Introducing Decision Intelligence

A New World?

In a world of uncertainty and unprecedented access to information, decisions are increasingly complex and collaborative. While big data and AI have become buzzwords synonymous with “innovation” and “data-driven decisions”, they are often disconnected from profitable business outcomes in practice.??

Per a recent Mckinsey survey, a staggering 74% of Fortune 500 executives report poor strategic decisions are as common as effective ones. Our research partner, Gartner, found that 60% of analytics and AI projects do not lead to business outcomes. Clearly, there is work to be done, and the answer isn’t simply pouring money into amassing and analyzing data. We’ve tried that.??

Achieving better outcomes boils down to effective decision making, and the common element in decisions is, of course, people. Yet, the way most companies make complex decisions is underdeveloped at best, and haphazard at worst. It overvalues data and undervalues the understanding of how decisions are made: the goals, cause-and-effect relationships, and thought processes behind each key decision.?

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A New Era?

Decision intelligence (DI) provides a systematic approach to executing complex decisions, unifying data-driven technologies under one umbrella. In many ways, DI is the natural extension of BI and AI in practice. It allows non-technical stakeholders to leverage powerful technologies without learning new jargon or theory, leading to tangible outcomes fast.??

Successfully Integrating DI into your organization starts with people, and more specifically, with executives. Department leadership must work cross-functionally and collaboratively, sharing valuable insights to maximize the probability of successful outcomes.?

When implemented effectively, DI affords a host of compelling benefits:?

? Faster, more proactive decision making?

? Fewer unintended consequences?

? Improved corporate performance?

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Supercharging Collaborative Work?

DI lends itself to improving complex, collaborative decision-making processes, such as introducing new products to market and evaluating new suppliers. Today, most analytics and AI practitioners work in data-first, collaboration-second frames of mind, leading to siloed information, miscommunication, and unintended consequences.?

"I would argue that companies have spent millions of dollars on ERP systems over the last two decades to improve the performance of transactional work, but many collaborative knowledge-based business activities have yet to be enabled effectively by technology. Here lies the opportunity for decision intelligence."

Allan Frank?

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Areas of Impact??

While DI methodologies help simplify and optimize complex strategic decisions, many of the same techniques apply to executing day-to-day operational decisions as well. Several examples of DI applied to core initiatives across the enterprise include, but are certainly not limited to:?

Sales: Proactively upsell existing customers and cross-sell products and services across customer segments.

Supply Chain: Anticipate disruptions across the supply chain and execute supply and demand planning with great accuracy.

Customer Support: Unlock a new level of customer engagement, anticipating changing customer needs to provide superior service.

Finance: Plan, forecast, and budget with improved accuracy while minimizing manual number crunching and ah-hoc analysis.

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Achieving Quick Wins?

Traditional analytics and AI implementations often take 6-12 months before any tangible value is generated. With a prudent approach to DI, however, leaders can tackle focused business challenges to move the needle forward immediately.?

It begins with a common understanding of the decision at hand, by all stakeholders involved. Technology can help with this, but decision-makers must put in the effort to collectively understand the key elements of each decision:??

? The desired outcomes?

? The actions they may take (decision levers)??

? The cause-and-effect relationships that result from taking these actions?

? The external circumstances that influence the outcomes?

Taking the First Step?

While the elements presented above are simple, achieving a common understanding among all stakeholders requires dedicated effort and the ability to recognize and moderate cognitive biases. However, this understanding affords investments in analytics & AI the best chance to flourish?and translate to more intelligent, profitable decisions.

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Clear Results?

In our recent Insights in Action installment, we partnered with a leading transportation company to augment decision making with data, generating a 180% ROI over 12 months.?

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About the Author?

Chris Andrassy is an entrepreneur and managing partner at Astral Insights, focused on transforming data into sustainable business value on a global scale. He began his career at PwC in New York City, supporting the digital transformation of mature organizations struggling to innovate in a hyper-competitive world. After experiencing the shortcomings of traditional analytics practices, he decided to begin a new chapter alongside colleagues and industry veterans. His departure from New York marked the inception of Astral Insights, a Raleigh-based decision intelligence firm helping mid-market and enterprise clients transform data into profit. Chris is also an investor focused on innovative technologies including synthetic biology, sustainable energy, and artificial intelligence. Outside of work, he is an avid musician, skier, traveler, and fitness enthusiast.?