Data management challenges that most brands face and how to overcome them

Data has become one of the most critical strategic assets for brands and retailers. But with the growing complexity of data management, they’re still struggling to leverage the data effectively.

From managing multiple fragmented data sources to ensuring consistent, accurate reporting across categories and markets, organizations are still facing the challenge of turning raw data into reliable and actionable insights.

This article explores the most common data management challenges that Consumer-Packaged Goods (CPG) and Consumer Healthcare (CHC) companies are facing and provides practical strategies for overcoming them.

Common data management challenges

1. Data volume and scale

One of the biggest challenges is the quantity of data that you have to manage. Where once a single dataset from a major data agency could provide you with a sufficient market view, today you must pull from multiple sources across multiple channels.

For example, if you’re a personal care manufacturer, you don’t have to track performance only in traditional grocery stores anymore. You have to include pharmacy, parapharmacy, and online data as well, each coming from different suppliers with different standards and inclusions.

Compared to CPG, in consumer healthcare the breadth of data sources is even greater, with many more local, country-level agencies operating alongside the global providers. To manage this expanded data ecosystem you need robust infrastructure, significant internal resources, and deep expertise in each individual source.

2. Data quality and inconsistencies

With more data sources, you also increase the risk of having data quality issues. Different agencies might define the same metric differently, apply different methodologies, or cover slightly different universes of outlets, which can lead to inconsistencies in the data. And if you don’t understand and manage these properly, you risk drawing misleading conclusions and poor business decisions.

This risk is especially acute in pharmacy and parapharmacy channels, where local data providers may have unique inclusions or exclusions that differ from the standards applied in grocery. Without a clear understanding of these nuances, you can easily find yourself drawing comparisons across non-comparable data sets.

3. Data heterogeneity

Related to quality, data heterogeneity refers to the structural and definitional differences between data sources. Different suppliers may use different product hierarchies, market definitions, time periods, or units of measurement.

Bringing these together into a single, coherent dataset requires careful mapping, transformation, and documentation which is both technically demanding and time-consuming.

4. Semantic interoperability

When you bring your data sources together, you must ensure that the harmonized data is still translatable back to the individual source data. This is very important, because internal teams, local market stakeholders and external retail partners all need to be able to use and verify figures that tie back to the original data sources.

If you manipulate or transform the data too aggressively in the process of building a global view, your teams could easily lose the ability to reconcile their internal numbers with what they see in a data provider’s own reporting tool or what a retailer presents in a joint business planning meeting.

So, maintaining traceability – the ability to link any aggregated figure back to the original local data point – is essential.

5. Time and cost

One of the data management challenges that still many companies underestimate is the time and cost required to manage the growing data complexity. What was once a straightforward process of onboarding a single dataset per country has expanded into a multi-supplier, multi-format effort in each market.

You have to invest considerable time to understand each new data source, from its coverage and methodology to the nuances and limitations before you can start using them reliably. And this effort compounds overtime as you add new channels or data sources, when your suppliers change their methodologies or there are changes in your teams.

The cost of getting this wrong — through poor decisions based on wrong data — can far exceed the investment required to manage data properly from the outset.

6. Data governance

Without clear policies and ownership around how data is collected, stored, accessed, and used, you can easily find your teams working from different versions of the truth. Data governance encompasses the rules, standards, and processes that ensure data integrity and consistency across the organization.

For CPG and CHC companies operating across multiple markets, this challenge is particularly significant. Each local team may have its own approach to data management, making it difficult to build a coherent global view without establishing overarching governance frameworks.

7. Legal and ethical considerations

With the proliferation of data sources comes increasing regulatory and ethical responsibility. You must ensure that you are using data in compliance with local laws, supplier contractual agreements, and broader data privacy regulations.

8. Domain-specific challenges

CHC companies face additional complexity as their data often involves a wider range of distribution channels, more complex regulatory environments, and a greater number of local data providers. While the fundamental data management challenges are similar, the scale and complexity are magnified, requiring even more rigorous approaches to data integration and governance.

