Datent | Revenue & Trends

The hottest Substack posts of Datent

And their main takeaways

The term 'data product' has become so broad that it lacks credibility and value.
Data professionals can learn a lot from actual product management and strategy.
Creating a taxonomy based on intention and proximity to the customer can improve the understanding and management of data products.

Strategies are guiding principles and need a clear purpose for decision-making.
Focus on maximizing the benefits of data through data product management, managing data culture, and running a data transformation program.
Feedback and continuous improvement are essential in developing effective data strategies.

Transitioning from a BI role to a data product team requires defining a Value Gateway to ensure projects deliver tangible benefits.
To manage the progress and accountability of data work, reporting on value at key points is crucial, showcasing the value realized and areas needing support.
Establishing a process around failing fast and doubling down on successful projects, supported by agile project management, is essential for efficient data product management.

Being data-dependent is different from being data-driven.
Investing in people's skills is crucial for transitioning to a data-driven organization.
Success in today's world comes from agility and innovation, not just relying on software applications.

The best predictions come from deep analysis of today's data challenges and trends.
Data oracles provide valuable insights for the future by understanding present data trends.
Data writers like Davenport, Moses, Madsen, and Thomas offer grounded observations and advice on data topics.

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Data leaders should take on all legacy issues and drive enterprise transformations.
CDOs should lead efforts to migrate Excel work to cloud-based environments, like the precedent of Jeff Bezos' 'API mandate' at Amazon.
Data transformation programs should be broken down into bold phases to convince boards of the vision and drive successful change.

Objective setting is a critical skill in the AI era but can be difficult to master.
When setting objectives for AI, consider the potential for unintended consequences.
AI tools like AutoGPT show the importance of human oversight and the need for careful objective setting.

Developing key systems is crucial for a successful data transformation.
Role creep in CDO/CDAO responsibilities is an ongoing concern.
Consider joining a data strategy book club for learning and community engagement.