Big data and associated analytics are shaking up the way decisions are made across organizations.
This isn’t the first time the art and science of decision-making has undergone a disruption. Until the 1970s or so, the most common approach was command and control, in which decisions were made in the upper echelons of organizations.
In the 1980s, as organizations were buffeted by global competition, enterprises recognized that employees further down the hierarchy had a great deal to contribute, and there needed to be ways to encourage their input. Approaches such as quality circles and flattened hierarchies came on the scene.
Now, the emergence of big data analytics is sparking a third revolution in decision-making, opening up new possibilities. It’s been a given that many low-level routine decisions – deciding if a customer is eligible for a discount, or rerouting orders to achieve greater speed – can be automated through an analytics-fueled rules engine. But dig data also reaches higher up the organization as well. All up and down the corporate ladder, decision-making is shifting outward – either to new players in the enterprise, or to machines.
On the people side, decisions are opening up to new players across enterprises. Michael Schrage, research fellow at MIT Sloan School’s Center for Digital Business and guru on all things related to big data, says as the dynamics of decisions change, executives need to rethink who gets involved. decisions should flow based on “decision rights” and the “RACI” framework, based on who’s responsible for completing the task; who’s accountable; who’s consulted; and who’s kept informed. As with everything else, big data is changing the traditional lines of decision making.
“As organizational decisions increasingly become more data driven, top managers need to assure decision rights are data-driven as well,” he states. For example, accountability for decisions – and thus decision rights — may shift from product groups to user experience (UX) teams as an organization rolls out more digital products and services.
Another question that must be taken up is whether machines (or software) assume some of those decision rights. Achieving the right balance between human-level and machine-based decisions is a key concern among 2,100 executives recently surveyed by PwC on the topic, who agree that data and analytics are important to decision-making processes, but they’re uncertain how to bring things about.
As data-driven decision-making expands in the enterprise, the challenge is deciding what is best handled by machines – or augmenting human decisions with data to support it. “Machines simply haven’t been put to work at what they do best,” the PwC authors observe. “For example, machines are far better at calculating nonlinear influences and handling many factors, such as complex pricing variables. Most executives don’t think they will get to where they think they should be by 2020.”
The PwC survey shows that most executives (59%) say their next big decision will rely mostly on human judgment, minds more than machines (47%). “However, with the emergence of artificial intelligence, we see a great opportunity for executives to supplement their human judgment with data-driven insights to fundamentally change the way they make decisions,” the report shows.
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