Insights
Short-form perspectives and frameworks shared on LinkedIn.
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Business Strategy First, Then Data and AI
Dazzled by the hype, organizations often look to solutions before looking at the problems to be solved. They start with planning: data platform, governance, tools — before it is even clear what for.
The Distinction That Decides Success or Failure with Data and AI
Migrating a data platform? Complicated — but plannable. The knowledge exists, the path is known.
The Planning Paradox in Data and AI
Organizations want to become data-inspired and start with a roadmap. "𝘞𝘩𝘢𝘵 𝘸𝘪𝘭𝘭 𝘪𝘵 𝘤𝘰𝘴𝘵, 𝘸𝘩𝘢𝘵 𝘢𝘳𝘦 𝘵𝘩𝘦 𝘮𝘪𝘭𝘦𝘴𝘵𝘰𝘯𝘦𝘴, 𝘢𝘯𝘥 𝘸𝘩𝘦𝘯 𝘸𝘪𝘭𝘭 𝘪𝘵 𝘣𝘦 𝘥𝘰𝘯𝘦?"
𝐃𝐚𝐭𝐚 𝐜𝐮𝐥𝐭𝐮𝐫𝐞 𝐢𝐬 𝐥𝐢𝐤𝐞 𝐚 𝐲𝐨𝐮𝐧𝐠 𝐩𝐥𝐚𝐧𝐭 — 𝐢𝐭 𝐝𝐨𝐞𝐬𝐧'𝐭 𝐠𝐫𝐨𝐰 𝐟𝐚𝐬𝐭𝐞𝐫 𝐣𝐮𝐬𝐭 𝐛𝐞𝐜𝐚𝐮𝐬𝐞 𝐲𝐨𝐮 𝐩𝐮𝐥𝐥 𝐨𝐧 𝐢𝐭
Despite billions invested in data and AI, most initiatives still fall short. Reports collect dust. AI pilots never scale. High investment, low return.
Data & AI: Five Prerequisites for Sustainable Success
Two years ago [I shared this infographic](https://jens-linden.de/insights/jenseits-des-hypes-5-saeulen-datenexzellenz).
How data culture can save lives
Many companies strive for a “good” data culture in order to use data and AI successfully. The desired target state is usually described like this:
AI readiness follows strategic clarity
Many executives try to get “*AI-ready*”. But buying technology, launching pilots or jumping into a data & AI strategy project should not be your first move.
The Data Culture Cheat Sheet
I have condensed the most critical concepts from my recent articles into a single page.
Seven Distinctions to Become Data-Inspired
The distinctions we use determine what we see in an organization, and what we stay blind to.
Focus fields for data culture analysis
Your data & AI initiatives are struggling, but you can't pinpoint why?
Data & AI Strategy vs. Business Strategy
Data Strategy Is a Buzzword – So Is AI Strategy
Data vs. Business Strategy – A Plea for a Common Language
Executives and strategy experts rely on well-established frameworks to design and implement business strategies. Yet, rather than applying these proven methodologies, many organizations attempt to redefine strategy in the context of data and AI as a foundation for becoming data-driven.
Two Types of Organizational Data & AI Demands – and Why the Difference Matters
Data, analytics, and AI demands arise from two fundamentally different sources: strategic or operational data demands. Understanding the difference is crucial for prioritizing resources, guiding investments, and making informed decisions about Return on Data Investment (RODI).
Data Strategy with a twist – there can be only one!
The link between data strategy and Highlander lies in the famous tagline of the 1986 fantasy action-adventure starring Christopher Lambert as Connor MacLeod. If you are of my generation, you might still recall it:
How Much Should You Invest in Data, Analytics, and AI?
When I speak with data leaders, a recurring question is:
Most organizations get data strategy wrong...
This might sound bold, but many data strategies fail to align with established strategy definitions used in business.
Every successful data plan starts with one thing: A clear strategy
**Every successful data plan starts with one thing:**
First Data Warehouse or Data Lake, then develop data use cases?
First Data Warehouse or Data Lake, then develop data use cases. Right? Or other way around?
Elevator pitch for BI and AI use cases?!
Sometimes, data use case names such as "Market Spend Efficiency" or "Predictive Maintenance" can trigger a different understanding to different stakeholders. When collecting or innovating data use cases in client projects or workshops, we usually document the use case in some kind of canvas including challenge, desired impact, stakeholders, risks and other aspects. This is good for documentation, but not for quick communication to a wide range of audiences.
And the winner is: Data literacy followed by data leadership & culture shift!
That's what the audience of my talk "How to master the data challenge" at the TDWI Munich voted when I asked for the biggest challenges during their data transformation. Thanks everybody for this very interactive session!
Sometimes I feel like a broken record.
Repeatedly talking about the obstacles companies face when turning their data into insights, actions and finally organisational value.
Data First Aid Kit
We thought hard about whether to give this away for free or not. But here it is - published for the first time: Our Data First Aid Kit.