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In sectors like energy, insurance, and telecoms — and particularly within the portfolio companies of private equity — data transformation has long been a strategic focus. We’ve had over a decade of effort across industries, and in the last five years, investment has surged. Yet despite this, few can say they’ve truly unlocked the full value from these programs. Why? What consistently causes data transformation to fall short of its promise? Having worked across energy, telecommunication and insurance firms — often in environments tangled with legacy systems — we’ve seen a recurring theme, the root cause is rarely the technology itself, it’s how we approach the transformation.
Technology-Led vs. Business-Led: The Crucial Distinction
Most failed or underwhelming transformations follow a familiar pattern. They are:
- Technology-led with business collaboration rather than
- Business-led and technology-enabled
This difference is more than just semantics. It defines whether transformation drives meaningful value or simply delivers incremental improvements.
When technology leads, you often get solid data stacks, capable pipelines, and decent analytics dashboards. You might even automate reporting and generate insights. But the impact remains confined. The systems touch parts of the business, not the whole. They don’t reach deeply into the operating model — especially in companies that have grown through M&A and are inherently complex.
Now imagine the same challenge approached from the business side. We visualised this using a “data plane” — a conceptual layer that runs across the entire organisation. Only when you see the business in its full complexity can you design data products that align to real strategic outcomes.
A great example of this in action comes from a Turkish telecom we studied, where the data transformation was led by CEO. By focusing on how data connected to growth levers — from customer churn to pricing models — the business didn’t just meet the market, it beat the market.
Where Is the Defensible Value in Data?
Another common question is: where does the most defensible value in data lie? Is it in customer insights? Operational efficiency? AI and automation?
The truth is: it depends on your vantage point.
- For a COO or CIO, operational efficiency is a must.
- For a CMO, customer insight is gold.
- For a CTO, delivery metrics and AI adoption might matter most.
- For a CEO, it’s the sum of all parts — even if priorities shift monthly.
This is why when building global B2B data platforms for Industry, we anchored everything in the target operating model. We assessed maturity across all value drivers — from supply chain to marketing, customer service to engineering — and then prioritized based on the most urgent gaps or growth opportunities.
But if there’s one constant across all roles, it’s this: data is no longer optional. It’s existential.
Companies that are data-guided — not just data-aware — have a structural edge. They move faster, learn quicker, serve better, and adapt more effectively to changing markets.
In Summary
If you're a private equity GP, LP, or operating partner, here’s the takeaway:
- Most data transformations don’t fail — but they don’t deliver their full value either.
- The critical difference is whether transformation is business-led or technology-led.
- Value in data isn’t one-size-fits-all — but it’s always foundational.
- A strong target operating model helps align data investment to strategic intent.
- Business-led data programs not only enhance performance but also increase attractiveness to future buyers.
In the next wave of value creation, those who embed data into the DNA of their portfolio companies — from C-suite to supply chain — will define the winners.
Photo credit: Logan Voss