Building a business case for data quality is a waste of time. Nobody really cares. Improving data quality for quality’s sake is a waste of money. Sounds funny coming from a data quality specialist, someone who has spent the last decade preaching data profiling and data quality. But the fact is people from the business side do not care about data quality. What they care about is the impact poor data quality has on their line of business.
When you look at how the business measures itself (after you get past revenue and profit), the talk is about key performance indicators (KPI). What are some of the KPIs for a call center? You will hear about goals of reducing talk time. The business wants to lower costs. You will hear about goals of decreasing hold times. The business wants to improve the customer experience.
Marketing will talk about the conversion or take rate of a mail campaign. It is looking to increase revenues. Or maybe marketing talks about reducing the catalog return rates. The growing problem with this KPI is most mail campaigns now send out mailings using bulk rates, so the catalogs are thrown away if they cannot be delivered. That problem aside, marketing is trying to reduce costs.
I often get a call from someone who says, “I have a meeting with the CIO, who is going to ask me to give an ROI for purchasing a data quality product. What do I say?” I suggest that he or she ask the CIO how the business measures performance. Then I ask how improving the quality of the data can improve those metrics. You must put the ROI in terms the business can understand. You might say that I can improve your conversion rates by 3 percent. (What you improve is the quality of the address data, so an additional 10 percent actually gets delivered.) Businesspeople understand such terms and immediately translate them into dollar terms of increased revenue. Or you might say that I can reduce your campaign costs by 10 percent with no change in the conversion rates. (What you are actually doing is removing duplicate records to shrink the number of pieces being mailed.)
When Richard Trapp, was the Director of Global Data Quality, for Avaya he said “I never talk about data quality. Businesspeople are not excited about good data quality. Businesspeople are excited about good marketing, increased sales, supply chain efficiencies, and reduced order-to-cash cycles.”
Another problem I run into is the wrong person is asked to put together the ROI for data quality. This conundrum brings up the issue of who owns the problem of data quality. IT says that the business should own the problem because it is their data. But business says IT manages the data, so IT should own the problem. It is not an either/or issue. There should be joint ownership of the problem. Most of the time, I am talking to IT about the problem and it does not have a clear understanding of the business metrics. Therefore, IT only talks about improving the quality of the data. We need to see business owners and IT celebrating accurate data because they collaborated together to clean up the data.
This was an introduction, the first of my seven part blog series discussing building a business case for data quality. This was what I mean by data quality and how you should approach the business case from a business perspective. The next blogs will discuss different approaches to go about building a business cases; an overview on the data quality assessment process – a process to follow to build and validate the business case for data quality. And the final four blogs will look at the detailed steps in building a business case:
- Analyze the data
- Categorize the anomalies
- Identify the business impact
- Create the business case
For more on the steps your organization should take to achieve pervasive data quality, read my recent white paper entitled “Informatica Data Quality Methodology – a Framework to Achieve Pervasive Data Quality Through Enhanced Business-IT Collaboration.”