From Rear View to Self Driving: Assessing the Analytic Health of your Data Vehicle
It’s a fair bet that every organization wants to be more predictive and prescriptive using the analytics horsepower it has. But I believe it’s just as important for data leaders to take a 360-degree view of how their analytics can guide them. I like to use a car analogy to explain this idea because it’s universally understood.
Rear-view analytics: invaluable perspective
When I drive my car, I have to use my rear-view mirror. That’s because it’s critical to see what’s happening in the past to understand where I came from. At a company level, some of our competition is driving very fast in small cars that are behind us right now, but they may not be tomorrow. Rear-view mirror analytics are important to be able to reflect on what has happened as a way to inform the future.
Real-time analytics: today’s line of sight
Realtime or near-realtime analytics educate us not about what’s going to happen years in the future, but what’s happening around us today. I equate this data to what you get using your windshield or side mirrors, or even glancing over your shoulder as you look to do a lane change. You need strong knowledge about today’s conditions to build and inform current actions.
Predictive analytics: the road ahead
No matter how good a driver you are, your line of sight only gets you so far in business. That’s where you have to rely on the Waze or Google Maps of predictive and prescriptive analytics to get you from point A to point B safely. You also have to anticipate other important questions, such as, Do we think traffic patterns for our business are going to remain the same as they appear now, or will they resolve quickly? Are there alternative routes we can take?
Similar to a crowdsourced navigation tool like Waze, a strong data organization will benefit from an ecosystem of “listening alerts” around the company and beyond its walls with customers. This kind of ecosystem provides even more intelligence to inform what the possible paths are in the future, or what issues exist in the road today.
Business process automation: self-driving insight
The final part of my analogy is the self-driving car. Autonomous vehicles used to be a fantastic utopia, but they are slowly coming into focus. I compare the autonomous vehicle to business process automation, where you are using machine learning or artificial intelligence with human intervention close by. That’s because even the best computing can’t fully replace human instinct and intuition, and no system is ever foolproof.
I remain confident that we’ll get to more autonomous businesses someday, but no organization can run on just predictive and prescriptive analytics. A robust data operation will rely on rear-view mirror analytics to help inform its direction but also draw on real-time analytics of the current view as well as the GPS of predictive analytics to inform a sound decision-making process. Just as in driving, a combination of analytics capabilities must be used in unison to navigate and steer your journey forward.