The Data-Driven Decision Making Blog

According to a D3M Labs poll, many data analysts who answered wanted to move into management, be it product management or people management. Behind the desired career move, was the interest to step back from the keyboard and focus on the communication and greater involvement in business and strategy.

AI is still magical to many people. Is that a major obstacle to AI adoption? Varshith H Anilkumar talked to myself and the D3M Labs community about what can be done to create a more AI-aware public and how that will help decrease bias and improve innovation.

This article summarizes the main takeaways as discussed in the panel „What is the future of AI adoption?“ at Rework’s Enterprise AI Summit in Berlin.

Technical debt in your data pipeline will impact your organization in ways that will annoy stakeholders, make the working lives of analysts tedious and frustrate data engineers. This debt can cause embarrassment in front of boards and investors, as numbers can be mismatching and unexplainable. And worse.

Data teams are often chaotic places to work, which leads to attrition, over hiring, burn-out and other bad side effects. Stevan Lazic, an experienced product engineering leader who has worked at numerous startups and scaleups, talks with Elizabeth Press about what he thinks is driving the unhealthy dynamic in many data teams and what measures can be taken so that data teams are properly resourced.

Babbel Live is a success story that product managers who are launching new digital products can learn from. Massive amounts of data are not necessary in order to use data to make good decisions. Starting simple and working in a data-informed way to prove or disprove hypotheses can yield positive results quickly.

Matt Brady, Founder of Zuma Recruiting and I talked about Data Strategy. We will start by covering data strategy and roadmaps before discussing how to treat data, data roles and where data should sit in an organization. Data teams add the best value to their organization when they are part of a holistic company strategy discussion and work as strategic partners with the stakeholders.

Consumer Insights is a natural partner of Data Analytics. While data analysts can show you in-depth what is happening, consumer insights can illuminate the how. The partnership between Consumer Insights, Data Analytics and other Insights functions can be powerful. This article will explain the benefits and how organizations can make it work,

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.

Is business intelligence a luxury?  Data – done right – is neither cheap nor easy. Most businesses wait until they are a certain size before investing in an in-house data competency. A greenfield assignment, the initial build-up of an inhouse data function, is an important early step in a company’s journey towards data maturity. Before the inception of an inhouse function dedicated to data, a company can be considered data immature, regardless of who uses the data or how long the company has been around.