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Auch in den Bundesministerien und im Bundeskanzleramt gibt es seit Kurzem Labore, allerdings ganz ohne solche Versuchsanordnungen aus dem Chemiebaukasten. Es handelt sich nämlich Datenlabore. Dort laufen allerhand komplexe Daten zusammen, die analysiert, verbreitet und genutzt werden. Denn auf Grundlage von Daten, so die Annahme, könnte das Land besser regiert werden. Was können Datenlabore und wie arbeiten sie konkret? Das wollen wir heute in dieser Podcastfolge herausfinden. Herzlich willkommen beim öFIT Podcast restart digital, eine Produktion des Fraunhofer Instituts für offene Kommunikationssysteme. Ich bin Philipp 1 und ich begrüße ganz herzlich meine beiden Co Hosts. Da sind zum einen Nicole Opiala. Sie ist Politologin, stellvertretende Leiterin des öFIT und ja beinahe in jeder Folge unseres Podcasts dabei. Heute ist sie, wie so meist, online zugeschaltet. Hallo Nicole. Hallo Philipp. Außerdem mit dabei Maximilian Kubi. Er ist Data Scientist und hat sich an der Herty School mit digitalem Wandel befasst. Er arbeitet außerdem als wissenschaftlicher Mitarbeiter am öfid und ist hier nach Berlin Tegel ins Studio gekommen mit Busersatzverkehr und allem, was dazugehört. Hallo Max. Guten Morgen, hi. Ja, schauen wir zunächst mal in die Praxis. Seit 20 21 haben Bundesbehörden mit dem Aufbau von Datenlaboren begonnen. Was das bringt und wie diese Datenlabore arbeiten, das habt ihr am Öffit in der aktuellen Studie gemeinsam mit dem Forschungsinstitut Neuland 21 untersucht. Fangen wir doch mal mit der Praxis an. Welche konkreten Probleme können durch die Arbeit von Datenlabore denn so gelöst werden? Ja, da kann man einmal eben ganz klein anfangen. Es gibt einfach Herausforderungen, die in der alltäglichen Arbeit, in der Ministerialverwaltung immer wieder auftauchen, dass Informationen zusammengesucht werden müssen für Reisen beispielsweise in bestimmte Wahlkreise oder in andere Länder, dass kleine Anfragen bearbeitet werden müssen, dass andere Referate beteiligt werden müssen bei bestimmten Entwicklungen oder Entscheidungen und das ist in der Praxis bisher sehr viel manueller Rechercheaufwand gewesen für die Mitarbeitenden, Dokumente zu durchsuchen, die teilweise beispielsweise, wenn's da Informationen des Währungsfonds geht, mehrere 100 Seiten umfassen, die dann möglichst zusammenzufassen und die entsprechenden Informationen da rauszuziehen. Und das ist zum Beispiel was, wo man mit Na ja, wo ist 'n Datenlabor mit ganz kleinen Anwendungen 'n großen Mehrwert schaffen kann, da entsprechende Informationen und Daten schneller auffindbar zu machen, KI gestützt, Dokumente zusammenzufassen oder eben auch über Dashboards gewisse Informationen einfach abzubilden, die aus verschiedenen Datenquellen stammen, die dann eben nicht mehr von den Mitarbeitenden händisch zusammengesucht werden müssen. Also das ist 'n ganz kleiner, konkreter Mehrwert in der Praxis, der in der alltäglichen Arbeit hilft, aber natürlich geht's da auch 'n bisschen das größere Bild, also auch Arbeit die Arbeitskultur zu verändern und die Mitarbeitenden zu befähigen und zu sensibilisieren, wie man mit Daten arbeiten kann und natürlich auch entsprechende Infrastrukturen zu schaffen, mit Daten zu arbeiten. Also die Datenlabore, die haben da ganz vielfältige Aufgaben, die sie dann erfüllen. Du hast ja schon so einige Beispiele genannt, so aus dem Arbeitsalltag 1 Ministeriums. Interessant find ich ja, dass die Entscheidung für die Datenlabore während der Coronapandemie fiel. Gibt's da auch grade im Bereich Gesundheit und Vorsorge, also auch Pandemiepläne zum Beispiel, Anwendungsfälle für solche Labore? Dass es grade der Zeitpunkt war, war, glaube ich, eher ein ein glückliches Zusammenspiel. 20 21 ist die Datenstrategie der Bundesregierung erschienen. Darin wurde der Auftrag erteilt, Datenlabore in allen Bundesministerien aufzubauen und dem NextGeneration EU Fundes einfach auch tatsächlich entsprechende Mittel bereit. Also man hatte dann die Chance, das auch tatsächlich direkt in die Praxis umzusetzen. Und wie sehr es solche Dateneinheiten braucht in der in der in den Bundesministerien, ist durch die Pandemie, glaube ich, einfach noch mal sehr deutlich klar geworden, wenn man festgestellt hat, dass an vielen Stellen die Daten einfach fehlen, dass man gar nicht weiß, wie groß ist der Bedarf? Wie viele Anspruchsberechtigte gibt es, wenn es Coronahilfen geht? Dass man Dinge nicht auszahlen konnte, weil entsprechende Daten, Kontodaten zu Personen fehlen et cetera und dass das in anderen Ländern ja auch ganz anders gelaufen ist, dass die, weil die eben nicht nur den großen Zensus alle paar Jahre haben, sondern regelmäßig in kleinerem Umfang Daten erheben und die auch zur Verfügung stehen, sehr viel besser, sehr viel genauer, sehr viel bedarfsgerechter durch diese Pandemie steuern konnten. Und ja, da kamen die Datenlabore dann quasi zur rechten Zeit, obwohl man auch sagen muss, manche haben schon direkt 20 21 auch mit der Arbeit beginnen können, wenn's entsprechende Vorarbeiten auch schon gab. Bei manchen anderen hat's dann doch deutlich länger gedauert, bis die dann richtig arbeitsfähig waren. Also teilweise die erst 20 23 so richtig mit der operativen Arbeit begonnen. Es ist ja, glaube ich, auch noch nicht abgeschlossen. Also die Datenlabore werden nach und nach weiter ausgebaut, installiert. Ja, schauen wir doch mal auf son paar Best Practices, Max. Ein Beispiel ist grad schon angeklungen, kleine Anfragen im Bundestag. Ist 'n großer Aufwand für die Verwaltung in den Ministerien, die zu bearbeiten. Jetzt gibt's aber KirAS, das ist so eine Art KI Rechercheassistent. Wie funktioniert das? Der hilft Mitarbeitenden in den Behörden, die kleinen Anfragen, die ausm Bundestag kommen, was ja doch regelmäßig vorkommt auf einfache Art, KI gestützt und über App oder auch Browserinterface, also Webschnittstellen, zu beantworten. Und aktuell wird er oder er wurde zum Zeitpunkt der Studie mit 1 Kernnutzerinnengruppe getestet. Also insgesamt 40 Personen konnten diesen Kiras Rechercheassistent ausprobieren und entsprechend helfen, dem weiterzuentwickeln. Mhm. Genau, der ist im BMUV entwickelt worden, aber ich hab's eben schon gesagt, kleine Anfragen beschäftigen natürlich alle Ministerien. Und deshalb hat das BMUV, die Datenlabore tauschen sich interministeriell sehr stark miteinander aus. Das ist auch eine große Besonderheit an diesen Einheiten, treffen sich regelmäßig auf Leitungsebene in der interministeriellen Arbeitsgruppe, aber auch in so einem Peer to Peer Netzwerk. Da sind die Leitenden dabei, aber auch die Mitarbeitenden. Es ist son bisschen selbstorganisiert und das BMUV hat dann seinen Rechercheassistent da mal vorgestellt, zur Diskussion gestellt und dann auch Testzugänge den anderen Ministerien zur Verfügung gestellt, weil das einfach natürlich 'n Thema ist, was alle beschäftigt. Und bevor man dann 16 parallele Anwendungen entwickelt, ist es natürlich sehr viel sinnvoller, wenn man sie kann das den anderen vielleicht zur Verfügung stellen. Denn natürlich sind die also die Inhalte und die fachlichen Fragen andere, aber rein technisch, genau, dass ich dass ich das KI gestützt machen kann, was es für Schnittstellen gibt, dass es irgendwelche Suchfunktionen gibt, dass ich das irgendwie prompten kann. Das sind ja so Aspekte, die in allen Ministerien auch zum Einsatz kommen. Deswegen, genau, kann man da sich auch gut austauschen und gemeinsam Entwicklungen anstoßen. Mhm. Also Synergien werden genutzt, das ist sicherlich verständlich. Wenn ich das jetzt so höre, dann klingt es ja auch so, dass am Ende Verwaltungsaufwand eingespart wird, also Arbeitszeit eingespart wird. Ich hab aber in eurer Publikation gelesen, da schreibt ihr auch, dass am Ende das Land eventuell besser oder auch effizienter regiert werden könne. Ist das so? Also was genau hilft dabei, tatsächlich besser zu regieren? Na, das hatte ja Nicole am Anfang schon angesprochen, dass quasi die Transparenz über die Tatbestände, jetzt mittler Beispiel Corona und die Transparenz eben über die Daten hilft, besser zu steuern und das Land besser. Und auch zu wissen, was was von den Sachen, die wir machen, was kommt da eigentlich tatsächlich an? Und auch eine Art Wirkungsmessung letztendlich durchführen zu können, die eben nur auf Basis von guten und verfügbaren Daten möglich ist. Mhm. Kommen wir mal noch zu 'nem anderen Beispiel, das ich auch ganz interessant fand, nämlich ein Migrations Dashboard. Also da kann praktisch online auf 'nem Monitor die gegenwärtige Migrationslage im Land abgebildet werden und Fachleute können zum Beispiel die Unterbringung von Geflüchteten oder auch die Integration besser koordinieren. Ja, wie funktioniert das? Es funktioniert aufm Prinzip des Dashboards, was man sich so vorstellen kann wie eine simple Benutzeroberfläche, die die wesentlichen Zahlen und die wesentlichen Trends in den Zahlen idealerweise den Entscheidungsträgerinnen zur Verfügung stellt. Und darauf basieren können dann Entscheidungen getroffen werden und eben Sachen nachgesteuert werden. Also die Dashboards sind in in der oder sind in den Datenlaboren, aber auch über die Datenlabore hinaus 'n sehr beliebtes Tool, würd ich mal sagen aktuell, Daten, mit Daten zu arbeiten, Daten transparent zu machen. Ich würd's mir jetzt so vorstellen, wirklich wie sone Art, ja, Monitor, auf dem alle Daten zusammenlaufen aus allen Bundesländern, aus ja, also Daten zum Thema Migration und ich dann wie sone Art ja Landkarte habe, die sich dort vor meinem Auge abzeichnet. Ist das ungefähr so richtig? Genau, das das BMI hat ja die Zahlen zur aktuellen Migrationslage. Das Datenlabor des BMI hat auch dieses Migrations Dashboard entwickelt und nach gewissen Schlüsseln werden dann ja eingewanderte Personen auf die Länder verteilt und wenn natürlich dann bekannt ist, wie sehen die Zahlen aktuell aus, wie entwickeln sich die Zahlen aktuell, erleichtert das natürlich die Planung für die Länder und dann auch für die Landkreise, die dann ja konkret auch Unterbringungsmöglichkeiten bereitstellen müssen. Deswegen, die Anwendung kommt sehr gut an. Aktuell wird wird die von allen Ländern und auch, na ja, unsere Studie, das ist dann der Stand von vom Ende letzten Jahres, da waren schon 2 Drittel der Landkreise, die dieses Dashboard auch genutzt haben. Also schon ein großer Hit, würd ich sagen. Ja, doch in auch relativ kurzer Zeit für Verwaltungsmaßstäbe. Jetzt haben wir sehr auf die Verwaltung geschaut. Ändern wir doch mal die Perspektive und blicken auf die Bürgerinnen und Bürger. Welche Vorteile haben die eigentlich durch die Arbeit von solchen Datenlaboren? Ich würd mal sagen, die Datenlabore in den Ministerien haben schon primär das Ziel, die Politik und die Politikgestaltung und die Arbeit der Ministerien eben zu erleichtern. Und die ist ja dann doch in groß, sag ich mal, relativ weit weg von den Bürgern. Aber es gibt auch Datenlabore außerhalb der Bundesministerien oder vor allem in sonem Smart City Kontext oder so, wo eben auch ähnliche Produkte wie jetzt oder ähnliche Anwendungen wie Chat Bots für Bürgerinnen und Bürger oder Dashboards, die irgendwelche beispielsweise, weiß ich von einem Dashboard, dass die nachhaltige Entwicklung 1 1 Region versucht, für die Bürger irgendwie zu machen. Also so was gibt's auch. Das heißt, das hilft einfach den Bürgern auch, mit dem Staat zu kommunizieren und auch über die aktuellen Staatsvorhaben immer aufm Laufenden zu bleiben. Also auch Transparenz und Interaktion, also solche Chat Bots werden oft auch für Anfragen der Bürgerinnen an beispielsweise das Bürgeramt genutzt und genau. Ich würd auch sagen, die Effekte sind in dem vielleicht eher 'n bisschen indirekter vielleicht. Also genau, was wir bisher dargestellt haben, arbeiten die Datenlabore in den Bundesministerien ja schon eher für für den für den Bund, für die Bundesressorts, also sorgen da einfach für eine Effizienzsteigerung, fürn besseres Wissensmanagement, für den Kompetenzaufbau, schaffen eben Infrastrukturen, mit Daten und auch mit KI natürlich arbeiten zu können, genau, oft mehr maschinenlesbare Daten überhaupt zur Verfügung stellen zu können und die dann auch nutzen zu können. Und letztlich zahlt das dann ja aber auch darauf ein, dass eben auch Politikgestaltung stärker evidenzbasiert, stärker datengestützt funktionieren kann. Und da ist natürlich dann die Hoffnung, dass das dann schon sehr deutlich bei den Bürgerinnen und Bürgern ankommt, wenn man schon 'n viel klareres Bild davon hat, was genau die Problemlagen sind und auch in welcher Form, weil man einfach die Daten dazu zur Verfügung hat, auch aktuelle Daten und dann auch weiß, so und so will ich steuern und das und das sind meine Ziele und so und so kann ich auch nachverfolgen, ob die intendierten Wirkungen dann erreicht werden und nicht mich auf Schätzungen oder alte Daten oder unvollständige Daten verlassen zu müssen, damit son bisschen im Blindflug zu sein und die Intention natürlich zu haben, was will ich mit den und den Gesetzesinitiativen erreichen, Aber dann auch Probleme habe zu erfassen, trifft es wirklich den Kern des Problems und werden die Wirkung erreicht? Und ich denke, das kommt uns allen und der ganzen Gesellschaft zugute, wenn das natürlich sehr viel näher an die Realität und an die realen Entwicklungen gekoppelt ist dadurch, dass man eben besser mit Daten arbeiten kann in den Ministerien. Gehen wir doch mal in den Maschinenraum dieser Datenlabore und gucken so ein wenig, wie die wirklich aufgebaut sind und wie die eingebunden sind in den Bundesbehörden. Ja, habt ihr da Beispiele, also grade auch jetzt in den Ministerien, wie die Datenlabore dort sozusagen situiert sind? Also ich würd sagen, der große Vorteil der Datenlabore, erst mal einen Schritt zurück, dass es eben in allen Bundesministerien ein Datenlabor gibt, ist, dass die Datenlabore auf diese Art, auf die jeweiligen Begebenheiten in den jeweiligen Häuser reagieren konnten. Deswegen sind die alle 'n bisschen unterschiedlich aufgebaut und haben auch unterschiedliche Größen, sind auch an unterschiedlichen Stellen sozusagen im in der Organisation verortet. Aber ganz generell kann man grob sagen, dass es immer so zwischen 5 und, sagen wir mal, 15 Teammitglieder sind. Da sind natürlich Data Scientists, also Leute, die mit Daten umgehen können, Data und Data Analysts. Das sind die klassischen, sagen wir mal, Rollen, die die bei mit Datenarbeit irgendwie wichtig sind. Das hatte ich mich gefragt, was macht eigentlich ein Data Scientist? Was macht ein Data Analyst? 