Define Critical Data
Critical data “keeps the lights on”. Identify critical data by focusing on the reports used to make decisions, data that provides the best customer experience, or data with regulatory, legal or compliance implications. Make the focus narrow enough that progress is made, and the scope can be widened as time allows. This data aligns to your business strategy meaning don’t worry about data that doesn’t drive your business strategy.
Establish a Data “Owner”
Who owns the data and takes responsibility for its accuracy? The owner should understand the process for creating or acquiring the data. They are responsible for supporting the process to create clean data, documenting metadata, and supporting questions about the data.
Establish and Measure Data Quality
What is clean data? The dimensions of data quality listed below create a comprehensive view into the data being interrogated. Measuring it over time will tell you if your data management processes are effective and help you focus on your next set of improvements.
- Complete – Business and System required fields are populated, e.g., address information.
- Unique – Degree to which duplication is managed. What is the source of truth?
- Accurate – Meets business rules, data standards, and validity parameters.
- Timely – Availability for use in a timely manner.
- Compliance – Used for intended purpose and data standards reflect statutory, regulatory, and legal requirements, as necessary.
- Consistent – Data is the same in definition, format, and values across all systems.
Document the processes from creation to end-use for critical data. Review the processes via the lens of data quality and data security. To find the areas for improvement as the following questions.
- Are the creation controls sufficient to create quality data?
- Is data knowledge readably available?
- Is data access/dissemination efficient and consistent?
- Is the beginning to end process timely?
Data Strategy – Building The Map
The clean data journey sets the stage for a Data Strategy. Data Strategy must come from an understanding of the data needs inherent in the business strategy: what data the organization needs, how it will get the data, how it will manage it and ensure its reliability over time and how it will utilize it.1 This will inform Data Management activities.
Data Management is the development, execution, and supervision of plans, policies, programs and practices that deliver, control, protect, and enhance the value of data and information assess throughout their lifecycles.2 Thought Logic defines the components of Data Management as: