Rakesh Jayaprakash, Product Manager at ManageEngine explains how the emergence of Decision Intelligence will drive predictable outcomes and ultimately improve decision making
Developing business strategies, improving customer journeys, coming up with new product features, and even recruitment, all start with making important decisions. To help organisations make the right choices, managers and analysts rely on large volumes of data.
In recent years, data has become easier and easier to collect, process, and analyse thanks to various tools and technologies such as automation and Artificial Intelligence (AI). While these modern data tools provide crucial insights, they can often leave organisations with more questions than answers. Although the insights generated from the data may be enlightening, the organisation can be left wondering what to do with the information they have learned.
This is where Decision Intelligence (DI) can help. This emerging discipline is the next evolution of Business Intelligence, but before we explore the reasons why, let’s take a look at the key differences between the two.
Business Intelligence vs Decision Intelligence
Business intelligence (BI) combines analytics, data mining, data visualisation, tools, and best practices to help organisations of any size make informed and practical decisions. BI relies on business intelligence software to collect all the complex data a business generates to then present it in a digestible way, for example in form of visual reports. But BI is not solely about generating reports; it also offers a way for people to examine current and historical data to understand trends and derive insights.
For example, Lebara, the UK-based telecommunications company, uses business intelligence analytics tools to achieve a complete overview of its help desk activities. This is so it can streamline its support team operations, predict the flow of incoming requests, and prepare contingency plans. By using analytics, Lebara is able to determine process lags that are causing backlogs and delaying resolutions. This also helps Lebara to improve SLAs percentages and increase customer satisfaction scores.
Decision intelligence (DI) is an extension of BI which allows organisations to utilise more accurate and usable data, leading to better-informed decisions being made. It will rapidly accelerate the journey towards these outcomes, enabling faster decision-making while eliminating the common errors associated with BI, such as human biases. Gartner predicts that by 2023, a third (33 percent) of large organizations will have analysts practicing decision intelligence, including decision modelling.
In comparison to BI alone, DI involves different decision-making techniques to design, model, align, implement, and track decision models and processes. When integrated with a combination of AI and machine learning (ML) algorithms, DI offers a decision-making framework with easy-to-understand and intuitive insights. As a consequence, data dashboards and business analytics become more comprehensive platforms that connect AI and human decision-making to form more intelligent conclusions. In turn, these lead to more favourable business outcomes.
How intelligent decision models work
An intelligent decision model needs to follow a certain series of steps in order to drive better decision-making. The first of these steps is to gather all of the relevant data that can help to determine the right outcome. Even data that might feel less relevant to the decision at hand is worth including as there is always a possibility it might be relevant in some way. A wide range of data sets can be useful at this first stage, from historical to behavioural, transactional, intern, and external data.
Before this data can be modelled, it needs to be understood . This is where AI and ML work to understand the data in the context of the insights that the organisation wants to derive. Then comes the modelling phase where the various potential actions that the organisation can take based on the data are generated and considered.
The next step is contextualisation. This is where the model presents the organisation with each of the potential courses of action that it has derived from the data analysis and modelling phases. Each of these potential actions should clearly communicate any negative business impacts that may arise as a result of each course of action being taken. This will provide an understanding of the potential trade-offs of each action and help to reach a faster decision on which action should be executed. Once the action is executed, the model can monitor and measure it. This (monitoring and measurement) serves as a feedback loop for the model to improve suggestions in the future.
The future of Decision Intelligence
DI is still in its early stages and has yet to evolve into a place where it can offer more intelligent suggestions for businesses to use. With the rate at which new technologies progress, it’s unlikely organisations will have to wait too long until they can implement and experience the benefits from DI. Not only will this support faster, more accurate and informed decision-making, DI also has the potential to reach the right decisions on its own. Eventually, DI will become a widespread strategic tool that organisations will rely on to effortlessly decide on the best possible business outcomes.