'N Data Scientist ist erst mal, wie der Name schon sagt, ein Datenwissenschaftler. Das heißt, da geht's viel Modellbildung und komplexere Anwendungen im weiteren Sinne, die er entwickelt, 'n Data Analyst und er oder sie, 'n Data Analyst oder eine Data Analystin analysiert, wie der Name ja auch sagt, Daten. Das heißt, es ist 'n bisschen oder 'n bisschen Praktischer, genau. Und ich Operativer und weniger konzeptionell. Und dann gibt's noch Data Engineers, die sind eigentlich für die Bereitstellung der Daten zuständig. Das ist aber nicht alles. Also diese Datenteams, die sind, fand ich, überraschend divers aufgestellt, ne. Also es gibt da alle möglichen Kompetenzen, die abgefragt werden. Welche sind euch da begegnet? Du hast es schon gesagt, ist relativ breit aufgestellt. Im Data Scientist ist jetzt auch nicht unbedingt 'n IT Hintergrund vonnöten gewesen, sondern das sind auch Sozialwissenschaftler*innen oder Naturwissenschaftler*innen, die mit sich mit Daten auskennen, Programmiererfahrung haben und eben auch empirische in empirischer Forschung oder in Datenanalyse Kompetenzen haben. Juristen findet man relativ wenige im Vergleich zur sonstigen Verwaltung und ansonsten Ökonominnen sind dabei, Coaches, also das geht wirklich 'n bisschen Querbeet Ja, finde ich der da mitarbeitet. Was genau macht den macht den Reiz aus, dass man sagt, wir wollen so diverse Teams haben in der Daten in der Datenarbeit. Ich weiß gar nicht, ob das von Anfang an vielleicht so intendiert war, aber die Arbeit mit Daten spielt einfach in in vielen Disziplinen ja eine eine steigende Rolle und allein deshalb hat sich das vermutlich auch irgendwo ergeben, dass man diverse Hintergründe erhalten hat der der Person, wenn man gesagt hat, es geht nicht jetzt einen bestimmten Abschluss in einem bestimmten Fach, sondern die tatsächlichen Fähigkeiten. Ich meine mich zu erinnern aus einem Interview, dass es dann auch einfach Tests gab, gewisse Aufgaben in Bezug auf Daten und Datenanalyse und es gibt eben verschiedene Disziplinen, in denen gut mit Daten gearbeitet werden kann. Die Leidenden haben ebenfalls sehr diverse Hintergründe. Die meisten kommen auch von eher von extern, da haben wir auch bis zur Astrophysik ist, glaube ich, alles alles vertreten. Aber natürlich, wenn man so verschiedene Disziplinen hat, schafft das natürlich auch eine eine gewisse Gemengelage, auch eine gewisse Spannung vielleicht, die dann aber natürlich auch zu mehr Kreativität und Produktivität führen kann, was man ja auch an interdisziplinären Teams auch in der Wirtschaft beispielsweise beobachten kann. Max, Du hast es gerade gesagt, diese sehr diversen Teams, die sind in der Praxis aber recht unterschiedlich organisiert in den Ministerien und Bundesbehörden. Also mal als Beispiel, manche Ministerien, da sind's 5 Leute, in anderen sind's 14 Mitarbeitende, die in so einem Datenlabor beschäftigt sind. Auch organisatorisch können die, so hab ich rausgelesen, recht unterschiedlich verortet sein. Gibt's da son Best Practice, wo ihr gesagt habt, ja, so müsste man's machen? Es gibt, glaub ich, Best Practices, die wieder vom Kontext abhängen. Aber ich würd mal ganz generell sagen, dass es schon gut, also sich bei den meisten als als sinnvoll herausgestellt hat, dass die Datenabore möglichst nah am Haus und an der Hausleitung dran sind, wenn's geht. Das heißt, eine nachgeordnete Behörde beispielsweise ist eventuell nicht der richtige Ort, aber kann auch da Gründe geben, warum das sinnvoll ist. Wie kann ich mir denn den Arbeitsalltag in so einem Datenlabor vorstellen? Also jetzt hantieren mit Reagenzgläsern und Flüssigkeiten haben wir ja schon ausgeräumt, das in jedem Falle nicht. Was macht einen was macht man in einem Datenlabor? Ich glaub, die Datenlabore vor allem zu dem an zu dem Zeitpunkt, an dem wir uns mit ihnen auseinandergesetzt haben, haben vor allem ganz viel Lobbyarbeit, wenn man so will, Daten Lobbyarbeit betrieben, also ganz viel in die Häuser gehen, mit Kolleginnen und Kollegen sprechen und zu versuchen, denen zu zeigen, was Daten eigentlich können, was man mit Daten eigentlich anstellen kann. Daneben die entsprechende Infrastruktur aufzubauen, die man braucht, zu arbeiten. Da gab's natürlich dann auch entsprechende Hürden, die vom von den von der Art und Weise, wie eben Verausgabungen in Bundesministerien oder in der Verwaltung generell geschehen, die da 'nem schnellen, sagen wir mal, Aufbauen der der Computerinfrastruktur, wenn man das jetzt mal so generell sagen will, teilweise oder das teilweise sehr erschwert haben. Und dann eben auch das Erstellen konkreter Produkte und konkreter konkreter Daten, Anwendungen. Und die wurden häufig auch wieder, auch wieder Mehrwert für fürs Haus zu liefern, eben mit Kolleginnen und Kollegen aus den Häusern umgesetzt, beziehungsweise auf Basis deren Bedarfe überhaupt erst entwickelt. Mhm. Also auch recht unterschiedliche Aufgaben, so von Marketing, sag ich mal, intern für Daten, ne. Warum sind Daten sinnvoll? Schulung bis hin zur Entwicklung. Wird denn auch in den Häusern selbst eine Lösung entwickelt? Zum Beispiel son Dashboard, über das wir gesprochen haben oder wird das dann extern herausgegeben? Die Idee ist schon, dass sie auch in den Häusern die entsprechenden Kompetenzen haben, solche Anwendungen zu entwickeln. Teilweise ist es natürlich nicht immer möglich, je nachdem wie umfänglich solche solche Anwendungen sind. Aber ja, also grade auch möglichst nah an den Mitarbeitenden und an den Anwenderinnen im Haus dranzubleiben, ist es wurden wurden viele dieser Anwendungen im Haus von den Teams in den Datenlaboren entwickelt. Ich hab verstanden, dass die Datenlabore zwischen den Ministerien und Bundesbehörden kooperieren, dass die gut zusammenarbeiten. Wie ist es aber, dass wie arbeiten denn aber die Datenlabore mit anderen Bereichen in der Verwaltung zusammen? Gibt's da auch starke Kooperation oder gibt's da auch vielleicht sogar Vorbehalte? Also das hatte ich grade schon angesprochen, es gibt dann quasi auch Datenlaborbeispiele, die dann in nachgeordneten Behörden beispielsweise verankert sind. Das heißt, da ist die Zusammenarbeit mit der nachgeordneten Behörde dann vermutlich eher intensiv und das entsprechend, denk ich mal, zielführend, Beispiel Statistisches Bundesamt, ne. Die arbeiten viel mit Daten, da 'n Datenlabor hinzusetzen und zu versuchen, Synergien zu schaffen, macht natürlich sehr viel Sinn. Ja. Also genau, es ist schon ja eher die Ministerialverwaltung, aber die nachgeordneten Behörden spielen da auch eine Rolle. Da gibt's natürlich auch welche, Du hast jetzt schon das prominenteste Beispiel genannt, die natürlich auch viel mit Daten arbeiten, aber da gibt's ja auch andere, beispielsweise die Bundesbank, also auch mit nachgeordnetenbehörden arbeiten die Datenlabor schon intensiv zusammen. Dann gibt's ja teilweise auch in den Bundesministerien noch andere Innovationseinheiten, irgendwelche Innovation Labs, Civic Innovation Lab, beispielsweise im Familienministerium. Auch da gibt es Austausche und Zusammenarbeit. Das Migrations Dashboard haben wir ja schon angesprochen, das wird ja auch von Ländern und auch den Landkreisen sehr intensiv genutzt beispielsweise. Blicken wir doch mal zum Schluss auf die Zukunft der Datenlabore, auch grade noch mal mit dem Fokus auf die öffentliche Verwaltung. Es gibt ja nur allerhand neue Technologien, auch grad in letzter Zeit. Wir haben schon über KI gestützte Modelle gesprochen, die ja auch schon Anwendungen gefunden haben. Welche neuen Technologien kommen euch dann noch in den Sinn, die die Arbeit von Datenlaboren beeinflussen könnte? Also auch, wenn wir mal so an Machine Learning denken oder auch die Bedeutung von Open Data? Ja, also ich würd sagen, das große Thema, das am meisten bestimmende Thema wird wahrscheinlich auch in naher Zukunft werden noch weiter die Sprachmodelle bleiben, weil da eben sehr viel Potenzial noch drinsteckt mit den, was ja in Behörden 'n großen Teil der Daten ausmacht, mit Texten eben zu arbeiten und diese entsprechend nutzbar zu machen. Darüber hinaus, klar, gibt's viele weitere Möglichkeiten, die anderen Daten, die Du hast grade angesprochen, also Open Data, die auch an unterschiedlichsten Stellen in in der Republik liegen, sag ich jetzt mal, die auch mehr noch zu nutzen und zusammenzuführen. Da gibt's auch gab's ja 'n 'n großen große Bewegung, möglichst viele Daten bereitzustellen. Und teilweise werden die teilweise mit sehr großen Aufwänden immer noch gepflegt und bereitgestellt, aber die Nutzung dessen der der der bereitgestellten Daten ist eher eingeschränkt. Also da gibt's nur wenige Beispiele, die die diese offenen bereitgestellten Daten auch tatsächlich nutzen. Und das weiter zu treiben, wird natürlich auch wichtig, 'n wichtiges Feld sein und und bleiben. Ist das, Nicole, auch eine ganz große Herausforderung? Also im Föderalismus, verschiedene Bundesländer eben, verschiedene Daten haben vielleicht nicht einen gemeinsamen Topf, wo die einfach reingespült werden, die auch wirklich nutzbar zu machen? Auf jeden Fall. Und das sind auch gerade diese genau diese Grundlagen, überhaupt mit Daten arbeiten zu können, Anwendungen darauf bauen zu können, etwas, woran die Datenlabor ja schon gearbeitet haben. Es wurde vom Bundeskanzleramt koordiniert, für so einen Datenpool mal einen Proof of Concept zu entwickeln. Wie könnte das denn überhaupt aussehen? Weil es ist ja schon schwierig, Daten allein über Ressortgrenzen auch teilen zu können. Da geht's auch erst mal darum, Datenkataloge zu erstellen. Was haben wir denn überhaupt für Daten, dass die auch halbwegs irgendwie einheitlich sind, dass man sie auch finden kann. Es ist vom Auswärtigen Amt gemeinsam mit der Bundesdruckerei eine Analyticsplattform, PLIN heißt die, für die Bundesverwaltung entwickelt worden, wo sie dann die Möglichkeit haben, Daten und auch Code untereinander zu teilen und da ihre Datenanalyseprojekte zu machen und 'n anderes Projekt ist beispielsweise KeePIDS, das ist vom BMI, von dem von der Initiative BEKI Beratungs- und Evalualisierungszentrum für Künstliche Intelligenz, auch gemeinsam mit den Datenlaboren entwickelt worden, den Bundesministerien Möglichkeiten zu geben, mit KI Anwendungen, speziell mit Sprachanwendungen, mal 'n bisschen rum zu experimentieren und da Erfahrungen zu machen. Genau, ich muss erst mal auch irgendwie eine Infrastruktur haben, auf der ich aufbauen kann und ich muss die Daten haben und dass die dass ich Daten habe, mit denen ich arbeiten kann, die in maschinenlesbarer Form vorliegen, die ich auch nutzen kann, weil Daten werden ja per Default erst mal als Verschlusssache eingestuft, da auch trotzdem Möglichkeiten zu haben. Open Data hast Du schon angesprochen, ist natürlich ein Weg, Daten zur Verfügung zu haben, mit denen ich arbeiten kann. Das ist 'n ganz, ganz großes Thema für die Datenlabore. Da hat haben sie schon große Schritte gemacht, hab ich grad 'n bisschen skizziert, aber da ist natürlich noch ein ganz noch ein weites Stück des Weges zu gehen. Und das ist für die Datenlabore gar nicht so einfach. Die waren erst mal, genau, das gibt's ja seit 20 21, waren sie erst mal finanziert bis 20 24. Dann gab's sone kleine Zitterpartie, jetzt ist die Finanzierung erst mal gesichert, allerdings auch wieder nur bis Ende nächsten Jahres. Das ist natürlich auch son bisschen schwierig, ohne eine langfristige Perspektive solche langfristigen Projekte anzugehen, wie eben eine entsprechende Infrastruktur zu schaffen. Datenkompetenzaufbau ist natürlich auch was, was was weitergehen muss in den Ministerien und was wir uns auch 'n bisschen wünschen würden, wäre eine stärkere Öffnung auch nach außen. Du hast ja schon es angesprochen, wie sieht's denn aus eigentlich mit Verwaltungen in Ländern und Kommunen beispielsweise, aber auch eben eine Öffnung mehr hin zur Öffentlichkeit, mehr in den Dialog gehen, auch mit Zivilgesellschaft, mit Wissenschaft, mit Wirtschaft, weil im Moment hat man schon mit von Ausnahmen abgesehen auf jeden Fall, da gibt's auch welche, die das 'n bisschen anders handhaben, aber ja schon diesen sehr starken Fokus auf Verwaltung und man kooperiert aber eher untereinander und nicht so sehr mit mit externen Akteuren, die ja vielleicht auch eine wertvolle Impulse, Kompetenzen et cetera noch zur Verfügung stellen könnten. Ich hab jetzt grade schon rausgehört, Zitterpartie, was die Finanzierung angeht. Wenn wir jetzt mal so 5 oder 10 Jahre in die Zukunft schauen, gibt's da noch Datenlabore oder was ist aus denen geworden? Ist eine spannende Frage. Ich glaub, diese Labore als als Vehikel, sag ich mal, sind oft was, was nicht auf ewig immer Bestand hat in solchen Organisationen. Also die sind meistens zum Etablieren 1 bestimmten Sache, zum Ausprobieren 1 bestimmten Sache genutzt. Bei den Datenlaboren bin ich mir, ehrlich gesagt, nicht so sicher. Da glaub ich, dass die aufgrund der breiten Anwendungen, die ja Daten in Ministerien finden und die auch die Mehrwerte, die Datenlabore entsprechend für die Ministerien liefern können, glaub ich, dass ich mir schon vorstellen kann, dass die auch langfristig eine Perspektive haben, Ob sie das dann tatsächlich auch so realisieren können und ob das so realisiert wird, hängt ja nicht nur an den Laboren, sondern in letztendlich auch politische Entscheidungen. Und da kann man nur hoffen, dass die Arbeit, die sie jetzt tun, dazu beiträgt, den Entscheidungsträgerinnen klarzumachen, dass das sehr wichtig ist, diese Datenlabore weiter zu institutionalisieren und weiter zu fördern und weiter in den Häusern idealerweise, wie wie wie schon angesprochen, in allen Häusern, aber trotzdem auch auf Zusammenarbeit angelegt zu etablieren. Könnte ja aber eben auch sein, dass in einigen Jahren die Arbeit mit Daten so selbstverständlich geworden ist, dass das Labor sich selbst überflüssig gemacht hat, ja, weil's eben in den Fachabteilungen angekommen ist. Nicole, was meinst Du? Das ist 'n guter Punkt. Das ist natürlich wünschenswert, dass die Arbeit der Datenlabore in 5 bis 10 Jahren vielleicht auf jeden Fall einen anderen Schwerpunkt haben wird, weil die Infrastrukturen inzwischen einfach da sind. Die müssen natürlich stetig weiterentwickelt werden und na ja, irgendwie, wenn wir sagen, wir wollen mehr datengestützte, mehr evidenzbasierte Politikgestaltung, dann müssen wir darauf auch irgendeine Antwort finden und die aktuelle Antwort lautet eben Datenlabore, diese Datenlabore zu zu installieren. Das war die Antwort darauf und es braucht auch 'n gewissen institutionalisierten Rahmen für so etwas. Und es ist natürlich auch sinnvoll, auch im Hinblick auf neue Technologien, die Du schon angesprochen hast, brauchen wir auch in Zukunft, auch in 5 und 10 Jahren noch Experimentier- und Lernräume, wo man einfach mal Neues ausprobieren kann mit Tri und Error, manches wird auch nicht funktionieren. Also irgendwas, was son Labcharakter hat, werden wir auf jeden Fall brauchen, obwohl natürlich die Hoffnung ist, dass genau Kompetenzen und vieles, was bisher die Datenlabore noch leisten müssen, schon sehr viel stärker in den Fachabteilungen verankert ist und selbstverständlich geworden ist. Wie Datennabore in Bundesbehörden arbeiten und warum sie so wichtig sind, das war restStart digital, der Podcast des Kompetenzzentrums Öffentlicher IT. Herzlichen Dank an meine Co Host Nicole Opiala und Maximilian Kubi. Schön, dass ihr hier wart. Vielen Dank. Danke dir. Die passende Publikation gibt's wie immer online unter öfid Punkt d e, öfid mit o e. Einen Link findet ihr auch in den Shownotes. Falls euch der Podcast gefallen hat, empfehlt uns weiter und folgt uns kostenfrei auf Spotify, Apple Podcasts und vielen weiteren Apps. 