Showcase how data quality directly impacts the bottom line. Poor data quality leads to costly errors, inefficiencies, and missed opportunities. For example, inaccurate data can result in incorrect business decisions, customer dissatisfaction, and compliance risks—all of which have a direct financial impact. Investing in data quality prevents these losses and drives better decision-making. Hence , enhancing Data quality is a smart investment rather than an expense and a key to optimally utilising Budget allocations.

Couple of Strategies to convince: 1)Data quality initiative should be associated with a business outcome/goal. We need to show the impact of bad quality data on business performance based on the data points collected from past. 2)Quantify the opportunity loss because of bad data by Performing an assessment to demonstrates the need for improvement. Combine the detailed data assessment with the impact of data quality. 3)Focus on competitive advantage: -Try to demonstrate on how improved data quality will help the organisation to perform better as compare to its competitors. 4)Predict future trends:-Gather evidence of the likely course of action and future business impact if the sponsor fails to act now on fixing the data quality issues?

Decision making is as good as the quality of the data from which decisions are made. Data is acts as inputs for decision makers, meaning poor quality data leads to poor decision making. It is paramount to prioritize quality data as this leads to informed decision making. Lacking to invest in data quality can have exponential costs to the organization in the future as decision making will be made based on erroneous, hence misleading data.

Here's a straightforward method to showcase the economic benefits of Data Quality in your organization: 1. Request a report of incidents from your platforms for the past month/year. 2. Identify those linked to data quality issues, from the obvious "bad data in reports" to the less direct "display errors in GUI.", but in the incident resolution, it is noted that the error was due to issues in data integration process. 3. Sum up the resolution time for these incidents. 4. Multiply by the average hourly rate in your company. Voilà! You've calculated the economic impact of implementing effective Data Quality processes.

Data quality is often Penny wise and Pound foolish, like many things. If you are looking at a concrete case, make the business case ad target it to specific managers. Think about increasing the value of data, reducing process interruptions, increasing predictability. It is about fixing recurring issues and preventing new issues. Make it specific and make sure it helps their KPIs. If you are looking at more generic initiatives, like a DQ Framework, making the business case may be complicated or tough. Try to go for vision, being in a place to re-use data at scale, saving time for the whole organisation. Target higher management, not the local department manager. I made that mistake several times, of course.