Companies today are under increasing pressure to make better business decisions in less time, with less risk, while producing higher quality results. The challenges are enormous, as are the many issues that can arise and potentially jeopardize success. Among the most pervasive problems companies face is the consistently poor quality of internal data that is used to draw conclusions and make decisions. Poor data quality is not a new problem; but solving it is easier than before, because companies no longer have to rely on methods that require them to boil the ocean. Streamlined approaches to data governance that incorporate new processes and data stewardship technologies enable more agile methods for improving data quality.
Problems With Top-Down Data Governance
The problem with data governance programs thus far has been that most companies were taking a top-down approach, while more pressing short-term business demands were derailing efforts and distracting resources. Executives responsible for these programs and line-of-business managers, whose cooperation was needed to make the processes successful, were always scrambling to make their revenue numbers, launch new products and meet other required business objectives, rather than focusing on the often laborious data governance process, which only produced intangible results.
As a result, only a very small percentage of companies have an active data governance program in place because in most cases a top-down approach doesnt work. In a typical top-down waterfall approach to data governance, a company spends six months forming a committee, an additional six months defining and stating the problems, another six months gathering requirements and another six months arguing about terminology. By the end of a two-year process, many people have wasted their time in endless meetings with little to show for it.
A Better Approach: Agile Data Governance
An alternative to top-down data governance and a better way to address the problem is for companies to adopt a more agile approach. By introducing more agile processes, companies can achieve quick wins by implementing data governance processes and policies in smaller pieces, learning from and adapting the approach with each segment, taking time off in between increments to focus on pressing business goals, and then coming back together to address another data domain, while making steady progress over time. For example, with an agile approach, a company could decide to focus its efforts on a master data management (MDM) project for its customer data, which it could actually solve within six to eight months - quite different from a top-down approach which takes years just to get started.
Companies that want to try an agile data governance approach should follow a number of guidelines that will assist them in a successful implementation. Before getting into the specific details of an agile process, lets take a step back and consider some of the broader issues that are key to the success of any project:
- Determining up front which data fixes are going to deliver the greatest business benefits and focusing on fixing those first will help guarantee that the project is a success.
- Limiting the size of the data governance team and allowing the team to evolve as different data is being addressed at each stage of the project will help streamline the process and eliminate many of the potential political issues.
- Drafting a solid team of data stewards for each piece of the project will help to ensure success.
Before the overall project begins, a company needs to select a small, core data governance board made up of executives who can authoritatively represent the business goals of the entire organization. The board needs to have enough visibility to be able to sort out the biggest data problems the enterprise faces and determine which critical few problems should be tackled first, including the very first project, which should deliver the biggest overall bang for the buck. Data governance problems that should be addressed are those where poor data quality has the most direct impact on bottom-line profitability, productivity, cycle times, customer satisfaction, risk, reputation, cost savings and employee morale. The data governance board will ultimately be responsible for overseeing the entire data governance process, but it will not manage individual pieces of the project. Beware of the practice of most organizations to assemble larger, rather than smaller, groups for data governance projects. The thinking is that larger groups help ensure that everyone has a voice, but in reality this can seriously compromise agility and drag out a project.
Steps to a Successful Data Governance Project
Each iteration of an agile data governance project should be managed by an implementation team who will work with others within the organization to follow a number of specific steps that will result in successful completion. The following are recommended steps companies should follow for success:
- Select the data governance implementation team for the first project. Once data priorities have been set, the core executive board should select the team that will manage the first piece of the project. Executives who will all benefit from fixing a specific type of data are most likely to work well on the team and ensure that a project gets completed in a timely fashion. Each piece of the project will require different team members, depending on which data area is being addressed. For example, if a company is resolving business to business (B2B) hierarchy data, that problem will require a different group of executives than if product catalog data is being addressed.