The Number One Thing Holding Your CPG Brand Back from Capitalizing on AI Is an Easier Fix Than You May Think

With AI dominating news headlines and social discourse, many CPG brands are scrambling to understand how to best leverage artificial intelligence to drive revenue growth. While Revenue Growth Management (RGM) focused technology is rapidly advancing, becoming more affordable, and improving exponentially on a daily basis, there is one key factor holding many brands back from taking advantage of these transformative technologies. What is this key limiting factor? Data. Fortunately, improving your brand's data infrastructure may be an easier fix than you think.

The Root of the Problem: What's Wrong with Your Brand's Data?

Understanding the common data challenges facing CPG brands is the first step toward building an AI-ready foundation. Most organizations face similar issues that, while complex in their implications, are surprisingly addressable with the right approach.

Competing Priorities

Within any organization, there are competing priorities that significantly impact how data is structured and managed. Different key functions often require specific data views that make little sense for other departments. Sales teams might need customer data organized by territory and account hierarchy, while marketing teams require the same information structured by demographic segments and campaign performance metrics. Finance, meanwhile, may need this data rolled up by cost centers and profit margins.

As a result, other functions are frequently forced to translate data into a format that works for their specific needs, or in more severe cases, they're compelled to use data structures that don't provide the visibility they require to make informed decisions. This translation process not only wastes valuable time but also introduces opportunities for errors and inconsistencies that compound over time.

Human Error and Unforeseen Complexity

Like most aspects of business operations, human error in data management often represents the biggest contributor to data inconsistency. When new products are launched and new customers are acquired, it's not uncommon for inconsistencies to develop in how the corresponding data is captured, categorized, and managed within existing systems.

New products or customers frequently have different requirements from existing data items, forcing human operators to make imperfect categorization decisions that may break established data governance rules. For example, a new product that spans multiple traditional categories might be incorrectly classified, creating downstream reporting issues that affect everything from inventory management to promotional planning.

These seemingly small decisions can have far-reaching consequences, especially when they become embedded in automated processes or when they're replicated across similar new items without proper review.

Resource Turnover

In many organizations, there are only one or two individuals who play an outsized role in managing master data. These "data champions" often become the unofficial guardians of institutional knowledge about data structures, business rules, and the rationale behind various categorization decisions.

In the absence of centralized guidance and documented processes, these individuals are often left to make critical data governance decisions independently. When these key resources leave the organization or transition to different roles, they frequently take that invaluable tribal knowledge with them, leaving gaps that are difficult to fill.

Even when new resources do their best to match existing data management patterns, it's highly likely that data inconsistencies will develop during the transition period. Without proper documentation and handoff procedures, new team members must reverse-engineer decision-making processes, often leading to different interpretations and approaches.

Inadequate Storage Infrastructure

While most organizations have become comfortable managing ERP data and traditional data warehouses, they often overlook the storage and management of data that doesn't natively belong in either system. Trade Promotion Management (TPM) data, Point of Sale (PoS) information, syndicated market research, and other marketing-related datasets are frequently stored and managed within their respective source systems, operating in isolation from the broader data management strategy.

This fragmented approach means that critical data points that could provide valuable additional context or insight are systematically excluded from the larger analytical consideration set. For instance, promotional performance data might live in the TPM system while baseline sales trends reside in the ERP, making it nearly impossible to develop a holistic view of promotional effectiveness.

These scattered data sources also create significant integration challenges when organizations attempt to implement new analytical tools or AI-powered systems that require comprehensive data access to function effectively.

Building Your AI-Ready Data Foundation: Four Essential Steps

Addressing these common data challenges requires a systematic approach that balances immediate needs with long-term strategic objectives. Here's how your brand can ensure its data infrastructure is prepared for the next stage of AI-driven growth.

1. Align Cross-Functionally on Key Data Definitions

In many cases, different business functions legitimately need to view certain key data elements in specific ways that serve their unique operational requirements. While varying data views may be necessary and even beneficial, it's crucial to ensure that different functions understand the rationale behind these variances and can effectively translate between different perspectives.

