Leveraging Predictive Analytics for Strategic Decision-Making

Business would be so much simpler if organisations could see into the future. They’d be able to anticipate trends and make decisions based on hard data to ensure that they stayed one step ahead of their competition. In the absence of a crystal ball, businesses can now make use of predictive analysis to achieve all these things. We look at what predictive analysis is and how organisations are using it for strategic decision-making.

What is predictive analysis?

Every organisation owns vast amounts of historical data about its customers, suppliers and employees including transactions, demographics, engagements and reviews. They also carry information about their own internal workings such as data from sensors and the Internet of Things, and have access to external sources such as weather forecasts, economic indicators, and even TikTok trends. 

All this information can be examined by machine learning, data analysis, statistical models and AI to create patterns that predict customer behaviour, outcomes, and risk, and offer insights into up-and-coming trends, sales and business outcomes. This enables organisations to make precise strategic decisions to enhance performance and boost revenue in what are the most likely business outcomes within a specified timeline.

Successful predictive analytics applications 

Every sector of business or industry can benefit from predictive analysis. Let’s look at some of the areas in which it’s currently being used. 

Banking/Insurance/Mortgages and Consumer Lending

A sector particularly prone to fraud, predictive analysis can highlight irregular behaviour and abnormal transactions in real-time to reduce cybercrime and assess credit risk, and also has applications in underwriting. It enables financial organisations to forecast and budget more accurately – vital in periods of growth or transformation – and will even take into account the effect of new financial regulations or Budget decisions. 

Healthcare

By utilising information from patient records and looking at factors such as medical history, demographic group and previous hospital admissions, predictive analysis can forecast expected admissions – particularly important during the annual NHS winter crisis when resource allocation is vital. It can delve into individuals’ healthcare history to provide accurate diagnoses and offer appropriate treatments, and can also enable personalised healthcare within the Life Sciences sphere, saving costs and enhancing outcomes. 

Manufacturing

Predictive analysis considers data such as market trends, consumer behaviour and seasonality to forecast demand, assist with inventory management, and ensure an effective supply chain. It can also suggest predictive maintenance schedules to prevent malfunctions and downtime, enhancing efficiency – something that’s particularly important in the aerospace industry, for example, where safety is paramount.

Pitfalls of predictive analysis 

Despite its myriad uses, there are some potential challenges for organisations when using predictive analysis. These include:

• Data – accurate predictive analysis depends entirely on the quality and quantity of data on which it is trained. Data should be relevant, complete and unbiased to achieve accurate and informative results.
• Ethics – unrepresentative or limited data can produce biased patterns or discriminatory practices, and can lead to unreliable predictions, reputational damage and unintended decisions.
• Expertise – the current skills gap in predictive analysis resonates with other AI and digital transformation issues, namely that a lack of skilled people is impacting organisations’ ability to innovate and grow.