Poor data quality destroys business value. Recent Gartner research shows that organisations estimate the average negative financial impact of poor data quality to be $9.7M per year. In other words, this is their estimate and the impact they believe it has on them per year
This is likely to worsen as information environments become increasingly complex. Data quality issues are faced by organisations of all sizes and complexity. Those with multiple business units and operations in several geographic regions with many customers, employees, suppliers and products will inevitably face more severe data quality issues.
Poor data quality contributes to a crisis in information trust and business value, such as financial and operational performance. Unless tangible and pragmatic steps are taken to understand, address and control data quality, the situation will worsen.
Many organisations struggle to successfully propose a programme for sustainable data quality improvement. Effective business engagement and funding may be limited for several reasons:
- Value to business may be unclear
- No connection might be evident between data quality improvement and business outcomes
- Business may not understand importance of their role in data quality improvement
- A business case for data quality improvement must start and end with the business outcome. The business case should demonstrate how any improvement in the underlying data creates better business outcomes.
Gartner has identified five steps to creating a business case for data quality improvement:
#1 Understand top business priorities and find right place to start
If a business case is to be taken seriously, it must be presented in the language of the business and speak to the critical and specific business priorities of key stakeholders. Understanding the business vision 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.
#2 Select business performance metrics to support the right business outcomes
Ironically, one of the main reasons for unsuccessful business cases for data quality improvement is because they focus on data quality. Successful business cases address the key components necessary to achieve the business vision, such as financial performance, operational performance, legal and regulatory compliance and customer experience.
#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. Data profiling should be carried out early and often. Establishing a benchmark at the initial level of data quality, prior to its improvement, will make it easier to objectively demonstrate the causal impact on business value after improvement and justify later requests for further funding.
#4 Describe the target state to achieve business improvements
Business leaders sometimes struggle to understand that data quality improvement is not a once-and-done activity. It is 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.
#5 Estimate 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 and must be tangible, quantifiable and desirable to the stakeholders.
Key takeaways
- A business case for data quality improvement must start and end with business outcome
- A good business case must identify anticipated benefits of the initiative and must be tangible, quantifiable, desirable to the stakeholders
- Business leaders sometimes struggle to understand that data quality improvement is not a once-and-done activity
- Organisations estimate average negative financial impact of poor data quality to be $9.7M per year
- Poor data quality destroys business value
- Poor data quality contributes to crisis in information trust and business value
An initiative to improve quality of data management is likely to progress by making a business case explains Gartner’s Saul Judah.