This alignment process should involve representatives from all major data-consuming functions including sales, marketing, finance, supply chain, and operations. The goal isn't to force everyone to use identical data views, but rather to create a common understanding of how and why different interpretations exist.

The ability to translate between different data perspectives will significantly reduce communication issues and ensure that the organization can make valid like-to-like comparisons when evaluating performance across different business dimensions. This foundational understanding becomes particularly critical when implementing AI tools that need to analyze data across functional boundaries.

2. Develop a Clearly Defined Master Data Management Process

While this may seem easier said than done, your organization needs a clearly defined, well-documented process for managing master data that goes beyond informal arrangements and tribal knowledge. This process should include several key components that work together to maintain data integrity over time.

Each critical data element should have a dedicated "data captain" who is ultimately responsible for making data-related decisions and ensuring consistency across the organization. Additionally, there should be designated personnel specifically responsible for data setup in ERP and other required systems, ensuring that data governance decisions are properly implemented at the technical level.

The process should also include formal mechanisms where impacted functions are notified of proposed changes and given opportunities to provide relevant feedback on data designations before they're finalized. This collaborative approach helps prevent downstream issues while ensuring that data structure changes support rather than hinder operational effectiveness.

3. Document, Document, Document

Master data management rules, business logic, and governance guidance should be comprehensively documented to ensure that policies are widely understood and consistently applied across the organization. This documentation should go beyond simple data dictionaries to include the rationale behind specific decisions, examples of proper application, and clear escalation procedures for handling edge cases.

Documentation should be regularly updated and maintained to reflect evolving business needs and lessons learned from practical application. Most importantly, this guidance should be structured and maintained in a way that ensures institutional knowledge can be easily transferred from resource to resource, reducing the organization's dependence on individual subject matter experts.

Consider implementing regular review cycles where documentation is evaluated for completeness, accuracy, and relevance to current business operations. This proactive approach helps prevent the gradual decay of data governance standards that often occurs when documentation becomes outdated or disconnected from actual practice.

4. Implement Centralized Data Storage Architecture

Organizations should take a comprehensive view of data storage and management that extends beyond traditional ERP and data warehouse boundaries. Implementing a data lake architecture to manage both structured and unstructured data represents the current best-in-class approach to enterprise data storage, offering the flexibility and scalability needed to support advanced AI applications.

Modern data lake solutions provide the capability to store raw data in its native format while supporting multiple analytical approaches and use cases. This flexibility is particularly valuable for AI applications that may require access to historical data patterns, unstructured content, or real-time streaming information that doesn't fit neatly into traditional database structures.

With numerous data lake solutions and management tools available in today's market, there are viable options to fit organizations of virtually any size and budget. The key is to select a solution that can grow with your organization's needs while providing the integration capabilities necessary to support both current operations and future AI initiatives.

Preparing for the AI-Driven Future

The rapid advancement of AI tools presents unprecedented opportunities for CPG brands to optimize revenue growth, improve operational efficiency, and enhance customer experiences. However, these opportunities will only be accessible to organizations that have invested in building robust, well-governed data foundations.

At Revenue Pit Crew, we encourage all current and potential clients to take a comprehensive view of their data needs and capabilities. As the CPG industry continues to evolve and embrace digital transformation, data quality and accessibility will become increasingly critical competitive differentiators.

The organizations that invest in proper data infrastructure today will be positioned to rapidly adopt and benefit from AI innovations as they emerge. Those that continue to operate with fragmented, poorly governed data will find themselves increasingly disadvantaged as competitors leverage AI to drive superior performance.

The time to address data challenges is now, while the solutions are still manageable and before the competitive gap becomes insurmountable. By implementing the strategies outlined above, your organization can build the data foundation necessary to capitalize on AI opportunities and maintain competitive advantage in an increasingly data-driven marketplace.

If you're interested in learning more about how your organization can stay ahead of evolving data challenges and prepare for AI-driven growth, reach out to ryan@revenuepitcrew.com to explore how we can help you build a comprehensive data strategy that supports your long-term business objectives.

- Real human thoughts, not generated by AI