What’s wrong with traditional BI & analytics?
In 2023, leaders must contend with an unprecedented volume of information, historic economic volatility, and global competition. We’ve all heard that leaders who want to get ahead in this environment must leverage established disciplines like data analytics, business intelligence (BI), and artificial intelligence (AI) to make better decisions. As a result, most enterprises have cultivated formidable BI and analytics teams, if not entire departments and centers of excellence (COEs). Ensuring these techniques translate into the achievement of desired business outcomes, however, is another beast entirely.
For most businesses, extracting insights from data is costly simply because they don’t know which data to focus on.
Research shows that 20% or less of an organization’s business data is actually relevant to achieving the strategic outcomes they desire. Yet most organizations take a blanket approach, treating all business data as more or less the same. They spend a considerable amount of time, effort, and money on massaging data in search of insights. In practice, many of these insights are irrelevant to making critical decisions, much to the dismay of all parties involved. Hefty investments are made, yet the gap between data and outcomes so often remains (the so-called “last mile” problem in analytics). Until recently, there wasn’t an explicit method to consistently bridge the gap between data and outcomes at the organizational level.
Enter decision intelligence (DI), a new discipline that brings humans and technology together like never before. One of Gartner‘s top technology trends for two years in a row, DI stands to become a $36B market in the coming decade.
Why Decision Intelligence?
DI is the natural extension of BI and AI in practice, providing a framework to effectively apply technology to executing the most complex and impactful business decisions. Instead of the ubiquitous data-centric approach of the last decade, DI starts with desired business outcomes and works backwards toward data and AI. It begins with people, not technology. It is still people, after all, who must ultimately make and be held accountable for high-impact business decisions.
Another unique element of DI is its accessibility to non-technical stakeholders; besides the term “decision intelligence” itself, DI requires no new jargon to understand or implement. By mirroring the way we naturally think about decisions in our daily lives, DI allows leaders to understand and successfully execute complex decisions, while mitigating unintended consequences down the road. It is for these reasons that DI “will solve the most difficult problems of our time” according to DI co-inventor and Astral Insights team member Dr. Lorien Pratt.
Ultimately, the value of your company is just the sum of decisions made and executed. The ability to make faster, more consistent, more adaptable and higher-quality decisions at scale defines the performance of your entire business.
How does DI work?
As a relatively young discipline, various DI approaches exist and continue to evolve. I will outline the methodology behind DI at a high level in this installment, diving into subtopics in more detail over the course of this year. It is worth noting that DI is a comprehensive field, meaning that its effective implementation requires people, processes, technology, and information working harmoniously together across your organization. It all begins with executives coming together to define their desired business outcomes, such as reducing financial leverage or expanding into a new market segment. By facilitating transparent conversations among decision makers, DI practitioners lay a solid foundation for the application of technology to decision making. After defining the outcomes, the team works to identify the actions that effectuate cause-and-effect chains leading to their desired outcomes. This process is known as decision modeling, and the resulting blueprint (alternatively known as a causal decision diagram, or CDD for short) enables DI practitioners to identify where data and technology are needed to support the decision. Notice the distinction between this approach and the aforementioned data-centric approach: it is only after stakeholders have a common understanding of all the key decision elements that terms like data management, predictive analytics, and BI join the conversation. The combination of DI with analytics tools we already know and love (think Tableau, Alteryx, Microsoft Power BI, etc.) is incredibly powerful, allowing leaders to make strategic decisions with improved speed and accuracy while cutting data management costs.
Companies that are slow to adopt DI risk losing market share to competitors, especially during a recessionary environment.
When is DI the right answer?
Given the transformative nature of DI, it should be no surprise that its applications are diverse and numerous, spanning the public and private sectors, global geographies, and industries. In fact, it is easier to define the sorts of decisions to which DI is not applicable. For many high-frequency, low-complexity decisions, AI can entirely automate the process and render human judgement unnecessary. An example of such a process is making sure invoices are paid in an automated fashion. These simple decisions are not a good fit for DI given the absence of a “human in the loop”.
However, we are still far from automating most important business decisions, especially those of higher complexity and uncertainty. Since these decisions are typically the most challenging to make (not to mention the most prone to negative unintended consequences), they represent the perfect stomping grounds for DI. Several such decisions include deciding which new product to introduce to the market and when, determining whether to proceed with a potential acquisition, and developing a new environmental, social, and corporate governance (ESG) policy for your organization. By creating an outcome-centric blueprint for each decision, mapping actions to outcomes, and highlighting ideal “insertion points” for technology, DI increases the likelihood of attaining desired outcomes while simultaneously reducing data management costs.
In the current post-Covid era of relentless global competition, changing customer needs, and economic volatility, the ability to cut through the noise and consistently make proactive, informed decisions will separate the most successful organizations from the rest.
Looking Ahead
Stay tuned for future articles, webinars, and events where we will dive deeper into the practical applications of DI and how to ease your organization into one of the most powerful business disciplines ever created.
Getting Started
Contact us to learn how decision intelligence can reduce costs and enable proactive decisions to navigate a volatile economic environment.
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 limitations 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.
