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Leading Practices for Strategic Data Governance

Data governance is an important practice in consumer packaged goods (CPG) companies, but it is often not considered a strategic priority. Here are five leading practices, along with a maturity model for companies that want to move from tactical to strategic data governance.

To get a pulse on the consumer packaged goods (CPG) industry, Kalypso sent questionnaires and interviewed clients about their data management practices, specifically around the role of the data custodian.

As one of our interviewees put it, “Data Quality is often fit for purpose but not seen as a strategic asset.” And even in those companies that recognize its strategic importance, the skills and knowledge needed to manage data well are lacking.

The Common Challenges of Data Governance

In our work with many clients around the world, either multinational, regional, or local, we observe the entire spectrum of maturity when it comes to data governance. We frequently find that upper/middle management recognizes the need to establish or maintain data governance, but they struggle to convince executives to invest. One of our interviewees noted that “Data governance is mainly driven by people with a personal interest in it, not by a clear strategy.”

Some visible indicators of poor data governance include:

  • Data governance is led by IT without business support
  • Data governance is mainly driven by people with a personal interest in data, often without executive backing
  • No definition exists for data governance roles and responsibilities
  • Frequent re-entering of data between different functions and systems
  • Inconsistencies between data in different systems or between information on a product and what is defined in a system
  • Limited reporting capabilities
  • No vision on how data can be a strategic asset to the firm

Five Leading Practices for Data Governance

There are a small number of companies that have managed to turn data into a strategic asset, and in some cases even base part of their business model on it. These organizations have put a strong emphasis on data governance and implemented a combination of systems, business rules, and roles and responsibilities that drive data throughout the entire organization, not just one function. One of our interviewees pointed out that, “You need to check a lot of boxes to establish governance throughout the organization; it is not a quick R&D or Quality-only project.”

Some specific leading practices include:

  1. Data quality and completeness (integrity) are driven as a strategic priority by both business and IT executives
  2. Clearly defined data governance responsibilities are embedded throughout the organization, at different levels of the hierarchy and in all functions
  3. Data standards and rules are defined and adhered to both internally and externally by suppliers, customers and other external stakeholders
  4. Master data management (MDM) solutions/systems are in place to drive consistency of data throughout the organization
  5. Scorecards and/or reporting solutions are used to monitor data quality and track improvement

Together, these practices create not only operational efficiencies but also the foundation for turning data into a strategic asset. They drive improved reporting capabilities and offer the potential for better big data analysis.

Moving from Tactical Data Management to Strategic Data Governance

The best practices above apply to companies in almost all industries. However, the CPG industry is significantly lower in maturity than other industries. In both the aerospace and the pharmaceutical industries, regulatory demands on product development drive an emphasis on data and information management that is nearly as big as the focus on developing the product itself.

In industries that are driven by mechanical design, there has always been a translation from product design to product data. This has been automated in recent years by integrating CAD, formulation and other design tools into product lifecycle management (PLM) and MDM solutions. Combined with the right processes and organizational roles, these solutions provide a strong foundation for good data governance.

In CPG companies, attention to data is often driven by tactical reasons instead of a more strategic vision and solution. Some examples include:

  • Retailers require logistical data such as packaging dimensions
  • Health and safety authorities demand data around specific materials or ingredients
  • Sourcing and procurement teams need to manage specific product specification data

These tactical needs have driven the implementation of specific point solutions and processes that are fit for their purpose but are often not part of an overall vision, and that are poorly integrated and managed. As a result, many CPG companies maintain a large set of point solutions that contain different but also overlapping sets of data without consistent integrity or quality controls between them. These inconsistencies lead to manual retyping of data, but also to risks of inconsistency. As one of our interviewees pointed out, “If R&D does not deliver the right data, downstream functions will hurt.”

When there is a focus on data, the program is frequently driven by IT, who are tasked with reducing total system ownership costs. In other situations, the need for data governance is driven by the implementation of a new system that requires data from different sources to be merged. In both cases, timelines and budgets often do not include data governance as a workstream. In addition, functional executives responsible for managing specific KPIs do not want to invest their scarce resources in areas that are not theirs.

Maturity Model for Data Governance

Through our work helping many Fortune 100 and 500 companies establish or improve their data governance capabilities, we started to recognize patterns of practices that indicate different levels of maturity.

Organizations are exposed to different levels of risk whether the data is of poor quality or high quality. This also gives an indication of the maturity of data management practices. Based on these insights, we developed a practical maturity model with five levels, ranging from Ignored to World-Class.

This data management maturity model can be used to benchmark companies on their level of maturity. Within CPG, most companies fall into the lower three levels of maturity. Very few companies in the industry are World-Class.

This model can help set a vision for the level of maturity a company aims to achieve when embarking on a data governance initiative. It helps guide decision making and budget allocation to the project.

Getting Started with Data Governance

A key challenge in setting up data governance is getting approval and support from both executive leadership and upper/middle management from different organizational functions. This is critical for obtaining the resource commitment required for the initiative to be successful. Typically, leadership requires a clear business case and roadmap for implementation.

Industry leaders take the following steps:

  1. Develop a vision statement for data governance for the entire organization, not a specific function
  2. Identify an executive sponsor to fund the development of the roadmap and business case based on the vision statement
  3. Benchmark the organization against leading practices and set realistic development targets
  4. Conduct a fit-gap analysis based on the as-is and the to-be situation
  5. Develop a prioritized roadmap towards achieving the development targets and corporate vision
  6. Focus on quick wins that will showcase the value of data governance to the rest of the organization
  7. Develop the business case. A key part if the business case should be the negative impacts the company experiences from current practices. These can include risks related to poor data management or organizational inefficiencies caused by manual interventions needed to overcome poor data quality
  8. Present the vision, roadmap and business case to executive leadership and cross-functional upper/middle management to gain commitment on the implementation of the roadmap

One specific practice that has proven successful is to set up a temporary data processes and standards team within the organization. The mission for this group is to identify significant improvement areas and propose solutions to resolve issues. According to one of our interviewees, “Such a group has to look for customers in the business and involve them to develop true end-to-end standards and processes.”

Though not a long-term solution, a group like this can be implemented relatively quickly for the short-term to help realize some of the quick wins. Over the long-term, this team should evolve into a structure that is fully embedded in the ongoing business organization.

To Sum It Up

Data governance is not a well-defined practice in CPG and its value not fully recognized by executives. There are, however, numerous examples of significant negative financial implications from poor data governance. CPG companies can learn from other industries that have already developed the systems, roles and processes that have proven to be successful.

Kalypso acknowledges the data custodians of the clients who provided input and comments for this article.
Thank you.

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