What is Decision Intelligence?
As data grows in volume, the human ability to process it for insights gets diminished. On the other hand, advanced computer-based algorithms can process humungous data but have no way of factoring in the equally important intangible factors. It has been shown that strategies based on a pure algorithmic pattern of decision making are almost as prone to underperform as those based on pure intuition. This is where Decision Intelligence (DI) comes in. DI is a framework for organisational decision-making that augments data science with social, managerial, and business dimensions.
Decision Intelligence is not about trusting technology to make the final decision. It is about using technology to empower decision-makers in complex situations. With DI, enterprise managers are presented with the most equitable options uncovered by advanced AI and ML algorithms running on large data sets, which can then be run through social, instinctual, and managerial filters to reach fast and accurate decisions.
Why is Decision Intelligence required?
Developing the most competitive business strategy, personalising customer-experience, determining the most optimal product features, or even hiring the right candidate for the job – it all starts with making the right decisions. And yet, most decisions are taken informally, relying on intuition rather than intelligence. With the explosion of data, and the constant pursuit of insights by an empowered workforce, technology has made some inroads into the art of decision-making, but with questionable efficacy.
To be fair, the human element is always a factor in strategic decision-making. Technology can replace many things, but not the gut. If a set of data points is given to different humans, chances are they will arrive at different decisions. But given identical inputs, different computers would always reach the same decision. The difference is in the pattern of deduction. The human brain works by association, which includes intangibles. Computers are purely algorithmic, relying on inferences alone. The best decisions in complex situations are usually arrived at through a combination of algorithmic and associative patterns. This is the driving principle for the evolution of Decision Intelligence, or DI, as an organizational discipline in new-age enterprises.
How is Decision Intelligence relevant to my organization?
As an emerging best-practice, DI applies advanced analytics and machine-learning techniques to the complex array of systems – and their interconnections – that characterise the digital enterprise. Decision Intelligence unifies the various components of decision-making across the enterprise and uses these as building blocks to “construct” decisions. It thus brings design and structure, or modelling, to the decision-making process, incorporating engineering principles like requirements analysis, development, testing, and re-use in the decision-making cycle.
A pure data science approach to construct decisions in the backdrop of increasing uncertainty, complexity, and decentralisation has limitations. For one, the pure data science approach is centred on the availability of structured data, such as a data warehouse. At a subtler level, the data science approach largely ignores the social, managerial, and instinctual dimensions of decision-making. As a result, decisions based on a pure data science approach are chiefly shaped by the existing technological capabilities of the enterprise. These enterprises mostly end up lagging the market.
An approach cantered on decision intelligence, however, is not based on existing capabilities but on new possibilities emerging from the evolving needs and expectations of the market in which the enterprise operates. DI provides the framework for applying AI techniques to data extracted from a data lake, characterised by voluminous data in varied forms, including social media feeds and sensory inputs. When combined with social and managerial aspects, this leads to the most optimal decision-making in complex situations. Given the centrality of decision-making to enterprise success, it is not surprising that enterprises embracing DI mostly end up leading their markets.
We already have analytics tools like BI. Must we still invest in DI?
DI is not a substitute for existing tools and processes like Business Intelligence (BI), advanced analytics, and Decision Support Systems (DSS). A BI software, for example, may provide optimal solutions to a business requirement, like analysing customer data to personalise marketing campaigns. DI is an organisational discipline focused on using data in diverse contexts as an aid to decision-making in complex situations. While analytics tools are technology-led products focused on automating the decisions using a software program, DI is a discipline embracing tool-based quantitative analysis and human perspicacity. As an analogy, if DI is equated to mathematical ingenuity to solve complex problems, analytical tools (like DSS) are about making the interim calculations.
How does DI work?
DI is not a technology. It is an organisational best practice. Much like customer orientation, quality focus, or business-technology alignment. As we said earlier, DI is the application of data-driven problem-solving skills at scale, using AI principles combined with social and managerial sciences.To understand how DI works, let us break this down into its constituents.
Data-driven problem-solving. This requires a blend of human and technological capabilities. An enterprise on the DI path must first boost its data literacy (refer https://kms-world.com/5-essential-tips-to-boost-your-data-literacy/).
Data at scale. Enterprises must have access to large volumes of data in all its varied forms, including unstructured data like paper-slips, social media feeds, and sensory inputs. The solution lies in ingesting data into enterprise data lakes (refer https://kms-world.com/7-things-to-consider-in-building-your-data-lake/).
Artificial Intelligence. AI algorithms working on large amounts of data not only empower the decision-maker with requisite insights in these situations but also allow the program to learn automatically from the patterns and features of the data, making it more efficient.
Social and managerial sciences. While technology is a strong enabler, it is the social and managerial aspects that ultimately drive the decision, by considering factors that are beyond the purview of machines. These include relationships, situations, cultural aspects (conflicting values), perceptions, instincts, emotions and politics, among others.
In Conclusion …
Ultimately, it is the combination of the technology-centric (big data, AI) and human-centric (social and managerial sciences, data literacy) approaches that enables optimal decisions in the transformed workplace.
The new business ecosystem embodies an intricate interplay of expanding data and increasingly complex algorithms to improve organisational decision-making capability. However, despite very innovative technological initiatives, most enterprises have not become better at decision-making. Decision Intelligence, by filling the gap between the reality of software-based systems and the ambiguity introduced by business, managerial, and social challenges, helps organisations achieve greater efficiency and effectiveness of decisions, and ultimately, vastly improved business outcomes.
