Companies that need to improve enterprise data quality are looking to data governance programs to help achieve their goals. In the past, data governance emphasized a time- and labor-intensive, top-down approach that did not yield immediate results. Today, new agile practices have evolved that incorporate streamlined processes and technologies that make data governance an effective way to solve many pressing data quality problems. In a prior article, “Agile Data Governance: the Key to Solving Enterprise Data Quality Problems,” we discussed why a top-down approach doesn’t work and gave an overview of how to implement an agile approach to data governance. This article describes the seven steps companies should follow when embarking on an agile data governance project.
Before the seven-step process begins, companies need to assemble a small, core data governance board made up of executives who can authoritatively represent the entire organization’s business goals. A good place to start when selecting board members is with existing audit and compliance groups or other organizations within the company responsible for upholding regulatory guidelines. If these formal organizations don’t exist, then companies should identify senior people within the organization who understand the value of data and will realize direct benefits from helping to fix the data problems. These people are often executives who run business units, manage manufacturing and distribution, or act as chief marketing or privacy officers. Once the board has been assembled, it needs to identify the biggest data problems the enterprise faces and decide which problem to fix first. When the first project has been determined, the organization is ready to embark on the seven steps it needs to follow to achieve success.
Step One: Selecting the Project Implementation Team
The data governance board should assemble the project implementation team, which will consist of five to eight members. The implementation team should be made up of managers who will benefit from fixing the specific type of data being addressed. The best way for the board to identify these individuals is to ask the IT organization for a list of systems involved in the project and the business owners of the data in each system. These data owners or a subset of this group should make up the implementation team, which will work together to ensure that the project gets completed in a timely fashion. For example, if customer data were being resolved, the team would be made up of the data owners of systems involved in the customer data lifecycle.
Step Two: Defining the Size and Scope of the Data Problem
The data governance project implementation team first needs to clearly define the business problems it is trying to solve by improving data quality. For example, if the problem is inconsistent customer data across business silos, the mission may be to ensure that all customer data is accurate and coordinated in all divisions of the company. Success would mean that the sales order-entry people have access to the same information as the customer support people and the accounting people. Specifically, the goal could be that whoever looks at customers across the entire company sees the same customer information when they need access to it. To achieve this goal, the systems that would need to be interrogated from a data quality perspective might be: finance and accounting, customer relationship management (CRM), fulfillment, data warehouse and any ecommerce, supply chain or enterprise data integration (EDI) systems that may be in use, etc.
Step Three: Drafting the Data Steward Team
Once the project implementation team members have defined the business problems they are attempting to solve, the systems that process the data and the timing of the project, they need to draft a data steward team. The best people for the team are those with the most knowledge of the data being addressed and who are also capable of overseeing the investigation and remediation of data problems. Data steward teams are usually made up of people within each division who work directly with one or more source systems and are intimately familiar with each system’s data problems. For a customer data project, for example, the systems would be those that capture customer data, interact directly with customers, integrate data from external sources, place orders from sales reps or capture information used by CRM analysts.