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"confidence": 0.8847656, "punctuated_word": "1.", "speaker": 0, "speaker_confidence": 0.7100551 } ], "summaries": null, "paragraphs": { "transcript": "\nSpeaker 0: Was kommt euch in den Sinn, wenn ihr von einem Labor hört? Also ich denk da an Menschen in weißen Kitteln mit Schutzbrillen, dann auf dem Tisch davor Reagenzgläser, Petrischalen, 'n Bunsenbrenner vielleicht. Das alles steht vor meinem inneren Auge da auf dem Tisch voller farbiger Substanzen, die vor sich hin brodeln, 'n Labor eben. Auch in den Bundesministerien und im Bundeskanzleramt gibt es seit Kurzem Labore, allerdings ganz ohne solche Versuchsanordnungen aus dem Chemiebaukasten. Es handelt sich nämlich Datenlabore.\n\nDort laufen allerhand komplexe Daten zusammen, die analysiert, verbreitet und genutzt werden. Denn auf Grundlage von Daten, so die Annahme, könnte das Land besser regiert werden. Was können Datenlabore und wie arbeiten sie konkret? Das wollen wir heute in dieser Podcastfolge herausfinden. Herzlich willkommen beim öFIT Podcast restart digital, eine Produktion des Fraunhofer Instituts für offene Kommunikationssysteme.\n\nIch bin Philipp 1 und ich begrüße ganz herzlich meine beiden Co Hosts. Da sind zum einen Nicole Opiala. Sie ist Politologin, stellvertretende Leiterin des öFIT und ja beinahe in jeder Folge unseres Podcasts dabei. Heute ist sie, wie so meist, online zugeschaltet. Hallo Nicole.\n\nSpeaker 1: Hallo Philipp.\n\nSpeaker 0: Außerdem mit dabei Maximilian Kubi. Er ist Data Scientist und hat sich an der Herty School mit digitalem Wandel befasst. Er arbeitet außerdem als wissenschaftlicher Mitarbeiter am öfid und ist hier nach Berlin Tegel ins Studio gekommen mit Busersatzverkehr und allem, was dazugehört. Hallo Max.\n\nSpeaker 2: Guten Morgen, hi.\n\nSpeaker 0: Ja, schauen wir zunächst mal in die Praxis. Seit 20 21 haben Bundesbehörden mit dem Aufbau von Datenlaboren begonnen. Was das bringt und wie diese Datenlabore arbeiten, das habt ihr am Öffit in der aktuellen Studie gemeinsam mit dem Forschungsinstitut Neuland 21 untersucht. Fangen wir doch mal mit der Praxis an. Welche konkreten Probleme können durch die Arbeit von Datenlabore denn so gelöst werden?\n\nSpeaker 1: Ja, da kann man einmal eben ganz klein anfangen. Es gibt einfach Herausforderungen, die in der alltäglichen Arbeit, in der Ministerialverwaltung immer wieder auftauchen, dass Informationen zusammengesucht werden müssen für Reisen beispielsweise in bestimmte Wahlkreise oder in andere Länder, dass kleine Anfragen bearbeitet werden müssen, dass andere Referate beteiligt werden müssen bei bestimmten Entwicklungen oder Entscheidungen und das ist in der Praxis bisher sehr viel manueller Rechercheaufwand gewesen für die Mitarbeitenden, Dokumente zu durchsuchen, die teilweise beispielsweise, wenn's da Informationen des Währungsfonds geht, mehrere 100 Seiten umfassen, die dann möglichst zusammenzufassen und die entsprechenden Informationen da rauszuziehen. Und das ist zum Beispiel was, wo man mit Na ja, wo ist 'n Datenlabor mit ganz kleinen Anwendungen 'n großen Mehrwert schaffen kann, da entsprechende Informationen und Daten schneller auffindbar zu machen, KI gestützt, Dokumente zusammenzufassen oder eben auch über Dashboards gewisse Informationen einfach abzubilden, die aus verschiedenen Datenquellen stammen, die dann eben nicht mehr von den Mitarbeitenden händisch zusammengesucht werden müssen. Also das ist 'n ganz kleiner, konkreter Mehrwert in der Praxis, der in der alltäglichen Arbeit hilft, aber natürlich geht's da auch 'n bisschen das größere Bild, also auch Arbeit die Arbeitskultur zu verändern und die Mitarbeitenden zu befähigen und zu sensibilisieren, wie man mit Daten arbeiten kann und natürlich auch entsprechende Infrastrukturen zu schaffen, mit Daten zu arbeiten.\n\nAlso die Datenlabore, die haben da ganz vielfältige Aufgaben, die sie dann erfüllen.\n\nSpeaker 0: Du hast ja schon so einige Beispiele genannt, so aus dem Arbeitsalltag 1 Ministeriums. Interessant find ich ja, dass die Entscheidung für die Datenlabore während der Coronapandemie fiel. Gibt's da auch grade im Bereich Gesundheit und Vorsorge, also auch Pandemiepläne zum Beispiel, Anwendungsfälle für solche Labore?\n\nSpeaker 1: Dass es grade der Zeitpunkt war, war, glaube ich, eher ein ein glückliches Zusammenspiel. 20 21 ist die Datenstrategie der Bundesregierung erschienen. Darin wurde der Auftrag erteilt, Datenlabore in allen Bundesministerien aufzubauen und dem NextGeneration EU Fundes einfach auch tatsächlich entsprechende Mittel bereit. Also man hatte dann die Chance, das auch tatsächlich direkt in die Praxis umzusetzen. Und wie sehr es solche Dateneinheiten braucht in der in der in den Bundesministerien, ist durch die Pandemie, glaube ich, einfach noch mal sehr deutlich klar geworden, wenn man festgestellt hat, dass an vielen Stellen die Daten einfach fehlen, dass man gar nicht weiß, wie groß ist der Bedarf?\n\nWie viele Anspruchsberechtigte gibt es, wenn es Coronahilfen geht? Dass man Dinge nicht auszahlen konnte, weil entsprechende Daten, Kontodaten zu Personen fehlen et cetera und dass das in anderen Ländern ja auch ganz anders gelaufen ist, dass die, weil die eben nicht nur den großen Zensus alle paar Jahre haben, sondern regelmäßig in kleinerem Umfang Daten erheben und die auch zur Verfügung stehen, sehr viel besser, sehr viel genauer, sehr viel bedarfsgerechter durch diese Pandemie steuern konnten. Und ja, da kamen die Datenlabore dann quasi zur rechten Zeit, obwohl man auch sagen muss, manche haben schon direkt 20 21 auch mit der Arbeit beginnen können, wenn's entsprechende Vorarbeiten auch schon gab. Bei manchen anderen hat's dann doch deutlich länger gedauert, bis die dann richtig arbeitsfähig waren. Also teilweise die erst 20 23 so richtig mit der operativen Arbeit begonnen.\n\nSpeaker 0: Es ist ja, glaube ich, auch noch nicht abgeschlossen. Also die Datenlabore werden nach und nach weiter ausgebaut, installiert. Ja, schauen wir doch mal auf son paar Best Practices, Max. Ein Beispiel ist grad schon angeklungen, kleine Anfragen im Bundestag. Ist 'n großer Aufwand für die Verwaltung in den Ministerien, die zu bearbeiten.\n\nJetzt gibt's aber KirAS, das ist so eine Art KI Rechercheassistent. Wie funktioniert das?\n\nSpeaker 2: Der hilft Mitarbeitenden in den Behörden, die kleinen Anfragen, die ausm Bundestag kommen, was ja doch regelmäßig vorkommt auf einfache Art, KI gestützt und über App oder auch Browserinterface, also Webschnittstellen, zu beantworten. Und aktuell wird er oder er wurde zum Zeitpunkt der Studie mit 1 Kernnutzerinnengruppe getestet. Also insgesamt 40 Personen konnten diesen Kiras Rechercheassistent ausprobieren und entsprechend helfen, dem weiterzuentwickeln. Mhm.\n\nSpeaker 1: Genau, der ist im BMUV entwickelt worden, aber ich hab's eben schon gesagt, kleine Anfragen beschäftigen natürlich alle Ministerien. Und deshalb hat das BMUV, die Datenlabore tauschen sich interministeriell sehr stark miteinander aus. Das ist auch eine große Besonderheit an diesen Einheiten, treffen sich regelmäßig auf Leitungsebene in der interministeriellen Arbeitsgruppe, aber auch in so einem Peer to Peer Netzwerk. Da sind die Leitenden dabei, aber auch die Mitarbeitenden. Es ist son bisschen selbstorganisiert und das BMUV hat dann seinen Rechercheassistent da mal vorgestellt, zur Diskussion gestellt und dann auch Testzugänge den anderen Ministerien zur Verfügung gestellt, weil das einfach natürlich 'n Thema ist, was alle beschäftigt.\n\nSpeaker 2: Und bevor man dann 16 parallele Anwendungen\n\nSpeaker 1: entwickelt, ist es natürlich sehr viel sinnvoller, wenn man sie kann das den anderen vielleicht zur Verfügung stellen. Denn natürlich sind die also die Inhalte und die fachlichen Fragen andere, aber rein technisch, genau, dass ich dass ich das KI gestützt machen kann, was es für Schnittstellen gibt, dass es irgendwelche Suchfunktionen gibt, dass ich das irgendwie prompten kann. Das sind ja so Aspekte, die in allen Ministerien auch zum Einsatz kommen. Deswegen, genau, kann man da sich auch gut austauschen und gemeinsam Entwicklungen anstoßen.\n\nSpeaker 0: Mhm. Also Synergien werden genutzt, das ist sicherlich verständlich. Wenn ich das jetzt so höre, dann klingt es ja auch so, dass am Ende Verwaltungsaufwand eingespart wird, also Arbeitszeit eingespart wird. Ich hab aber in eurer Publikation gelesen, da schreibt ihr auch, dass am Ende das Land eventuell besser oder auch effizienter regiert werden könne. Ist das so?\n\nAlso was genau hilft dabei, tatsächlich besser zu regieren?\n\nSpeaker 2: Na, das hatte ja Nicole am Anfang schon angesprochen, dass quasi die Transparenz über die Tatbestände, jetzt mittler Beispiel Corona und die Transparenz eben über die Daten hilft, besser zu steuern und das Land besser. Und auch zu wissen, was was von den Sachen, die wir machen, was kommt da eigentlich tatsächlich an? Und auch eine Art Wirkungsmessung letztendlich durchführen zu können, die eben nur auf Basis von guten und verfügbaren Daten möglich ist. Mhm. Kommen wir\n\nSpeaker 0: mal noch zu 'nem anderen Beispiel, das ich auch ganz interessant fand, nämlich ein Migrations Dashboard. Also da kann praktisch online auf 'nem Monitor die gegenwärtige Migrationslage im Land abgebildet werden und Fachleute können zum Beispiel die Unterbringung von Geflüchteten oder auch die Integration besser koordinieren. Ja, wie funktioniert das?\n\nSpeaker 2: Es funktioniert aufm Prinzip des Dashboards, was man sich so vorstellen kann wie eine simple Benutzeroberfläche, die die wesentlichen Zahlen und die wesentlichen Trends in den Zahlen idealerweise den Entscheidungsträgerinnen zur Verfügung stellt. Und darauf basieren können dann Entscheidungen getroffen werden und eben Sachen nachgesteuert werden. Also die Dashboards sind in in der oder sind in den Datenlaboren, aber auch über die Datenlabore hinaus 'n sehr beliebtes Tool,\n\nSpeaker 0: würd ich\n\nSpeaker 2: mal sagen aktuell, Daten, mit Daten zu arbeiten, Daten transparent zu machen.\n\nSpeaker 0: Ich würd's mir jetzt so vorstellen, wirklich wie sone Art, ja, Monitor, auf dem alle Daten zusammenlaufen aus allen Bundesländern, aus ja, also Daten zum Thema Migration und ich dann wie sone Art ja Landkarte habe, die sich dort vor meinem Auge abzeichnet. Ist das ungefähr so richtig?\n\nSpeaker 1: Genau, das das BMI hat ja die Zahlen zur aktuellen Migrationslage. Das Datenlabor des BMI hat auch dieses Migrations Dashboard entwickelt und nach gewissen Schlüsseln werden dann ja eingewanderte Personen auf die Länder verteilt und wenn natürlich dann bekannt ist, wie sehen die Zahlen aktuell aus, wie entwickeln sich die Zahlen aktuell, erleichtert das natürlich die Planung für die Länder und dann auch für die Landkreise, die dann ja konkret auch Unterbringungsmöglichkeiten bereitstellen müssen. Deswegen, die Anwendung kommt sehr gut an. Aktuell wird wird die von allen Ländern und auch, na ja, unsere Studie, das ist dann der Stand von vom Ende letzten Jahres, da waren schon 2 Drittel der Landkreise, die dieses Dashboard auch genutzt haben. Also schon ein großer Hit, würd ich sagen.\n\nSpeaker 0: Ja, doch in auch relativ kurzer Zeit für Verwaltungsmaßstäbe. Jetzt haben wir sehr auf die Verwaltung geschaut. Ändern wir doch mal die Perspektive und blicken auf die Bürgerinnen und Bürger. Welche Vorteile haben die eigentlich durch die Arbeit von solchen Datenlaboren?\n\nSpeaker 2: Ich würd mal sagen, die Datenlabore in den Ministerien haben schon primär das Ziel, die Politik und die Politikgestaltung und die Arbeit der Ministerien eben zu erleichtern. Und die ist ja dann doch in groß, sag ich mal, relativ weit weg von den Bürgern. Aber es gibt auch Datenlabore außerhalb der Bundesministerien oder vor allem in sonem Smart City Kontext oder so, wo eben auch ähnliche Produkte wie jetzt oder ähnliche Anwendungen wie Chat Bots für Bürgerinnen und Bürger oder Dashboards, die irgendwelche beispielsweise, weiß ich von einem Dashboard, dass die nachhaltige Entwicklung 1 1 Region versucht, für die Bürger irgendwie zu machen. Also so was gibt's auch. Das heißt, das hilft einfach den Bürgern auch, mit dem Staat zu kommunizieren und auch über die aktuellen Staatsvorhaben immer aufm Laufenden zu bleiben.\n\nAlso auch Transparenz und Interaktion, also solche Chat Bots werden oft auch für Anfragen der Bürgerinnen an beispielsweise das Bürgeramt genutzt und genau.\n\nSpeaker 1: Ich würd auch sagen, die Effekte sind in dem vielleicht eher 'n bisschen indirekter vielleicht. Also genau, was wir bisher dargestellt haben, arbeiten die Datenlabore in den Bundesministerien ja schon eher für für den für den Bund, für die Bundesressorts, also sorgen da einfach für eine Effizienzsteigerung, fürn besseres Wissensmanagement, für den Kompetenzaufbau, schaffen eben Infrastrukturen, mit Daten und auch mit KI natürlich arbeiten zu können, genau, oft mehr maschinenlesbare Daten überhaupt zur Verfügung stellen zu können und die dann auch nutzen zu können. Und letztlich zahlt das dann ja aber auch darauf ein, dass eben auch Politikgestaltung stärker evidenzbasiert, stärker datengestützt funktionieren kann. Und da ist natürlich dann die Hoffnung, dass das dann schon sehr deutlich bei den Bürgerinnen und Bürgern ankommt, wenn man schon 'n viel klareres Bild davon hat, was genau die Problemlagen sind und auch in welcher Form, weil man einfach die Daten dazu zur Verfügung hat, auch aktuelle Daten und dann auch weiß, so und so will ich steuern und das und das sind meine Ziele und so und so kann ich auch nachverfolgen, ob die intendierten Wirkungen dann erreicht werden und nicht mich auf Schätzungen oder alte Daten oder unvollständige Daten verlassen zu müssen, damit son bisschen im Blindflug zu sein und die Intention natürlich zu haben, was will ich mit den und den Gesetzesinitiativen erreichen, Aber dann auch Probleme habe zu erfassen, trifft es wirklich den Kern des Problems und werden die Wirkung erreicht?