The financial impact of bad data is experienced through direct and indirect costs. Consider these examples of each.
When your foundation is broken, you waste time and money fixing it (even if you just keep patching it to keep things running).
Fixing The Data
This cost is greater than you may guess. If your source data is inaccurate or incomplete, it’s probably being fixed multiple times by multiple people. And it’s probably not being “fixed” consistently across the business. People in Sales, Customer Support, Accounting, and Analytics would all access the same data for different purposes. If the data isn’t correct or complete, each person must make a judgment call on how to handle it. That costs time and resources in each area and leaves you no better off where your source data is concerned.
Another drain on time and resources shows up in customer relationship management. Inaccurate data is especially problematic if you have automated processes that rely on it. Marketing to someone who has requested not to be contacted, offering a particular pricing plan to someone ineligible, and asking an active customer to come back are a few situations that will eat into your Customer Care team’s time. They're stuck repairing damage rather than building a great customer experience.
When you invest time or money in patching a data problem, those resources aren't directed toward growth opportunities.
Consider the money spent in the “Direct Cost” areas. With cleaner data and a process for keeping it that way, that money could instead be invested in acquiring new customers or increasing the value of each of your current customer relationships.
All companies have data, and those that have robust and accurate information AND know how to use it are ahead of the game. When you can communicate with and serve your customers with excellence (or even competence), you stand a better chance of gaining market share. If you’re working with bad data, you operate with blind spots and that shows up in your customer interactions.
Data is a powerful asset for driving improvements in marketing, customer service, and operations. Consider any form of predictive analytics applied for these purposes. Statistical models are optimized according to the data they’re given and then operationalized on future data. If either data source is bad, these highly effective tools won’t work as intended.
Planning and Budgeting
You need your data for budgeting, forecasting, and guiding strategy for the years ahead. Accurate data leads to well-informed decisions. Dirty data can leave you to your assumptions or, worse, lead you in the wrong direction.
Consider this example for quantifying the impact of poor data quality for 1 year:
In this case, doing nothing to improve the source data used for business decisions has a cost of over $3.4 million in just one year. Imagine the compounding effect over time!
My favorite ROI calculator is from Dun&Bradstreet. Check it out!
At SPUD, I offer an analytics subscription to help you use data to your advantage.
Book a call with me to determine whether this is a good option for your company!