Data Analytics

The role of Data in Board-level decision-making

Authored by Duncan Shaw, Lecturer in Information Systems at Nottingham University Business School and member of Corndel’s Data Board


The first thing to say about Board-level data skills is that it’s not really about data – it’s what you can do with data. Generating strategic options, making structural decisions and creating platforms that reorganise and unify all levels of the business.

It seems like a lot, but emerging data technologies include all that is happening with AI, the Internet of Things, decentralised finance platforms and many other changes to the nature of organisations.

The bottom line is that all this radically changes what the board can do as well as what the board is responsible for. It’s not just the capabilities of the business that are changing, the capabilities of the board to oversee those changes are changing too.


Cultivating the business model

Organisations are constellations of business models. Each product and service, each division, each business unit, they all have a business model. Each one describes how stakeholders in that business model can mutually satisfy each other with their capabilities and resources. Change any of these variables and you change the business model ‘equation’.

New capabilities from technologies like Robotic Process AutomationMachine Learning, and blockchain-based decentralised finance are disrupting, rebalancing and reinventing many business models. Scarce resources like human decision-making or specialised expertise are becoming more plentiful through automation and global communications systems. Stakeholders are joining and leaving, or just changing their roles.

Products are turning into services as a deeper understanding of customers’ lives suggest new ways to monetise relationships. And gathering and analysing customer/ user journey data is the key to product and service design. Also, every transaction in a relationship is a chance to learn more about what that customer will pay for next, because more sales breeds more sales.

As always, this is about steering the boat not rowing it, but that requires a sound understanding what the boat is now capable of.


Navigating through uncertain times

Ongoing and interlinked events are forcing business to transform but at the same time they make it very hard to foresee how to do so. Uncertainties include Covid, world-wide shipping problems, shortages of key skills and materials, energy cost spikes and increasing concerns from consumers and regulators about climate change and the use of plastics.

But new data sources and new ways to use them are giving boards access to the big picture, like never before. For example, large firms are developing early warning systems to prepare themselves, and forming teams of experts to check emergency decisions when the crisis hits. The reducing costs of data and online analytics tools have also opened options for smaller firms.

As ever, clear thinking is key but knowing that these new information sources exist is the first step. Next is understanding how to weave them together to regularly review the vision, mission and values of your business.


Decision-making is becoming more nuanced and evidence-based

‘Who’ decides ‘what’ is an issue that many boards have to deal with. In the past, management hierarchies were defined by access to information and huge experience.

Now everyone is an analyst these days. Anyone in an organisation can get access to virtually any online information. And use online tools like Qlik, Tableau and Power BI to manipulate it.

So, who should make which decisions on organisations today? This calls into question the structure of the whole business because organisational structures are decision-making structures.

Data sharing between organisations is the life blood of Internet of Things ecosystems, and it is changing many external relationships. Which ecosystems should your business join? Or should you start an ecosystem and it build up?

Supply chains mostly move and transform materials, information and other resources in a single direction. Ecosystems are different, they move resources in all directions, between all stakeholders.

Most businesses are part of several ecosystems at the same time. Which means entering completely new markets when new business partners have customers that need your help.

It also means fighting off competition from outside your sector. Like new competitors from industries you never heard of. Every customer buys from many different markets, each with their own special view of that customer. So out-of-sector competitors have access to data that you do not.

Whether it’s new data or new ways of finding patterns in data, boards have to get used to radically different levels of speed, precision, and scales of decision-making. Not just for the organisation’s managers but in the board processes that oversee and guide them.

Trade-offs like quick versus accurate choices, or selective/personalised versus common/standardised are increasingly not required for many management decisions. Which has negative as well as positive implications.

Industry structures are morphing, your workforce’s values and roles are altering, and the fundamental purpose of many organisations is being questioned.

’Data’ is both the enabler of much of these changes, as well as the tool for dealing with them.


‘Data’ is a Board responsibility

Ethics, transparency, liability and accountability are all Board level responsibilities that data technologies are impacting.

New data capabilities may not match the ethical framework that a business works within, which might have cultural, branding and regulatory repercussions. AI systems and other data technologies are not unethical. But the human designs that they follow might be. Biased data samples that train sexist and racist AI models are one well known example.

Transparency is important, not just for data protection but it is also a fundamental requirement for customer trust. And transparency means being clear with how you will use customer data, as well as not being creepy when you do it.

If customers don’t trust you then they will not share their data with you. But they will share it with a trustworthy competitor.

New data technologies bring new liabilities and new risk profiles. Will accidents with driverless cars be a manufacturer liability rather than one for the driver’s insurer?

If the Board is ultimately accountable, then how can it prove due diligence when no one can explain why some types of AI make the decisions that they do? The AI software and how it works on a line-by-line basis is easy to describe. But the internal complexity of some AI software makes it impossible to say how this translates into particular decisions.


Skills about data not data skills

The challenge for the Board is not to master data skills, but to understand how the firm can do that, what these capabilities can be used for and what the potential options and repercussions might be.

This is what I call ‘qualitative data science’. At a lower level it enables jobs like Mckinsey’s analytics translator role, which connect new analytics capabilities to the rest of the business. At the Board level it’s more about building and leading a data-driven organisation.

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