Vetting New Data: Questions to Ask Before You Buy
Our fast, practical guide to separating valuable data from a shiny distraction.
Quick Recap: Where You Left Off
If you’ve already completed the 3‑Step Exercise in Part 1, you know whether a data feed deserves deeper evaluation. This article builds on that foundation to help you confidently assess the data without getting lost in endless calls and documents.
We’ve used this approach to vet hundreds of data sources. In our experience, it reduces the go-no go decision from dozens of hours over months to a few hours over a few weeks.
Before You Dive In
Why this matters: Buying a new data source isn’t like ordering office supplies, so there’s no standard process. And every data set has quirks, limits, and hidden assumptions. The goal here is to understand what you’re really buying.
Set yourself up for success:
Schedule a 1–2 hour discovery call with the provider. Ask them to include their sales engineer or product manager.
Request the data dictionary and pricing in advance.
Invite a technical partner or data-savvy colleague, but keep the meeting small.
Follow this step-by-step evaluation framework to prepare and get the most from your discovery call.
Step-by-Step Data Evaluation Framework
When you are finished preparing, this list should reflect your organization’s needs and priorities. Get input from key stakeholders like primary users, budget holders, and technical partners. And distinguish between what’s truly required versus just nice-to-have.
💡 Note: If you’re comparing several providers, add a simple rating scale to keep your evaluations consistent.
Want more detail? Click here for a customizable (and free) Google Doc template with our list of standard questions.
1: What’s in the Data
Data Fields, Coverage, and Industry-Specific Nuances
Confirm what’s measured, which data fields are included, and how key fields are defined.
Clarify data coverage (geography, demographics, categories, etc.). Get specific as no single data set will cover everything you need.
Ask about data segmentation and how each segment is defined. Request examples and definitions as they may differ from yours.
💡 Note: Always ask for a documented list of fields, their definitions, and segment definitions.
📚 Example: If evaluating a childrens book database, we’d ask whether coverage includes these parts of the market:
Category: Graphic novels, comics
Format: Print, digital, or audio
Publisher type: Major houses, independents, or self-published
Language: English only or multilingual
🛑 STOP: Does the data’s scope match your needs? If not, now’s the time to walk away.
2: Frequency, History, and Change
Clarify what time periods are available (weekly, daily, monthly) and how much history is available.
Ask how the provider handles market changes. Industries evolve as brands change, companies merge, and new innovations. If changes aren’t tracked well, the data can get messy fast.
📚 Example: “When a book title changes publishers, is it updated in the main database? How is that change captured?”
🛑STOP: If this doesn’t line up with your needs, check competitors or assess workarounds.
3: Data Collection, Cleaning, and Processing
Your goal: assess the stability and quality of their data.
Where does it come from and how clean is it?
Ask how the provider sources the data (collected firsthand or purchased and aggregated).
Dig into their quality controls and validation process.
Have them walk you through their data cleaning and processing from receipt to delivery.
Ask about any AI or Machine Learning used and how they verify its accuracy.
💡 Note: This can get technical. If something’s unclear, ask what it means and why it matters. Understanding their process now saves big headaches later.
🛑 STOP: If they can’t clearly explain their sources or cleaning methods, that’s a red flag.
Want more detail? Click here for a customizable (and free) Google Doc template with our list of standard questions.
4: Logistics, Delivery, and Support
Clarify delivery schedule.
Confirm formats and transfer methods (CSV, API, etc.). This is usually negotiable.
Discuss support channels: Who do you contact if something looks off? What’s the response time?
💡 Note: Build delivery timelines and service expectations into the contract.
🛑 LAST STOP: Compare what you’ve learned vs. your initial needs list. Does this data source still look like a fit?
Typical Next Steps
If this data is still a good fit:
Schedule a follow-up call within 1-2 weeks.
Request test files, data documentation, and draft contract terms.
Bring in technical partners to discuss integration and testing (if you’ve not done so already).
Beyond the Data
This guide focuses on the data itself: what it is, how it’s collected, and how it’s delivered. But before signing anything, also vet:
Company stability and track record.
Pricing structure and renewal terms.
Support model and service‑level commitments.
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