Authored by Duncan Shaw, Lecturer in Information Systems at Nottingham University Business School and member of Corndel’s Data Board
When we lead, our role is to steer the boat, not to row it. As a non-technical leader then the priority is to understand the part of the business that is using data analytics – not to understand the technologies themselves.
Non-technical leaders need to specify the project, develop the business case and evaluate project outcomes. That means understanding WHAT data can do for the business, and WHERE to use it; it’s not about the detail of HOW it does so.
With that in mind, there are five data skills that non-technical leaders need, to take advantage of new data technologies:
1. Understanding what data projects are for – to specify, buy in and evaluate them
The purpose of data analytics projects is to find patterns in data that can guide decisions – board decisions, management decisions, all levels of decision making.
Patterns of data tell us which customers might like which products, or which problems might occur for which people, machines or processes. Data patterns help us predict events so that we can pre-plan; and group things in order to prioritise resources.
But, in the current world, there is too much data and too many data sources – it’s overwhelming and confusing. Businesses sit on vast datasets that they never use; whilst other businesses hold tonnes of data that, if shared, would give you valuable additional insights, like viewpoints on your customers.
Every mobile app, online software tool, or Internet of Things device can potentially harvest data. And when it comes to analysing that data, there are plenty of new technologies: like AI and machine learning, not to mention simpler analysis software like Power BI, Tableau and Qlik.
Leaders should not start by thinking about data. They should start with the decisions and actions that are needed – and then work backwards to determine what data is needed and how to analyse it.
Focus on the processes that need fixing; the problems that need removing; and the opportunities that need to be assessed.
2. Journey-based thinking – to decide where to use data analytics technologies
Journey-based thinking is a highly effective way to focus resources. It shows leaders which decisions can be improved via a data project; and it develops the business case. It’s similar to business process mapping and drawing flow charts – but it’s subjective rather than objective.
Coming from work on analysing customer journeys, journey-based thinking describes the individual experiences of any stakeholder – not just customers. This includes machines as well as people – every staff member experiences their job differently; every car has its own maintenance record. A journey analysis tells you where to aim a data project. The possibilities of data-driven marketing and customer services have long been understood. Adopting journey-based thinking allows this exciting, effective and evidence-based approach to be adopted for all stakeholders and machines.
Better, quicker, smoother and cheaper work or life journeys for all stakeholders benefits them as well as the organisation.
3. Forming and running the project steering board – to oversee but not project manage
A steering board meets regularly – before, during and after the project – to keep a strategic eye on planned and actual activities, and how they are supporting the original purpose of the project. The steering board is also there to remove barriers and be the fundamental source of sponsorship and resources.
Leaders are familiar with project steering boards and the mix of change management and business transformation work that they oversee. The difference here is that data projects deal with global as well as local; and large scale as well as very personal issues; all at the same time.
4. Understanding data governance and data ethics – to manage new risks not just benefits
Non-technical leaders are customers of an analytics projects – and intelligent customers need to be aware of the unique risks of using data, not just the benefits. Data governance and data ethics are key issues for every non-technical leader to understand. Understanding how the data project fits within the organisation’s data protection responsibilities; and ensuring those responsibilities are met are, ultimately, leadership roles.
Even if legal requirements are fulfilled, the leader needs to understand the additional ethical risks of the large-scale use of personal data. There are some risks that are perfectly legal; but which impact the organisation’s brand, or are inconsistent with the organisation’s purpose. Facebook and Google are obvious examples. Are their users ‘customers’? If the answer is ‘no’; then how should they be treated, especially if many of them expect to be treated as customers?
5. Continuing the transformation – to become a data-driven organisation
Most organisations are just starting to be data-driven. The advantages are clear: to vastly improve the customer and stakeholder experiences; to increase the efficiency of how all resources are used; and to reengineer the firm’s business model by using the new capabilities that data technologies provide.
In the midst of data project #1 (whilst doing the ‘day job’), leaders also need to think about the next project, and the one after that.
Which parts of the organisation can build on the knowledge and capabilities that we learned from project #1? Who needs to be convinced, and what additional resources are required? Do we need to reskill and upskill those staff with years of precious organisational knowledge, or those who can pick up prized skills?
Data technologies are also part of an arms race. Competitors will be using them – so leaders must consider how to best defend themselves. Just look at how online advertising data disrupted traditional newspaper revenues.
Non-technical leaders do not need to become technical – even in a data-driven business. Leaders need translation skills – to make sense of the new resources, capabilities, environments, business models and strategies of our increasingly data-driven world.
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