Data quality is far more than a technical checkbox; it’s a strategic imperative. In modern retail, accurate data underpins every smart decision, powers reliable analytics, and protects businesses from costly pricing blunders.
Before using market data to adjust prices or guide strategy, you need absolute confidence in its accuracy.
Data quality isn’t just about technical precision—it’s about business relevance. Inaccurate or stale data doesn’t just hurt operational performance; it erodes your brand’s credibility and bottom line.
To keep data reliable, we rely on two core sets of guardrails:
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Technical rules: These check the intrinsic health of the data—completeness, accuracy, and freshness.
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Business rules: These ensure the data makes sense in a retail context, validating its functional value, consistency, and usability for commercial teams.
In Conversation with Gregory, Data Expert at Retail Shake
Why is data quality so critical when it comes to competitor price tracking?
Gregory: Our clients rely on our insights to make high-stakes commercial decisions. If the data isn’t spot-on, they can’t act with confidence.
How does Retail Shake guarantee the accuracy of its data?
Gregory: Beyond manual audits at every stage of the data pipeline, we built an internal verification engine called Datacrop. It performs automated sanity checks right before data is written to our database.
Which data quality KPIs do you track, and how do you refine them?
Gregory: We keep a close eye on the volume of items scraped by each spider, using sector-specific visual dashboards during our daily checks. We’re currently moving towards even finer granularity to better benchmark our collection runs.
What does your data quality lifecycle look like in practice?
Gregory: It follows a strict four-step workflow:
1. Local scraping tests on a limited scope.
2. Automated test scripts for every individual spider.
3. Dedicated monitoring dashboards to track volumes visually.4. Continuous QA and extraction health checks.
Can you share a real-world example where data quality paid off for a client?
Gregory: Take our enterprise client Leroy Merlin. They use Retail Shake to shape their pricing strategy and automatically update electronic shelf labels in-store. Here, data accuracy directly impacts the price tags customer see on the shop floor every day.
How do you handle missing or dirty data?
Gregory: If a key attribute like an EAN/GTIN is missing, we enrich the product record by cross-referencing alternative sources.
For dirty data, our Datacrop layer sits between extraction and ingestion to clean and normalize values. For example, different retailers list weights differently (“kg” vs “kilogram”). Datacrop standardizes these attributes on the fly so everything matches seamlessly.
What’s the biggest data hurdle for retailers, and how do you solve it?
Gregory: Benchmarking Private Label (Own-Brand / MDD) ranges is always the hardest part because there are no shared EANs. We solved this with an intuitive, Tinder-style matching tool that pre-selects candidate products, making cross-retailer comparisons effortless for Category Managers.
How does high-quality data drive customer trust and retention?
Gregory: When data is consistently reliable, it shows you truly understand the client’s business. Clients feel confident, adopt the platform more deeply, and suggest new features—which feeds right back into our product roadmap.
How do you ensure full data transparency and traceability?
Gregory:
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Transparency: Clients access their data either directly through the Retail Shake app or via raw data exports. Whichever they choose, they can always see the exact source and collection timestamp.
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Traceability: Every single data point is timestamped and audited. We know precisely which spider scraped it, when it ran, and when it was last updated.
What tech stack do you use to maintain data integrity?
Gregory: I’m currently testing dbt-core to automate data testing against strict business requirements. The data ecosystem is moving fast, so staying ahead of the curve is part of the job.
Any recent tech innovations you’ve rolled out?
Gregory: We recently launched a pre-production environment for our task orchestrator (Apache Airflow) for client exports. This allows the engineering team to run full QA checks before pushing data live.
It also acts as a sandbox to test new automation tools. For instance, I recently helped our CSM, Hannah, set up Selenium IDE so she can record and replay site verification routines without doing manual checks.