Poor data quality destroys business value. Recent Gartner research has found that organizations believe poor data quality to be responsible for an average of $15 million per year in losses.
This is likely to worsen as information environments become increasingly complex — a challenge faced by organizations of all sizes. Those with multiple business units and operations in several geographic regions, and with many customers, employees, suppliers and products will inevitably face more severe data quality issues.
Create compelling business cases that connect data quality improvement with key business priorities
Speaking ahead of the October Gartner Data & Analytics Summit 2018 in Frankfurt, Ted Friedman, vice president and distinguished analyst at Gartner, says, "As organizations accelerate their digital business efforts, poor data quality is a major contributor to a crisis in information trust and business value, negatively impacting financial performance."
Many organizations are struggling to successfully propose a program for sustainable data quality improvement. Effective business engagement and funding may be limited for several reasons:
- There is no understandable connection between data quality improvement and business outcomes.
- The business may not understand the criticality of its role in data quality improvement.
"Data and analytics leaders need to understand the business priorities and challenges of their organization. Only then will they be in the right position to create compelling business cases that connect data quality improvement with key business priorities," explains Friedman.
He shares five steps to create a business case for data quality improvement.
Step No. 1. Understand business priorities and find the right place to start
If a business case is to be taken seriously, you must present it in the language of the business and speak to the critical and specific business priorities of key stakeholders. Understanding the business goals of your organization will not only enable you to identify senior-level support for your business case, but also help to identify and engage the right level of senior business sponsorship.
Step No. 2. Select business performance metrics to support the right business outcomes
Ironically, one of the primary reasons for unsuccessful business cases for data quality improvement is because they focus on data quality. To be successful, business cases must address the key components necessary to achieve the business goals, such as financial performance, operational performance, legal and regulatory compliance, and customer experience. Linking data quality to these metrics is critical.
Step No. 3. Profile the current state of data quality and its business implications
Once the scope of the business case has been agreed on, initial data profiling can begin. Carry out data profiling early and often. Establish a benchmark at the initial level of data quality, prior to its improvement, to help you objectively demonstrate the causal impact on business value and justify ongoing funding.
Step No. 4. Describe the target state to achieve business improvements
Business leaders sometimes struggle to understand that data quality improvement is not a "one and done" activity. It's very important to make it clear that unless a sustainable environment for data quality improvement is established, it will rapidly revert to its original poor state. The target state for data quality must be described in terms of how it can positively and sustainably improve critical business metrics such as financial results.
Step No. 5. Estimate the financials for the business case
A "go" or "no-go" decision for business case proposals often comes down to the financials, and this is no different for data quality improvement. A good business case must identify the anticipated benefits of the initiative, which must be tangible, quantifiable and desirable to the stakeholders.
This article has been updated from the original, published on January 9, 2017 to reflect new events, conditions or research.