Regardless of the driving business strategy or focus, organizations are turning to data to leverage key insights and help improve the organization’s ability to realize its vision, key goals, and objectives.
Poor quality data, however, can negatively affect time-to-insight and can undermine an organization’s customer experience efforts, product or service innovation, operational efficiency, or risk and compliance management. If you are looking to draw insights from your data for decision making, the quality of those insights is only as good as the quality of the data feeding or fueling them.
Improving data quality means having a data quality management practice that is sustainably successful and appropriate to the use of the data, while evolving to keep pace with or get ahead of changing business and data landscapes. It is not a matter of fixing one data set at a time, which is resource and time intensive, but instead identifying where data quality consistently goes off the rails and creating a program to improve the data processes at the source.
This research is designed for:
- A CIO or data management executive looking to improve data quality, reduce data complexity, and build a data quality practice.
- Data owners and stewards who are tasked with the duty of improving data quality and/or currently managing data initiatives.
- Align your data quality initiative with the business, exercising just enough effort to making it fit for purpose.
- Avoid common pitfalls and challenges that derail data quality initiatives.
- Recognize any organizational data quality gaps and deficiencies and improve them.
- Get to the root of data quality issues to fix data quality issues where they start.
With the business demand for useful data and the rate of data proliferation showing no signs of slowing down, users are struggling with getting quality data to meet their business needs and to support timely decision making. Even when the data gets to users, they don’t trust the data and complain about getting different answers while running the same report.
- IT is struggling to define what quality means in the context of meeting the needs of data users. Data quality is not an absolute. Perfect data quality is unattainable and a waste of time.
- Organizations lack a systematic and sustainable way to establish and ensure data quality because of the lack of integration of data quality into the organization’s data management and data governance program.
- Our four-step, practical approach helps you to improve the organization’s enterprise data quality practices while systematically addressing specific data quality improvement initiatives:
2. Analyze - To begin addressing specific business-driven data quality projects, you must identify and prioritize the data-driven business units. This will ensure that data improvement initiatives are aligned to business goals and priorities.
3. Fix - After determining whose data is going to be fixed based on priority, determine the specific problems that they are facing with data quality, and implement an improvement plan to fix them.
4. Sustain - Without being embedded into the organization’s long-term data management program, data quality will remain a band-aid fix. Sustain data quality improvements by incorporating data quality practices into the data governance program.