Data Stewardship postfour key areas of focus that when managed intentionally lead to a solid data stewardship environment. We also created a “Data Stewardship Checklist” that can provide structure to assist in creating and maintaining the missing link. Click here to get the checklist.
Today we’ll focus on arguably the simplest of the four key focus areas related to Data Stewardship, which is also the one that most often gets overlooked. In order to be successful in this effort you need to put someone in charge of it and create an understanding of its importance. This involves four components:
- Establish stewardship priorities and a common understanding of “why”
- Assign accountability to data owners
- Provide process resources and tools to support improvement
- Measure results and plan for improvement
There is always inherent value in clarity. A foundational success factor is to clearly define your purpose for data management and quality. Explain why it’s important. This seems elementary, but clarity of purpose should never be implied.
Create a practical connection between data and your desired outcomes. You can do this by outlining the critical decisions that are made based on data. Create a data map indicating the data flow from data collection to making that critical decision. Use practical examples of data quality variation and show how it leads to lower quality strategic and operational decisions. Show your commitment to data stewardship by confidently including it in your organizational fabric. Invite debate about the program design and the processes involved. Build data ownership into both your operational processes and improvement projects as a rule.
In order to be successful, someone needs to be accountable. This does not imply that Data Stewardship is a one-person effort – quite the contrary. While every person, process and system that collects and manages information in your organization plays a role in data stewardship, the assignment of someone to the overall accountability of a data stewardship program is critical. This ensures focus and attention to the overall effort. It also allows for a much higher level of consistency in both data accuracy and process efficiency.
As part of defining accountability, be clear about the role each person plays. Define the specific activities to assist people, processes and systems in creating a higher quality data environment. Outline peoples’ role in managing data prioritization, data quality definitions and process improvement. Allow each role to manage data improvement projects. Position each data owner in the organization as a resource to assist people in both data collection and data management roles as well as decision makers to be more effective and confident in the information they use.
Demonstrate that you are dedicated to data quality by committing resources to support improving processes. Fix your data problem at its source or appropriate process point, not as an audit-based correction. While inspections and audits always play a part in a data stewardship program, focusing on designing quality processes should dramatically reduce the need for audit. Implement automated data analysis tools to assist you in catching abnormalities and incomplete data. Do simple things like designing targeted, exception-only based action lists so your team only has to act on high value data.
Make data quality and data stewardship a part of your leadership mantra. Talk about it, set expectations, plan improvement and measure results. Stick with your vision that you define for your data priorities and your definition of the “right” level of data quality. Prove its importance by making improvements that move you toward the goal. Show and celebrate how the process improvements are benefiting the organization and your team. Importantly, only make a big deal about data quality in areas that warrant the attention. Make a plan and follow the plan – if is it has been defined as critical, treat it appropriately.