CPG Data Playbook Fundamentals

The consumer packaged goods (CPG) industry is in the midst of a transformation.  The rise of ecommerce, rapidly advancing technologies, changing consumer behavior and the arrival of big data have all tipped data strategy from a competitive advantage to a business imperative. If CPG companies wish to survive and thrive in this highly competitive industry, they must harness the power of their data for better business intelligence to drive revenue and growth.

Navigating the Changing CPG Landscape with Digital Transformation

With the increasing popularity of digital commerce among consumers, CPG companies have had to swiftly adapt and abandon traditional brick-and-mortar comfort zones. As digital transformation takes hold, implementing new tactics and technologies allow organizations to leverage data better, no matter their channel strategy. Turning this raw data into trustworthy, actionable business intelligence helps companies identify opportunities, achieve critical objectives and better compete in the emerging ecommerce arena. But to do so, companies need to innovate their data strategy and capabilities to enable necessary changes to long-standing business models, processes and corporate culture.

Another change CPG companies are experiencing is the consolidation of businesses in the space. Mergers and acquisitions (M&A) activity in the CPG market has seen explosive growth in recent years, sparked by the initial reticence of many larger CPG companies to enter the online marketplace. Small startups were happy to fill this online void, offering innovative new products and approaches that also make them attractive acquisition targets. Larger CPG’s have been acquiring these companies at a frenzied pace to expand product offerings, target new consumer audiences, reach emerging markets, increase distribution channels and elevate their digital strategy. But, like digital transformation, M&A also presents complex data challenges for these acquiring organizations who need to consolidate systems and infrastructure, streamline processes and eliminate redundancies.

Whether the initiative is digital transformation or M&A consolidation, companies face many of the same challenges and require an enterprise data strategy to overcome them. They must define and prioritize critical business objectives, evaluate the current state and gaps of their digital landscape, identify business-critical data processes and assets, and develop a plan for execution. To be successful, any digital strategy must be rooted in a foundation of data governance. Let’s explain why.

Surveying the Data Terrain

 As companies look to transition to a data-driven business model, strengthen their digital strategy and/or bring together disparate systems and processes, they must first survey their data landscape. They need five basic capabilities in order to consistently answer critical questions, such as: 

1. The “Whys” for Change — which meaningful business outcomes does the organization wish to achieve by leveraging data as a strategic asset? For example: Accelerating M&A customer and supply chain synergies; accelerating time to market for new products; reducing days sales outstanding; removing wasteful spend.
2. Value Measurement Framework — how will we consistently measure our success / failure in attaining of those outcomes? For example: KPI’s, metrics, process performance measures, risk exposure/tolerances.
3. Critical Data Focus — what specific data assets are most critical to key business processes? For example: Ability to discover, govern and control the 1-5 % of our data that drives 80%+ of our business decisions and outcomes.
4. Sponsorship & Ownership — who are the critical data owners and stakeholders for setting data strategy? For example: CxOs, line of business leaders, process & functional SMEs, data stewards & maintainers.
5. Cohesive Strategy & Operational Plans — by when can we expect to see demonstrable success for our initiative(s)? For example: Strategic program roadmap; operational data migration plans for systems consolidation; tactical data cleansing initiatives to eliminate data quality vulnerabilities within the data supply chain.

Once a company has completed their assessment and set priorities and objectives, they must establish a solid framework of data governance in which to execute their strategy.

Establishing Enterprise Data Governance

No matter the outcomes and objectives a company wishes to achieve through the power of data, they first need a foundation of data governance, or the formal orchestration of people, processes and technology that allow organizations to leverage data as an enterprise asset.

Effective data governance assigns ownership and accountability among data owners, stewards and stakeholders, establishes a central repository of accessible data sets and sources, and allows data consumers to easily understand and leverage the most relevant assets for analysis and reporting. Data governance features critical capabilities to develop enterprise data literacy and ensure that everyone is speaking the same data language. Business glossaries define data, terms and business attributes; data dictionaries detail data sources, usage and flow within systems and processes; and data lineage traces data origin and flow through systems and processes.

Together, these governance capabilities enable organizations to achieve critical business outcomes in areas such as system and data migration, process optimization, omni-channel sales, product transparency or growth strategies.

But data governance provides the framework for success; the other essential element is data quality.

Integrating Data Quality

There’s little point to governing data that is inconsistent, inaccurate or irrelevant. Bad data will sit untrusted and unused—or worse yet, it will generate faulty analysis and lead to flawed business decisions. That’s why it’s critical that data governance efforts include integrated data quality to protect and improve the accuracy of data across the data supply chain.

Organizations need to evaluate data quality levels across both environments and processes, and identify where data is most at risk. Implementing data integrity checks for data consistency, conformity, completeness and timeliness can protect data quality as data flows through systems and processes. More advanced quality rules for automated reconciliation and transaction tracking can also be used to provide end-to-end data validation throughout the organization, building trust and data value.

For example, recent CPG client benchmarks have proven the possibility of 25-30% reduction in new product introduction cycle times, improved PMO capacity upwards of 20%, and the ability to remove millions in wasted spend or missed discounts with strategic suppliers.

Beyond quality checks and rules, machine learning may also be used to monitor and improve data quality levels, and implement data quality scores to build consumer trust around data assets’ fitness for purpose and reliability.

As the CPG market continues its evolution, data must play an essential role in migrating systems, driving product transparency and ultimately increasing revenue.  Data governance done right, with integrated data quality, increases operational efficiencies, lowers costs, optimizes processes and allows organizations to derive maximum value from their strategic data assets.

If you’d like to learn more how to accelerate M&A integrations to better leverage your strategic business assets through digital transformation, download the white paper below.