Does your organization have a data quality control strategy in place?
If you want to improve data quality and reduce operational costs across your organization, having a strategy in place to manage quality and set organizational standards is critical.
In order to develop and execute a data quality strategy, you will need to have a team and process in place. The following section provides guidelines on how to structure a team and develop processes.
Team
The first step in implementing a data quality strategy is to recruit a data governance team that will establish clear data definitions, develop comprehensive policies, oversee documentation by which internal business units collect, steward, disseminate, and integrate data on behalf of their organization.
This team must be experts across different functions in the organization. Having a cross-functional governance team in place will ensure the success of the long-term vision as well as build a sustainable and trusted data ecosystem. Furthermore, establishing a cross-functional team will develop a data-driven culture and creates data champions across the organization.
The governance team starts with a team leader, which is often the Chief Data Officer. The team leader is responsible for overseeing the entire team. This includes managing team focus, communicating procedures and monitoring success. The team lead will develop an executive committee that includes executive leaders from across different functions of the organization such as finance and marketing. Once the executive committee is established, they will be responsible for developing and overseeing the data governance processes and policies.
The team will also recruit mid-level managers across the different functions of the organization. These managers will be responsible for championing the data governance strategy and collaborating across the various functions of the organization. The managers will define processes, data quality metrics, and best practices.
Lastly, the managers will assign data owners, stewards, and users. The data owner is responsible for compliance, administration and access control of data. Data stewards are responsible for being the conduit between business users where they will interpret data sets and develop usable reports. Users are the organization members directly responsible for entering and using data as part of their daily task, they are responsible for reporting data irregularities to the owners and stewards.
Process
Define Scope
When implementing a data quality strategy, determine business processes that can be readily affected through improved data quality. The project should have definable and recognizable issues. Initial projects should be smaller in scope, allowing shorter iterations and faster results, which will provide immediate value. This will improve future executive support of larger projects.
All projects should include associated implementation costs and a timeline of how long the project will take and when results can be expected.
Map Data to Key Business Processes
Once it has been determined which key business process will be affected by the scope of the initial data quality project, the flow of data within the process will need to be mapped.
Mapping the data flow provides the big picture of how the data is being used downstream, what other business processes it affects, and ultimately what business initiative metrics it is being used for.
Plot Financial Implications on the Business
After the data flow of the process is defined, a deeper understanding of the financial implications can be achieved. It may be determined that the poor data is affecting more areas of the business than originally hypothesized, showing a greater cost savings opportunity.
Whatever the case may be, it will be important to plot out all of the financial implications the project will have on the business. It will be important to work with business management, accounting, and the finance department to ensure accuracy of the implications as well as gain trust and support for future projects.
Select the Right Technology to Help Facilitate the Process
When you’re ready to begin moving forward with implementation, you will need to determine what technology you will leverage to facilitate the data quality evaluation process.
The organization will need to utilize a diagnostics tool for data discovery and profiling. This tool will be key throughout the entire data cleansing process. It will be used to evaluate data set differences over time, quantify probable outcomes from cleansing and estimate the ROI of your project.
Determine Data Quality Metrics
Next, metrics will need to be selected that will capture the business impact. Metrics can range from simple to complex (i.e. measuring across several different data elements).
Once the metrics are established, you will need to determine what indicators will be measured, detailed in this previous post. The indicators are relevance, completeness, timeliness, structure, accuracy, precision, consistency, uniqueness, accessibility, understandability, community, and interoperability.
Link these metrics to key business initiatives to communicate the organizational value of data quality projects.
Establish Data Quality Project Best Practices
Lastly, as you continue to learn from each iteration of your data quality projects, you will want to begin establishing best practices. Best practices should be used consistently as the importance of data quality is communicated throughout the organization. By advocating best practices and communicating the importance of data quality through measurable results you will be able to influence a cultural shift in how the organization views data quality.