Data has often been likened to oil, but this well-used analogy does have its limitations, given that data is a resource that will never run dry. Indeed, many organisations are drowning in the stuff, having been forced by the Covid crisis to accelerate their digital transformations. The volume of crude data pouring into their systems from various sources, particularly consumers, is increasing exponentially.
What they are lacking is the refined material: actionable data – the rocket fuel of commerce.
This is still difficult to come by. Many business leaders who authorised the digitalisation of their companies – ploughing vast sums into new IT infrastructure, breaking down silos and creating data lakes – are eager to see the fruits of their investment. The digital transformation of business has done only so much for them. Now they want to see a data-led transformation.
Achieving data-powered decision-making across all parts of a business is a lofty goal, but it’s necessary for two reasons. First, many supply chains and markets are heading in this direction. Second, the number of data-related critical business risks is growing.
But why is actionable data still hard to come by in so many enterprises that have seemingly achieved digital transformations?
“There’s so much data in an organisation that it can be hard to know where to start,” says Darren Mitchell, former global COO at law firm Hogan Lovells. “Many businesses favour the approach of putting a robust data architecture and governance system in place first. In my experience, such initiatives lose momentum and enthusiasm, as they can all too easily get bogged down in processes and policy-making.”
Making data an asset
It’s likely that firms in many industries, in their rush to transform their operations, didn’t think deeply enough about how they would use all the extra material that would be coming their way. They employed IT professionals who obsessed about how costly new systems should be implemented without first defining what real-world business problems these all-singing, all-dancing tech stacks were meant to solve.
“Data needs to be treated like any other critical asset: it must be discussed at the top of the company, both at C level and in the boardroom,” says Theos Evgeniou, professor of decision sciences and technology management at Insead. “This is about leaders understanding the ‘art of the possible’ with data – and about having data leaders at C level. It all starts from the business problems that need to be solved. You work backwards from there. Identify your top business goals and then ask what data you require to best achieve them.”
Mitchell stresses that data needs to be accessible, comprehensible and usable by non-technical strategists in the business if it’s to have value. This is not always the case, of course.
“The data is often sitting there and waiting to be used, but business leaders simply aren’t asking the right questions that help us to structure it in such a way that we can deliver answers,” he says. “Senior executives should really be engaging to define which measures, metrics and data points would really help them to make actionable business decisions and then work from there.”
The case for such an approach is strong. More than half (52%) of the 1,700 European business decision-makers surveyed by Dun & Bradstreet in December 2021 doubted that their firms could survive without having relevant, up-to-date and compliant business data to hand, for instance. Actionable data is not merely nice to have; it’s essential.
“It is vital that every CEO, CFO and COO is on board when it comes to embracing a data-driven approach. More than a quarter of the respondents we polled were looking to improve their data literacy,” says Dun & Bradstreet’s chief data scientist and senior vice-president, Anthony Scriffignano. “Data forms the bedrock of business processes. There is no doubt that we’re living in a data-led economy. So, if businesses want to thrive, it is imperative that their leaders focus on data and the critical insights that can be derived from it.”
Start with the use case
But business leaders can’t simply wave a magic wand and expect their organisation to be transformed by actionable data overnight, of course. The exponential growth in data, the increased complexity of operations and the continual digital transformation of businesses all mean that there are many moving parts. Choosing the right data is a challenge in itself. This is why starting with a use case or a real commercial problem that needs solving with data can be the most fruitful approach.
“Remember that data has meaning only in a specific business context,” Evgeniou stresses. “You can’t outsource its interpretation to technical people alone. Business leaders need to ensure that they are closely involved in using the data and guiding people across their organisation to understand it, interpret it and make the best use of it.”
Case studies that most impress board members tend to be those that are easy to understand and show what cost savings can be achieved when data insights are used. If the project is repeatable and scalable across the organisation, and its return on investment is high and rapid, so much the better. Spreading the word about such data-led projects inside the organisation will then help to build momentum.
