If Your Data Fails, Your Systems Will Too
I hope everyone who attended POI had a thought-provoking experience filled with networking and meaningful discussion. POI is always a great time to catch up with colleagues, make new connections, and most importantly, learn about the new technologies that can help your business take the next leap in its Revenue Growth journey. However, there is one critical insight that is easy to overlook when evaluating the latest TPM and RGM tools: if your data quality is poor, your new system will perform even worse—regardless of implementation costs.
What are the most common data issues that constrain system performance? Here is a brief overview of the most frequently encountered TPM and RGM data challenges:
1. Customer Hierarchy Challenges
While many brands are confident that they know their customers, failure to properly align your brand's understanding of its customers with market realities is one of the most common causes of failed implementations. It is not uncommon for different functions within an organization to view customers differently. ERP systems typically view customers through a financial or logistical lens, which doesn't always align with how Sales must plan and forecast. Lift-and-shift migrations of ERP hierarchies often lead to planning inefficiencies and inaccuracies that make TPM planning unreliable or unnecessarily difficult.
2. Product Hierarchy Disorganization
Products represent another fundamental area where different organizational functions can have vastly different perspectives and preferences regarding data organization. In some organizations, Marketing and Finance may prefer product data aggregations that do not align with Sales planning and forecasting. As a result, Sales may be forced to plan items separately even when there is no compelling customer-facing reason to do so. In other cases, there may be opportunities to consolidate product groupings in ways that facilitate sales planning. Product hierarchies should be structured with Sales planning in mind to ensure optimal efficiency, which will ultimately result in higher-quality system outputs.
3. Lack of Alignment with Consumption Data
Both sell-in and sell-out data are critical elements of Sales planning and Post-Event Analysis. However, many brands struggle to adequately provide both data sources and ensure proper alignment between them. While sell-out data presents numerous challenges—including unit of measure conversions, display activity tracking, and timing issues—the most common problem is simply the lack of available data. Brands may only have access to certain retailers' sell-out data, may need to access multiple customer portals, and often encounter data in a wide variety of formats. The first step toward managing sell-out data effectively is ensuring both access and creating an internal process that establishes a consistent and comprehensive view of that data.
4. Translation Issues
There will be times when data structures must differ across systems to accommodate specific business needs. As discussed earlier, the structure of data in ERP may vary from the preferred view in TPM or RGM. While creating different views certainly adds complexity from an integration perspective, there is nothing inherently wrong with maintaining different views—provided all required data can be viewed correctly in each respective system of record. Ensuring this requires the ability to translate data between systems through both technological and process-based solutions. If middleware or intermediary systems are used, the organization must ensure that data is correctly reported in each system and that stakeholders understand when integrations between systems may lead to temporary misalignments.
5. Inadequate Data Export Planning
While brands invest in systems with the expectation that most required analysis will occur within the system itself, there is often substantial analysis that cannot be performed reliably or cost-effectively within the platform. Furthermore, the rapid advancement of new analytical RGM tools often requires the export of massive data volumes from TPM systems. As a result, during implementations, brands should consider TPM system data export as a key integration on par with other critical data integrations that bring essential data into the system.
The Path Forward
While this list could be considerably longer and significantly more detail could be devoted to each of these data issues, the key insight is this: it is far more cost-effective to solve these issues before you decide to implement—or even purchase—a new system. Resolving critical data issues prior to implementation is not only the best way to protect your investment, it is also one of the most important steps you can take to ensure long-term system success. Don't rush into an implementation without addressing these foundational issues first, and consider hiring a skilled mechanic to help expedite the process.
We welcome your feedback on what we may have missed and how we can improve upon this guidance.
