Autor: Elizabeth Press

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.

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.

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.

A child of the Cold War, I grew up hearing stories and learning deeply about World War 2. Much of my youth was spent pondering the new and old-world order. For that reason, I found the panel at the Data Festival in Munich inspiring. I hope it inspired others in the tech scene to support the use of data in defending European freedom.

The industry seems to be disillusioned by centralized data assets, data warehouse and data lake alike and looking for the next big thing.

At the AI Guld Dinner, I was asked by a couple of people about what I look for when I hire data scientists. This advice can be scaled to all data professionals – and beyond.

The right mix of governance and freedom in architecture is still up for debate, end-to-end solutions are often-heard recommendations, low and no-code is expanding access to data and the customer journey could be seen as a source of revenue are some insights I gleaned from this spring’s Big Data World.