In my previous post I emphasized the importance of demonstrated project management fundamentals as a key enabler of effective data quality delivery. In this blog, I will discuss why an understanding of corporate financial concepts is so important to data quality success.
Despite the continued evolution of data management technologies and the growing awareness of the challenges and promises of data quality, business buy-in is still a major barrier to the widespread adoption of data quality as another lever to achieve operational effectiveness. One of the key reasons for limited adoption is a clear linkage between data quality and a business’s performance, which is measured in a myriad of ways from operational metrics to managerial reports to formal KPIs. But, eventually, the enterprise’s performance is summarized in three key financial statements; the income statement, the balance sheet and the cash flow statement. Positioning data quality impacts or improvements in the context of these financial statements begins to “connect the dots” and moves data quality from the abstract to the concrete and from the theoretical to the practical. To illustrate this point, let’s take a look at the impacts and implications of a simple data quality issue like “undeliverable” billing addresses.
If billing address date defects within a company’s billing system prevent the delivery of customer invoices, there will be an increase in a company’s return mail volume. The obvious implication of this additional return mail is an increase in a company’s shipping and handling expenses associated with the analysis and correction of billing address data defects and subsequent invoice reprocessing. While a localized problem like this is typically not material from an accounting standpoint (i.e. important/significant), when combined with inefficiencies and costs associated with other data defects found throughout the enterprise, the impacts can manifest themselves as higher operating expenses on the income statement, which reduces operating income.
Another impact associated with “undeliverable” billing addresses is the delay in invoices being received by the customer. However, the implications of billing delays are less obvious. If the undeliverable billing address issue is large enough, invoice delays will likely have an adverse effect on the company’s Days Sales Outstanding (DSO – the average number of days it takes to collect revenue after a sale has been made), and therefore on the Cash Conversion Cycle (CCC – the time between outlay of cash and cash recovery). Additionally, an increase in DSO can have a negative impact on collections since there is often a correlation between the age of receivables and write offs. To counter the increased risk associated with customer non-payment, the company will have to increase its “reserve for bad A/R” on the balance sheet. The net implication of delayed billing will be a weakened cash position as reflected on the company’s cash flow statement. This “tightening” of cash increases the company’s need for, and cost of, borrowing for expansion, inventory, product development, sales and marketing efforts, etc.
While proactively eliminating billing address data defects can sufficiently mitigate these risks to business performance, gaining business buy-in and support is still a necessary first step. However, simply telling the business that they have data quality challenges that need to be corrected, without communicating the impacts and implications in the context of business performance, misses the mark and makes the job more difficult.
In my next post, I will examine knowledge of commercial enterprise applications as a key enabler of effective data quality delivery.