\n\nUnd ich denke, das kommt uns allen und der ganzen Gesellschaft zugute, wenn das natürlich sehr viel näher an die Realität und an die realen Entwicklungen gekoppelt ist dadurch, dass man eben besser mit Daten arbeiten kann in den Ministerien.\n\nSpeaker 0: Gehen wir doch mal in den Maschinenraum dieser Datenlabore und gucken so ein wenig, wie die wirklich aufgebaut sind und wie die eingebunden sind in den Bundesbehörden. Ja, habt ihr da Beispiele, also grade auch jetzt in den Ministerien, wie die Datenlabore dort sozusagen situiert sind?\n\nSpeaker 2: Also ich würd sagen, der große Vorteil der Datenlabore, erst mal einen Schritt zurück, dass es eben in allen Bundesministerien ein Datenlabor gibt, ist, dass die Datenlabore auf diese Art, auf die jeweiligen Begebenheiten in den jeweiligen Häuser reagieren konnten. Deswegen sind die alle 'n bisschen unterschiedlich aufgebaut und haben auch unterschiedliche Größen, sind auch an unterschiedlichen Stellen sozusagen im in der Organisation verortet. Aber ganz generell kann man grob sagen, dass es immer so zwischen 5 und, sagen wir mal, 15 Teammitglieder sind. Da sind natürlich Data Scientists, also Leute, die mit Daten umgehen können, Data und Data Analysts. Das sind die klassischen, sagen wir mal, Rollen, die die bei mit Datenarbeit irgendwie wichtig sind.\n\nSpeaker 0: Das hatte ich mich gefragt, was macht eigentlich ein Data Scientist? Was macht ein Data Analyst?\n\nSpeaker 2: 'N Data Scientist ist erst mal, wie der Name schon sagt, ein Datenwissenschaftler. Das heißt, da geht's viel Modellbildung und komplexere Anwendungen im weiteren Sinne, die er entwickelt, 'n Data Analyst und er oder sie, 'n Data Analyst oder eine Data Analystin analysiert, wie der Name ja auch sagt, Daten. Das heißt, es ist 'n bisschen oder 'n bisschen Praktischer, genau. Und ich Operativer und weniger konzeptionell. Und dann gibt's noch Data Engineers, die sind eigentlich für die Bereitstellung der Daten zuständig.\n\nSpeaker 0: Das ist aber nicht alles. Also diese Datenteams, die sind, fand ich, überraschend divers aufgestellt, ne. Also es gibt da alle möglichen Kompetenzen, die abgefragt werden. Welche sind euch da begegnet?\n\nSpeaker 1: Du hast es schon gesagt, ist relativ breit aufgestellt. Im Data Scientist ist jetzt auch nicht unbedingt 'n IT Hintergrund vonnöten gewesen, sondern das sind auch Sozialwissenschaftler*innen oder Naturwissenschaftler*innen, die mit sich mit Daten auskennen, Programmiererfahrung haben und eben auch empirische in empirischer Forschung oder in Datenanalyse Kompetenzen haben. Juristen findet man relativ wenige im Vergleich zur sonstigen Verwaltung und ansonsten Ökonominnen sind dabei, Coaches, also das geht wirklich 'n bisschen Querbeet\n\nSpeaker 0: Ja, finde ich der\n\nSpeaker 1: da mitarbeitet.\n\nSpeaker 0: Was genau macht den macht den Reiz aus, dass man sagt, wir wollen so diverse Teams haben in der Daten in der Datenarbeit.\n\nSpeaker 1: Ich weiß gar nicht, ob das von Anfang an vielleicht so intendiert war, aber die Arbeit mit Daten spielt einfach in in vielen Disziplinen ja eine eine steigende Rolle und allein deshalb hat sich das vermutlich auch irgendwo ergeben, dass man diverse Hintergründe erhalten hat der der Person, wenn man gesagt hat, es geht nicht jetzt einen bestimmten Abschluss in einem bestimmten Fach, sondern die tatsächlichen Fähigkeiten. Ich meine mich zu erinnern aus einem Interview, dass es dann auch einfach Tests gab, gewisse Aufgaben in Bezug auf Daten und Datenanalyse und es gibt eben verschiedene Disziplinen, in denen gut mit Daten gearbeitet werden kann. Die Leidenden haben ebenfalls sehr diverse Hintergründe. Die meisten kommen auch von eher von extern, da haben wir auch bis zur Astrophysik ist, glaube ich, alles alles vertreten. Aber natürlich, wenn man so verschiedene Disziplinen hat, schafft das natürlich auch eine eine gewisse Gemengelage, auch eine gewisse Spannung vielleicht, die dann aber natürlich auch zu mehr Kreativität und Produktivität führen kann, was man ja auch an interdisziplinären Teams auch in der Wirtschaft beispielsweise beobachten kann.\n\nSpeaker 0: Max, Du hast es gerade gesagt, diese sehr diversen Teams, die sind in der Praxis aber recht unterschiedlich organisiert in den Ministerien und Bundesbehörden. Also mal als Beispiel, manche Ministerien, da sind's 5 Leute, in anderen sind's 14 Mitarbeitende, die in so einem Datenlabor beschäftigt sind. Auch organisatorisch können die, so hab ich rausgelesen, recht unterschiedlich verortet sein. Gibt's da son Best Practice, wo ihr gesagt habt, ja, so müsste man's machen?\n\nSpeaker 2: Es gibt, glaub ich, Best Practices, die wieder vom Kontext abhängen. Aber ich würd mal ganz generell sagen, dass es schon gut, also sich bei den meisten als als sinnvoll herausgestellt hat, dass die Datenabore möglichst nah am Haus und an der Hausleitung dran sind, wenn's geht. Das heißt, eine nachgeordnete Behörde beispielsweise ist eventuell nicht der richtige Ort, aber kann auch da Gründe geben, warum das sinnvoll ist. Wie kann\n\nSpeaker 0: ich mir denn den Arbeitsalltag in so einem Datenlabor vorstellen? Also jetzt hantieren mit Reagenzgläsern und Flüssigkeiten haben wir ja schon ausgeräumt, das in jedem Falle nicht. Was macht einen was macht man in einem Datenlabor?\n\nSpeaker 2: Ich glaub, die Datenlabore vor allem zu dem an zu dem Zeitpunkt, an dem wir uns mit ihnen auseinandergesetzt haben, haben vor allem ganz viel Lobbyarbeit, wenn man so will, Daten Lobbyarbeit betrieben, also ganz viel in die Häuser gehen, mit Kolleginnen und Kollegen sprechen und zu versuchen, denen zu zeigen, was Daten eigentlich können, was man mit Daten eigentlich anstellen kann. Daneben die entsprechende Infrastruktur aufzubauen, die man braucht, zu arbeiten. Da gab's natürlich dann auch entsprechende Hürden, die vom von den von der Art und Weise, wie eben Verausgabungen in Bundesministerien oder in der Verwaltung generell geschehen, die da 'nem schnellen, sagen wir mal, Aufbauen der der Computerinfrastruktur, wenn man das jetzt mal so generell sagen will, teilweise oder das teilweise sehr erschwert haben. Und dann eben auch das Erstellen konkreter Produkte und konkreter konkreter Daten, Anwendungen. Und die wurden häufig auch wieder, auch wieder Mehrwert für fürs Haus zu liefern, eben mit Kolleginnen und Kollegen aus den Häusern umgesetzt, beziehungsweise auf Basis deren Bedarfe überhaupt erst entwickelt.\n\nSpeaker 0: Mhm. Also auch recht unterschiedliche Aufgaben, so von Marketing, sag ich mal, intern für Daten, ne. Warum sind Daten sinnvoll? Schulung bis hin zur Entwicklung. Wird denn auch in den Häusern selbst eine Lösung entwickelt?\n\nZum Beispiel son Dashboard, über das wir gesprochen haben oder wird das dann extern herausgegeben?\n\nSpeaker 2: Die Idee ist schon, dass sie auch in den Häusern die entsprechenden Kompetenzen haben, solche Anwendungen zu entwickeln. Teilweise ist es natürlich nicht immer möglich, je nachdem wie umfänglich solche solche Anwendungen sind. Aber ja, also grade auch möglichst nah an den Mitarbeitenden und an den Anwenderinnen im Haus dranzubleiben, ist es wurden wurden viele dieser Anwendungen im Haus von den Teams in den Datenlaboren entwickelt.\n\nSpeaker 0: Ich hab verstanden, dass die Datenlabore zwischen den Ministerien und Bundesbehörden kooperieren, dass die gut zusammenarbeiten. Wie ist es aber, dass wie arbeiten denn aber die Datenlabore mit anderen Bereichen in der Verwaltung zusammen? Gibt's da auch starke Kooperation oder gibt's da auch vielleicht sogar Vorbehalte?\n\nSpeaker 2: Also das hatte ich grade schon angesprochen, es gibt dann quasi auch Datenlaborbeispiele, die dann in nachgeordneten Behörden beispielsweise verankert sind. Das heißt, da ist die Zusammenarbeit mit der nachgeordneten Behörde dann vermutlich eher intensiv und das entsprechend, denk ich mal, zielführend, Beispiel Statistisches Bundesamt, ne. Die arbeiten viel mit Daten, da 'n Datenlabor hinzusetzen und zu versuchen, Synergien zu schaffen, macht natürlich sehr viel Sinn. Ja.\n\nSpeaker 1: Also genau, es ist schon ja eher die Ministerialverwaltung, aber die nachgeordneten Behörden spielen da auch eine Rolle. Da gibt's natürlich auch welche, Du hast jetzt schon das prominenteste Beispiel genannt, die natürlich auch viel mit Daten arbeiten, aber da gibt's ja auch andere, beispielsweise die Bundesbank, also auch mit nachgeordnetenbehörden arbeiten die Datenlabor schon intensiv zusammen. Dann gibt's ja teilweise auch in den Bundesministerien noch andere Innovationseinheiten, irgendwelche Innovation Labs, Civic Innovation Lab, beispielsweise im Familienministerium. Auch da gibt es Austausche und Zusammenarbeit. Das Migrations Dashboard haben wir ja schon angesprochen, das wird ja auch von Ländern und auch den Landkreisen sehr intensiv genutzt beispielsweise.\n\nSpeaker 0: Blicken wir doch mal zum Schluss auf die Zukunft der Datenlabore, auch grade noch mal mit dem Fokus auf die öffentliche Verwaltung. Es gibt ja nur allerhand neue Technologien, auch grad in letzter Zeit.\n\nSpeaker 2: Wir haben\n\nSpeaker 0: schon über KI gestützte Modelle gesprochen, die ja auch schon Anwendungen gefunden haben. Welche neuen Technologien kommen euch dann noch in den Sinn, die die Arbeit von Datenlaboren beeinflussen könnte? Also auch, wenn wir mal so an Machine Learning denken oder auch die Bedeutung von Open Data?\n\nSpeaker 2: Ja, also ich würd sagen, das große Thema, das am meisten bestimmende Thema wird wahrscheinlich auch in naher Zukunft werden noch weiter die Sprachmodelle bleiben, weil da eben sehr viel Potenzial noch drinsteckt mit den, was ja in Behörden 'n großen Teil der Daten ausmacht, mit Texten eben zu arbeiten und diese entsprechend nutzbar zu machen. Darüber hinaus, klar, gibt's viele weitere Möglichkeiten, die anderen Daten, die Du hast grade angesprochen, also Open Data, die auch an unterschiedlichsten Stellen in in der Republik liegen, sag ich jetzt mal, die auch mehr noch zu nutzen und zusammenzuführen. Da gibt's auch gab's ja 'n 'n großen große Bewegung, möglichst viele Daten bereitzustellen. Und teilweise werden die teilweise mit sehr großen Aufwänden immer noch gepflegt und bereitgestellt, aber die Nutzung dessen der der der bereitgestellten Daten ist eher eingeschränkt. Also da gibt's nur wenige Beispiele, die die diese offenen bereitgestellten Daten auch tatsächlich nutzen.\n\nUnd das weiter zu treiben, wird natürlich auch wichtig, 'n wichtiges Feld sein und und bleiben.\n\nSpeaker 0: Ist das, Nicole, auch eine ganz große Herausforderung? Also im Föderalismus, verschiedene Bundesländer eben, verschiedene Daten haben vielleicht nicht einen gemeinsamen Topf, wo die einfach reingespült werden, die auch wirklich nutzbar zu machen?\n\nSpeaker 1: Auf jeden Fall. Und das sind auch gerade diese genau diese Grundlagen, überhaupt mit Daten arbeiten zu können, Anwendungen darauf bauen zu können, etwas, woran die Datenlabor ja schon gearbeitet haben. Es wurde vom Bundeskanzleramt koordiniert, für so einen Datenpool mal einen Proof of Concept zu entwickeln. Wie könnte das denn überhaupt aussehen? Weil es ist ja schon schwierig, Daten allein über Ressortgrenzen auch teilen zu können.\n\nDa geht's auch erst mal darum, Datenkataloge zu erstellen. Was haben wir denn überhaupt für Daten, dass die auch halbwegs irgendwie einheitlich sind, dass man sie auch finden kann. Es ist vom Auswärtigen Amt gemeinsam mit der Bundesdruckerei eine Analyticsplattform, PLIN heißt die, für die Bundesverwaltung entwickelt worden, wo sie dann die Möglichkeit haben, Daten und auch Code untereinander zu teilen und da ihre Datenanalyseprojekte zu machen und 'n anderes Projekt ist beispielsweise KeePIDS, das ist vom BMI, von dem von der Initiative BEKI Beratungs- und Evalualisierungszentrum für Künstliche Intelligenz, auch gemeinsam mit den Datenlaboren entwickelt worden, den Bundesministerien Möglichkeiten zu geben, mit KI Anwendungen, speziell mit Sprachanwendungen, mal 'n bisschen rum zu experimentieren und da Erfahrungen zu machen. Genau, ich muss erst mal auch irgendwie eine Infrastruktur haben, auf der ich aufbauen kann und ich muss die Daten haben und dass die dass ich Daten habe, mit denen ich arbeiten kann, die in maschinenlesbarer Form vorliegen, die ich auch nutzen kann, weil Daten werden ja per Default erst mal als Verschlusssache eingestuft, da auch trotzdem Möglichkeiten zu haben. Open Data hast Du schon angesprochen, ist natürlich ein Weg, Daten zur Verfügung zu haben, mit denen ich arbeiten kann.\n\nDas ist 'n ganz, ganz großes Thema für die Datenlabore. Da hat haben sie schon große Schritte gemacht, hab ich grad 'n bisschen skizziert, aber da ist natürlich noch ein ganz noch ein weites Stück des Weges zu gehen. Und das ist für die Datenlabore gar nicht so einfach. Die waren erst mal, genau, das gibt's ja seit 20 21, waren sie erst mal finanziert bis 20 24. Dann gab's sone kleine Zitterpartie, jetzt ist die Finanzierung erst mal gesichert, allerdings auch wieder nur bis Ende nächsten Jahres.\n\nDas ist natürlich auch son bisschen schwierig, ohne eine langfristige Perspektive solche langfristigen Projekte anzugehen, wie eben eine entsprechende Infrastruktur zu schaffen. Datenkompetenzaufbau ist natürlich auch was, was was weitergehen muss in den Ministerien und was wir uns auch 'n bisschen wünschen würden, wäre eine stärkere Öffnung auch nach außen. Du hast ja schon es angesprochen, wie sieht's denn aus eigentlich mit Verwaltungen in Ländern und Kommunen beispielsweise, aber auch eben eine Öffnung mehr hin zur Öffentlichkeit, mehr in den Dialog gehen, auch mit Zivilgesellschaft, mit Wissenschaft, mit Wirtschaft, weil im Moment hat man schon mit von Ausnahmen abgesehen auf jeden Fall, da gibt's auch welche, die das 'n bisschen anders handhaben, aber ja schon diesen sehr starken Fokus auf Verwaltung und man kooperiert aber eher untereinander und nicht so sehr mit mit externen Akteuren, die ja vielleicht auch eine wertvolle Impulse, Kompetenzen et cetera noch zur Verfügung stellen könnten.\n\nSpeaker 2: Ich hab\n\nSpeaker 0: jetzt grade schon rausgehört, Zitterpartie, was die Finanzierung angeht. Wenn wir jetzt mal so 5 oder 10 Jahre in die Zukunft schauen, gibt's da noch Datenlabore oder was ist aus denen geworden?\n\nSpeaker 2: Ist eine spannende Frage. Ich glaub, diese Labore als als Vehikel, sag ich mal, sind oft was, was nicht auf ewig immer Bestand hat in solchen Organisationen. Also die sind meistens zum Etablieren 1 bestimmten Sache, zum Ausprobieren 1 bestimmten Sache genutzt. Bei den Datenlaboren bin ich mir, ehrlich gesagt, nicht so sicher. Da glaub ich, dass die aufgrund der breiten Anwendungen, die ja Daten in Ministerien finden und die auch die Mehrwerte, die Datenlabore entsprechend für die Ministerien liefern können, glaub ich, dass ich mir schon vorstellen kann, dass die auch langfristig eine Perspektive haben, Ob sie das dann tatsächlich auch so realisieren können und ob das so realisiert wird, hängt ja nicht nur an den Laboren, sondern in letztendlich auch politische Entscheidungen.