Every modern business should invest in predictive analytics as part of its business strategy

Martin Butler, Professor of Digital Transformation, shares his opinion on predictive analytics

Martin Butler

By Martin Butler

Professor of Management Practice

28 February 2024

Technology is playing an increasingly important role in both tactical and strategic decisions – and not only in operational decisions. If data is the new oil – as the famous quote goes – then predictive analytics is the infrastructure to exploit the data and turn it into value.

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Information technology has assisted organisations with decision-making for the better part of a century. The traditional role of IT was to make sense of the past – and managers used insight about past events to make decisions about the future. Over the last two decades, two important aspects concerning technology in managerial decision-making have changed. Firstly, significantly more extensive, and richer data sets are available – and they contain new levels of business insight if processed correctly. At the same time, algorithms that enable us to interpret and extrapolate from these datasets have developed in leaps and bounds. These algorithms allow us to include some of the tacit knowledge we used to make decisions in the past. As a result of these two changes, predictive analytics has grown both in stature and capability.

What are the major benefits of using predictive analytics for compliance?

As internal processes, customer interactions, and business partner engagements become more digitised, the risk of compliance breaches also increases. However, with the use of predictive analytic data models and algorithms, it is now possible to quickly and accurately identify and monitor data movements and processes in real time. Organisations can identify potential non-compliance issues immediately and respond to them instantaneously. Moreover, sophisticated models can flag potential compliance breaches hidden in large data or subtle deviations from existing processes. Compliance teams who investigate these potential breaches can now move from searching for a needle in a haystack to a pebble in a shoe.

What are some of the challenges associated with predictive analytics?

The system's business value is determined by the underlying data models created, or machine learning models trained on the data. It is essential to avoid the solutionist trap and believe that vendors will bring solutions to the table. Vendors provide technology. Organisations must provide the business context, skills and insight to gain full value from predictive analytics. A predictive analytics project should never be vendor-driven. Organisations need to take ownership of predictive analytics projects and ensure that multi-skilled teams with excellent business insight in the application domain steer the initiative. Having the appropriate skillsets and mindset to take ownership and be clear about the business value, remains the biggest obstacle to successful implementation.

Where is the future of predictive analytics heading?

The previous two decades are characterised by significant progress in data visualisation and presenting it in formats that suit quick insight and appropriate actions. This trend is set to continue as we create more intuitive and flexible interfaces to present data in a creative format for decision-making. Yet the most exciting trend in predictive analytics is the power of AI across the entire data value chain. AI can help with data ingestion, processing, governance, and dissemination. Self-service analytics, powered by AI, can display insights suited for the intended purpose. And it will enable humans to add value to the decision process by providing the last layer of insight to make the best possible decisions.

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Martin Butler

Martin Butler

Professor of Management Practice