A Data Lake won’t solve your problems (but here’s what will)

Hello, Data Enthusiasts!

This is my first newsletter in English, and I’m excited to take this step with you. Let’s talk about something many companies get wrong: the belief that a data lake alone will magically solve all their data-driven decision-making challenges.

The Data Lake Illusion

Some time ago, I joined a meeting where stakeholders were eagerly awaiting the implementation of a data lake. To them, this was the missing piece that would finally enable the company to make better, data-driven decisions. The excitement was understandable—but also misplaced.

What is a Data Lake?

A data lake is a centralized repository designed to store vast amounts of structured, semi-structured, and unstructured data. Unlike traditional databases, which enforce a rigid schema, data lakes allow raw data ingestion in its native format. This flexibility is useful for advanced analytics, AI, and machine learning use cases. However, without proper governance and structure, a data lake can quickly turn into a "data swamp" — an unmanageable pool of disorganized information. A data lake is a powerful tool, but it’s just that: a tool. It stores data, but it doesn’t inherently create value. The real game-changer? Data Products.

What are Data Products?

Think of data products as curated, business-ready assets designed to serve specific needs. Unlike raw data sitting in a lake, data products are built with usability, quality, and accessibility in mind. They transform raw information into actionable insights, empowering teams to make informed decisions.

Example of a Data Product

A great example of a data product is a customer churn prediction model. Instead of merely storing customer interaction data in a data lake, a data product could be built that processes this data, applies machine learning models, and delivers a dashboard that alerts sales and support teams about high-risk customers. This enables proactive engagement and significantly reduces churn rates.

Selling the Data Lake (the right way)

So, does that mean a data lake is useless? Absolutely not! But to make it work, you need to position it correctly:

1. Align It With Business Goals: ensure leadership understands that a data lake is an enabler, not a solution by itself. It supports the development of valuable data products.
2. Focus on Usability: create structured, accessible data layers that serve real business needs.
3. Integrate Governance & Quality: without governance, a data lake can quickly become a “data swamp.” Prioritize data quality and usability.
4. Drive a Cultural Shift: adoption is key. People need to trust and use data for decision-making, which means investing in training and fluency.

A Data Stack That Transforms Culture

To truly drive a data-driven culture, a company needs more than just infrastructure. The stack that makes a difference includes:

• Data Governance & Quality: well-managed data ensures trust and usability.
• Self-Service Analytics: empowering business users to explore and extract insights.
• Clear Business Use Cases: data should solve specific problems, not just exist in a system.
• Adoption & Education: without people understanding and using data, even the best stack is worthless.
• Data & AI Literacy: training teams to interpret and use data effectively is essential. Without data literacy, even the most advanced data stack will fail to drive real impact.

Final Thought: If you’re investing in a data lake, make sure it’s not just a storage solution. Build data products that generate value, integrate governance, and drive adoption. That’s how you create a real data-driven culture.

What’s your experience with data lakes and data products? Let’s discuss!