What Companies That Accurately Predict the Future Do Differently

With access to data and the technological power to analyze it, the ability to more accurately predict the future should be within the grasp of many, if not most, enterprises.

What separates those who can actually bring these forces to bear in order to reduce uncertainty while creating advantage and those who remain subject to their whims is more organizational than technological or data driven.

The attitudes and actions required may seem counterintuitive, like leaning into your skis as you go down a mountain, rather than giving way to natural instinct and pulling back. The concept of letting go is not an easy one for most executives and leaders to take. They tend to believe that they got where they are by tightening the reins and micro-managing rather than letting a thousand flowers bloom. But that is precisely what organizations - especially those that have become institutions - must do in order to succeed in a global economy defined by an expanding web of relationships whose connections, impacts and implications can not always be precisely determined, let alone controlled.

Acknowledging that all wisdom does not reside within the confines of one's self-constructed box is the important first step. In this economy, the leverage necessary to expand comes from securing relationships outside one's control. That includes information and the ability to analyze it, then to act on the intelligence generated.

Investing in people and ideas and their potential, even if it's ROI can not be guaranteed, is another essential element. The great fortunes were not generally  made by incrementalists. There is a place for that, but there must also be an appetite for risk that defies the easy formulae generated solely by spreadsheet. 

Ultimately, those who succeed manage risk better than others. And they do so because they accept the challenge that risk offers rather than trying to pretend it can be eliminated. JL

 Jeanne Harris and Mark McDonald comment in Harvard Business Review:

“The future is already here, it’s just unevenly distributed.” The predictive is valuable because it’s unevenly distributed and therefore in demand. Finding this future requires investing in management, information-intensive relationships, and a broader view of analytics in the enterprise.
If knowledge is power, then predictive analytics promises the ultimate knowledge — that of the future. Such knowledge does not come easily, but the increasing density of digital information, deeper automated connections across companies, and increased storage and computing power create new options for enterprise leaders. For the first time in history, the predictive future — the increasing awareness and likelihood of potential future actions and outcomes — is within reach. No wonder, then, that executives have placed predictive analytics at the top of the executive agenda since 2012, according to a recent Accenture survey.
But to know more about potential future actions and outcomes and their probability — and to act on that knowledge — organizations are engaging in new kinds of relationships. We have found that the most forward-looking organizations do these three things:
(1) Look to the outside: The main focus of analytics has until recently been internal, directed toward high-frequency, standardized, repeatable processes that connect variance with intervention. Using analytics, organizations have deployed bigger data sets, cheaper cloud computing power, and more aggressive algorithms to successfully standardize previously non-standard processes such as sales and service, making them more repeatable, predictable, and amenable to analytics.
To apply analytics to the future, though, self-knowledge is insufficient. The information most likely to influence the future comes from looking out the window, not in the mirror. Sheer computing power isn’t the key differentiator either, because the predictive future relies less on additional statistical mastication than on a greater diversity of inputs. Consider the example of a manufacturer of production equipment that collects sensor-based telemetry about its machines’ operations, the status of their parts, their performance, their resource consumption, and other data. This monitoring turns up an anomaly at a key customer that indicates a failure is imminent. Such a failure would cause a significant cost and damage the customer’s brand. The manufacturer notifies the customer, which pulls the machine off line and repairs it, saving millions of dollars in lost production and damage to its brand. Business continues as usual and the equipment manufacturer has a very grateful customer.
In this example, information that was critical to the customer came from outside its walls. But while such information exchanges have become technically feasible, they are not yet financially beneficial to the information provider and difficult for the customer to value and incorporate into their management systems. Turning information exchange into value and revenue involves changing the nature of information relationships as well as management’s abilities to act on that information. The most forward-thinking companies are developing new business models to create value from these kinds of information exchanges.
(2) Develop open multiple multi-sided relationships: Altruism or openness alone will not give rise to ready access to the diversity of data required to understand the predictive future. The availability and veracity of the data involved in the predictive future requires creating multiple multi-sided relationships with customers, suppliers, trading partners, and just about anyone else with potentially beneficial information. It is no longer enough to share information one-to-one with partners. Increasing predictive power rests in positioning yourself at the center of multiple information flows.
Current information-based services, such as Bloomberg, involve an information provider selling a single set of information with segmented services to multiple customers. Such models play a part in the predictive future, but the industrial Internet and expanded communications capabilities change the nature of information products. From one product distributed to many customers, the move is underway toward products that feed information from many sources to a single party, which rearranges and redistributes the information to many customers. In short, from many to one to many.
There’s a demand for this type of information, and thus product and market opportunities, but in an information services marketplace where people want everything for nothing, it is not easy to monetize information products. We expect, though, that a viable market will emerge as commercial terms evolve to support the multiple multi-sided relationships that give subscribers unique access to information and therefore value. Whether the information source is commercial brokers or existing commercial relationships, diverse information sources fuel the predictive future.
(3) Update management and leadership practices: An extended analytics engine fueled by multiple information sources, however, can accomplish little without the ability to act on future predictions. The practice of management itself must evolve for this capability to emerge.
It is hard enough to act on solid information about the past. The level of difficulty rises when management is asked to deal with a set of predictive futures rather than projections based on past performance. Effective use of predictive analytics involves mastering a new set of management, operational, and financial techniques and disciplines.
Managerially, organizations need to revise management practices, including: increasing the use of experiments and pilots to enhance risk-taking based on external and incomplete data; incorporating test-and-learn experiences into decisions and action; enhancing awareness of the differences between causation, correlation, and coincidence; and placing tangible value on avoiding adverse effects and missed opportunities.
Operationally, organizations need to establish their own trust and execution mechanisms for multi-sided, information-based relationships. These mechanisms entail creating new analytics capabilities, securing access to third-party information and capabilities, continuously refreshing sources, and determining which data need to remain private to retain their value.
Financially, organizations require new models to account for information assets beyond treating them as intangibles. Financial arrangements have to evolve to handle pricing and payments for value based on possible futures. The ultimate goal is to treat information as a tangible flow rather than an intangible asset stuck on the balance sheet.
“The future is already here, it’s just unevenly distributed.” William Gibson’s dictum, though overused and abused, remains true. The predictive future is valuable precisely because it’s unevenly distributed and therefore in demand. Finding this future in the deluge of information available requires doing a better job of boiling the ocean. It requires investing in management, information-intensive relationships, and a broader view of analytics in the enterprise.