Do you Really Need to Embrace Analytics?
If you have not witnessed the deluge of big data and business analytics media coverage to date, then welcome back from the coma you were apparently in for the last couple of years. For the rest of you, perhaps you have the same nagging question that I have: Are big data and business analytics such a big deal that if our organization is late to the party in deploying them, we will never catch up to our competitors?
I go back and forth on wondering if applying analytics now is an urgent imperative for an organization to survive or if it is a “nice to have” relative to more critical “must have” capabilities that an organization should ideally possess.
Examples of critical capabilities are strategy maps and a balanced scorecard for strategy execution; advanced managerial accounting practices to reliably understand and have visibility of product, service, channel and customer profitability; driver-based forecasting and planning; process improvement (e.g., lean and Six Sigma management); leadership skills; and enterprise risk management.
Which is More Critical: Management Methods or Analytics?
These examples are some of the core components of enterprise and corporate performance management methods and systems. They are all basically modeling methods which increasingly embed statistical analytical techniques (e.g., correlation, segmentation analysis) into the methods. Hence, a case can be made that there is sufficient competitive edge gaining power from simply using these managerial improvement methods without needing advanced statistical tools with them.
Another cause for speculation that applying analytics today is not so urgent is by simply reflecting on my own career. I graduated more than 40 years ago with a Bachelor of Science degree from Cornell University in industrial engineering and operations research. It was 1971, and I was going to go out into the work world and optimize everything. To me at the time, the world was a big machine, and we simply needed to fine-tune its pulleys, levers, gears and dials to maximize performance and results.
But I had a rude awakening. Business and government was not ready to embrace the mentality of optimization derived from having a strong analytics capability. Then the problems were not complex enough, the need for significant improvement was not sufficient, and the computing power was not adequate in those times.
Thus, my skepticism of the urgency for analytics is based on doubt formed by my lengthy career, since most companies gradually improve without any deep analytics capability.
How Much Things Have Changed
But I have changed my tune from skeptic to advocate regarding the urgent need for an analytics capability.
I am rapidly recognizing that embracing analytics is indeed a requirement for successful organizational performance. The majority of today’s professionals grew up with computers and digital devices. They understand this imperative. They are the “newlyweds” to embracing analytics. The marriage is their passionate brainpower with the maturing and acceptance of analytics. Part of the analytics imperative is the current challenge of how to cope with the V’s of big data: volume, variety, velocity, viability, and value.
I classify the other professionals who prefer to rely on gut feel, intuition, and experience for making decisions as “newly dead.” Fortunately there increasingly fewer of them today since most rising managers are tech savvy.
My shift to recognizing the compelling need and urgency to embrace analytics was reinforced when I attended the annual conference of the Institute for Operations Research and Management Science on business analytics and operations research. There were roughly 125 presentations and 72 poster board displays. All of them were a combination of being inspirational, mind-numbing and visionary. They demonstrated the extensive reach and feasibility of applying analytics, data mining, sensors and big data to a wide variety of problems and solutions in every industry and public sector. Here are just a few examples:
- Supply chain management profitability maximization.
- Solar and wind farm energy optimization.
- Dynamic airline re-scheduling due to weather or maintenance delays.
- Hospital patient flow forecasting and nurse scheduling.
- Debottlenecking manufacturing production flows.
- Dynamic antiviral treatment to prevent a global pandemic influenza.
- Intermodal truck and rail route profit optimization.
- Optimal bank loan credit scoring.
- Maximizing ROI from marketing campaigns.
- Assortment planning and optimization in a retail store.
- Using social network data to predict customer churn.
- Slot machine optimization in casinos.
You get the idea. If the situation is complex with lots of data and an objective to maximize, minimize or optimize, then the analytics capability day has arrived. Analytics are essential to effectively framing the problem, modeling the solution, making decisions and taking action.
Note to Reader: Get on the Bus or be Under It
Throughout my career I have been an active and loud advocate, even cheerleader, for deploying the enterprise and corporate performance management methods mentioned earlier. I still do make noise about their high value and nag organizations who still use primitive precursors (e.g., management by objectives, standard cost accounting).
However, I now choose analytics capabilities as more critical EPM/CPM capabilities. Part of my reasoning is that I now see solid evidence, no longer just aspirations, of the realization of applying statistical and analytical to a very broad range of problems. I personally am now getting excited. It’s here. Analytics can be done. Moneyball. Synchronizing street traffic lights. Altering the trajectory of an earthbound civilization-threatening asteroid. Choose your own desired application for advanced analytics.
It is no longer a dream. It is no longer theory. Analytics and the impact of big data is the real deal. The type of managers, hopefully only a few, who do not embrace having a strong analytics capability will risk the consequences of being classified as Medieval.