\n\nUnd da kann man nur hoffen, dass die Arbeit, die sie jetzt tun, dazu beiträgt, den Entscheidungsträgerinnen klarzumachen, dass das sehr wichtig ist, diese Datenlabore weiter zu institutionalisieren und weiter zu fördern und weiter in den Häusern idealerweise, wie wie wie schon angesprochen, in allen Häusern, aber trotzdem auch auf Zusammenarbeit angelegt zu etablieren.\n\nSpeaker 0: Könnte ja aber eben auch sein, dass in einigen Jahren die Arbeit mit Daten so selbstverständlich geworden ist, dass das Labor sich selbst überflüssig gemacht hat, ja, weil's eben in den Fachabteilungen angekommen ist. Nicole, was meinst Du?\n\nSpeaker 1: Das ist 'n guter Punkt. Das ist natürlich wünschenswert, dass die Arbeit der Datenlabore in 5 bis 10 Jahren vielleicht auf jeden Fall einen anderen Schwerpunkt haben wird, weil die Infrastrukturen inzwischen einfach da sind. Die müssen natürlich stetig weiterentwickelt werden und na ja, irgendwie, wenn wir sagen, wir wollen mehr datengestützte, mehr evidenzbasierte Politikgestaltung, dann müssen wir darauf auch irgendeine Antwort finden und die aktuelle Antwort lautet eben Datenlabore, diese Datenlabore zu zu installieren. Das war die Antwort darauf und es braucht auch 'n gewissen institutionalisierten Rahmen für so etwas. Und es ist natürlich auch sinnvoll, auch im Hinblick auf neue Technologien, die Du schon angesprochen hast, brauchen wir auch in Zukunft, auch in 5 und 10 Jahren noch Experimentier- und Lernräume, wo man einfach mal Neues ausprobieren kann mit Tri und Error, manches wird auch nicht funktionieren.\n\nAlso irgendwas, was son Labcharakter hat, werden wir auf jeden Fall brauchen, obwohl natürlich die Hoffnung ist, dass genau Kompetenzen und vieles, was bisher die Datenlabore noch leisten müssen, schon sehr viel stärker in den Fachabteilungen verankert ist und selbstverständlich geworden ist.\n\nSpeaker 0: Wie Datennabore in Bundesbehörden arbeiten und warum sie so wichtig sind, das war restStart digital, der Podcast des Kompetenzzentrums Öffentlicher IT. Herzlichen Dank an meine Co Host Nicole Opiala und Maximilian Kubi. Schön, dass ihr hier wart.\n\nSpeaker 1: Vielen Dank. Danke dir.\n\nSpeaker 0: Die passende Publikation gibt's wie immer online unter öfid Punkt d e, öfid mit o e. Einen Link findet ihr auch in den Shownotes. Falls euch der Podcast gefallen hat, empfehlt uns weiter und folgt uns kostenfrei auf Spotify, Apple Podcasts und vielen weiteren Apps. Bis zum nächsten Mal, ich bin Philipp 1.", "paragraphs": [ { "sentences": [ { "text": "Was kommt euch in den Sinn, wenn ihr von einem Labor hört?", "start": 2.08, "end": 5.7799997 }, { "text": "Also ich denk da an Menschen in weißen Kitteln mit Schutzbrillen, dann auf dem Tisch davor Reagenzgläser, Petrischalen, 'n Bunsenbrenner vielleicht.", "start": 6.08, "end": 14.42 }, { "text": "Das alles steht vor meinem inneren Auge da auf dem Tisch voller farbiger Substanzen, die vor sich hin brodeln, 'n Labor eben.", "start": 14.625, "end": 21.205 }, { "text": "Auch in den Bundesministerien und im Bundeskanzleramt gibt es seit Kurzem Labore, allerdings ganz ohne solche Versuchsanordnungen aus dem Chemiebaukasten.", "start": 21.505, "end": 29.765 }, { "text": "Es handelt sich nämlich Datenlabore.", "start": 30.57, "end": 32.51 } ], "start": 2.08, "end": 32.51, "num_words": 80.0, "speaker": 0 }, { "sentences": [ { "text": "Dort laufen allerhand komplexe Daten zusammen, die analysiert, verbreitet und genutzt werden.", "start": 33.37, "end": 38.67 }, { "text": "Denn auf Grundlage von Daten, so die Annahme, könnte das Land besser regiert werden.", "start": 39.05, "end": 43.71 }, { "text": "Was können Datenlabore und wie arbeiten sie konkret?", "start": 44.925003, "end": 47.985 }, { "text": "Das wollen wir heute in dieser Podcastfolge herausfinden.", "start": 48.205, "end": 50.465 }, { "text": "Herzlich willkommen beim öFIT Podcast restart digital, eine Produktion des Fraunhofer Instituts für offene Kommunikationssysteme.", "start": 64.14, "end": 70.72 } ], "start": 33.37, "end": 70.72, "num_words": 57.0, "speaker": 0 }, { "sentences": [ { "text": "Ich bin Philipp 1 und ich begrüße ganz herzlich meine beiden Co Hosts.", "start": 72.125, "end": 75.985 }, { "text": "Da sind zum einen Nicole Opiala.", "start": 76.045006, "end": 77.745 }, { "text": "Sie ist Politologin, stellvertretende Leiterin des öFIT und ja beinahe in jeder Folge unseres Podcasts dabei.", "start": 78.045006, "end": 84.125 }, { "text": "Heute ist sie, wie so meist, online zugeschaltet.", "start": 84.125, "end": 87.41 }, { "text": "Hallo Nicole.", "start": 87.630005, "end": 88.83 } ], "start": 72.125, "end": 88.83, "num_words": 45.0, "speaker": 0 }, { "sentences": [ { "text": "Hallo Philipp.", "start": 88.83, "end": 89.65 } ], "start": 88.83, "end": 89.65, "num_words": 2.0, "speaker": 1 }, { "sentences": [ { "text": "Außerdem mit dabei Maximilian Kubi.", "start": 90.350006, "end": 92.350006 }, { "text": "Er ist Data Scientist und hat sich an der Herty School mit digitalem Wandel befasst.", "start": 92.350006, "end": 97.090004 }, { "text": "Er arbeitet außerdem als wissenschaftlicher Mitarbeiter am öfid und ist hier nach Berlin Tegel ins Studio gekommen mit Busersatzverkehr und allem, was dazugehört.", "start": 97.40501, "end": 105.825005 }, { "text": "Hallo Max.", "start": 105.965004, "end": 106.785 } ], "start": 90.350006, "end": 106.785, "num_words": 45.0, "speaker": 0 }, { "sentences": [ { "text": "Guten Morgen, hi.", "start": 106.845, "end": 107.825005 } ], "start": 106.845, "end": 107.825005, "num_words": 3.0, "speaker": 2 }, { "sentences": [ { "text": "Ja, schauen wir zunächst mal in die Praxis.", "start": 108.365005, "end": 110.22 }, { "text": "Seit 20 21 haben Bundesbehörden mit dem Aufbau von Datenlaboren begonnen.", "start": 110.3, "end": 114.880005 }, { "text": "Was das bringt und wie diese Datenlabore arbeiten, das habt ihr am Öffit in der aktuellen Studie gemeinsam mit dem Forschungsinstitut Neuland 21 untersucht.", "start": 115.340004, "end": 125.12 }, { "text": "Fangen wir doch mal mit der Praxis an.", "start": 125.58, "end": 127.435 }, { "text": "Welche konkreten Probleme können durch die Arbeit von Datenlabore denn so gelöst werden?", "start": 127.895, "end": 132.875 } ], "start": 108.365005, "end": 132.875, "num_words": 64.0, "speaker": 0 }, { "sentences": [ { "text": "Ja, da kann man einmal eben ganz klein anfangen.", "start": 133.175, "end": 137.035 }, { "text": "Es gibt einfach Herausforderungen, die in der alltäglichen Arbeit, in der Ministerialverwaltung immer wieder auftauchen, dass Informationen zusammengesucht werden müssen für Reisen beispielsweise in bestimmte Wahlkreise oder in andere Länder, dass kleine Anfragen bearbeitet werden müssen, dass andere Referate beteiligt werden müssen bei bestimmten Entwicklungen oder Entscheidungen und das ist in der Praxis bisher sehr viel manueller Rechercheaufwand gewesen für die Mitarbeitenden, Dokumente zu durchsuchen, die teilweise beispielsweise, wenn's da Informationen des Währungsfonds geht, mehrere 100 Seiten umfassen, die dann möglichst zusammenzufassen und die entsprechenden Informationen da rauszuziehen.", "start": 137.96, "end": 187.815 }, { "text": "Und das ist zum Beispiel was, wo man mit Na ja, wo ist 'n Datenlabor mit ganz kleinen Anwendungen 'n großen Mehrwert schaffen kann, da entsprechende Informationen und Daten schneller auffindbar zu machen, KI gestützt, Dokumente zusammenzufassen oder eben auch über Dashboards gewisse Informationen einfach abzubilden, die aus verschiedenen Datenquellen stammen, die dann eben nicht mehr von den Mitarbeitenden händisch zusammengesucht werden müssen.", "start": 188.275, "end": 216.55501 }, { "text": "Also das ist 'n ganz kleiner, konkreter Mehrwert in der Praxis, der in der alltäglichen Arbeit hilft, aber natürlich geht's da auch 'n bisschen das größere Bild, also auch Arbeit die Arbeitskultur zu verändern und die Mitarbeitenden zu befähigen und zu sensibilisieren, wie man mit Daten arbeiten kann und natürlich auch entsprechende Infrastrukturen zu schaffen, mit Daten zu arbeiten.", "start": 216.695, "end": 242.285 } ], "start": 133.175, "end": 242.285, "num_words": 219.0, "speaker": 1 }, { "sentences": [ { "text": "Also die Datenlabore, die haben da ganz vielfältige Aufgaben, die sie dann erfüllen.", "start": 242.745, "end": 250.44499 } ], "start": 242.745, "end": 250.44499, "num_words": 13.0, "speaker": 1 }, { "sentences": [ { "text": "Du hast ja schon so einige Beispiele genannt, so aus dem Arbeitsalltag 1 Ministeriums.", "start": 251.385, "end": 256.15 }, { "text": "Interessant find ich ja, dass die Entscheidung für die Datenlabore während der Coronapandemie fiel.", "start": 256.77, "end": 262.45 }, { "text": "Gibt's da auch grade im Bereich Gesundheit und Vorsorge, also auch Pandemiepläne zum Beispiel, Anwendungsfälle für solche Labore?", "start": 262.45, "end": 270.07 } ], "start": 251.385, "end": 270.07, "num_words": 46.0, "speaker": 0 }, { "sentences": [ { "text": "Dass es grade der Zeitpunkt war, war, glaube ich, eher ein ein glückliches Zusammenspiel.", "start": 271.54498, "end": 276.205 }, { "text": "20 21 ist die Datenstrategie der Bundesregierung erschienen.", "start": 276.505, "end": 280.11227 }, { "text": "Darin wurde der Auftrag erteilt, Datenlabore in allen Bundesministerien aufzubauen und dem NextGeneration EU Fundes einfach auch tatsächlich entsprechende Mittel bereit.", "start": 280.11227, "end": 300.185 }, { "text": "Also man hatte dann die Chance, das auch tatsächlich direkt in die Praxis umzusetzen.", "start": 300.245, "end": 304.345 }, { "text": "Und wie sehr es solche Dateneinheiten braucht in der in der in den Bundesministerien, ist durch die Pandemie, glaube ich, einfach noch mal sehr deutlich klar geworden, wenn man festgestellt hat, dass an vielen Stellen die Daten einfach fehlen, dass man gar nicht weiß, wie groß ist der Bedarf?", "start": 304.965, "end": 324.505 } ], "start": 271.54498, "end": 324.505, "num_words": 106.0, "speaker": 1 }, { "sentences": [ { "text": "Wie viele Anspruchsberechtigte gibt es, wenn es Coronahilfen geht?", "start": 324.505, "end": 329.145 }, { "text": "Dass man Dinge nicht auszahlen konnte, weil entsprechende Daten, Kontodaten zu Personen fehlen et cetera und dass das in anderen Ländern ja auch ganz anders gelaufen ist, dass die, weil die eben nicht nur den großen Zensus alle paar Jahre haben, sondern regelmäßig in kleinerem Umfang Daten erheben und die auch zur Verfügung stehen, sehr viel besser, sehr viel genauer, sehr viel bedarfsgerechter durch diese Pandemie steuern konnten.", "start": 329.145, "end": 356.845 }, { "text": "Und ja, da kamen die Datenlabore dann quasi zur rechten Zeit, obwohl man auch sagen muss, manche haben schon direkt 20 21 auch mit der Arbeit beginnen können, wenn's entsprechende Vorarbeiten auch schon gab.", "start": 356.98502, "end": 369.25998 }, { "text": "Bei manchen anderen hat's dann doch deutlich länger gedauert, bis die dann richtig arbeitsfähig waren.", "start": 369.4, "end": 374.36 }, { "text": "Also teilweise die erst 20 23 so richtig mit der operativen Arbeit begonnen.", "start": 374.36, "end": 379.115 } ], "start": 324.505, "end": 379.115, "num_words": 139.0, "speaker": 1 }, { "sentences": [ { "text": "Es ist ja, glaube ich, auch noch nicht abgeschlossen.", "start": 379.115, "end": 380.555 }, { "text": "Also die Datenlabore werden nach und nach weiter ausgebaut, installiert.", "start": 380.555, "end": 383.695 }, { "text": "Ja, schauen wir doch mal auf son paar Best Practices, Max.", "start": 384.555, "end": 388.255 }, { "text": "Ein Beispiel ist grad schon angeklungen, kleine Anfragen im Bundestag.", "start": 388.54, "end": 392.0 }, { "text": "Ist 'n großer Aufwand für die Verwaltung in den Ministerien, die zu bearbeiten.", "start": 392.94, "end": 396.72 } ], "start": 379.115, "end": 396.72, "num_words": 53.0, "speaker": 0 }, { "sentences": [ { "text": "Jetzt gibt's aber KirAS, das ist so eine Art KI Rechercheassistent.", "start": 397.82, "end": 401.36002 }, { "text": "Wie funktioniert das?", "start": 401.9, "end": 403.12 } ], "start": 397.82, "end": 403.12, "num_words": 14.0, "speaker": 0 }, { "sentences": [ { "text": "Der hilft Mitarbeitenden in den Behörden, die kleinen Anfragen, die ausm Bundestag kommen, was ja doch regelmäßig vorkommt auf einfache Art, KI gestützt und über App oder auch Browserinterface, also Webschnittstellen, zu beantworten.", "start": 403.465, "end": 421.04 }, { "text": "Und aktuell wird er oder er wurde zum Zeitpunkt der Studie mit 1 Kernnutzerinnengruppe getestet.", "start": 421.5, "end": 429.18002 }, { "text": "Also insgesamt 40 Personen konnten diesen Kiras Rechercheassistent ausprobieren und entsprechend helfen, dem weiterzuentwickeln.", "start": 429.18002, "end": 437.965 }, { "text": "Mhm.", "start": 438.585, "end": 439.085 } ], "start": 403.465, "end": 439.085, "num_words": 63.0, "speaker": 2 }, { "sentences": [ { "text": "Genau, der ist im BMUV entwickelt worden, aber ich hab's eben schon gesagt, kleine Anfragen beschäftigen natürlich alle Ministerien.", "start": 439.145, "end": 446.445 }, { "text": "Und deshalb hat das BMUV, die Datenlabore tauschen sich interministeriell sehr stark miteinander aus.", "start": 447.44998, "end": 455.44998 }, { "text": "Das ist auch eine große Besonderheit an diesen Einheiten, treffen sich regelmäßig auf Leitungsebene in der interministeriellen Arbeitsgruppe, aber auch in so einem Peer to Peer Netzwerk.", "start": 455.44998, "end": 466.35498 }, { "text": "Da sind die Leitenden dabei, aber auch die Mitarbeitenden.", "start": 466.655, "end": 468.835 }, { "text": "Es ist son bisschen selbstorganisiert und das BMUV hat dann seinen Rechercheassistent da mal vorgestellt, zur Diskussion gestellt und dann auch Testzugänge den anderen Ministerien zur Verfügung gestellt, weil das einfach natürlich 'n Thema ist, was alle beschäftigt.", "start": 469.29498, "end": 485.63 } ], "start": 439.145, "end": 485.63, "num_words": 107.0, "speaker": 1 }, { "sentences": [ { "text": "Und bevor man dann 16 parallele Anwendungen", "start": 485.63, "end": 485.97287 } ], "start": 485.63, "end": 485.97287, "num_words": 7.0, "speaker": 2 }, { "sentences": [ { "text": "entwickelt, ist es natürlich sehr viel sinnvoller, wenn man sie kann das den anderen vielleicht zur Verfügung stellen.", "start": 485.97287, "end": 