I read a blog by Thomas Redman in the Harvard Business Review (Make the Case for Better Quality Data) that stated, “It costs ten times as much to complete a unit of simple work when the data are flawed in any way as it does when they’re perfect.” I’ve had many discussions with colleagues about whether executives care about data quality or not. The summary of those discussions are that executives do care about data quality but don’t always appreciate and recognize the costs of obtaining quality data and the cost of not ensuring quality data.
The consensus is that generalizations about costs aren’t enough to get executive interest and buy in. A classic example is the use of spreadsheets and the following justifications for data quality.
- The cost of errors – spreadsheets are notoriously error-prone and even if the errors don’t result in a need to restate earnings, they often result in poor decisions that negatively impact revenue, profitability and the ability to compete.
- Opportunity costs – often more time is spent trying to compile data, validate the accuracy, and reconcile inconsistencies than analyzing the business, which reduces productivity and responsiveness to changing business conditions.
- Maintenance and audit costs – as businesses grow the number and complexity of spreadsheets increases usually with minimal or no documentation, which results in a significant amount of time and money being, spent reviewing and validating formulas, macros, links between workbooks and data security.
The cost of errors is a potential risk, but executives are usually not given any tangible or quantifiable evidence. Is poor data leading to a misrepresentation of earnings? If so by how much? What’s the scope of the issue?
Similarly with opportunity costs without a direct link to business objectives and outcomes there is no real connection for executives. Is poor data impacting the closing of deals? If so what impact will that have on revenue and profit?
And unless you can quantify maintenance and audit costs they are just a cost of doing business and issues for departmental and/or IT groups to address with the budget allocated to them. How many man hours are spent validating the source and validity of data in reports? What is the labor cost per hour related to these efforts? How could executive level objectives be impacted by reallocation of the resources spent on validating data?
Redman uses the example of customer billing to develop a business case that helps data quality make the cut among competing priorities. This started me thinking about other use cases to show executives quantifiable business impact. A few possibilities include;
- Sending multiple mailings to a given customer/household – It should be possible to calculate costs based on the total number of mailings per year, the estimated percentage of duplicate records, and the average cost per mailing.
- Inability for sales to follow up on leads – It should be possible to calculate costs based on the number leads generated per a given time frame, the percentage of leads not followed up on because of missing contact information, lead to close ratio, and average selling price.
- Not complying with legal hold regulations – There are a variety of country and industry specific regulations about retention periods for different categories of information. Since fines are usually documented for infractions, and legal fees for representation can be estimated it should be possible to calculate costs.
I would love to hear your opinions. Do you think executives care about data quality? Do you think the use cases I outlined above would get executive attention and buy in? What other use cases would you suggest? There are certainly more opportunities to estimate costs around analytics or business intelligence, operational inefficiencies, and data lifecycle compliance. In fact, free cost calculators or a free information management benchmark assessment could help you see how much data quality impacts your bottom line.