6 ways to check whether your CPG data is good enough

In a highly competitive environment like the Consumer-Packaged Goods (CPG) industry, the quality of the data that brands have to work with can make or break the performance of the business.

Poor quality data leads to faulty analysis and inaccurate decisions that might cost companies millions in lost revenue.

The companies that get this right don’t just collect data, but they connect it, harmonize it, and validate it continuously. They make data quality a strategic priority, because they understand that accuracy drives better pricing, forecasting, and market execution. And this is where the right partner can make all the difference.

At Redslim, we process billions of data points across markets and categories every month, supporting global CPGs in turning siloed, raw inputs into reliable, decision‑ready insights. Our day-to-day work with the different sources and client use cases gives us in-depth visibility into the common patterns, pitfalls, and success factors that determine whether the data is trustworthy.

So, how can you check if your data is good enough?

Continue reading and we’ll guide you through 6 fundamental aspects you should pay attention to, to make sure your data reflects reality and takes you to reliable conclusions.

1. Check if you see a consistent story through data sources

As a first step when checking data quality, ask yourself if your different data sources tell the same story. Do they show the same trend? Then you can move to the next step. But what if your retail direct data source shows growth in the category, but your consumer panel data indicates a decline? That’s a signal.

In this case, you have to investigate to understand the reason behind it. The issue might still be valid, but you have to review trends across the key data sources on a monthly basis and understand why before making decisions on conflicting stories.

Said that, having people with technical expertise to ingest data is not enough. You need people with a deeper understanding of the different datasets. People who understand the sources’ limitations and where and when you should use them.

2. Verify alignment in product attribution and taxonomy

If you’re working with different data providers, across various countries and categories, product attributes are not coded in the same way. Each provider defines categories differently or uses a different hierarchy. Depending on the countries you’re operating in, there might be differences also in the prices and measures (€ vs. $ or kg vs. lb).

So, the next thing you have to check is if the taxonomies are consistent and your datasets have the right “building blocks” to create a consistent view of your performance. And this is exactly what most CPG companies are struggling with when integrating data from disparate sources.

Make sure that all data follows your global taxonomy and that each source provides the attributes (size, pack type, flavor etc.) needed for the mapping.

3. Validate if aggregation is done in the right way

The next thing on our checklist is about aggregation, the caveats of regrouping things together. From a data perspective, there are certain measures which can’t be simply added up, even if from a technical point of view, it seems an easy-to-do task.

Distribution is a great example of this. You can’t add up SKU distributions to get the brand distribution because you can’t see the overlap at store level. If you have “SKU A” and “SKU B” each with 5% distribution, those might be the same 5% of stores. Or there may be some overlap or there may even be no overlap at all.

So, instead of just checking if you can technically aggregate your data, you have to understand what that means in terms of the usability of the data and how to work around that to get a meaningful figure.

4. Check period and refresh cycle alignment to prevent historical drift

Usually, different data sources update with different frequency (weekly, bi-weekly, monthly etc.) and restate historical data at varying schedules. Sometimes more often than many teams would expect.

Simply adding the latest data without checking for historical changes results in variations between your back data and what the data providers show. Historical restatement frequency varies also by country, even within the same agency. For example, one provider’s Asia Pacific data might restate more often than Europe, requiring different ingestion approaches.

At Redslim, we’ve been working with the global data providers for years, so we can advise on the best way to work with the data received from them. And quite often it requires a different approach. Most importantly, it’s essential to understand things like restatement patterns – where and what those changes usually are.

5. Install pre- and post-harmonization quality checks

We recommend checking the quality of your data twice. First, you should assess input on its own as you get it from the data providers. Check the data sets for completeness, format consistency and outliers before even starting the data harmonization process.

Then, you have to validate the data after the harmonization as well. After applying mapping and aggregation, validate that the results actually make sense. Also, check if the agencies restated sales, changed attribution or shifted category definitions. Catching these before they hit dashboards prevents data-freeze moments.

6. Confirm the first line defense for data governance and democratization

According to a recent Gartner research, to achieve data quality, a fundamental thing is to have an effective data management and data governance in place.

Yet, many brands tend to think about it like an afterthought. Another McKinsey study shows that only 40% of consumer-goods companies that have made digital and analytics investments are achieving returns above the cost of capital. Most of them think that it’s because they still don’t have enough data. But the truth is that they simply lack data governance.

Data democratization amplifies quality issues even further. While once data was analyst-controlled who caught problems first, now, with dashboards everywhere, quality issues become instantly public.

To avoid that happening, your data must be clean before release. Once your business users see contradictory numbers, even if corrected, their trust in the data is already damaged. So, robust governance should include preventative validation as well.

What you can do is to build quality gatekeeping that validates data before it reaches users. This might seem like limiting access to end users. But in reality, it ensures that when people get access, they’re getting trustworthy data.

Build confidence in your data

When you’re preparing your data sets for analysis, the goal is to help business users to make confident decisions based on the harmonized data. When teams stop second-guessing numbers and start acting on them, you’ve crossed from “good enough” to genuinely useful data.

CPG companies that win aren’t those with the most data or sophisticated algorithms—they’re the ones whose data tells the true story that people can trust enough to bet the business on. Hopefully, these six checks will help you get there.

Feeling overwhelmed by the growing complexity of your data? Is maintaining high‑quality data taking time away from what really matters?

The Redslim is ready to support you. We specialise in data management, harmonisation, and integration, so your teams can focus on impact, not infrastructure.

If you’re ready to outsource the heavy lifting and unlock the true value of your data, let’s talk.