I recently started reading Gary's blogs and thought we shared both a point of view (a higher level what's good for the business POV) and that little voice in the back of our heads that's always asking - is that really true? Hope you enjoy this one. Many of Gary's current blogs appear here.
Guest blog by Gary Cokins, Jun 25, 2015
A quick quiz: What is a good nine- or 10-letter word, ending in “-al” that describes the emerging interest in business analytics and big data?
A choice that may come to mind for many is “hysterical.” This choice reflects frenzied excitement about opportunities for business analytics to solve problems often resulting from big data. Advocates – actually enthusiasts – of analytics have become energized by the growing interest in the fields of business intelligence and data mining.
But perhaps a less obvious choice of “skeptical” is an equally valid answer. Doubters and naysayers of business analytics believe the interest in these topics is overblown and misguided.
Where are the skeptics coming from?
Let’s start with the view of the skeptics of analytics. Who are they? What is their profile? What is their objection to embracing and deploying analytics?
I will probably exaggerate, but here is my take on the skeptics of analytics. They are likely to be over the age of 50. When they took a statistics course in college, many of them probably just wanted to pass with a “D” or passing grade and get the course behind them.
When the skeptics’ careers were shaped, they did not have PlayStations, Nintendos or Xboxes – no video games at all. They did not have 150 channels on cable, satellite TV, video movies or DVDs. They had no surround-sound, no smartphones, no personal computers and no Internet.
During the skeptics’ early careers, they observed and participated in company firefighting. This was actually fun for them. It kept them busy. The assertive skeptics were continuously promoted to higher job positions because of their quick wit and intuition. These types of managers are not self-serving sycophants and political players (although some are). They are hard workers.
Skeptics’ reliance on buffers to protect against errors
The career experiences of skeptics did not involve punctuated change and volatility as occurs in today’s sped-up world. However, it was not easy for them. Skeptics were not free from solving problems or evaluating opportunities. Those are eternal tasks in anyone’s workday. What was different about the solutions developed when skeptics’ careers were shaped is the solutions just weren’t that elegant – they were like Swanson TV dinners, oven-baked in aluminum trays.
When skeptics’ careers were shaped, there were no crystal balls to predict the future. But they did not need them. For example, in manufacturing a forecast was somehow produced every month or so, and it was used to establish plans for determining what and how much inputs to buy (e.g., raw materials, component parts) and what types of resources to hire or purchase.
When skeptics’ careers were shaped, they survived using buffers to protect them from errors and missed delivery schedules. Buffers were the magic elixir that kept problems from becoming larger or more painful. There were three types of buffers that skeptics relied on related to time, flow and resources. They would start manufacturing products earlier to buffer expected finish dates, build and stock extra inventory to buffer material shortages, and add more people and equipment to buffer capacity.
Let’s fast-forward to today. Skeptics recognize that the world has changed. It is much more volatile. But they still challenge the need to embrace deep or advanced analytics. When their careers were shaped they observed people measuring something with a micrometer, marking it with a piece of chalk, and cutting it with a swinging ax. They behaved this way too. Why be precise? Few organizations recognized the penalties and extra buffer-related expenses and investments that mitigated against imprecision and errors. Inefficiencies, long delivery lead times, and temporary shortages were tolerated by both suppliers and their customers.
Where are the enthusiasts of analytics coming from?
Let’s look next at the opposite view – that of the analytical enthusiasts whose careers have been more recently shaped. Who are they? What is their profile? What is their enthusiasm – sometimes hysterically so – for embracing and deploying analytics?
Again I will probably exaggerate to make my points, but here is my take on the enthusiasts. Many are likely to be under the age of 40. When they took their university statistics courses, they had hand calculators and laptop computers. (Believe it or not, in my first two years in college we only had slide rules. I graduated in engineering and operations research in 1971.)
During enthusiasts’ careers, firefighting was not an occasional need – it was ongoing and never-ending. And its intensity is not just because there are more problems (although there are). It is because there are more opportunities requiring a sense of urgency. Today enthusiasts have much greater ability to investigate and analyze – and with more computing power and more functional software.
In the movie Moneyball, Brad Pitt plays the role of the Oakland Athletics baseball team general manager Billy Beane. Just before Pitt fires the team’s head baseball scout, he says (paraphrasing), “OK. My turn. When you visit the homes of an aspiring young baseball player you tell his parents that he has a good chance of being a major league player, you don’t know. You don’t know.” He repeats that to make his point.
This also applies to organizations, especially where skeptics dominate. Skeptics may think they know, but do they really know?
Enthusiasts use analytics to replace buffers
As previously discussed, in the past the skeptics solved operational problems by relying on buffers. But enthusiasts’ careers today have been shaped by buffers that must be paper-thin. Buffers that the skeptics enjoyed to protect them from the impact of problems are too costly and unaffordable today. These costs and penalties include surplus inventory, excessively long production and delivery lead times, extra equipment capacity, poor customer service levels, and more employees than are needed.
Analytics is reducing the size of and replacing the protective crutch of buffers. Today the enthusiasts for analytics are imaginative and visionary. Their thinking doesn’t stop with enterprise resource planning (ERP) systems, which can schedule part production and purchasing based on forecast product demand volume, supplier delivery rates and assembly lead times. Enthusiasts are far more imaginative than that – plus they have the analytics and computing power to be creative.
What enthusiasts do is think forwardly with probabilistic what-if scenario analysis. They do not view product distribution in a supply chain as a linear tree, branch and leaf structure that sequences parts like elephants’ trunk-to-tails in a circus all the way from production to the customer. Enthusiasts see opportunities in an integrated network of parts and products that exist or can be made anywhere. And they then perform iterative trade-off analysis of the interrelated variables in real time.
Iterative scenario analysis gives enthusiasts power to answer many questions, such as, “What is the additional inventory carrying cost if we want to improve service levels from 97 to 99 percent for our strategic customers?” These capabilities are no longer a dream or vision. They exist today.
Enthusiasts can win buy-in from the skeptics
Admittedly my profiles of skeptics versus enthusiasts were exaggerated. They are not polar opposites but rather people residing along a continuum. But this does not remove the challenge of creating a culture for analytics.
My experience is that an effective way to drive change, overcome resistance, and gain buy-in is through example. Enthusiasts can be role models. Lead by example. Demonstrate what can be done and it will be done.
About the author
Gary Cokins is an internationally recognized expert, speaker, and author in advanced cost management and performance improvement systems. He is the founder of Analytics-Based Performance Management, an advisory firm located in Cary, North Carolina at garycokins.com. Gary’s most recent book, co-authored, is Predictive Business Analytics. He received a B.S. with honors in industrial engineering/operations research from Cornell University and an MBA from Northwestern University’s Kellogg School of Management. Gary began his career as a strategic planner at FMC’s Link-Belt Division and then served as financial controller and operations manager. In 1981 he began his management consulting career with Deloitte Consulting. With KPMG consulting in 1988 Gary was trained on ABC by Harvard Business School professors Robert S. Kaplan and Robin Cooper. From 1997 until recently Gary was in business development with SAS, a leading provider of enterprise performance management and business analytics and intelligence software.