Analytics alone aren't enough to guide your business

Everywhere you look, businesses of all shapes and sizes are looking to transform themselves into digital businesses. This digital transformation tidal wave is often predicated on building a more robust, data-driven organization.

Executives want to make more informed, more strategic decisions, and see analytics technologies, from big data to predictive analytics to good old fashioned business intelligence, as the silver bullet to do so. It’s no secret that businesses are buying in big. Worldwide revenues for big data and business analytics are expected to grow 50 percent, up from $122 billion in 2015 to $187 billion in 2019, according to research from IDC.

But are the businesses investing in these massive solutions actually making better decisions?

That’s the $122 billion-dollar question. The answer is… sort of.

The big data predictive analytics tools executives use look at the past to identify correlations in historical data and extrapolate future trends in their businesses based on these correlations. Analytics inform their estimates for product demand, their sales forecasts and their investment strategies including how they prioritize updates and improvements to their products and infrastructure. It’s all based on looking at what has already happened, and simplifying it into something digestible and actionable.

Analytics, however, cannot provide insights into events that have never occurred before where data about historic correlations don’t exist. And it’s an ability to predict the cascading effects of a possible event -- think massive power outage, security breach or epidemic outbreak -- is minimal. In order to understand these, complex systems modeling, which uses algorithms to identify the likelihood of an event transpiring, even if it has never done so before, is required.

Large, complex businesses that need to understand both how the past affects their businesses and how future events are likely to, are in a unique position to leverage both analytics and complex systems modeling to optimize their decision-making today and in the future. Companies that try to use just one approach are doomed to either live in the past without looking to the future, or lose the historic knowledge critical to running a business’s day to day operations.

For example, data analytics can tell key decision-makers at large retail brands which products moved the most units over the past three months, or which stores saw the greatest increase in same day sales as a result of a promotions campaign. For investors, it allows them to look back at the previous year to ascertain the precise gains stocks and bonds contributed to their portfolios in various industries, or which of their investment companies has seen a dip in revenue. And analytics can tell logistics companies how many miles per gallon their fleets of vehicles recorded over the past year, to inform how much they might pay for fuel in the coming year. Analytics inherently allow a business to look at what happened in the past to glean insights to guide decisions today, critically important in an evermore competitive business landscape.

But businesses face plenty of decisions each and every day for which there is no historical analog, no precedent to revisit to glean insights. That’s where complex systems modeling comes in. By leveraging domain experts and cutting edge modeling technologies, retailers can obtain insight into the likely success of a campaign they haven’t run yet, based on consumer spending trends, social media sentiment and pending changes in the tax rate. Complex systems modeling can give those logistics companies, who look to analytics to determine how much fuel they should buy the ability to accurately predict how the price of fuel is likely to be affected in the coming year based on all of the interconnected factors that affect that price -- from weather, to new pipelines, to changes in policy, giving decision-makers new insight into how to forecast for fuel spend. And for investors, complex systems modeling can predict how certain sectors of the stock market may perform, based on factors such as current corporate tax rates, commodity prices and current exchange rates, helping investors guide where they should invest next.

As businesses continue to spend exorbitant sums on analytics technologies, it’s imperative they recognize what analytics can do -- and what it cannot. Going all in on analytics as a way to inform all decisions would be a mistake, but businesses that use analytics to understand the past and complex systems modeling to predict the future will be well on their way to making the optimal decision, whatever the scenario.

Image Credit: Syda Productions / Shutterstock

Michel Morvan, PhD and Eisenhower Fellow, is co-founder and executive chairman of The CoSMo Company, a global technology company that helps the C-suite make optimal decisions. He has worked on complex systems all of his adult life. Michel is a French citizen and a  U.S. resident for the past 2 years. He is dedicated to helping C-level executives, public leaders and others make optimal decisions by creating the tools that allow them to account for the complexity that characterizes the world’s most challenging problems.