Web data is the fuel for modern business. It powers pricing models, market intelligence, product development, and even entire corporations.
Yet, when they are buying a data feed or service, many teams walk away from the experience feeling frustrated.
That’s not because data is unavailable. On the contrary, there are more data sources and acquisition capabilities than ever.
But, too often, the process of buying can feel like walking through fog.
I have worked with thousands of data buyers, helping them scope and cost data project requirements that are technically feasible, commercially viable, and legally compliant.
When they share stories about their past data engagements, I rarely hear: “We couldn’t find any data.” More often, I hear:
“We didn’t get what we actually needed.”
“We weren’t so sure about what we really needed”
“We did not commit to a project specification early enough.”
“We underestimated what it would take to make the data usable.”
Some charge ahead, only to realize the data they bought is unusable, incomplete, or difficult to integrate.
Some fall into the trap of treating web data like a commodity, focusing only on price or volume, thinking more is always better – until poor quality or a failed integration forces them to start over.
And many try to scale prematurely, assuming early experiments will work just as well at full production, only to find unexpected maintenance and operational costs.
The problem? Not bad luck or bad vendors. The real issue: buyers hit different challenges at different stages of the web data journey — but often solve for the wrong ones at the wrong time.
When teams don’t understand which stage they’re at, they try to force a one-size-fits-all solution onto a complex, evolving process. Some projects aren’t ready to scale, others don’t yet require extensive flexibility, and some only need a small proof of value to move forward.
That’s why understanding your data archetype is critical to matching your strategy to your real needs.
| Type | Challenge | Mindset | Common Pitfalls |
|---|---|---|---|
| Explorer | Feasibility Gap | “We see the value of data but we’re unsure how to get it.” | Unclear needs, premature commitments, analysis paralysis |
| Discoverer | Adaptability Trap | “We know it’s valuable. Now we need flexibility as we figure things out.” | Locked-in too early, expensive change requests, or frequent vendor switching |
| Optimizer | Scaling Wall | “It works—Now, can it scale sustainably, reliably and cost-effectively?” | Unprepared for scaling challenges and operational surprises |
So let’s meet the three data buyers, examine what each should pay attention to, and how they can find data vendors that match their priorities.
Explorers are either new to web data or applying it to a problem for the first time. They see its potential for their business but they are not yet sure how to get it. Their main goal is to validate feasibility before making a larger investment.
Most buyers don’t start with a clear understanding of what’s possible, realistic, or valuable. To them, every supplier sounds confident. Every quote feels like a mix of excitement and caution. They may have a high level idea of the type of data they need—pricing data, product listings, business leads—but, without internal expertise or relevant past experience, they can’t spell out the details, nor can they gauge the cost or value the project will entail.
Explorers ask, but often struggle to answer, key questions like:
What data is possible to get?
What does it cost to get it reliably?
Why are some things simple and others surprisingly hard—and therefore costly?
How much effort will it take to make the data usable?
Without guidance, some Explorers end up overengineering and overcommitting too early. I once worked with an e-commerce startup that spent six months building an in-house scraping system, only to realize a simple off-the-shelf dataset would have provided the data needed in a week. Others hesitate indefinitely, stuck in analysis paralysis – opportunities get lost simply because they didn’t have the right data at the right time.
Recommended approach:
Buy ready or build small. At this stage, buying off-the-shelf datasets, using an AI data gathering tool or running a vendor-assisted proof of concept is likely the best investment. Proof of concept data may not fit your use case 100% but is often enough to test value. Overspending on infrastructure or custom solutions too early can be a waste. Start lean and then iterate.
Prioritize usability over volume. Early on, you don’t need all the data, you need enough data that helps you validate your business goal. If you want to plan ahead, asking the vendor questions about data formatting, enrichment, and integration capabilities can help streamline future efforts.
Plan for compliance. Even in the exploration phase, ignoring legal and ethical concerns can backfire later, especially if you are in it for the long term. Ensure any data you deal with aligns with applicable laws and regulations in your region.
Discoverers have validated the value of web data. They have confirmed its business relevance, integrated it into real workflows, and defined short-term requirements. But their needs are still evolving, and flexibility becomes critical.
What worked during experimentation now feels limiting. Data needs evolve, business goals shift, priorities change, fields need adjusting, regional websites get added, integrations need tweaking.
By locking into/committing to systems or contracts too soon and too rigidly, before fully understanding their evolving needs, Discoverers may undo their ability to adapt later on.
Some get stuck paying for the wrong data, wasting money and slowing momentum. Others swing the other way, constantly switching providers, burning time and resources without ever stabilizing. They’re prone to paying for yesterday’s needs with today’s budget.
Recommended approach:
Balance cost with flexibility. Avoid long-term contracts unless they come with options to adapt as your use case matures. Seek out a data partner that offers the flexibility and data compliance you need.
Track ROI closely. Since the use case is still solidifying, tracking the impact of data on business outcomes helps determine whether to scale, adjust, or pivot to a different dataset.
Optimize acquisition speed. If you have the technical capacity, leveraging web scraping tools such as proxy management or unblocking solutions can speed up project delivery.
Partition projects. Verify whether your data collection effort could be refactored rather than treated as a monolithic. New data sources could be explored in one sub-project while an established defined pipeline is being optimized in another.
Eventually, success brings scale. Data volume grows, sources accumulate, websites behave more aggressively, and pipelines groan under the edge cases. What worked in a pilot buckles under production loads.Optimizers start losing sleep fighting the maintenance monsters.
Optimizers have stable, well-defined data needs. They know what they want. Workflows are in place. Now their priority is keeping data reliable, scalable, and disruption-free.
Data feeds must be consistent. Quality can't drop. Reliability hinges on robust maintenance workflows, automated monitoring, clear service level agreements, and dedicated support. Without them, what once looked like an asset could turn into a liability—breaking forecasts, dashboards, and even revenue streams.
Recommended approach:
Lean in to reliability. Your project may be live and pulling in data, but will it stay functional beyond the first few runs? Optimisers ensure their data feeds don’t break and continue producing accurate data.
Optimize for cost predictability. Costs for collecting web data depend on volume, complexity, and delivery format. Avoid surprises—find out how the pricing model accounts for these factors and ask for volume discounts whenever applicable.
Re-assess your reliance. At this stage, your data gathering process is either self-run or outsourced. But Optimizers will continue asking themselves the “Build or buy?” question, even when a project is up and running.
Choose long-term partners, not just vendors. Seek trusted, reliable data providers with a proven track record of supporting enterprises. Minimize in-house maintenance costs by partnering with technical experts specializing in scaling and maintaining data infrastructure. Scaling is cheaper and smoother when you work with experts who understand your environment and growth trajectory.
Most teams move through these stages in order—but not always. Some skip steps. Some circle back. Some run multiple stages in parallel. One quarter, you’re an Optimizer; the next, you’re an Explorer again.
You might be exploring a brand-new use case in one department while another operates a mature, scaled-up pipeline. Or you might realize that what worked last year now struggles under today’s demands.
The path isn’t always linear. If your requirements are clear and simple, you may even transform from Exploring straight to Optimizing. Other projects don’t progress at all. If you can’t prove ROI or the initiative loses relevance in the business, there’s no reason to move forward.
Just as you wouldn’t use the same map for navigating a jungle and crossing a desert, one’s approach to data procurement must adapt to one’s context. By being aware of these stages, you can be more confident that you are solving the right problems, at the right time, with the right partners.