Managing Data to Manage Your Business

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Co-written by Gael Corbion, Manager EU, Master Data management


Every pharma company aims to apply Business Intelligence (BI) to support business decisions. However, despite the critical importance of BI, project failure rates sit at a terrifying 80 percent. Even more alarming, more than 70 percent of companies still have concerns about data leakage and incompleteness.

Correct data can support better decision making, but you need more than just the right CRM and BI tools. This post explores how you can improve the reporting framework by leveraging Master Data Management (MDM) when building your KPIs. (The KPI framework must be defined first.) To define the right KPIs, what do you need to ensure you have the right data and insights?

A typical, real world example

A global Life Sciences company is building a consolidated Data Warehouse (DWH) for EMEA countries to provide Business Intelligence reporting to senior management. There is no unified CRM or MDM solution in place. The project has launched and challenges pile up rapidly. Soon the project is put on hold. What is behind this failure, and what can we learn?

When building reports and KPIs, source data is one of the most important success factors. A BI initiative is not only about tool selection, system architecture and cost. Data management must cover some essential considerations:

  1. Data ConsistencySame fields must have the same values and identifiers between applications and across the enterprise. With better Data Consistency across the enterprise, companies will be able to relate customers across CRM, Financial Systems and Spend Transparency solutions. More importantly, without this consistency across reporting systems, activities can be duplicated and KPIs misreported.
  2. Standards and AlignmentWhen information doesn’t have a unique definition, management rules or calculation formula across the enterprise. A good example of the benefits of improving Data Standards and Alignment in your Enterprise is the challenge of Product Information. The same Product can be named differently depending on the context (e.g., substance name, regulated medicinal name, different brand names in different countries). Proper standardization establishes a cross reference between product names, enabling precise identification of the product across situations and systems. Reporting at any level of the product hierarchy across countries is finally possible, and the benefits of standardization become clear. It ensures all affiliates share the same understanding of key company nomenclature. Definition of top-level channels should cascade to every country. Each interaction with a Customer will be reported in the appropriate channel, allowing precise consolidation of activities across geographies.
  3. Lack of Data Quality and Accuracy – When the data doesn’t conform to business definitions, is missing/lacking information, has integrity issues or is duplicated. Duplicate Customer information inevitably complicates day-to-day field activities for representatives and decreases analytics capabilities. Overall reporting is impacted. MDM discipline needs to be applied to all data elements required by a business intelligence initiative.

How MDM can help

Operational data is analyzed across dimensions and attributes. MDM provides “governed” dimensions for master data entities such as Product, Customer or Employee

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Data consistency, accuracy and quality is enforced across the enterprise using data governance processes, supported by MDM to maintain data standardization, ensure duplicate identification and provide merge capabilities.

A key activity when establishing strong data governance at the enterprise level is the implementation of a Corporate Business Glossary. This enables a common definition of the data elements used across the enterprise, ensuring activity or sales data can be compared in a relevant manner and easily aggregated. Sales performance and field activity can then be compared across brands and geographies.

MDM eliminates duplicates at the source. Trying to eliminate them at the DWH level is a painful activity requiring extensive effort to design and build data loading procedures. Furthermore, it does not prevent errors (sometimes process-breaking) during future data loads or merges. MDM resolves this by providing a “golden”, unique identifier that ensures an object’s uniqueness. MDM leverages:

  • Cross referencing capability to maintain the source identifier of each object in each connected system to understand and identify multiple duplicates in each system and across systems.
  • Merge capability to eliminate duplicates using merging rules tailored to each source or each type of duplicate.
  • Data Quality monitoring procedures to ensure quality remains optimal over time.

MDM also enables adaptation of hierarchies (e.g., for products or organizations) and flexible data modeling. For example, even with different SKUs in different countries for the same product, and different products within a brand family, you can easily align global reporting to understand brand performance and maintain the integrity of local SKU analysis needed to understand the impacts of specific campaigns and contracts by specific SKU.

Similarly, the DWH can benefit from these hierarchies and leverage the aggregation capabilities safely to allow flexibility when building complex KPIs.8 MDM capabilities will therefore be applied from an end-to-end perspective when creating KPIs.

Conclusion

Results of reports, ad hoc queries and analyses using data in the DWH can be correlated with the same quality data observed in an MDM system. When fed to the DWH, these KPIs produce better information because they are based on “trusted” MDM dimensions. In simpler terms, if you make sure you’re not putting garbage in, you’ll increase your chances of not getting any garbage out.

For optimal results when developing your KPI Dashboards and Reports, apply these simple guidelines in the following order:

  1. Harmonize company operating procedures, hierarchies and data, directly at the source(s)
  2. Map data where needed
  3. Extrapolate and manipulate data if necessary

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Building your insights on comparable quality data is key to accurate reporting and avoids misleading results and poor decision making. MDM is a requirement of any BI initiative that aims to have fast access to data processing, with accurate drill-down and transformation capabilities to support business-minded analytics.

To learn more, please contact Gaël Corbion or Antonio Pregueiro of HighPoint Solutions’ MDM and Commercial Excellence Practices.

Tags: business intelligence, Data Management, Life Sciences, KPI

   

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