Four keys to using big data to unlock better strategy

The era of ‘big data analytics’ and the rise of artificial intelligence are throwing up many challenges for businesses.

How do they deal with the constant flow of structured and unstructured information? How should they integrate that into their operations? And how can they make the most of big data's potential when developing strategy?

These are not easy questions to grapple with. Understanding the relationship between big data and strategy is a complex task in itself.

The impact that big data can have on the day-to-day practices that shape an organisation's strategy can be hard to judge. Harnessing that data to formulate an effective strategy can be harder still.

The term 'big data analytics' can cover many things. For the purposes of this discussion, I include everything from data collection to the organisation, storage, retrieval, analysis and dissemination of that data.

Artificial intelligence can feed into this in many ways. It can help with pattern recognition, predictive modelling, and natural language processing, among other things.

I worked with Yassine Talaoui, Marko Kohtamaki, and Mikko Ranta, of the University of Vaasa in Finland, to review 228 papers that have been published on this topic since 1995.

Our study found that academic discussions of the relationship between big data and strategy tend to fall into one of two main camps.

How are data analytics and strategy connected?

The first perspective views big data analytics and strategy as quite separate. Big data offers a computational input that flows into an organisation and its decision-making processes, leading ultimately to strategy outputs.

Under this input/output model, a better understanding and awareness of technology leads to better business decisions and potential outcome.

This is because the organisation develops computational capabilities that in turn allows for improved intelligence across organisational layers and better-informed decisions.

The second way of looking at the relationship offers a more complex picture, in which the two elements are more entwined. Big data helps to inform strategy, but strategy also informs the collection and use of big data.

This ‘entanglement’ model puts more emphasis on the social processes and networks within an organisation.

The relationship between big data and strategy is not pre-defined. Instead, it can be constructed by actors and teams. That means the role of people is far more significant.

This in turn opens up the possibility for power dynamics and office politics to play a bigger role.

How should firms use big data when developing strategy?

There might be disagreements between those who are more data-savvy and those who are more resistant to the idea of rapid change.

While the input/output model focuses more heavily on technology and processes, the entanglement view gives more weight to interpersonal engagement and the roles of teams, practices, and activities.

Both models have their respective shortcomings. However, they each offer a useful lens through which to view the use of big data and can help organisations identify the best approach to take.

Of the two, the more complex entanglement model perhaps gives a better sense of how a business can make the most of big data analytics.

If managers within an organisation focus solely on big data as a computational capability, they could easily miss its potential to create more meaningful change.

There are four key areas that managers should focus on to make the most of big data’s potential to inform and shape their organisation’s strategy.

1 Laying the right tech foundations

Companies need to ensure they have the right technological foundations in place before they try to use big data to build and shape strategy.

The most useful technologies will vary from business to business. So will the details of how, when, and where to invest in them.

If your organisation is not particularly data-savvy, it won’t necessarily need to have the latest and most expensive technology straight away.

However, it will need the right tools to meet the organisation’s needs and to help it fulfil its potential.

It might be better to start off with smaller projects that focus on parts of the organisation where the necessary capabilities already exist, before launching into more ambitious technology endeavours that require greater investment and buy-in from internal stakeholders.

2 Collaborating with external partners

Building the right relationships with other organisations can help to develop and enhance your own company’s technological capabilities. This is something that even the world’s largest tech companies recognise and engage in from time to time.

A good example is Microsoft’s relationship with OpenAI, the company behind ChatGPT.

The US software giant already had its own AI capability. However, it was worried about the strides that its rival, Google, was making in the space.

To be regarded as one of the leaders in the AI race, Microsoft had to find a partner to boost their efforts and OpenAI seemed to be a good fit. 

As a result Microsoft invested $1 billion in OpenAI in 2019. That was followed by a further investment in 2021 and another $10 billion in 2023.

3 Developing key skills in-house

Working with third parties can help firms to fast-track their technological capabilities, but they must also have the right skills within their organisation to exploit the potential of big data analytics.

Managers need to think about what kind of skills their company requires today, but they also need to consider what skills might be useful in the future.

One way to acquire these skills is to identify and recruit the talent the organisation is currently lacking.

Another is to consider how to train employees to enhance the company’s overall knowledge and capability.

4 Organisational fitness

Managers need to consider whether the business is fit enough to make the most of the potential offered by big data.

A company might have started experimenting and found that some of the strategic ideas and initiatives being generated were positive or even transformative. But even in those situations, it is easy for inertia to assert itself.

In order to harness big data analytics in the most effective way, organisations need to ensure they have the ability to enhance their capabilities.

This means continuing to innovate, invest in, and engage with new technologies to boost their current operations and develop new business models. 

Ultimately, robust data capabilities are vital for any company that aims to tap into the potential of big data analytics to shape effective strategy. However, they are not sufficient in themselves.

To truly use big data to improve strategy, those capabilities must be married to the right organisational culture and talent, along with an appetite for innovation. Only then will firms enjoy the full benefits of big data.

Further reading:

How to measure what really drives business performance

Algorithmic inertia: the flaw that made Moody's blind to the financial crash

Three steps to integrate AI into your organisation

Are managers going to be replaced by AI

Sotirios Paroutis is Professor of Strategic Management at Warwick Business School. He teaches Strategy and Practice on the Full-Time MBA, Executive MBA, and Global Online MBA. He also teaches Digital Strategy and Agile Transformation on the WBS Master's portfolio. 

Learn more about strategy on the four-day Executive Education course Creating Strategic Advantage at WBS London at The Shard.

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