495.545 }, { "text": "Denn natürlich sind die also die Inhalte und die fachlichen Fragen andere, aber rein technisch, genau, dass ich dass ich das KI gestützt machen kann, was es für Schnittstellen gibt, dass es irgendwelche Suchfunktionen gibt, dass ich das irgendwie prompten kann.", "start": 495.765, "end": 510.46997 }, { "text": "Das sind ja so Aspekte, die in allen Ministerien auch zum Einsatz kommen.", "start": 510.93, "end": 515.25 }, { "text": "Deswegen, genau, kann man da sich auch gut austauschen und gemeinsam Entwicklungen anstoßen.", "start": 515.25, "end": 520.455 } ], "start": 485.97287, "end": 520.455, "num_words": 85.0, "speaker": 1 }, { "sentences": [ { "text": "Mhm.", "start": 520.755, "end": 520.91504 }, { "text": "Also Synergien werden genutzt, das ist sicherlich verständlich.", "start": 520.91504, "end": 523.815 }, { "text": "Wenn ich das jetzt so höre, dann klingt es ja auch so, dass am Ende Verwaltungsaufwand eingespart wird, also Arbeitszeit eingespart wird.", "start": 524.59503, "end": 531.175 }, { "text": "Ich hab aber in eurer Publikation gelesen, da schreibt ihr auch, dass am Ende das Land eventuell besser oder auch effizienter regiert werden könne.", "start": 531.29004, "end": 539.95 }, { "text": "Ist das so?", "start": 540.97003, "end": 541.69 } ], "start": 520.755, "end": 541.69, "num_words": 58.0, "speaker": 0 }, { "sentences": [ { "text": "Also was genau hilft dabei, tatsächlich besser zu regieren?", "start": 541.69, "end": 545.075 } ], "start": 541.69, "end": 545.075, "num_words": 9.0, "speaker": 0 }, { "sentences": [ { "text": "Na, das hatte ja Nicole am Anfang schon angesprochen, dass quasi die Transparenz über die Tatbestände, jetzt mittler Beispiel Corona und die Transparenz eben über die Daten hilft, besser zu steuern und das Land besser.", "start": 545.075, "end": 558.98004 }, { "text": "Und auch zu wissen, was was von den Sachen, die wir machen, was kommt da eigentlich tatsächlich an?", "start": 558.98004, "end": 564.04004 }, { "text": "Und auch eine Art Wirkungsmessung letztendlich durchführen zu können, die eben nur auf Basis von guten und verfügbaren Daten möglich ist.", "start": 564.34, "end": 573.435 }, { "text": "Mhm.", "start": 573.435, "end": 573.935 }, { "text": "Kommen wir", "start": 574.47504, "end": 574.79504 } ], "start": 545.075, "end": 574.79504, "num_words": 77.0, "speaker": 2 }, { "sentences": [ { "text": "mal noch zu 'nem anderen Beispiel, das ich auch ganz interessant fand, nämlich ein Migrations Dashboard.", "start": 574.79504, "end": 579.215 }, { "text": "Also da kann praktisch online auf 'nem Monitor die gegenwärtige Migrationslage im Land abgebildet werden und Fachleute können zum Beispiel die Unterbringung von Geflüchteten oder auch die Integration besser koordinieren.", "start": 579.755, "end": 592.51 }, { "text": "Ja, wie funktioniert das?", "start": 593.85004, "end": 595.23004 } ], "start": 574.79504, "end": 595.23004, "num_words": 50.0, "speaker": 0 }, { "sentences": [ { "text": "Es funktioniert aufm Prinzip des Dashboards, was man sich so vorstellen kann wie eine simple Benutzeroberfläche, die die wesentlichen Zahlen und die wesentlichen Trends in den Zahlen idealerweise den Entscheidungsträgerinnen zur Verfügung stellt.", "start": 595.29004, "end": 614.07996 }, { "text": "Und darauf basieren können dann Entscheidungen getroffen werden und eben Sachen nachgesteuert werden.", "start": 614.07996, "end": 619.3 }, { "text": "Also die Dashboards sind in in der oder sind in den Datenlaboren, aber auch über die Datenlabore hinaus 'n sehr beliebtes Tool,", "start": 619.36, "end": 629.175 } ], "start": 595.29004, "end": 629.175, "num_words": 68.0, "speaker": 2 }, { "sentences": [ { "text": "würd ich", "start": 629.175, "end": 629.41504 } ], "start": 629.175, "end": 629.41504, "num_words": 2.0, "speaker": 0 }, { "sentences": [ { "text": "mal sagen aktuell, Daten, mit Daten zu arbeiten, Daten transparent zu machen.", "start": 629.41504, "end": 633.41504 } ], "start": 629.41504, "end": 633.41504, "num_words": 12.0, "speaker": 2 }, { "sentences": [ { "text": "Ich würd's mir jetzt so vorstellen, wirklich wie sone Art, ja, Monitor, auf dem alle Daten zusammenlaufen aus allen Bundesländern, aus ja, also Daten zum Thema Migration und ich dann wie sone Art ja Landkarte habe, die sich dort vor meinem Auge abzeichnet.", "start": 633.41504, "end": 648.44 }, { "text": "Ist das ungefähr so richtig?", "start": 648.44, "end": 649.74 } ], "start": 633.41504, "end": 649.74, "num_words": 48.0, "speaker": 0 }, { "sentences": [ { "text": "Genau, das das BMI hat ja die Zahlen zur aktuellen Migrationslage.", "start": 650.04, "end": 654.54 }, { "text": "Das Datenlabor des BMI hat auch dieses Migrations Dashboard entwickelt und nach gewissen Schlüsseln werden dann ja eingewanderte Personen auf die Länder verteilt und wenn natürlich dann bekannt ist, wie sehen die Zahlen aktuell aus, wie entwickeln sich die Zahlen aktuell, erleichtert das natürlich die Planung für die Länder und dann auch für die Landkreise, die dann ja konkret auch Unterbringungsmöglichkeiten bereitstellen müssen.", "start": 655.32, "end": 681.99 }, { "text": "Deswegen, die Anwendung kommt sehr gut an.", "start": 682.465, "end": 685.425 }, { "text": "Aktuell wird wird die von allen Ländern und auch, na ja, unsere Studie, das ist dann der Stand von vom Ende letzten Jahres, da waren schon 2 Drittel der Landkreise, die dieses Dashboard auch genutzt haben.", "start": 685.425, "end": 699.2 }, { "text": "Also schon ein großer Hit, würd ich sagen.", "start": 699.26, "end": 701.66003 } ], "start": 650.04, "end": 701.66003, "num_words": 125.0, "speaker": 1 }, { "sentences": [ { "text": "Ja, doch in auch relativ kurzer Zeit für Verwaltungsmaßstäbe.", "start": 701.66003, "end": 703.92 }, { "text": "Jetzt haben wir sehr auf die Verwaltung geschaut.", "start": 705.26, "end": 707.44 }, { "text": "Ändern wir doch mal die Perspektive und blicken auf die Bürgerinnen und Bürger.", "start": 708.06, "end": 712.265 }, { "text": "Welche Vorteile haben die eigentlich durch die Arbeit von solchen Datenlaboren?", "start": 712.405, "end": 715.465 } ], "start": 701.66003, "end": 715.465, "num_words": 41.0, "speaker": 0 }, { "sentences": [ { "text": "Ich würd mal sagen, die Datenlabore in den Ministerien haben schon primär das Ziel, die Politik und die Politikgestaltung und die Arbeit der Ministerien eben zu erleichtern.", "start": 716.405, "end": 726.48505 }, { "text": "Und die ist ja dann doch in groß, sag ich mal, relativ weit weg von den Bürgern.", "start": 726.48505, "end": 730.78 }, { "text": "Aber es gibt auch Datenlabore außerhalb der Bundesministerien oder vor allem in sonem Smart City Kontext oder so, wo eben auch ähnliche Produkte wie jetzt oder ähnliche Anwendungen wie Chat Bots für Bürgerinnen und Bürger oder Dashboards, die irgendwelche beispielsweise, weiß ich von einem Dashboard, dass die nachhaltige Entwicklung 1 1 Region versucht, für die Bürger irgendwie zu machen.", "start": 730.78, "end": 756.52 }, { "text": "Also so was gibt's auch.", "start": 756.9, "end": 758.02 }, { "text": "Das heißt, das hilft einfach den Bürgern auch, mit dem Staat zu kommunizieren und auch über die aktuellen Staatsvorhaben immer aufm Laufenden zu bleiben.", "start": 758.02, "end": 764.34 } ], "start": 716.405, "end": 764.34, "num_words": 132.0, "speaker": 2 }, { "sentences": [ { "text": "Also auch Transparenz und Interaktion, also solche Chat Bots werden oft auch für Anfragen der Bürgerinnen an beispielsweise das Bürgeramt genutzt und genau.", "start": 764.34, "end": 775.475 } ], "start": 764.34, "end": 775.475, "num_words": 23.0, "speaker": 2 }, { "sentences": [ { "text": "Ich würd auch sagen, die Effekte sind in dem vielleicht eher 'n bisschen indirekter vielleicht.", "start": 775.475, "end": 779.955 }, { "text": "Also genau, was wir bisher dargestellt haben, arbeiten die Datenlabore in den Bundesministerien ja schon eher für für den für den Bund, für die Bundesressorts, also sorgen da einfach für eine Effizienzsteigerung, fürn besseres Wissensmanagement, für den Kompetenzaufbau, schaffen eben Infrastrukturen, mit Daten und auch mit KI natürlich arbeiten zu können, genau, oft mehr maschinenlesbare Daten überhaupt zur Verfügung stellen zu können und die dann auch nutzen zu können.", "start": 779.955, "end": 815.51 }, { "text": "Und letztlich zahlt das dann ja aber auch darauf ein, dass eben auch Politikgestaltung stärker evidenzbasiert, stärker datengestützt funktionieren kann.", "start": 816.05, "end": 826.47504 }, { "text": "Und da ist natürlich dann die Hoffnung, dass das dann schon sehr deutlich bei den Bürgerinnen und Bürgern ankommt, wenn man schon 'n viel klareres Bild davon hat, was genau die Problemlagen sind und auch in welcher Form, weil man einfach die Daten dazu zur Verfügung hat, auch aktuelle Daten und dann auch weiß, so und so will ich steuern und das und das sind meine Ziele und so und so kann ich auch nachverfolgen, ob die intendierten Wirkungen dann erreicht werden und nicht mich auf Schätzungen oder alte Daten oder unvollständige Daten verlassen zu müssen, damit son bisschen im Blindflug zu sein und die Intention natürlich zu haben, was will ich mit den und den Gesetzesinitiativen erreichen, Aber dann auch Probleme habe zu erfassen, trifft es wirklich den Kern des Problems und werden die Wirkung erreicht?", "start": 827.495, "end": 875.27997 } ], "start": 775.475, "end": 875.27997, "num_words": 241.0, "speaker": 1 }, { "sentences": [ { "text": "Und ich denke, das kommt uns allen und der ganzen Gesellschaft zugute, wenn das natürlich sehr viel näher an die Realität und an die realen Entwicklungen gekoppelt ist dadurch, dass man eben besser mit Daten arbeiten kann in den Ministerien.", "start": 875.81995, "end": 887.985 } ], "start": 875.81995, "end": 887.985, "num_words": 40.0, "speaker": 1 }, { "sentences": [ { "text": "Gehen wir doch mal in den Maschinenraum dieser Datenlabore und gucken so ein wenig, wie die wirklich aufgebaut sind und wie die eingebunden sind in den Bundesbehörden.", "start": 889.645, "end": 898.16003 }, { "text": "Ja, habt ihr da Beispiele, also grade auch jetzt in den Ministerien, wie die Datenlabore dort sozusagen situiert sind?", "start": 898.86005, "end": 905.52 } ], "start": 889.645, "end": 905.52, "num_words": 46.0, "speaker": 0 }, { "sentences": [ { "text": "Also ich würd sagen, der große Vorteil der Datenlabore, erst mal einen Schritt zurück, dass es eben in allen Bundesministerien ein Datenlabor gibt, ist, dass die Datenlabore auf diese Art, auf die jeweiligen Begebenheiten in den jeweiligen Häuser reagieren konnten.", "start": 905.855, "end": 920.175 }, { "text": "Deswegen sind die alle 'n bisschen unterschiedlich aufgebaut und haben auch unterschiedliche Größen, sind auch an unterschiedlichen Stellen sozusagen im in der Organisation verortet.", "start": 920.175, "end": 931.8 }, { "text": "Aber ganz generell kann man grob sagen, dass es immer so zwischen 5 und, sagen wir mal, 15 Teammitglieder sind.", "start": 931.86, "end": 939.845 }, { "text": "Da sind natürlich Data Scientists, also Leute, die mit Daten umgehen können, Data und Data Analysts.", "start": 940.065, "end": 945.985 }, { "text": "Das sind die klassischen, sagen wir mal, Rollen, die die bei mit Datenarbeit irgendwie wichtig sind.", "start": 945.985, "end": 952.64 } ], "start": 905.855, "end": 952.64, "num_words": 116.0, "speaker": 2 }, { "sentences": [ { "text": "Das hatte ich mich gefragt, was macht eigentlich ein Data Scientist?", "start": 952.64, "end": 955.04004 }, { "text": "Was macht ein Data Analyst?", "start": 955.04004, "end": 956.5 } ], "start": 952.64, "end": 956.5, "num_words": 16.0, "speaker": 0 }, { "sentences": [ { "text": "'N Data Scientist ist erst mal, wie der Name schon sagt, ein Datenwissenschaftler.", "start": 956.72003, "end": 960.02 }, { "text": "Das heißt, da geht's viel Modellbildung und komplexere Anwendungen im weiteren Sinne, die er entwickelt, 'n Data Analyst und er oder sie, 'n Data Analyst oder eine Data Analystin analysiert, wie der Name ja auch sagt, Daten.", "start": 960.4, "end": 976.07 }, { "text": "Das heißt, es ist 'n bisschen oder 'n bisschen Praktischer, genau.", "start": 976.07, "end": 982.47 }, { "text": "Und ich Operativer und weniger konzeptionell.", "start": 982.47, "end": 984.57 }, { "text": "Und dann gibt's noch Data Engineers, die sind eigentlich für die Bereitstellung der Daten zuständig.", "start": 984.94995, "end": 989.52997 } ], "start": 956.72003, "end": 989.52997, "num_words": 82.0, "speaker": 2 }, { "sentences": [ { "text": "Das ist aber nicht alles.", "start": 990.795, "end": 991.835 }, { "text": "Also diese Datenteams, die sind, fand ich, überraschend divers aufgestellt, ne.", "start": 991.835, "end": 996.955 }, { "text": "Also es gibt da alle möglichen Kompetenzen, die abgefragt werden.", "start": 996.955, "end": 999.835 }, { "text": "Welche sind euch da begegnet?", "start": 999.835, "end": 1001.21497 } ], "start": 990.795, "end": 1001.21497, "num_words": 31.0, "speaker": 0 }, { "sentences": [ { "text": "Du hast es schon gesagt, ist relativ breit aufgestellt.", "start": 1001.835, "end": 1004.495 }, { "text": "Im Data Scientist ist jetzt auch nicht unbedingt 'n IT Hintergrund vonnöten gewesen, sondern das sind auch Sozialwissenschaftler*innen oder Naturwissenschaftler*innen, die mit sich mit Daten auskennen, Programmiererfahrung haben und eben auch empirische in empirischer Forschung oder in Datenanalyse Kompetenzen haben.", "start": 1004.71497, "end": 1029.425 }, { "text": "Juristen findet man relativ wenige im Vergleich zur sonstigen Verwaltung und ansonsten Ökonominnen sind dabei, Coaches, also das geht wirklich 'n bisschen Querbeet", "start": 1031.965, "end": 1045.0599 } ], "start": 1001.835, "end": 1045.0599, "num_words": 72.0, "speaker": 1 }, { "sentences": [ { "text": "Ja, finde ich der", "start": 1045.22, "end": 1046.02 } ], "start": 1045.22, "end": 1046.02, "num_words": 4.0, "speaker": 0 }, { "sentences": [ { "text": "da mitarbeitet.", "start": 1046.02, "end": 1046.6799 } ], "start": 1046.02, "end": 1046.6799, "num_words": 2.0, "speaker": 1 }, { "sentences": [ { "text": "Was genau macht den macht den Reiz aus, dass man sagt, wir wollen so diverse Teams haben in der Daten in der Datenarbeit.", "start": 1047.0599, "end": 1053.395 } ], "start": 1047.0599, "end": 1053.395, "num_words": 23.0, "speaker": 0 }, { "sentences": [ { "text": "Ich weiß gar nicht, ob das von Anfang an vielleicht so intendiert war, aber die Arbeit mit Daten spielt einfach in in vielen Disziplinen ja eine eine steigende Rolle und allein deshalb hat sich das vermutlich auch irgendwo ergeben, dass man diverse Hintergründe erhalten hat der der Person, wenn