Using Neuroscience to Realize Big Data’s Value
EUC with HCI: Why It Matters
How organizations can recoup 60 to 80 percent of their massive investment in big data.
At an accelerating rate, in countless business meetings, conferences and journals, the topic is big data and its promise to deliver better decision-making, better actions and better results.
That promise still exists today, but is largely unrealized so far. Exceptions can be found in data-built companies such as Google, Amazon and Facebook, or in organizations enabled by data, such as Zara, Progressive Insurance and Harrah’s. Traditional organizations seem to have come to the painful realization that big data is not delivering on the promise of consistent value, and their leaders and managers are not adopting it.
Why? There are many complex explanations. But in part, organizations are learning the truth of what management consultant Peter Drucker described in his 1967 essay, “The Manager and the Moron”: “At present the computer is the greatest possible obstacle to management information, because everybody has been using it to produce tons of paper. Now, psychology tells us that the one sure way to shut off all perception is to flood the senses with stimuli. That’s why the manager with reams of computer output on his desk is hopelessly uninformed.”
Drucker’s thinking has been validated and enhanced by neuroscience, which suggests that because of human and institutional biases and the way the brain operates, organizations may gain little or no insight from the data they try to use.
How the Nervous System Works in Responding to Information
The brain has two separate systems that respond in different ways to an assault by large amounts of data. The auto-responder system bases actions either by ignoring data that does not conform or selects data that conforms to preset rules. The deliberate/thinking system takes one of two routes: If the information is squarely in the area of expertise of the person receiving it and the person has the time, the brain differentiates between signal and noise and generates genuine insight; or if expertise and time are not available, the brain looks for data that confirms the person’s preexisting bias and does not generate any new insight.
One key factor that does enhance insight generation is a clear story or crisp visual to convey the meaning of the data.
The neurological system of the human being and that of the organization, it turns out, are very much the same because an organization suffers from a collective bias of the individuals in it. Both usually consider information agreeing with their propositions to be the most important. They typically focus on data they can understand, rejecting new data if they can’t easily make sense of it. And both often find huge amounts of information to be overwhelming, so they ignore most of it and simply apply prebuilt assumptions to it.
Organizations should understand what their needs are, and they should have a clear message that conveys the meaning of the large amounts of information they gather. To achieve success, an organization needs to decide what actions to take on a daily/monthly/yearly basis and decide what is inside the data that will enable it to make this decision—not just confirm it. It is important that an organization focuses on decision-making and not confirmation of what it already knows. Neuroscience also tells us that when successfully acting on decisions, an organization (like an individual) must convey the meaning of the information through a visual or textual narrative—a story-based method of explaining complicated information in a clear, understandable manner.
As an example of how an organization might determine the need of a functional area that uses data, consider the supply chain function. The organization may leverage information to determine how much of each product to stock in each store. But, in addition to the traditional data of prior sales, price, and promotions, and other data, could it leverage social chatter, weather trends, local economic conditions or search patterns? What further value could be derived from the investment by analyzing the additional information?
Three things differentiate this approach from traditional big data approaches. Organizations should start by crisply establishing the specific business need, and then:
*Create a set of sensors to collect relevant data
*Design attenuator filters to filter contextually relevant data
*Deliver a compelling visual or textual narrative to enable decisions
Create External and Internal Sensors
Since our brains cannot take in every piece of available information to enable the decisions an organization needs to make, we turn to technology to collect information from all existing sources. We scan the information with sensors created to provide signals that tell us when they detect something needing our attention. This acts much like our bodies, which have sensors to inform us of heat, cold, light and dark—providing us with the information we need to react appropriately to our environment.
This is where technology is most valuable. Big data gives these sensors the largest possible database from which to select the most relevant information. But only when the organization (like the individual) pays attention to the specific sensor-provided signals is it capable of doing anything about the complex information assaulting it.
To correctly stock a product, for example, the organization needs to have sensors that send out signals to notice a variety of issues. These might include the organization’s promotions and marketing, what is being said about the product on social media, which influential people are (or are not) buying the product, and competitors’ action in the space. These sensor signals will help the organization begin to narrow the information to what is needed to enable the right decisions.
Design Attenuator Filters
There’s still too much information, and it’s too unfiltered to be useful. The organization must focus on the most relevant parts of the data highlighted by the sensors. It does this by refining the data with attenuator filters, which act on a set of rules between incoming and outgoing data. These filters extract even more exact information from the existing sensor signals to create a cohesive view of the situation. Information on product promotion, for example, might be filtered to show specific percentages of the marketing budget used to promote the product in the digital, store and media channels. Social media information can be filtered to not only show what influencers are saying about the product but to describe how consumers are using the product, how they perceive its value, and how the product compares to competitive offerings.
With these attenuator filters, the organization further narrows the data to bite-size chunks of specific information that matter in making business decisions and in taking actions to drive value.
Develop the Narrative
Organizations need to leverage their marketing and communications skill sets in using the filtered information to develop the narrative needed to tell the story behind the complex, nuanced data. Unbounded problems, with their large complexity of information, have multiple possible outcomes. Therefore, organizations should provide visual or textual narratives with an easily understood storyline from which employees can make decisions.
For example, a narrative for stocking a product might include some of the information mentioned earlier, as well as subtler but equally important statements, such as the fact that many buyers today focus on product life, color and package design—elements that go beyond the product’s function.
Today’s approach to realizing big data’s value is like putting your hand in a haystack hoping the missing needle will prick a finger—and it’s hardly strategic. Organizations that don’t deploy a neuroscience model to understand their needs and build these narratives are ignoring between 60 and 80 percent of the information available to them through big data. As a result, much of their investment in big data and in the people working with it is wasted, diminishing their ability to grow future revenue from insights. By using the knowledge of neuroscience, organizations can take charge of the decisions they need to make in a clear and understandable way.
About the Authors:
Ann Dozier is senior vice president, chief information officer at Southern Wine & Spirits. She can be reached at AnnDozier@southernwine.com.
Suketu Gandhi is a partner in the strategic IT practice of A.T. Kearney, a global management consulting firm. He can be reached at Suketu.Gandhi@atkearney.com
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