A Complete Guide To Self-Service Analytics
By Rob Baldwin, PwC US & Global Data Analytics Network Leader
Data-driven companies are becoming more the norm — 39% of executives say their companies are already highly-data driven, according to PwC’s Global Data and Analytics Survey 2016: Big Decisions™. Data savvy companies may bring to mind data scientists noodling complex algorithms — PhDs with a firm grasp of machine learning, experimental design, and statistical modeling. Passionate statisticians who know scripting languages like Python, and can captivate executives with data stories as enthralling as Harry Potter.
But do you always need a team of data scientists to get value from data and analytics? No, you don’t. I’m not suggesting that you fire your data scientist or scrap plans to hire one. These gurus can be invaluable in helping your company get a handle on its data, utilizing advanced technologies like prescriptive or autonomous analytics, and empowering C-suite executives with speed and clarity around high-stakes decisions.
But this shouldn’t preclude the rest of the workforce from accessing data and applying basic analytics. The proliferation of analytic technologies, combined with falling costs, enables professionals — from sales reps to tax and marketing professionals — to harness basic data and analytics to do their jobs differently, and faster, than ever before.
For instance, visualization software, which once required intensive training, is now user-friendly. Non-technical professionals can pick up the basics through tuning in to a five-minute YouTube video. A plethora of easy to use tools and techniques means that you no longer need to wait weeks or months for IT. You can leverage data on your own.
We are starting to see a pick-up in self-service analytics — within our clients as well as within our own firm. I think we’ll see more companies moving toward this model in the coming year, and the implications for tax and other functions are significant.
Self-service in action
What if your entire workforce had a basic level of data and analytics skills? When you combine this with each person’s knowledge and experience, can you imagine how your company can rapidly begin to unlock new sources of value?
Say a sales/use tax refund or tax compliance professional starts the monthly remittance process — accumulating data from thousands of transactions and using sample sizes to extract general insights. What if the same professional — instead of manually analyzing monthly, quarterly, or annual transactions, could load 10 years of data into visualization software and identify anomalies like underpayments or overpayments within seconds?
The professional, a first year associate, drills down on the data and pinpoints the person or vendor responsible for the errors. The company now has a diagnostic tool and tax professionals shift from simply keeping up with monthly data to rapidly identifying issues and root causes. The company delivers valuable new insights, and proactively suggests how to reduce these errors going forward.
A mindset shift
The impact of elevating everyone’s analytic IQ is extraordinary — as everyone begins to quickly deliver new insights and value. A self-service approach also empowers highly trained data scientists to spend less time educating and tackling mundane problems — and more time solving complex business issues through sophisticated data and analytics.
While a self-service model is within everyone’s grasp, the mindset shift is often challenging. Leadership must realize that a highly trained analytics team is not always necessary. Even when it is, value can increase across every business dimension as more people move up the learning curve.
The first step is to elevate everyone’s knowledge so they can integrate basic data and analytics into their daily work. The vice president of tax can ask a junior employee to do some research and educate others across the team. Start by empowering those closest to the data — but realize that analytics is a team sport. Find ways to crowdsource and leverage all levels of experience. Organize your data and visualization capabilities so that everyone participates and applies their unique experiences.
Avoid the notion that the effort is too time-consuming. While employees may recoil at the mere mention of data and analytics — as if they are being asked to scale Mount Everest on top of their existing work — they will soon realize that the mountain isn’t that tall. In fact, our clients will often talk about the five new types of value they were able to identify instead of the time it took to do so.
Analytics is a team sport. We have quarterbacks, running backs, offensive linemen and special teams throughout the enterprise. The key to creating value through data and analytics is to get everyone involved — and the benefits will increase exponentially. In my next post, I’ll talk about applying a self-service model to the five stages of analytics. If you are using a self-service approach, what path did you take to get there?