Then why not do it yourself? Many organizations cite a lack of suitable resources or budget (or both) as impediments to getting starting with a data analytics program. They rely exclusively on external auditors or cost recovery firms to execute one-off programs where little is learned internally. Take a quick look at the following quotes that I’ve heard numerous times from senior managers at external audit firms.
“Never ask the customer what they want to do. We always tell them what they want to do.”
“We shouldn’t give a customer their analytic results within a month, when we’re billing them for six.”
This attitude is not uncommon, but it makes me wonder why more companies don’t at least try to start a program in-house. A senior auditor at a well-known producer of breakfast cereals (over $10bn revenue) recently told me that they pay a cost recovery firm 34% of all savings identified per engagement, despite having the skills to potentially own this project themselves. In short, it might be worth starting a data analytics program to reduce those external costs. This doesn’t mean taking on the entire data universe within your organization – just in one or two key areas that have been identified as high-risk (eg: Order to Cash/Accounts Receivable, Vendor Management) before maturing to broader areas (operational, or even compliance-related testing). Of course, companies require the right tools, training, and at least some human capital to begin such a project, but then they also gain 100% ownership of the program’s return on investment when efficiencies are found.
It also creates a more independent environment for a data analytics project to grow, rather than relying on an external audit firm to prescribe their own scope of consulting derived from their own audit results. It has long been argued that such segregation should be made into law for publicly listed companies. That’s my opinion too, but then, who doesn’t have a biased opinion these days? Oh yes – you.