man gesagt hat, es geht nicht jetzt einen bestimmten Abschluss in einem bestimmten Fach, sondern die tatsächlichen Fähigkeiten.", "start": 1053.6951, "end": 1081.145 }, { "text": "Ich meine mich zu erinnern aus einem Interview, dass es dann auch einfach Tests gab, gewisse Aufgaben in Bezug auf Daten und Datenanalyse und es gibt eben verschiedene Disziplinen, in denen gut mit Daten gearbeitet werden kann.", "start": 1081.765, "end": 1098.29 }, { "text": "Die Leidenden haben ebenfalls sehr diverse Hintergründe.", "start": 1098.29, "end": 1101.11 }, { "text": "Die meisten kommen auch von eher von extern, da haben wir auch bis zur Astrophysik ist, glaube ich, alles alles vertreten.", "start": 1101.49, "end": 1109.8451 }, { "text": "Aber natürlich, wenn man so verschiedene Disziplinen hat, schafft das natürlich auch eine eine gewisse Gemengelage, auch eine gewisse Spannung vielleicht, die dann aber natürlich auch zu mehr Kreativität und Produktivität führen kann, was man ja auch an interdisziplinären Teams auch in der Wirtschaft beispielsweise beobachten kann.", "start": 1110.4651, "end": 1131.2301 } ], "start": 1053.6951, "end": 1131.2301, "num_words": 179.0, "speaker": 1 }, { "sentences": [ { "text": "Max, Du hast es gerade gesagt, diese sehr diversen Teams, die sind in der Praxis aber recht unterschiedlich organisiert in den Ministerien und Bundesbehörden.", "start": 1132.255, "end": 1140.275 }, { "text": "Also mal als Beispiel, manche Ministerien, da sind's 5 Leute, in anderen sind's 14 Mitarbeitende, die in so einem Datenlabor beschäftigt sind.", "start": 1140.495, "end": 1148.84 }, { "text": "Auch organisatorisch können die, so hab ich rausgelesen, recht unterschiedlich verortet sein.", "start": 1149.14, "end": 1153.2999 }, { "text": "Gibt's da son Best Practice, wo ihr gesagt habt, ja, so müsste man's machen?", "start": 1153.2999, "end": 1157.3999 } ], "start": 1132.255, "end": 1157.3999, "num_words": 72.0, "speaker": 0 }, { "sentences": [ { "text": "Es gibt, glaub ich, Best Practices, die wieder vom Kontext abhängen.", "start": 1158.295, "end": 1162.535 }, { "text": "Aber ich würd mal ganz generell sagen, dass es schon gut, also sich bei den meisten als als sinnvoll herausgestellt hat, dass die Datenabore möglichst nah am Haus und an der Hausleitung dran sind, wenn's geht.", "start": 1162.535, "end": 1173.74 }, { "text": "Das heißt, eine nachgeordnete Behörde beispielsweise ist eventuell nicht der richtige Ort, aber kann auch da Gründe geben, warum das sinnvoll ist.", "start": 1173.96, "end": 1183.82 }, { "text": "Wie kann", "start": 1184.275, "end": 1184.515 } ], "start": 1158.295, "end": 1184.515, "num_words": 71.0, "speaker": 2 }, { "sentences": [ { "text": "ich mir denn den Arbeitsalltag in so einem Datenlabor vorstellen?", "start": 1184.515, "end": 1187.315 }, { "text": "Also jetzt hantieren mit Reagenzgläsern und Flüssigkeiten haben wir ja schon ausgeräumt, das in jedem Falle nicht.", "start": 1187.315, "end": 1191.895 }, { "text": "Was macht einen was macht man in einem Datenlabor?", "start": 1192.195, "end": 1194.535 } ], "start": 1184.515, "end": 1194.535, "num_words": 36.0, "speaker": 0 }, { "sentences": [ { "text": "Ich glaub, die Datenlabore vor allem zu dem an zu dem Zeitpunkt, an dem wir uns mit ihnen auseinandergesetzt haben, haben vor allem ganz viel Lobbyarbeit, wenn man so will, Daten Lobbyarbeit betrieben, also ganz viel in die Häuser gehen, mit Kolleginnen und Kollegen sprechen und zu versuchen, denen zu zeigen, was Daten eigentlich können, was man mit Daten eigentlich anstellen kann.", "start": 1195.395, "end": 1217.515 }, { "text": "Daneben die entsprechende Infrastruktur aufzubauen, die man braucht, zu arbeiten.", "start": 1218.2949, "end": 1223.815 }, { "text": "Da gab's natürlich dann auch entsprechende Hürden, die vom von den von der Art und Weise, wie eben Verausgabungen in Bundesministerien oder in der Verwaltung generell geschehen, die da 'nem schnellen, sagen wir mal, Aufbauen der der Computerinfrastruktur, wenn man das jetzt mal so generell sagen will, teilweise oder das teilweise sehr erschwert haben.", "start": 1223.815, "end": 1245.165 }, { "text": "Und dann eben auch das Erstellen konkreter Produkte und konkreter konkreter Daten, Anwendungen.", "start": 1246.265, "end": 1253.2 }, { "text": "Und die wurden häufig auch wieder, auch wieder Mehrwert für fürs Haus zu liefern, eben mit Kolleginnen und Kollegen aus den Häusern umgesetzt, beziehungsweise auf Basis deren Bedarfe überhaupt erst entwickelt.", "start": 1253.34, "end": 1267.04 } ], "start": 1195.395, "end": 1267.04, "num_words": 170.0, "speaker": 2 }, { "sentences": [ { "text": "Mhm.", "start": 1267.865, "end": 1267.945 }, { "text": "Also auch recht unterschiedliche Aufgaben, so von Marketing, sag ich mal, intern für Daten, ne.", "start": 1267.945, "end": 1273.5449 }, { "text": "Warum sind Daten sinnvoll?", "start": 1273.5449, "end": 1274.9249 }, { "text": "Schulung bis hin zur Entwicklung.", "start": 1275.625, "end": 1277.085 }, { "text": "Wird denn auch in den Häusern selbst eine Lösung entwickelt?", "start": 1277.5449, "end": 1280.39 } ], "start": 1267.865, "end": 1280.39, "num_words": 35.0, "speaker": 0 }, { "sentences": [ { "text": "Zum Beispiel son Dashboard, über das wir gesprochen haben oder wird das dann extern herausgegeben?", "start": 1280.85, "end": 1284.79 } ], "start": 1280.85, "end": 1284.79, "num_words": 15.0, "speaker": 0 }, { "sentences": [ { "text": "Die Idee ist schon, dass sie auch in den Häusern die entsprechenden Kompetenzen haben, solche Anwendungen zu entwickeln.", "start": 1286.2101, "end": 1292.39 }, { "text": "Teilweise ist es natürlich nicht immer möglich, je nachdem wie umfänglich solche solche Anwendungen sind.", "start": 1292.4501, "end": 1297.7949 }, { "text": "Aber ja, also grade auch möglichst nah an den Mitarbeitenden und an den Anwenderinnen im Haus dranzubleiben, ist es wurden wurden viele dieser Anwendungen im Haus von den Teams in den Datenlaboren entwickelt.", "start": 1298.1749, "end": 1311.24 } ], "start": 1286.2101, "end": 1311.24, "num_words": 66.0, "speaker": 2 }, { "sentences": [ { "text": "Ich hab verstanden, dass die Datenlabore zwischen den Ministerien und Bundesbehörden kooperieren, dass die gut zusammenarbeiten.", "start": 1312.58, "end": 1318.5199 }, { "text": "Wie ist es aber, dass wie arbeiten denn aber die Datenlabore mit anderen Bereichen in der Verwaltung zusammen?", "start": 1318.98, "end": 1324.635 }, { "text": "Gibt's da auch starke Kooperation oder gibt's da auch vielleicht sogar Vorbehalte?", "start": 1324.635, "end": 1329.055 } ], "start": 1312.58, "end": 1329.055, "num_words": 46.0, "speaker": 0 }, { "sentences": [ { "text": "Also das hatte ich grade schon angesprochen, es gibt dann quasi auch Datenlaborbeispiele, die dann in nachgeordneten Behörden beispielsweise verankert sind.", "start": 1330.555, "end": 1339.9701 }, { "text": "Das heißt, da ist die Zusammenarbeit mit der nachgeordneten Behörde dann vermutlich eher intensiv und das entsprechend, denk ich mal, zielführend, Beispiel Statistisches Bundesamt, ne.", "start": 1340.11, "end": 1351.045 }, { "text": "Die arbeiten viel mit Daten, da 'n Datenlabor hinzusetzen und zu versuchen, Synergien zu schaffen, macht natürlich sehr viel Sinn.", "start": 1351.045, "end": 1358.165 }, { "text": "Ja.", "start": 1358.165, "end": 1358.645 } ], "start": 1330.555, "end": 1358.645, "num_words": 67.0, "speaker": 2 }, { "sentences": [ { "text": "Also genau, es ist schon ja eher die Ministerialverwaltung, aber die nachgeordneten Behörden spielen da auch eine Rolle.", "start": 1358.645, "end": 1365.7699 }, { "text": "Da gibt's natürlich auch welche, Du hast jetzt schon das prominenteste Beispiel genannt, die natürlich auch viel mit Daten arbeiten, aber da gibt's ja auch andere, beispielsweise die Bundesbank, also auch mit nachgeordnetenbehörden arbeiten die Datenlabor schon intensiv zusammen.", "start": 1365.7699, "end": 1381.645 }, { "text": "Dann gibt's ja teilweise auch in den Bundesministerien noch andere Innovationseinheiten, irgendwelche Innovation Labs, Civic Innovation Lab, beispielsweise im Familienministerium.", "start": 1381.705, "end": 1392.11 }, { "text": "Auch da gibt es Austausche und Zusammenarbeit.", "start": 1393.61, "end": 1396.51 }, { "text": "Das Migrations Dashboard haben wir ja schon angesprochen, das wird ja auch von Ländern und auch den Landkreisen sehr intensiv genutzt beispielsweise.", "start": 1397.05, "end": 1403.23 } ], "start": 1358.645, "end": 1403.23, "num_words": 106.0, "speaker": 1 }, { "sentences": [ { "text": "Blicken wir doch mal zum Schluss auf die Zukunft der Datenlabore, auch grade noch mal mit dem Fokus auf die öffentliche Verwaltung.", "start": 1405.625, "end": 1411.085 }, { "text": "Es gibt ja nur allerhand neue Technologien, auch grad in letzter Zeit.", "start": 1412.665, "end": 1415.705 } ], "start": 1405.625, "end": 1415.705, "num_words": 34.0, "speaker": 0 }, { "sentences": [ { "text": "Wir haben", "start": 1415.705, "end": 1415.945 } ], "start": 1415.705, "end": 1415.945, "num_words": 2.0, "speaker": 2 }, { "sentences": [ { "text": "schon über KI gestützte Modelle gesprochen, die ja auch schon Anwendungen gefunden haben.", "start": 1415.945, "end": 1419.6 }, { "text": "Welche neuen Technologien kommen euch dann noch in den Sinn, die die Arbeit von Datenlaboren beeinflussen könnte?", "start": 1420.16, "end": 1426.24 }, { "text": "Also auch, wenn wir mal so an Machine Learning denken oder auch die Bedeutung von Open Data?", "start": 1426.24, "end": 1430.02 } ], "start": 1415.945, "end": 1430.02, "num_words": 47.0, "speaker": 0 }, { "sentences": [ { "text": "Ja, also ich würd sagen, das große Thema, das am meisten bestimmende Thema wird wahrscheinlich auch in naher Zukunft werden noch weiter die Sprachmodelle bleiben, weil da eben sehr viel Potenzial noch drinsteckt mit den, was ja in Behörden 'n großen Teil der Daten ausmacht, mit Texten eben zu arbeiten und diese entsprechend nutzbar zu machen.", "start": 1430.32, "end": 1451.25 }, { "text": "Darüber hinaus, klar, gibt's viele weitere Möglichkeiten, die anderen Daten, die Du hast grade angesprochen, also Open Data, die auch an unterschiedlichsten Stellen in in der Republik liegen, sag ich jetzt mal, die auch mehr noch zu nutzen und zusammenzuführen.", "start": 1451.63, "end": 1469.115 }, { "text": "Da gibt's auch gab's ja 'n 'n großen große Bewegung, möglichst viele Daten bereitzustellen.", "start": 1469.415, "end": 1474.635 }, { "text": "Und teilweise werden die teilweise mit sehr großen Aufwänden immer noch gepflegt und bereitgestellt, aber die Nutzung dessen der der der bereitgestellten Daten ist eher eingeschränkt.", "start": 1474.9349, "end": 1485.8301 }, { "text": "Also da gibt's nur wenige Beispiele, die die diese offenen bereitgestellten Daten auch tatsächlich nutzen.", "start": 1485.8301, "end": 1490.865 } ], "start": 1430.32, "end": 1490.865, "num_words": 151.0, "speaker": 2 }, { "sentences": [ { "text": "Und das weiter zu treiben, wird natürlich auch wichtig, 'n wichtiges Feld sein und und bleiben.", "start": 1490.865, "end": 1496.8049 } ], "start": 1490.865, "end": 1496.8049, "num_words": 16.0, "speaker": 2 }, { "sentences": [ { "text": "Ist das, Nicole, auch eine ganz große Herausforderung?", "start": 1497.505, "end": 1500.005 }, { "text": "Also im Föderalismus, verschiedene Bundesländer eben, verschiedene Daten haben vielleicht nicht einen gemeinsamen Topf, wo die einfach reingespült werden, die auch wirklich nutzbar zu machen?", "start": 1500.465, "end": 1509.67 } ], "start": 1497.505, "end": 1509.67, "num_words": 33.0, "speaker": 0 }, { "sentences": [ { "text": "Auf jeden Fall.", "start": 1510.13, "end": 1511.11 }, { "text": "Und das sind auch gerade diese genau diese Grundlagen, überhaupt mit Daten arbeiten zu können, Anwendungen darauf bauen zu können, etwas, woran die Datenlabor ja schon gearbeitet haben.", "start": 1511.25, "end": 1521.935 }, { "text": "Es wurde vom Bundeskanzleramt koordiniert, für so einen Datenpool mal einen Proof of Concept zu entwickeln.", "start": 1522.235, "end": 1528.875 }, { "text": "Wie könnte das denn überhaupt aussehen?", "start": 1528.875, "end": 1530.715 }, { "text": "Weil es ist ja schon schwierig, Daten allein über Ressortgrenzen auch teilen zu können.", "start": 1530.715, "end": 1536.1 } ], "start": 1510.13, "end": 1536.1, "num_words": 67.0, "speaker": 1 }, { "sentences": [ { "text": "Da geht's auch erst mal darum, Datenkataloge zu erstellen.", "start": 1536.16, "end": 1539.04 }, { "text": "Was haben wir denn überhaupt für Daten, dass die auch halbwegs irgendwie einheitlich sind, dass man sie auch finden kann.", "start": 1539.04, "end": 1544.4 }, { "text": "Es ist vom Auswärtigen Amt gemeinsam mit der Bundesdruckerei eine Analyticsplattform, PLIN heißt die, für die Bundesverwaltung entwickelt worden, wo sie dann die Möglichkeit haben, Daten und auch Code untereinander zu teilen und da ihre Datenanalyseprojekte zu machen und 'n anderes Projekt ist beispielsweise KeePIDS, das ist vom BMI, von dem von der Initiative BEKI Beratungs- und Evalualisierungszentrum für Künstliche Intelligenz, auch gemeinsam mit den Datenlaboren entwickelt worden, den Bundesministerien Möglichkeiten zu geben, mit KI Anwendungen, speziell mit Sprachanwendungen, mal 'n bisschen rum zu experimentieren und da Erfahrungen zu machen.", "start": 1544.4, "end": 1590.44 }, { "text": "Genau, ich muss erst mal auch irgendwie eine Infrastruktur haben, auf der ich aufbauen kann und ich muss die Daten haben und dass die dass ich Daten habe, mit denen ich arbeiten kann, die in maschinenlesbarer Form vorliegen, die ich auch nutzen kann, weil Daten werden ja per Default erst mal als Verschlusssache eingestuft, da auch trotzdem Möglichkeiten zu haben.", "start": 1590.44, "end": 1611.045 }, { "text": "Open Data hast Du schon angesprochen, ist natürlich ein Weg, Daten zur Verfügung zu haben, mit denen ich arbeiten kann.", "start": 1611.045, "end": 1617.14 } ], "start": 1536.16, "end": 1617.14, "num_words": 199.0, "speaker": 1 }, { "sentences": [ { "text": "Das ist 'n ganz, ganz großes Thema für die Datenlabore.", "start": 1617.14, "end": 1619.56 }, { "text": "Da hat haben sie schon große Schritte gemacht, hab ich grad 'n bisschen skizziert, aber da ist natürlich noch ein ganz noch ein weites Stück des Weges zu gehen.", "start": 1620.5801, "end": 1630.255 }, { "text": "Und das ist für die Datenlabore gar nicht so einfach.", "start": 1630.395, "end": 1633.115 }, { "text": "Die waren erst mal, genau, das gibt's ja seit 20 21, waren sie erst mal finanziert bis 20 24.", "start": 1633.115, "end": 1639.375 }, { "text": "Dann gab's sone kleine Zitterpartie, jetzt ist die Finanzierung