From Data Lakes to Value Streams: Building Data Products That Matter.

From Data Lakes to Value Streams: Building Data Products That Matter.

From data lakes to monetisation—how businesses can build #dataproducts that create value, spark trust, and fuel industry change.

We live in a time when data is the new capital—but capital left idle is wasted potential. The real transformation comes not from collecting more, but from shaping that raw material into products that people use, trust, and pay for. This post takes you through the shift from #datalakes to #dataproducts, and finally, to #datamonetisation.

It explains why storing alone is not enough, how companies can embrace product thinking, and why culture and trust are as important as tech. Along the way, it points to examples across industries—from banking and retail to healthcare and logistics—showing how data products are reshaping business models.

This is not a technical manual. It’s a call to leaders—CIOs, CTOs, CEOs, and academic thinkers—to move from passive collection to active creation. The companies that rise will not be those with the biggest lakes, but those with the most valuable streams.

The Moment Data Stopped Sleeping

For over a decade, businesses rushed to collect. The phrase “data is the new oil” drove billions in investment into #bigdata platforms. We celebrated terabytes like trophies. #Datalakes became the boardroom obsession.

But here’s the thing: oil is only valuable when refined. And data, left untouched, is no different.

Executives soon realised that petabytes in storage didn’t mean better forecasts, smarter products, or higher profits. Instead, they were spending more on cloud bills than they were making in returns.

Then came the shift: what if data were treated not as oil in the ground, but as a finished good on the shelf? What if it behaved like a product—designed, refined, packaged, and delivered to those who need it most?

That’s when the era of data products began. #DataProducts #DataStrategy #DataMonetisation

Data as a Sleeping Giant

Why Storing Isn’t Enough

When Hadoop, Spark, and cloud warehouses took off, everyone wanted a lake. Banks, telcos, e-commerce giants—all rushed to build storage at scale.

But leaders soon faced three hard truths:

1. Data without context is noise. Collecting every log or clickstream without understanding business value only creates clutter.

2. Access without design is chaos. If analysts and managers can’t navigate it, the lake is just a swamp.

3. Storage without purpose is a cost. Cloud bills pile up; ROI doesn’t.

A 2023 survey by NewVantage Partners found that over 65% of firms admitted they failed to turn their data investments into measurable business value. That’s not a lack of tech—it’s a lack of direction.

The challenge was never about size. It was about use.

And use comes from product thinking. #BigData #Cloud #CIO

The Birth of Data Products

From Pipelines to Products

A pipeline moves data from A to B. A product moves people from problem to solution. That’s the difference.

A #dataproduct is:

• User-centric: built for someone, not for storage.

• Outcome-driven: designed to deliver results—insight, automation, or growth.

• Sustainable: with feedback loops that make it better over time.

Examples Across Industries

• Netflix & Spotify: Recommendation engines are not just algorithms—they are full-fledged products that drive engagement and retention.

• Banking: Fraud detection systems evolve daily to prevent billions in losses.

• Retail: Predictive inventory planning saves millions in overstock and waste.

• Healthcare: Data-driven diagnostic tools guide doctors and improve patient outcomes.

These aren’t “dashboards.” They are products. They are built, tested, improved, and marketed like any other product.

This is where the shift happens: #dataengineering gives way to #dataproductthinking.

Designing for Trust and Usability

Why Adoption Beats Accumulation

The best algorithm is useless if no one uses it. This is the single biggest gap in most corporate data strategies.

Executives reject tools they don’t trust. Analysts ignore dashboards they can’t rely on. Engineers abandon systems that constantly break.

Adoption beats accumulation. That’s why design matters.

Core Design Principles for Data Products

1. Usability → Speak human, not SQL. Build interfaces people can use without training manuals.

2. Trust → Embed governance, lineage, and transparency. People need to know where data came from and how it was processed. #AIethics

3. Speed → Deliver insights when decisions are made, not weeks later. Latency kills adoption.

Look at #Tesla. Its self-driving system is a data product. If it delivered late updates or lacked transparency, adoption would collapse. Instead, Tesla treats feedback as fuel, constantly refining the product.

Trust and usability transform shelfware into everyday allies. #DataGovernance #AI #DigitalTrust

Monetisation—The Next Frontier

From Internal Tools to Market Value

The strongest signal that data products have arrived is monetisation.

Companies aren’t just using data for themselves—they’re turning it into revenue.

• Banks: Fraud detection offered as a service to partners.

• Retailers: Demand forecasts are sold to suppliers.

• Telecoms: Location insights packaged for advertisers.

• Healthcare: Genomic data services licensed to research institutions.

This is data as a business model.

A McKinsey study estimated that data monetisation could unlock $3–5 trillion annually across industries by 2030.

But note—monetisation isn’t “selling raw data.” That’s risky and often legally blocked. True monetisation is selling solutions, insights, and services built on data.

That’s the path to sustainable growth. #DataEconomy #Monetisation #DigitalBusiness

The Human Side of Data Products

Why Culture Matters More Than Code

Even the best algorithms fail in the wrong culture.

Too often, companies treat data as an afterthought—something engineers “handle.” But successful firms treat it as a deliverable.

That means:

• Hiring data product managers, not just engineers.

• Aligning incentives so people are rewarded for usage, not just collection.

• Fostering cross-functional teams—business, IT, design—working together.

Culture matters more than code. A toxic culture can sink even the smartest model. A product mindset can elevate even basic tools.

The future belongs to firms that marry culture and code. #Leadership #Culture #DataDriven

What Leaders Must Ask

Questions to Spark Transformation

C-suite leaders cannot afford to stay passive. Here are three questions every CIO, CTO, or CEO should ask today:

1. What products have we built from our data? If the answer is “dashboards,” you’re behind.

2. Do these products have users? Adoption is the only measure that matters.

3. Do they create revenue? If not, you’re running a cost centre, not a value centre.

These are not technical questions—they are strategic ones. They separate data-rich firms but value-poor from those that are truly value-rich. #CIO #CTO #DigitalLeadership

The Road Ahead

From Insights to Impact

The next decade will not be defined by who stores the most. It will be defined by who uses the best.

• Data Lakes → gave us storage.

• Data Products → give us usage.

• Monetisation → gives us impact.

The winners will not be the biggest collectors. They will be the boldest creators.

We are moving toward a world where #AI, #cloud, and #data converge—not to generate reports, but to build new industries. #FutureOfWork #AI #Innovation

The Call to Act

The age of passive data is over. The age of active products has begun.

Whether you are in #fintech, #healthcare, #retail, #AI, or #manufacturing—the principle is the same: build products that matter.

Don’t measure terabytes. Measure trust. Measure adoption. Measure revenue.

Because when data moves, industries shift. And when industries shift, leaders rise.

So here’s my challenge to you: what data product are you building?

Let’s start a conversation. Share your thoughts below.