“Any data-led transformation project, no matter its size, doesn’t exist in a vacuum,” notes Ravi Mayuram, chief technology officer and senior vice-president of engineering at Couchbase. “Even if, at first glance, it affects only one part of the business, its impact will ripple beyond this and require every member of the C-suite to pay attention. For instance, Tesco implemented a data-based project in 2020 to optimise its delivery scheme. This enabled a faster service to customers and created priority slots for the most vulnerable. It gave them significant value.”
Executing any data-driven project is not without its challenges, of course. Businesses have to create ‘data ecosystems’ that make sense of information in context. No single source of data will drive action. Information silos and territorial disputes concerning which department owns what material can be a limiting factor. Without all of its functions acting in unison, with interoperable data, an organisation risks missing a crucial part of the data puzzle.
Any data-led transformation therefore calls for a scientific approach. Data fluency shouldn’t be the preserve of analytics teams; it needs to exist across the whole organisation and be especially prevalent at C level. As with a digital transformation, it isn’t just the domain of the CIO or the IT department. It should be embedded in everyone’s remit, from the most junior member of staff all the way up to the chairman.
“You need empirical rigour around data,” Scriffignano stresses. “You must also be able to replicate methods. This enhances the analysis process and addresses potential biases. The strongest leaders in this field have noted the importance of specific epistemologies, belief systems and best practices.
It means that they have a holistic view of the data ecosystem, including what they believe and why they believe it. Such rigour allows them to challenge beliefs as the commercial environment changes. If employees are to use data, this grounding needs to be in place from the C suite, so that staff have clear guidance on how data can be used.”
A range of strategies
Although compartmentalisation is rarely the best strategy, some organisations have gone down the easier route of centralising many of these decisions under one central authority, the chief data officer. If it can get this right, the organisation can then obtain the maximum return on investment from its data.
Other approaches are just as feasible. Adopting a ‘data fabric architecture’ is also one step closer to achieving a data-led transformation, for instance. This is a powerful way to standardise data management practices. It is also designed specifically to help organisations solve complex data problems, although boards may baulk at the further IT costs that the approach typically entails.
“A data fabric architecture seamlessly connects all sources of data and makes them accessible to everyone in the organisation,” explains Ebru Binboga, director of data AI and automation at IBM UK and Ireland. “It is the foundational layer for an agile digital business that can quickly seize on market opportunities by harnessing insights from data.”
A new culture governing the use of data will certainly be needed. Very few organisations can yet say that they are truly data-driven businesses. But the speed at which market conditions are changing has never been greater, as is an organisation’s need for both agility and operational resilience.
“The huge driver of change is customers’ expectations,” says Mark Woods, chief technical adviser for US software firm Splunk in EMEA. “People want businesses to react instantly, with their performance in this respect measured in milliseconds. The cloud-first world and the digitalisation of businesses means that individual employees are no longer able to come to the boardroom and brief their leaders about everything that’s going on. This simply isn’t practical anymore. What businesses need are data-led analytics and data-led decision-making maturity.”
Whether this is coming soon depends on the investments that businesses make beyond their digital transformation programmes. New ways of thinking about how data is valued also matter. If it is treated like a critical asset, this could shift the dial. ‘Data mesh’, ‘data as a service’ or ‘data as a product’ are ways that organisations are starting to reframe the issue. Sugar-coating data with AI can also help.
Most companies have come to think of data as an asset, but few have come to view it as an economic asset that could appreciate in value if they could do more with it. There is little incentive, therefore, for them to package and refine it as if it were a product or a service.
Yet forward-looking businesses are starting to apply ‘product thinking’ to data. If an organisation were to start treating data with the same care as the process of selling a product to a customer, it could be a game-changing move. This approach, which would entail investing in product development and quality assurance, puts much more emphasis on the value of data and the problems it solves. For that reason alone, it’s a space very much worth watching.
