Kategorie: Data Products

Posted in AI Strategy, Change management, Data as an asset, Data Leadership, Data product management, Data Products, Deutsch

Das Produktlebenszyklusmanagement im Zeitalter der intelligenten Geräte – ein Interview mit Eric JoAchim Liese

Wie managt man ein Data Science Produkt, Teil 2: Da die Geräte immer intelligenter werden, muss sich das Produktlebenszyklusmanagement weiterentwickeln, um die Daten als langfristigen Wert und Teil der Kundenbeziehung zu betrachten. Eric Joachim Liese spricht über Edge Computing und Geräteautonomie als Voraussetzung für ein gutes Kundenerlebnis. Er erklärt auch, wie traditionelle Hardware-Hersteller ihre Betriebsabläufe weiterentwickeln und Fachkräfte einstellen können, um diesen Weg erfolgreich zu beschreiten

Posted in AI Strategy, Change management, Data as an asset, Data Leadership, Data product management, Data Products

Product lifecycle management in the era of smart devices – an Interview with Eric JoAchim Liese

How to Manage the Data Science Product, Part 2: As devices get smart, product lifecycle management for hardware needs to evolve to encompass the view of data as a long-term asset and as an active, even pro-active part of the customer relationship. Eric JoAchim Liese talks about edge computing and device autonomy as being requisite to providing a good customer experience. He also explains how traditional hardware manufacturers can evolve their operations and hire in expertise to successfully navigate the journey.

Posted in Data governance, Data Leadership, Data Products, DataOps, Deutsch, Technical debt

Data Mesh – Wie man verhindert, dass es sich in ein geldverschlingendes Chaos verwandelt – ein Podcast

Data Mesh ist eine analytische Datenarchitektur und ein Betriebsmodell, bei dem Daten wie ein Produkt behandelt werden und den Teams gehören, die sie produzieren, d. h. den Geschäftsbereichen. Wie können sich Unternehmen auf den Weg zu Data Mesh machen, ohne ihre Budgets zu sprengen und letztlich einen großen, unübersichtlichen und teuren Datensumpf zu schaffen? Höre dir den Podcast an. Lese den Blog.

Posted in Data governance, Data Leadership, Data Products, DataOps, Technical debt

Data Mesh – How to prevent it from turning into a money draining mess – A podcast

Data Mesh is an analytical data architecture and operating model where data is treated like a product and owned by teams who produce it, i.e the busness domains. How can organizations embark on their data mesh journeys without exploding their budgets and ultimately creating a big, mess, expensive data swamp? Listen to the podcast. Read the blog.

Posted in AI Strategy, Data as an asset, Data Leadership, Data Products, Deutsch

Das Management des Data Science Produktes – ein Interview mit Anna Hannemann, PhD

Wie managt man ein Data Science Produkt, Teil 1: Algorithmen sind Produkte, die gemanagt werden müssen, um geschäftliche Ergebnisse zu erzielen. Anna Hannemann, PhD von Metro.digital erzählt, was sie als Pionierin im Produktmanagement für Datenwissenschaft gelernt hat. Sie spricht auch über den organisatorischen Aufbau, die Kompetenzen, die vorhanden sein müssen, und darüber, wie neue Tools das Management von Data-Science-Produkten beeinflussen.

Posted in AI Strategy, Data as an asset, Data Leadership, Data Products

Managing the data science product – an interview with Anna Hannemann, PhD

How to manage the data science product, Part 1: Algorithms are now products that need to be managed for business impact. Anna Hannemann, PhD of Metro.digital shares what she has learned as a pioneer in data science product management. She shares some key success factors for data science product development to drive monetization and growth .She also talks about organizational design, competencies that need to be in place and how new tools are impacting how data science products are managed.

Posted in AI Strategy, Data as an asset, Data product management, Data Products, Deutsch

Wie managt man ein Data Science Produkt?  – Eine Serie von D3M Labs

Die Datenwissenschaft entwickelt sich von der Forschung und Entwicklung zu Produkten – sowohl online als auch offline….

Posted in AI Strategy, Data as an asset, Data product management, Data Products

How to manage the data science product, a D3M Labs Series.

Data science is moving from R&D into products – both online and off. Managing data products requires…

Posted in AI Strategy, AI use case, Data education, Data politics, Data Products, Education

The prevalence of AI and importance of engaging in dialogue

AI is becoming omnipresent in our lives and is shaping our world. Thus wider public involvement in determining how AI is designed and used is important for society. Understanding AI and getting involved in how it is applied and governed might seem daunting, but Varsh Anilkumar offers some ways to get involved and learn about AI.

Posted in AI Strategy, AI use case, Data Leadership, Data Products

My top takeaways from the Berlin AI Summit: Understand the problem and don’t neglect operations.

The major challenges to AI implementation are often mind-set based rather than technical. Problems in production and implementation of AI often stem from organizations‘ and practitioners‘ lack of ability and/or desire to thoroughly scope out and define the problem they are trying to solve. Consequently, they often don’t select the right tools, capabilities and processes to implement successfully. Organizations can also negelct operations (such as MLOps), which are important for work efficacy and scale.