Retail, CPG and Logistics

Leading practices for CPG data governance

All businesses these days, including Consumer Packaged Goods (CPG) manufacturers, are awash with data. Big data, enhanced by the application of AI and ML technologies, had promised to deliver a significant reduction in costs, an increase in revenue, an improvement in operational efficiency, and greater customer satisfaction. However, the reality has not matched the expectations. The old saying in computer science, GIGO—' garbage in, garbage out’ can be modified to read ‘poor quality data in, poor analysis and actionable results out’! The reasons for this situation are many. Especially with CPG manufacturers with their multitude of products, brands, price-points, promotions, customer segmentation, and supply chains, the sheer volume of data generated daily is staggering; to add to that, the data comes from multiple sources, is stored all over the company in a variety of structured and unstructured formats, and is used by many different channels – companies are increasingly using e-commerce in addition to the traditional retail channels. All these factors have led to the recognition of the importance of Data Governance (DG) and Data Quality (DQ).

DG refers to the policies and rules for the gathering, storing, and maintenance of all the data in the company to ensure that they are timely, accurate, secure, and accessible. Only then can the data be properly analysed to produce reliable, actionable insights that will truly benefit the company. Beyond that, DG is also essential to ensure that the company meets all compliance standards.

Implementing effective DG requires several steps

1. Implementing DG requires both the commitment of top management, to support the efforts and to fund them adequately, as well as the participation of all the stakeholders in gathering and maintaining data in the company. A DG team led by a senior manager – and that includes all the departmental managers involved – should set overall policies and guidelines for data management in the company.
   
2. As with any change effort, to start with, identify areas that need immediate attention; what are the most significant problems in terms of data quality and prioritise areas that you will initially focus on. Identify not just the problems but also the metrics (for example, the percentage of customer data that is accurate and up-to-date) by which you can assess if you have achieved your goals and create KPIs you can monitor.
   
3. For the chosen areas, make a concerted effort to integrate all the diverse repositories of data to ensure that there is a single source of truth for each category of data; for example, there should be a single source of dependable, up-to-date customer data. This principle should be applied to other relevant categories of data such as products (including packaging, description, and other related data). Efforts must be made to identify and eliminate duplicate entries of data to avoid problems with the trustworthiness of the data. Clear standards must be defined for data validation, conflict resolution, and implementation of security. DG should ensure that people who need specific data for effectively running the company have access to the necessary data and also that only those who need it have access.
   
4. Based on the previous effort, identify data owners and data stewards and give them the responsibility to ensure the quality of the data. Data owners are ultimately responsible for all aspects of specific areas of data while data stewards are in charge of overseeing the actual hands-on maintenance of that data.
   
5. Data producers and data consumers: Data producers are the staff that enter the data. They need to be supported by software that automatically does data validation and flags errors. They also need to be trained well to appreciate the importance of their work downstream. Data Consumers are often the staff with direct contact with the outside environment – such as Customers and suppliers. They are also the ones who are in the best position to judge the value of the data for the task at hand; hence, they should be empowered to escalate issues to the data stewards promptly and comprehensively.
   
6. Then we have the data custodians, usually the IT Department, that is charged with the procurement and upkeep of hardware and software used to capture, maintain, update, and eventually archive data during its life cycle.
   
7. Last, but not least, is the commitment enforced by the DG team to continuously improve the DG framework – policies, procedures, roles and responsibilities. This requires continuous attention to KPIs and metrics that evaluate the quality of the data and prompt response to issues as they arise.

For big data to deliver its promises to drive growth, efficiency, and customer satisfaction, CPG companies must implement effective DG and deliver good DQ. This is all the more important in the highly competitive, constantly evolving, multi-channel world of CPG manufacturers.

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