9. Versioning and updates

Data is not static. Suppliers regularly update their methodologies, revise historical data, or change their coverage universes. Managing these changes — while maintaining comparability over time and communicating updates to stakeholders — adds another layer of complexity to already demanding data operations.

10. Stakeholder cooperation

To effectively manage data, you need buy-in and cooperation from multiple internal and external stakeholders. Local market teams, regional management, data suppliers, and retail partners all have roles to play. Aligning these groups around common data standards, definitions, and processes can be as challenging as the technical integration work itself.

Strategies for overcoming data management challenges

We illustrated the 10 biggest challenges that CPG and CHC companies usually face. And while they are real and can mean a significant threat to both brands and retailers, it doesn’t mean there is nothing you can do to overcome them.

If you implement the right strategies, tools, and ways of working, you can transform these challenges into a competitive advantage. So, what are the most effective approaches?

1. Clearly define your data management objectives

Before selecting any platform or process, you should be clear about what you want to get from your data. Are you looking to make better commercial decisions or improve your supply‑chain operations and the collaboration with retailers? Or do you want to accelerate product innovation?

Well‑defined objectives serve as a compass that helps you choose the sources you should prioritize, influences how reporting is structured, and determines the level of governance you need. Since you might operate in different markets, the goal‑setting process should reflect both a global need for comparable, aggregated insight and local demands for granular, market‑specific data.

2. Implement strong data governance

A well‑designed governance framework defines data ownership at a granular level, the policies that regulate the collection, storage, and access, and the standards which keep metrics consistent across regions.

By clarifying data ownership and recording the steps for resolving issues, you can guarantee that every team, from global insights or local analysts, has access to the same “single source of truth.” Without this foundation, even the most advanced technology will have a hard time producing reliable and trustworthy results.

3. Simplify data integration

Investing in an integration layer that pulls together various sources and creates a single view helps to avoid double‑counting, reduces discrepancies, and maintains the audit trail back to the original data points. Once you have your data harmonized and enriched, you can implement data visualization software tools (like Redslim SPRINT or Power BI) to see both the global picture and the local breakdowns and enable your users to effortlessly alternate between strategic and operational views without the need for manual reconciliation.

4. Make data security a top priority

With the expansion of data and an increasing number of users accessing it, security should be an integral part of the design rather than an afterthought. By implementing role‑based access controls and detailed audit trails, and complying with supplier‑mandated usage rights, you can protect your sensitive commercial and health‑related information. In this way, you can reduce the risk of breaches, regulatory penalties, and loss of partner trust.

5. Partner with a data management company

Manual data preparation is tedious, prone to errors, and heavily reliant on an individual’s expertise. What can you do instead?

You can choose collaborating with an external partner for data ingestion, cleaning, harmonization, and enrichment who can do the heavy lifting for you by delivering up-to-date, reliable data to allow your teams to concentrate on extracting value from the data.

6. Purchase data quality solutions

Inadequate data quality can be extremely costly and hinder forecasting, negotiations, and decision‐making. With real‐time quality‐management systems you can detect and fix errors, duplicates and gaps in your data automatically. On the other hand, detailed source documentation explains the extent of available data and suitable use cases, thereby helping to avoid costly misinterpretations.

7. Develop a data-driven culture

Having the right technology and partners will not bring about value on its own. You have to take care of promoting data quality and data usability at every level of the organization. With regular training, a strong leadership commitment and organized knowledge‐sharing, you can ensure that employees are well-equipped with the skills, responsibility, and organizational memory to treat data as a common, strategic resource.

Summary

The data management challenges that CPG and CHC companies are facing every day are growing and evolving together with the industry.

The good news is that the solutions exist. With the right systems, governance, and partners in place, overcoming today’s data management challenges is not just possible – it’s a direct path to stronger performance and sustainable competitive advantage.

At Redslim, we help CPG and CHC companies take control of their data landscape with a combination of advanced technology and deep industry expertise. Our harmonization, integration, and data management services are designed to simplify complexity, improve data quality, and enable confident decision‑making across markets, functions, and teams.

Learn more about our data harmonization services or contact our experts to discuss your particular needs and understand how we can help.