How do businesses make decisions? It seems a trivial question, but it is a question with many answers. Throughout corporate history we have been guilty of making many terrible decisions and a multitude more good ones. But given that bad decisions have the potential to wipe out entire businesses, or sometimes even whole industries, we have a duty to reduce our bad decisions to a minimum. Research points to humans making anything up to35,000 decisions a day. Most of these decisions are unimportant and are made intuitively without ever engaging our consciousness. But some decisions are important. These decisions require us to do more than rely on experience, and so we gather data, derive analytics, and discuss our options extensively. But does this lead to better decisions?
The move towards data driven decision making has seen a revolution in business analytics. Corporations have invested heavily in data lakes, we use a wide range of statistics and machine learning to garner insight, and decision makers have a seemingly endless stream of information to learn from. There can be no doubt that we understand our businesses better than we ever have before. So surely companies are making better decisions? Seemingly not when you consider that companies are failing at a slightly higher rate than at any previous moment in history. And this stretches all the way from small start-ups to corporate behemoths. With so much information at our finger tip show is it that corporates are still plagued by bad decision making?
To understand this, we need to think about all aspects of decision making and how the elements of decision making have changed over time. The most basic form of human decision making requires little conscious thought.
Our experiences amass to form knowledge, this knowledge produces our intuition, which leads us to ‘gut instinct’ decision making. As recently as 2002 many business leaders told a Harvard Business Review in a corporate survey that they still relied upon instinct for “the majority of” their business decisions.
This type of decision making is a loop and over time, good or bad decisions will feed back into our experience to create ‘better’ intuition.
Over the past decade advances in computer power and data storage have re-written our decision-making process. Decision makers are faced with a barrage of information. From customer data to real-time sales information, decision makers have a deeper appreciation of business drivers than ever before. And yet, for all these advances the mechanics of decision making have not really progressed at all.
Data and analysis may have deepened our experience and extended our intuition, but decision making continues to be a leap of faith, with instinct still playing a significant part. How then can we use all the technological advances that we have made to drive better decisions?
Decision Intelligence is a new way of thinking that attempts to deal with these limitations. If we think of Business Intelligence as being an understanding of what has happened, Decision Intelligence is a forward-looking approach that links decisions to outcomes.
Decision Intelligence is a framework to deploy intelligence inside decision models. They are a graphical and quantitative representation of the structure and dynamics of a business. In recreating this structure, it is possible to model how a decision will cascade through an organisation. A decision model could incorporate data, forecasts, and even intuition.
Building decision models is surprisingly intuitive. There is no requirement for any specialist skill beyond an understanding of how a business works. I could draw out a decision model using a simple pen and paper, sketching out the causal steps that make up my business processes. But of course, it is by building quantitative computational decision models that I can develop the capability to quickly test decisions and have the machine recommend optimal interventions.
Decision models avoid the persistent problem of trying to derive the structure of a business from data alone. Too often there is a disconnect between analysis and reality resulting in the perceived problem that data is ‘unclean’. But in fact, company data, however ‘unclean’, is an accurate reflection of the processes at play within an organisation. To make better decisions those imperfections need to be accounted for instead of being glossed over or ignored. Only by understanding how data is produced can we wholly understand the workings of a business and ultimately be able to make better decisions.
The Decision Intelligence approach has many advantages but perhaps most important of all is that it is a forward-looking approach. As a decision model is stepped forward in time (technically, simulated) it produces forward looking data “data from the future” that can be used to analyse how decisions taken today will play out. The forward-looking nature of Decision Intelligence is why corporations are investing in this area and why they are choosing to work with Silico. If you would like to find out more about how Silico’s software is helping our customers to begin their Decision Intelligence journey, please visit us at www.silicoai.com.