accelerating train going through station captured in time lapse
PHOTO: Sawyer Bengtson

"… if analytics does not lead to more informed decisions and more effective actions, then why do it at all?" ― Mike Gualtieri, Forrester 

Years ago, when I was a consultant teaching business intelligence and analytics classes for TDWI, I had a colleague, Dave Wells (formerly director of education for TDWI), who started all of his data warehouse classes with the following question:

"The data warehouse brings no value — only cost; true or false?"

Despite suspecting trickery with this question, most students answered false. After all, what self-respecting data warehouse or data lake practitioner would sign up to say these analytical applications bring no value?

The answer was true. And the point Dave was trying to bring home, which may seem blindingly obvious when you consider the quote by Mike Gualtieri at the start of this article, often gets lost in the day to day running of an organization. Dave called it the data-to-value chain. Modernizing Dave’s original concept, I call it accelerating analytics to value.

The High Cost of Analytics Inaction

Either way, it goes like this. There is a clear cost to generating both data and analytics: storage and systems costs to capture, maintain and operationalize the data; people costs in the time needed to gain the experience and knowledge to effectively analyze the data; and opportunity costs when the data or analytics are not deployed in a timely way or are lacking the context necessary to make them relevant to the situation.

Dave would tell his classes, and I agree, that value is realized only when positive outcomes are generated from the data. Or, as Mike posits, when the analytics leads to more informed decisions and effective actions.

Driving value from analytics is becoming increasingly important, imperative even. Tom Davenport and Randy Bean recently wrote an article where they discussed a big data and AI survey conducted by NVP group. They confirmed what Dave Wells and Mike Gualtieri already know — that analytics is more important than ever, but value is still elusive. Ninety-two percent of survey respondents reported that the pace of their big data and AI investments is accelerating; 88% indicated a greater urgency to invest in big data and AI; and 75% cited fear of disruption as a motivating factor for big data/AI investment. In addition, 55% of companies reported that their investments in big data and AI now exceed $50 million, up from 40% just last year.

At the same time, however, an eye-opening 77% reported that business adoption of Big Data/AI initiatives is a major challenge, up from 65% last year, 69% said they have not created a data-driven organization, and 52% admitted they are not competing on data and analytics.

Adding to the bad news is a Gartner prediction that in 2019 and beyond growth will be the CEO’s main priority, and many will look to the CMO to deliver that growth. And yet as an HBR study on real-time analytics shows, many marketers still struggle to deploy analytics in their CX initiatives, with only 16% ranking themselves as very effective at delivering real-time customer interactions across touchpoints and devices.

Related Article: The Data-Driven Organization Is an Endangered Species

Decisioning Will Drive the Analytics Value of the Future

This is where decisioning comes into play. As we say at SAS (and I've written in this column before):

“Data doesn’t change the organization. Decisions do. Every decision drives value — from big strategic choices to thousands of operational micro-moments. Success will come to those who can make the right decisions in context, in the moment, for every moment, automating and scaling those decisions with powerful and trusted analytics.”  

The increasing amount of data, expanding customer touchpoints and progressively connected organizations have converged to make it almost impossible for humans to keep up with the sheer volume of operational decisions that must be made every day. Making a credit decision, identifying fraud, validating a claim, determining the next best action to take for a customer as they progress through their customer journey and pricing a product or policy are just a few examples of the thousands of daily operational decisions facing companies and impacting customer experiences today. 

What's more — many of these decisions are inter-connected and the consequences of a single bad decision can compound rapidly. The imperative to solve the growing customer experience problem — engaging with customers in the moment — requires a speed and capacity that must be driven by automation and analytics.

Related Article: You're Drowning in Data – Now What?

The Analysts Agree

Analysts are catching on to this concept as well — and a growing buzz surrounds decisioning, both conceptually and as a technology solution. Technopedia has recently updated its definition of decision management to one which embraces analytics and automation:

“Enterprise decision management (EDM) is an enterprise approach that applies analytical and rule-based systems to manage and deploy all operational decisions, such as relationships with employees, suppliers and customers. The computerized EDM movement has altered the enterprise decision-making process by incorporating information-based decisions based on historical behavioral data, as well as prior decisions and their outcomes.”

Gartner put continuous intelligence as number three in its Top 10 Data and Analytics Technology Trends for 2019. It defines continuous intelligence as a design pattern providing decision automation in which real-time analytics are integrated within a business operation, processing current and historical data to prescribe actions in response to events providing decision. Continuous intelligence leverages multiple technologies such as augmented analytics, event stream processing, optimization, business rule management and machine learning. 

Rita Sallam, research vice president at Gartner, calls continuous intelligence “a grand challenge and a grand opportunity” for analytics teams to help businesses make smarter real-time decisions in 2019. She predicts that by 2022, more than half of major new business systems will incorporate continuous intelligence that uses real-time context data to improve decisions.

Forrester calls analytics-driven automated decisioning the dawn of digital decisioning. Digital decisioning merges Forrester’s existing systems of insight, real-time interaction management, and digital intelligence concepts; it encompasses software that automates immediate insight-to-action cycles for digital business, addressing not only digital customer engagement cycles, but operational processes as well.

The case for decisioning is clear. Analytics must drive CX decisions — and the software that automates them will be critical to developing a data-driven culture, competing on analytics, and deriving value from analytics moving forward.