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Data and decision making

Data has an increasing role to play in helping companies confront the array of global challenges, says Yu-wang Chen.

Decision-making is obviously a huge part of all of our lives. You decide which product you are going to buy in a supermarket, whether you will have a new vaccine, or maybe how you allocate tasks to members of your team at work.

Some decisions are clearly more important than others. As recent weeks have amply demonstrated, if you are Prime Minister or Chancellor then the decisions you take can have huge consequences.

But for the rest of us there can also be huge variations in terms of the importance of the decisions we make. To quote the earlier example, there is a huge difference between deciding whether to be vaccinated or buy a soft drink.

Decision-making in business

Making decisions in business and management can be particularly challenging and are often bounded by resources, information, personal experiences and subjective knowledge.

There are a number of specific challenges. Firstly, the decision often involves different forms of uncertainty and unknown outcomes, and can also be based on incomplete knowledge and limited information.

Secondly, many problems require business leaders to make trade-offs between multiple criteria, such as balancing return and risk in investment decision-making. Indeed, multiple criteria decision-making, known as MCDM, is a widely studied field in the discipline of management science and operations research.

And thirdly, when making decisions it is also essential to take into consideration human factors and make good use of prior knowledge from key stakeholders.

Data driven decision-making

The effective use of data can help overcome many of these challenges. For instance, a recent IBM survey found that more than half of business leaders need better data to make more efficient and informed decisions.

Indeed, in recent years data-driven decision making (DDDM) has become widely accepted as fundamental to improving the situation of decision-making in business and management. Put simply, it promotes the use of data science and Artificial Intelligence (AI) techniques to support decision-making and validate alternative courses of action before a business makes specific commitments.

Responding to challenges

There are tangible benefits for businesses to practice DDDM, in terms of improving the decision-making process and decision outcomes.

Importantly, it allows business leaders to respond more efficiently to the increasingly uncertain and changing environment we are all experiencing. Challenges such as the Covid-19 pandemic, climate change and geopolitical issues have all made decision-making even harder.

For example, in the retail industry huge volumes of operational data - such as marketing data, sales, transactions, customer services and finance data - can be used for implementing customer analytics to better understand how the pandemic has changed customers’ purchasing behaviours.

In the telecommunication sector, customers’ socio-demographic information, billing and payment data, complaints and even geographical factors can be combined together to predict customer churn and support decisions on customer retention.

Case studies

In our Knowledge Transfer Partnership (KTP) with Connect Childcare, a nursery management software provider in the UK, data-driven models were developed to enable the company and its clients to respond promptly to changes in childcare policy and market conditions. It also contributed to government policy discussions by utilising data analytics to inform the decision-making process.

In another example, the primary objective of our ongoing KTP project with Forensic Testing Service, a leading drug and alcohol testing laboratory, is to develop data-driven AI tools which can make more accurate drug and alcohol misuse recommendations for courts.

A key driver in this case is that in digital forensics it becomes difficult for the human overseeing the decisions to understand all the information and make effective use of heterogeneous data from various sources.

Research and teaching

Myself and colleagues in the Decision and Cognitive Science Research Centre at AMBS are continuing to research how we can extend the boundaries of decision sciences in the digital age.

For instance, one of our research themes is combining data as inputs to machine learning and AI models with decision makers’ knowledge to support reliable and informed business decision-making.

Ensuring that the next generation of business leaders use data effectively is also paramount. That is precisely why our MSc Business Analytics programme is designed to teach students fundamental theories, approaches and analytical toolkits of data analytics, decision sciences, applied operational research and statistics.

The programme has been ranked second in the UK for four consecutive years by the QS Business Masters Rankings and the undergraduate course unit of Business Data Analytics which I co-ordinate attracts wide interest from students across multiple programmes.

This only further shows how developing data and digital skills are now regarded as increasingly important for all of us as we continue to make our daily decisions.

Yu-wang Chen is a Professor in Decision Sciences and Business Analytics.

Blog posts give the views of the author, and are not necessarily those of Alliance Manchester Business School and The University of Manchester.

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