The Data-Driven Decision Making Blog

Ingenieure und Customer Success Teams sind die unbesungenen Helden des IT-Betriebs.
Hast du dich schon einmal gefragt, was sie über deine Kaufgewohnheiten denken? Vor allem in virtuellen Organisationen können Ingenieure und Kundenerfolgsteams bei Anbietern in ihrer Programmierhöhle leben, hart arbeiten und für andere unsichtbar sein. Wenn man impulsive coole Tools kauft, ohne die Ingenieure mit einzubeziehen, kann das nicht nur diese schwer zu rekrutierenden Fachleute in den Wahnsinn treiben und die Beziehungen zu den Anbietern belasten, sondern auch die Entwicklung Ihrer Plattform und wichtige Projekte wie die Datenmigration zum Scheitern bringen. Das könnte Ihre Ingenieure so frustrieren, dass sie kündigen.

Engineers and customer success teams are unsung heroes of IT Operations.
Have you wondered what your they think of your purchasing habits? Especially in virtual organizations, engineers and customer success counterparts at vendors can live in their coding-cave, working hard, invisible to others. Impulsive cool tool purchasing without including engineers might not only be driving those difficult to recruit professionals crazy and burn vendor relationships, it could derail your platform development and important projects such as data migration. It might frustrate your engineers enough to quit.

Coole Tools sind oft die heiße Hookup des Data Leaders (oder Stakeholders), um am nächsten Morgen mit den Klamotten von gestern in einer kalten Gasse zu landen. Coole Tools werden gekauft, installiert, ausprobiert und für den nächsten vielversprechenden Anbieter aufgegeben. Und das bedroht ernsthaft die finanzielle und operative Überlebensfähigkeit des Data Teams. Warum sind Data Tools so verführerisch? 

Wie können wir verhindern, dass die Beschaffung zu einem Boulevard der geplatzten Träume und nicht realisierten Projekte wird?

Cool tools are often the Data Leader’s (or stakeholder’s) hot tech hookup, ending up in a cold alley with yesterday’s clothes the morning after. Cool tools get purchased, installed, tried out and abandoned for the next promising vendor. And it’s seriously threatening data teams‘ financial and operational viability. 
Why are data tools so seductive? 

How can we prevent procurement from ending up in a boulevard of broken dreams and unrealized projects?

The first Decision Lab Round Table covered the topic of how to make Analytics a revenue-generating function. We had a cross functional discussion involving data professionals, as well as adjacent professions who are working in Europe and the USA. This blog covers the discussion points, as well as D3M Labs commentary about how analytics should be a business function.

Der erste Decision Lab Round Table befasste sich mit dem Thema, wie man die Analytik zu einer Umsatz generierenden Abteilung machen kann. Wir hatten eine funktionsübergreifende Diskussion unter Beteiligung von Datenexperten und benachbarten Berufsgruppen, die in Europa und den USA arbeiten. Dieser Blog enthält die Diskussionspunkte sowie einen Kommentar von D3M Labs dazu, wie Analytik eine Geschäftsfunktion sein sollte.

Natural language processing (NLP) is a set of techniques that aim to interpret and analyze human languages. By using it in more complex pipelines, we can solve predictive analytics tasks and extract valuable insights from unstructured text data.
A major breakthrough was made in the field of NLP by the introduction of transformers, which paved the way for large language models (LLMs) and generative AI research (e.g. BERT, BART, GPT).
In this article, we walk through different concepts of NLP. In the first section, we summarize the architecture of transformers and highlight its core concepts, such as the attention mechanism. Then, in the second section, we focus on BERT, one of the most popular Transformer-based LLMs, and we present examples of how it is used in data science applications.

Ashish Kalra is an experienced data leader who has been reading about self-service analytics over LinkedIn from different Data Leaders for some time. He has observed that everyone has their own definition of „Self-Service Analytics.“ In this article, Ashish publishes his own view on the topic and is open to peer and stakeholder feedback.

Contextualizing our world with data, part 4: Journalism. Solving the speed vs. quality dilemma and growing the New York Times, also during the Trump years. Shane Murray, Field Chief Technology Officer at Monte Carlo and former Senior Vice President of data & insights at The New York Times, talks with about experimentation and growing a digital subscriber business, the New York Times. Shane talks about how to solve the experimentation speed vs. quality dilemma – and often outright conflict – between business stakeholders and data teams. Shane also talks about how the New York Times transformed itself into a digital subscription product and tech company.

Contextualizing our world with data, part 3: Public Relations. Liuka Lobarieva, co-founder and coordinator at the Ukrainian PR Army, has been volunteering as a coordinator for Food Safety and Nuclear Safety since Russia invaded Ukraine. She is driven by her conviction that it is important to tell the truth about the war caused by Russia in the very center of Europe today. She does this while she is working as Public Relations and Communications Manager at Datuum.ai, a startup using AI to automate data pipelines. Liuka gives a unique glimpse into the virtual world of PR professionals telling Ukraine’s story and narrates her own experiences before and since the Russian invasion of Ukraine. The Ukrainian PR Army is data-driven. Liuka tells us how.