erst mal gesichert, allerdings auch wieder nur bis Ende nächsten Jahres.", "start": 1640.475, "end": 1646.83 } ], "start": 1617.14, "end": 1646.83, "num_words": 88.0, "speaker": 1 }, { "sentences": [ { "text": "Das ist natürlich auch son bisschen schwierig, ohne eine langfristige Perspektive solche langfristigen Projekte anzugehen, wie eben eine entsprechende Infrastruktur zu schaffen.", "start": 1647.21, "end": 1657.305 }, { "text": "Datenkompetenzaufbau ist natürlich auch was, was was weitergehen muss in den Ministerien und was wir uns auch 'n bisschen wünschen würden, wäre eine stärkere Öffnung auch nach außen.", "start": 1657.7051, "end": 1668.665 }, { "text": "Du hast ja schon es angesprochen, wie sieht's denn aus eigentlich mit Verwaltungen in Ländern und Kommunen beispielsweise, aber auch eben eine Öffnung mehr hin zur Öffentlichkeit, mehr in den Dialog gehen, auch mit Zivilgesellschaft, mit Wissenschaft, mit Wirtschaft, weil im Moment hat man schon mit von Ausnahmen abgesehen auf jeden Fall, da gibt's auch welche, die das 'n bisschen anders handhaben, aber ja schon diesen sehr starken Fokus auf Verwaltung und man kooperiert aber eher untereinander und nicht so sehr mit mit externen Akteuren, die ja vielleicht auch eine wertvolle Impulse, Kompetenzen et cetera noch zur Verfügung stellen könnten.", "start": 1668.665, "end": 1708.16 } ], "start": 1647.21, "end": 1708.16, "num_words": 150.0, "speaker": 1 }, { "sentences": [ { "text": "Ich hab", "start": 1708.62, "end": 1708.78 } ], "start": 1708.62, "end": 1708.78, "num_words": 2.0, "speaker": 2 }, { "sentences": [ { "text": "jetzt grade schon rausgehört, Zitterpartie, was die Finanzierung angeht.", "start": 1708.78, "end": 1713.02 }, { "text": "Wenn wir jetzt mal so 5 oder 10 Jahre in die Zukunft schauen, gibt's da noch Datenlabore oder was ist aus denen geworden?", "start": 1713.02, "end": 1720.495 } ], "start": 1708.78, "end": 1720.495, "num_words": 32.0, "speaker": 0 }, { "sentences": [ { "text": "Ist eine spannende Frage.", "start": 1721.5951, "end": 1722.795 }, { "text": "Ich glaub, diese Labore als als Vehikel, sag ich mal, sind oft was, was nicht auf ewig immer Bestand hat in solchen Organisationen.", "start": 1722.795, "end": 1733.61 }, { "text": "Also die sind meistens zum Etablieren 1 bestimmten Sache, zum Ausprobieren 1 bestimmten Sache genutzt.", "start": 1733.8301, "end": 1739.51 }, { "text": "Bei den Datenlaboren bin ich mir, ehrlich gesagt, nicht so sicher.", "start": 1739.51, "end": 1741.8301 }, { "text": "Da glaub ich, dass die aufgrund der breiten Anwendungen, die ja Daten in Ministerien finden und die auch die Mehrwerte, die Datenlabore entsprechend für die Ministerien liefern können, glaub ich, dass ich mir schon vorstellen kann, dass die auch langfristig eine Perspektive haben, Ob sie das dann tatsächlich auch so realisieren können und ob das so realisiert wird, hängt ja nicht nur an den Laboren, sondern in letztendlich auch politische Entscheidungen.", "start": 1741.8301, "end": 1768.0499 } ], "start": 1721.5951, "end": 1768.0499, "num_words": 124.0, "speaker": 2 }, { "sentences": [ { "text": "Und da kann man nur hoffen, dass die Arbeit, die sie jetzt tun, dazu beiträgt, den Entscheidungsträgerinnen klarzumachen, dass das sehr wichtig ist, diese Datenlabore weiter zu institutionalisieren und weiter zu fördern und weiter in den Häusern idealerweise, wie wie wie schon angesprochen, in allen Häusern, aber trotzdem auch auf Zusammenarbeit angelegt zu etablieren.", "start": 1768.9099, "end": 1791.25 } ], "start": 1768.9099, "end": 1791.25, "num_words": 54.0, "speaker": 2 }, { "sentences": [ { "text": "Könnte ja aber eben auch sein, dass in einigen Jahren die Arbeit mit Daten so selbstverständlich geworden ist, dass das Labor sich selbst überflüssig gemacht hat, ja, weil's eben in den Fachabteilungen angekommen ist.", "start": 1791.87, "end": 1801.9501 }, { "text": "Nicole, was meinst Du?", "start": 1801.9501, "end": 1803.345 } ], "start": 1791.87, "end": 1803.345, "num_words": 38.0, "speaker": 0 }, { "sentences": [ { "text": "Das ist 'n guter Punkt.", "start": 1804.5449, "end": 1805.505 }, { "text": "Das ist natürlich wünschenswert, dass die Arbeit der Datenlabore in 5 bis 10 Jahren vielleicht auf jeden Fall einen anderen Schwerpunkt haben wird, weil die Infrastrukturen inzwischen einfach da sind.", "start": 1805.505, "end": 1816.7849 }, { "text": "Die müssen natürlich stetig weiterentwickelt werden und na ja, irgendwie, wenn wir sagen, wir wollen mehr datengestützte, mehr evidenzbasierte Politikgestaltung, dann müssen wir darauf auch irgendeine Antwort finden und die aktuelle Antwort lautet eben Datenlabore, diese Datenlabore zu zu installieren.", "start": 1816.7849, "end": 1833.395 }, { "text": "Das war die Antwort darauf und es braucht auch 'n gewissen institutionalisierten Rahmen für so etwas.", "start": 1833.535, "end": 1839.9551 }, { "text": "Und es ist natürlich auch sinnvoll, auch im Hinblick auf neue Technologien, die Du schon angesprochen hast, brauchen wir auch in Zukunft, auch in 5 und 10 Jahren noch Experimentier- und Lernräume, wo man einfach mal Neues ausprobieren kann mit Tri und Error, manches wird auch nicht funktionieren.", "start": 1840.255, "end": 1857.22 } ], "start": 1804.5449, "end": 1857.22, "num_words": 139.0, "speaker": 1 }, { "sentences": [ { "text": "Also irgendwas, was son Labcharakter hat, werden wir auf jeden Fall brauchen, obwohl natürlich die Hoffnung ist, dass genau Kompetenzen und vieles, was bisher die Datenlabore noch leisten müssen, schon sehr viel stärker in den Fachabteilungen verankert ist und selbstverständlich geworden ist.", "start": 1857.6, "end": 1872.21 } ], "start": 1857.6, "end": 1872.21, "num_words": 42.0, "speaker": 1 }, { "sentences": [ { "text": "Wie Datennabore in Bundesbehörden arbeiten und warum sie so wichtig sind, das war restStart digital, der Podcast des Kompetenzzentrums Öffentlicher IT.", "start": 1875.09, "end": 1883.59 }, { "text": "Herzlichen Dank an meine Co Host Nicole Opiala und Maximilian Kubi.", "start": 1883.97, "end": 1887.7251 }, { "text": "Schön, dass ihr hier wart.", "start": 1887.7251, "end": 1888.8651 } ], "start": 1875.09, "end": 1888.8651, "num_words": 37.0, "speaker": 0 }, { "sentences": [ { "text": "Vielen Dank.", "start": 1889.005, "end": 1889.5651 }, { "text": "Danke dir.", "start": 1889.5651, "end": 1890.4651 } ], "start": 1889.005, "end": 1890.4651, "num_words": 4.0, "speaker": 1 }, { "sentences": [ { "text": "Die passende Publikation gibt's wie immer online unter öfid Punkt d e, öfid mit o e.", "start": 1891.0851, "end": 1896.785 }, { "text": "Einen Link findet ihr auch in den Shownotes.", "start": 1896.8451, "end": 1899.025 }, { "text": "Falls euch der Podcast gefallen hat, empfehlt uns weiter und folgt uns kostenfrei auf Spotify, Apple Podcasts und vielen weiteren Apps.", "start": 1899.3401, "end": 1906.88 }, { "text": "Bis zum nächsten Mal, ich bin Philipp 1.", "start": 1907.1001, "end": 1909.68 } ], "start": 1891.0851, "end": 1909.68, "num_words": 53.0, "speaker": 0 } ] }, "entities": null, "translations": null, "topics": null } ], "detected_language": "" } ], "utterances": [ { "start": 2.08, "end": 5.7799997, "confidence": 0.9909261, "channel": 0, "transcript": "Was kommt euch in den Sinn, wenn ihr von einem Labor hört?", "words": [ { "word": "was", "start": 2.08, "end": 2.32, 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Aber ganz generell kann man grob sagen, dass es immer so zwischen 5 und,", "words": [ { "word": "im", "start": 929.54, "end": 930.04, "confidence": 0.84814453, "punctuated_word": "im", "speaker": 2, "speaker_confidence": 1.0 }, { "word": "in", "start": 930.18, "end": 930.42, "confidence": 0.99609375, "punctuated_word": "in", "speaker": 2, "speaker_confidence": 1.0 }, { "word": "der", "start": 930.42, "end": 930.66, "confidence": 1.0, "punctuated_word": "der", "speaker": 2, "speaker_confidence": 1.0 }, { "word": "organisation", "start": 930.66, "end": 931.16, "confidence": 0.99902344, "punctuated_word": "Organisation", "speaker": 2, "speaker_confidence": 1.0 }, { "word": "verortet", "start": 931.3, "end": 931.8, "confidence": 0.93082684, "punctuated_word": "verortet.", "speaker": 2, "speaker_confidence": 1.0 }, { "word": "aber", "start": 931.86, "end": 932.18, "confidence": 1.0, "punctuated_word": "Aber", "speaker": 2, "speaker_confidence": 1.0 }, { "word": "ganz", "start": 932.18, "end": 932.58, "confidence": 1.0, "punctuated_word": "ganz", "speaker": 2, "speaker_confidence": 1.0 }, { "word": "generell", "start": 932.58, "end": 932.98, "confidence": 1.0, "punctuated_word": "generell", "speaker": 2, "speaker_confidence": 1.0 }, { "word": "kann", "start": 932.98, "end": 933.14, "confidence": 0.9951172, "punctuated_word": "kann", "speaker": 2, "speaker_confidence": 1.0 }, { "word": "man", "start": 933.14, "end": 933.3, "confidence": 1.0, "punctuated_word": "man", "speaker": 2, "speaker_confidence": 1.0 }, { "word": "grob", "start": 933.3, "end": 933.54, "confidence": 1.0, "punctuated_word": "grob", "speaker": 2, "speaker_confidence": 1.0 }, { "word": "sagen", "start": 933.54, "end": 933.94, "confidence": 1.0, "punctuated_word": "sagen,", "speaker": 2, "speaker_confidence": 1.0 }, { "word": "dass", "start": 933.94, "end": 934.02, "confidence": 0.99902344, "punctuated_word": "dass", "speaker": 2, "speaker_confidence": 1.0 }, { "word": "es", "start": 934.02, "end": 934.305, "confidence": 0.9892578, "punctuated_word": "es", "speaker": 2, "speaker_confidence": 1.0 }, { "word": "immer", "start": 934.625, "end": 934.785, "confidence": 1.0, "punctuated_word": "immer", "speaker": 2, "speaker_confidence": 1.0 }, { "word": "so", "start": 934.785, "end": 935.02496, "confidence": 0.99902344, "punctuated_word": "so", "speaker": 2, "speaker_confidence": 1.0 }, { "word": "zwischen", "start": 935.02496, "end": 935.505, "confidence": 1.0, "punctuated_word": "zwischen", "speaker": 2, "speaker_confidence": 1.0 }, { "word": "5", "start": 935.505, "end": 935.985, "confidence": 0.99902344, "punctuated_word": "5", "speaker": 2, "speaker_confidence": 1.0 }, { "word": "und", "start": 935.985, "end": 936.485, "confidence": 0.7993164, "punctuated_word": "und,", "speaker": 2, "speaker_confidence": 1.0 } ], "speaker": 2, "id": "7abcdc03-e6a4-4adb-b955-f68a8270ae47" }, { "start": 937.105, "end": 947.045, "confidence": 0.951416, "channel": 0, "transcript": "sagen wir mal, 15 Teammitglieder sind. Da sind natürlich Data Scientists, also Leute, die mit Daten umgehen können, Data und Data Analysts. 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Das hatte ich mich gefragt, was macht eigentlich ein Data Scientist? Was macht ein Data Analyst? 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Also auch recht unterschiedliche Aufgaben, so von Marketing,", "words": [ { "word": "mhm", "start": 1267.865, "end": 1267.945, "confidence": 0.907959, "punctuated_word": "Mhm.", "speaker": 0, "speaker_confidence": 0.6781585 }, { "word": "also", "start": 1267.945, "end": 1268.105, "confidence": 0.9980469, "punctuated_word": "Also", "speaker": 0, "speaker_confidence": 0.6781585 }, { "word": "auch", "start": 1268.105, "end": 1268.1849, "confidence": 0.9951172, "punctuated_word": "auch", "speaker": 0, "speaker_confidence": 0.6781585 }, { "word": "recht", "start": 1268.1849, "end": 1268.4249, "confidence": 0.9951172, "punctuated_word": "recht", "speaker": 0, "speaker_confidence": 0.6781585 }, { "word": "unterschiedliche", "start": 1268.4249, "end": 1268.9049, "confidence": 0.99902344, "punctuated_word": "unterschiedliche", "speaker": 0, "speaker_confidence": 0.6781585 }, { "word": "aufgaben", "start": 1268.9049, "end": 1269.3049, "confidence": 0.8330078, "punctuated_word": "Aufgaben,", "speaker": 0, "speaker_confidence": 0.6781585 }, { "word": "so", "start": 1269.3049, "end": 1269.465, "confidence": 0.99902344, "punctuated_word": "so", "speaker": 0, "speaker_confidence": 0.6781585 }, { "word": "von", "start": 1269.465, "end": 1269.705, "confidence": 0.97265625, "punctuated_word": "von", "speaker": 0, "speaker_confidence": 0.6781585 }, { "word": "marketing", "start": 1269.705, "end": 1270.205, "confidence": 0.95874023, "punctuated_word": "Marketing,", "speaker": 0, "speaker_confidence": 0.6781585 } ], "speaker": 0, "id": "566ab3e1-baec-4f4c-a9a6-4c980bb782aa" }, { "start": 1271.3049, "end": 1274.9249, "confidence": 0.9387207, "channel": 0, "transcript": "sag ich mal, intern für Daten, ne. 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Also auch, wenn wir mal so an Machine Learning denken oder auch die Bedeutung von Open Data? 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Wie könnte das denn überhaupt aussehen? 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Da geht's auch erst mal darum, Datenkataloge", "words": [ { "word": "auch", "start": 1534.72, "end": 1534.9601, "confidence": 0.9951172, "punctuated_word": "auch", "speaker": 1, "speaker_confidence": 0.94524765 }, { "word": "teilen", "start": 1534.9601, "end": 1535.36, "confidence": 1.0, "punctuated_word": "teilen", "speaker": 1, "speaker_confidence": 0.94524765 }, { "word": "zu", "start": 1535.36, "end": 1535.6, "confidence": 1.0, "punctuated_word": "zu", "speaker": 1, "speaker_confidence": 0.94524765 }, { "word": "können", "start": 1535.6, "end": 1536.1, "confidence": 0.9741211, "punctuated_word": "können.", "speaker": 1, "speaker_confidence": 0.94524765 }, { "word": "da", "start": 1536.16, "end": 1536.3201, "confidence": 0.9970703, "punctuated_word": "Da", "speaker": 1, "speaker_confidence": 0.94524765 }, { "word": "geht's", "start": 1536.3201, "end": 1536.56, "confidence": 0.95947266, "punctuated_word": "geht's", "speaker": 1, "speaker_confidence": 0.94524765 }, { "word": "auch", "start": 1536.56, "end": 1536.64, "confidence": 0.9838867, "punctuated_word": "auch", "speaker": 1, "speaker_confidence": 0.94524765 }, { "word": "erst", "start": 1536.64, "end": 1536.8, "confidence": 0.9589844, "punctuated_word": "erst", "speaker": 1, "speaker_confidence": 0.94524765 }, { "word": "mal", "start": 1536.8, "end": 1536.9601, "confidence": 0.9970703, "punctuated_word": "mal", "speaker": 1, "speaker_confidence": 0.7184534 }, { "word": "darum", "start": 1536.9601, "end": 1537.4601, "confidence": 0.99609375, "punctuated_word": "darum,", "speaker": 1, "speaker_confidence": 0.7184534 }, { "word": "datenkataloge", "start": 1537.52, "end": 1538.02, "confidence": 0.9949951, "punctuated_word": "Datenkataloge", "speaker": 1, "speaker_confidence": 0.7184534 } ], "speaker": 1, "id": "f27b2291-9dcf-40ed-8c6e-92c839b2a4bf" }, { "start": 1538.48, "end": 1545.06, "confidence": 0.9751282, "channel": 0, "transcript": "zu erstellen. 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