Buying a data platform is a big decision, and most teams approach it carefully. Yet the difference between a purchase that delivers and one that disappoints is usually set early — in how clearly the problem is framed and how the process is run, long before any vendor enters the room.
That's encouraging, because it means the most important factors are the ones you control. We wanted to understand them better, so we ran two studies with business analytics students at Tampere University into how organizations buy data products, and looked at what the wider research says about B2B buying. Here's what stood out, and how to put it to work. (More on the study at the end.)
The single clearest lesson: the hardest part isn't choosing a supplier. It's defining the problem.
Key takeaways
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The biggest risk is a poorly defined problem — invest there first.
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Ownership is shared; find the person who bridges IT and business.
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Weigh total cost of ownership, and quantify the benefit, not just the price.
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Expect a long timeline — early ambiguity is what stretches it.
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Get IT, business, and suppliers anchored on the same concrete outcome.
1. Start With the Problem, Not the Solution
Across both studies, problem definition came out as both the biggest challenge and the phase that most decides the outcome. In our survey, it was named the top difficulty by a wide margin; in the interviews, it surfaced again and again as the place where projects quietly go wrong. The pattern is consistent: teams feel pressure to move, so they jump to evaluating solutions before they understand the root cause. Several buyers described investments that only revealed themselves as the wrong choice once the project was finished — because nobody had stopped to ask what they were really solving.
This isn't unique to data. Gartner describes B2B buying as a set of "jobs" buyers loop through, and reports that 99% of B2B purchases are triggered by organizational change — so the real problem is usually broader than the technical symptom that surfaced it. Time spent articulating the actual business outcome before you scan the market does more for the result than any later step.
What to do: Run a structured first conversation focused on the outcome, not the technology. Name the problem, who it hurts, and what success looks like in numbers. Skip it, and the cost resurfaces later in the project.
2. Know Who Actually Owns the Decision
Responsibility rarely sits in one place. The lead is usually IT or a technical leader, but ownership is fragmented and assembled ad hoc per purchase — architecture, security, procurement, and a business sponsor pulled together for the occasion. Gartner puts the average B2B buying group at six to ten stakeholders, each doing their own research. The technology is bought through IT, but the case is made in business terms.
What to do:
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Find the bridging person early — often a CDO or CIO-level role who speaks both architecture and business value. They unblock decisions.
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Line up architecture, security, and procurement sign-offs before you need them. The process stalls when those checks arrive late as surprises.
3. Budget for Total Cost, Not the Sticker Price
When asked what would make buying easier, buyers were blunt: clearer pricing first, then proven savings and compatibility with the existing environment. Experienced buyers no longer look at purchase price alone — they weigh total cost of ownership over five to ten years, including development, integration, and the people to keep it running.
The hard part is the value side. Many couldn't put a number on the benefit, which made ROI tough to prove and internal approval tough to win. Cost is concrete; benefit is fuzzy. That asymmetry slows decisions more than any price tag.
What to do: Demand lifecycle cost clarity up front, and express expected benefit in measurable terms. A purchase that can't articulate its own return won't clear approval.
4. Expect the Timeline the Ambiguity Creates
These purchases take time — on average around eleven months in our survey, and in the public-sector interviews, six to nine months was a typical baseline for regulated procurement. Length had no clear link to organization size or supplier mix. The strongest signal was something else: ambiguity at the start stretches everything that follows. The shortest path is a clear starting point.
Sourcing is also overwhelmingly relationship-driven — networks, colleagues, and trusted partners far ahead of search engines or events. Trust is built before the first sales conversation, not during it. Your existing network is a better filter than a cold market scan, so cultivate it before you need it.
What to do: Treat early clarity as your fastest accelerator, and bring trusted peers into the room before vendors do.
5. Keep Everyone Speaking the Same Language
Buyers, sponsors, and suppliers often don't share the same vocabulary — a term that means one thing to IT means another to the business. You don't need agreed definitions, just a shared anchor: the concrete outcome you defined at the start. That keeps the problem from drifting as it passes between people over a months-long process.
And What About AI? Will It Reshape Everything — or Not?
AI is still mostly at pilot stage in most organizations — an accelerator of existing analytics, not an autonomous decision-maker. It's tempting to assume AI will compress these slow cycles and make the discipline above unnecessary. It won't, at least not the way people expect.
AI changes the speed of building, not the difficulty of deciding what to build. Faster tooling just gets you to a bad outcome quicker if the problem is wrong. Where it does shift the equation is on the build side — and the advantage compounds. An AI-ready platform, with consistent semantics, governed flows, and fresh, reliable data, is what lets AI tools produce useful output instead of confident guesses. A model is only as good as the context you give it, and well-managed metadata is that context. Raw capability isn't enough either: the gap between demo and production is the harness around the model — the guardrails, validation, and workflow that make output trustworthy and repeatable. This is the part we work on at Agile Data Engine: turning metadata into reliable context and wrapping it in the automation and guardrails that keep AI-era data work dependable at scale. Organizations investing in both context and harness now get steadily more from every AI tool that follows. Those without the foundations re-solve the same data problems for each new use case — bearing out the industry estimate that 70–80% of AI project time still goes to data wrangling.
The Common Thread
Across both studies and the wider research, the advice converges on one idea: a good purchase is decided at the start, not at signature. Define the real problem before scanning the market. Find the person who bridges IT and business. Insist on total-cost clarity and a quantifiable benefit. Expect a long cycle, and shorten it by removing ambiguity early. The vendor matters — but the discipline you bring to the front of the process matters more, and that part is yours to shape.
Thinking about a data platform purchase? That early clarity is exactly where we help. Talk to ADE to pressure-test your problem definition and map the path to value before you commit.
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About the research: This post draws on two studies conducted with business analytics students at Tampere University in spring 2026 — a survey of fifteen organizations and in-depth interviews with four enterprises across the private and public sectors. Two teams used different methods independently, yet reached closely aligned conclusions. External figures are from [Gartner's B2B buying research](https://www.gartner.com/en/sales/insights/b2b-buying-journey) and widely cited industry estimates.