Exemplary Methodologies for CPG Data Governance & Quality

Data governance is a disciplined method for managing data during its life cycle, from collection to use to disposal.

Every business needs data governance. As companies across all industries continue their digital transformation efforts, data has rapidly become their most important asset.

For strategic company choices, senior managers want reliable and timely data. Marketing and sales professionals require reliable data to comprehend what customers desire. Personnel responsible for procurement and supply chain management want precise data to maintain inventories and reduce production costs. Compliance officers must demonstrate that data is managed by both internal and external regulations. And so forth.

The consumer packaged goods (CPG) business is undergoing significant change.

The rise of e-commerce, quickly advancing technologies, a shift in customer behaviour, and the advent of big data have transformed data strategy from a competitive advantage to a corporate need.

In the highly competitive CPG business, data governance and quality have been and will continue to be crucial to an organisation's capacity to survive and develop. So let's delve deeper into the present situation of consumer packaged goods (CPG) and identify the top business intelligence methods that promote revenue and growth.

How to Thrive in the Rapidly Evolving CPG Industry?

CPG companies have had to quickly change and shed their conventional brick-and-mortar comfort zones in response to the rising popularity of digital shopping among customers.

Implementing new strategies and technology will enable firms to exploit data more effectively, regardless of their channel strategy, as digital transformation gains traction.

The transformation of this raw CPG data into reliable, usable business intelligence assists organizations in identifying opportunities, achieving crucial goals, and competing more effectively in the burgeoning e-commerce market. To do this, however, businesses must reinvent their data strategy and capabilities to make the required changes to their long-standing business models, procedures, and corporate culture.

Additionally, CPG companies are witnessing the effects of industry consolidation.

Mergers and acquisitions (M&A) activity in this market has exploded in recent years, driven by the initial hesitation of many large CPG firms to enter the internet marketplace.

It is a hole that small companies have been eager to fill with their inventive new goods and strategies, which have also made them attractive acquisition targets. More giant consumer packaged goods corporations have been purchasing these companies at a frantic rate to expand product offerings, target new consumer populations, enter emerging countries, strengthen distribution channels, and improve their digital strategy.

However, like digital transformation, mergers and acquisitions provide complicated data issues for acquiring firms that must consolidate systems and infrastructure, optimize processes, and eliminate redundancies.

Whether the aim is digital transformation or M&A consolidation, businesses encounter many of the same obstacles and need an organizational data strategy to solve them. They must establish and prioritize critical business objectives, assess their digital landscape's present condition and gaps, identify business-critical data processes and assets, and formulate an execution strategy.

Any digital strategy that is to be successful must be founded on data governance.

Analyzing the CPG Data Landscape:

Before transitioning to a data-driven business model, strengthening their digital strategy, or integrating disparate systems and processes, CPG companies must conduct a data landscape analysis. In addition, they must possess the following five abilities to respond to crucial questions consistently:

1)Cohesive strategy & operational plans:

When do you think we'll start seeing results from our initiatives?

?Plans for the future of a program, the consolidation of systems, and the elimination of data quality risks in the data supply chain are all examples of strategic and operational data migration and data cleansing projects.

After completing the evaluation and establishing priorities and objectives, the next step in developing a successful data governance framework is to implement the strategy.

2)The reasons for the change:

What tangible business results do the organisation hope to accomplish by employing data as a strategic asset? (such as speeding M&A customer and supply chain synergies, expediting time to market for new goods, minimizing days sales remain outstanding, and eliminating unnecessary spending)

3)Methodology for evaluating worth:

To what extent can we gauge progress toward those goals reliably? (including but not limited to key performance indicators, metrics, process performance measures, and risk exposure/tolerances)

4)Focus on Vital Statistics:

Determine which data assets are critical to core business operations. (For instance, being able to identify, manage, and exert influence on the 5% of data responsible for producing 80% or more of the company's outcomes)

5)Financial backing and ownership transfer:

Who should be considered while determining data strategy—the data owners and key stakeholders? executives, managers, subject matter experts (SME) in processes and functions, data stewards and maintainers, etc.

Developing Business Data Governance:

Companies in the CPG sector require strong data governance to lay the groundwork for achieving their data-driven outcomes and goals.

Data governance is the formal coordination of people, processes, and technology that enables organizations to use data as a corporate asset.

Establishing a central repository of accessible data sets and sources and facilitating data consumers' understanding of and using the most relevant assets for analysis and reporting are all components of effective data governance.

Critical components of data governance include the promotion of business data literacy and maintaining a typical data lexicon. Data, words, and qualities are defined in business glossaries; data sources, usage, and flow across systems and processes are outlined in data dictionaries; and data lineage is followed to discover where specific data originated and how it was used.

Companies can benefit significantly from these governance capacities when implementing important business initiatives like system and data transfer, process optimization, omnichannel sales, product transparency, and growth plans.

However, the foundation for success is provided by data governance, and data quality is another crucial component.

Incorporating Data Quality:

There is no point in enforcing CPG regulations if the data is inconsistent, erroneous, or irrelevant.

When businesses have access to unreliable information, it might be exploited to make poor judgments or be left unused and untrustworthy. That's why organizations need to safeguard and enhance the precision of their data throughout the data supply chain by implementing integrated data quality as part of their data governance initiatives.

Companies in the consumer packaged goods industry need to assess the state of their data quality in their environments and processes to determine the most vulnerable areas.

Data quality as it moves through systems and processes can be safeguarded by implementing data integrity checks for data accuracy, consistency, completeness, and timeliness. In addition, end-to-end data validation across the company can be achieved by implementing more sophisticated quality criteria for automatic reconciliation and transaction tracking, increasing confidence and data value.

Conclusion:

What is the ultimate reward for companies achieving all of this?

Data quality can be monitored and improved with the help of machine learning, which can also be used to apply data quality scores to increase confidence in the usefulness and trustworthiness of data assets among consumers.

CPG data is crucial for system migration, product transparency, and revenue growth in a constantly shifting market.

When properly implemented, data governance with integrated data quality may help businesses save money, streamline their operations, and maximize the value of their most valuable assets—their key datasets.