Kategorie: Data strategy

Ist Business Intelligence ein Luxus? Daten – richtig gemacht – sind weder billig noch einfach. Die meisten Unternehmen warten, bis sie eine gewisse Größe erreicht haben, bevor sie in eine interne Datenkompetenz investieren. Ein Greenfield-Projekt, der erste Aufbau einer internen Datenfunktion, ist ein wichtiger erster Schritt auf dem Weg eines Unternehmens zur Datenreife. Vor der Einrichtung einer internen Datenfunktion kann ein Unternehmen als Daten unreif gelten, unabhängig davon, wer die Daten nutzt oder wie lange das Unternehmen bereits besteht.

Irinia Nikiforova explains what data governance is and why it is essential for any business that not only wants to survive, but use insights from data or data-driven products to drive growth.

The AI mystique might be the biggest obstacle to AI adoption. The artisanal data scientist who works on an alchemy of code output the magical algorithm impedes discussion on what is needed to commercialize and scale AI solutions. AI needs to be treated like a product and an item to be manufactured and scaled on an industrial level.

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.

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.

Monetizing customer relevancy through data-driven insights is key for any successful modern marketing campaign. Modern marketers work in a hypotheses-driven manner, using data to gain customer insight. Consumers and B2B customers have grown used to marketers understanding who they are, their behaviors, as well as when and how they want to communicate with you.

In Berlin habe ich mehr als ein paar Mal den Kommentar „das ist nur Business“ gehört. In New York habe ich niemals einen solchen Kommentar vernommen. Ganz im Gegensatz zu Berlin wird es in New York über Unternehmensstrategie, Marktentwicklung und Marketingstrategie eifrig diskutiert.

Data-as-a-Service (DaaS) can be described as productized data-driven insight on demand. DaaS allows business users to access the data and insights they need at the timing they desire. The data and insights can be consumed by multiple individuals simultaneously, location-independent of where the data has been sourced and managed.