3 leadership behaviors you need to make your organization data-driven

What’s keeping your organization from being more data-driven? The answers may surprise you

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What lessons can be learned from businesses embracing the opportunities offered by big data and analytics? Bing Windt, an MSc student in Digital Business at the University of Amsterdam, explored the topic for his master’s thesis – Understanding challenges and responses of leadership in data-driven transformation. He identified typical leadership behaviors required to make the leap from intuition to facts and figures. Here, he writes about his findings.

Bing Windt gives a presentation at the University of Amsterdam based on his master's thesis: Understanding challenges and responses of leadership in data-driven transformation.

Colleges and universities around the world are at a crossroads. They have long faced fierce competition for funding and talent, and now education needs are evolving at an ever-accelerating pace.

A variety of recent studies suggest that unless they can take a critical look at their role in delivering knowledge, some institutions face closure. This seems to chime with the public view of higher education – in 2016, the Pew Research Center found that “just 16% of all Americans think that a four-year degree prepares students very well for a well-paying job in today’s economy.”

There are other moving parts to consider. Corporations frustrated by the gaps in technology education (ed-tech) have established their own academies to train the workforces of the future. In addition, an aging population and the rate at which tech is developing have resulted in a growing demand for lifelong learning.

If you want your institution to thrive, you can no longer afford to make decisions based on reputation or experience alone – you need to ensure that data is in the driving seat when it comes to future planning.

Why should you become more data-driven? I found many answers to that question, but basically, you can’t afford not to. Other organizations – your competitors – are already moving in that direction. These are the companies that have better performance figures and higher profits. That’s because measuring, analyzing and understanding the impact of their activities allows them to future-proof their businesses.

The tools required for data analysis are relatively easy to acquire; the challenges lie elsewhere. It’s about managing that transition within the company. Another difficulty is the availability and experience of data scientists, but the pool of analysts is constantly growing.

The barriers to becoming data-driven are gradually disappearing, and what the data is showing is that you can’t afford to miss the boat.

For my thesis, I spoke with four companies with analytical competencies and expertise that have made strong strides towards a data-driven model. At each company, I interviewed three people: a middle management employee, a member of the senior management team, and a data analyst or data scientist.

The four companies were:

How to shape a data-driven future

Through observing these companies, I found that there are three actions or responses that leaders should display if they want to secure a successful transformation.

1. Communicate and explain the value of data-driven decision making

You can’t get quality data analytics without the experience, support and expertise of colleagues. But in some cases, it’s difficult for leaders to make this happen. Perhaps other senior management or departments don’t see the value. Perhaps they’ve been working there for 20 years or more and are used to the status quo. That’s where sharing examples and use cases can be really helpful as they provide concrete examples of how this transformation has worked for other companies or in other departments.

Executive conferences about data and analytics allow leaders to discuss and share the problems or challenges they’ve encountered along the way, as well as the approaches that are working. Communicating the value of transition is vital for success and so it’s important leaders understand what that value is.

2. Secure and manage critical resources

Quality data is critical for performing analyses. If you don’t have that, you can’t create valuable insights. Leaders have a crucial role to play in ensuring that the data scientists get access to the information they need.

The desire for a transformation like this often comes from the top and then it’s possible to ensure that everyone makes it a priority and it’s captured in their objectives. Some companies really get this, and you see it reflected in their reporting structures with a senior analytics member of staff or Chief Data Officer reporting directly to the CEO or the board. That provides them with the authority needed to make things happen.

Another challenge businesses face is that it’s often people creating the data, not computers. One company I spoke with was trying to move beyond simple, transactional data by finding metrics to capture the full experience of a customer interaction. The human interpretation, the emotional response, it’s all part of the interaction. If the customer was angry… why? How angry were they? And what does it teach the company about their services and their customers? There aren’t any easy solutions to capturing these details, but analysts are working on it.

Leaders are also crucial in selecting, growing and enabling their analysts and giving them the freedom to identify what needs to be done and the tools required.

3. Build a data-driven culture

It’s important for leaders to integrate the data analysts they employ within existing teams. Employees might have a lot of people skills but less knowledge of data. By involving your data analyst in daily operations, you give them an opportunity to develop their sense of business applications and inspire non-data analytical people. They can help you educate peers and superiors about the power of data and transform the business from the bottom-up. That means it’s important they have good people and time management skills.

Another key learning was that it’s important to think about that word leadership. What does it entail? In this case, it’s someone who has the authority to not only ensure the quality of the data but manage the change required. You need a strong leader to make a shift like this.

I found a number of differences between the companies I interviewed, many of which can be attributed to their differing industries. Financial companies have historically been more data-driven, so the change is perhaps smaller for them. Telcos are already highly tech-centred and digitally-enabled, which helps.

While an airline is data-driven, the dynamics there are very different. Roles within the company can vary enormously and there are thousands of employees who need daily management. How do you determine what the measure should be for each of those departments? And, how do you analyse such different measures? It can be very difficult to align data needs.

If you were to replicate this study in a year’s time, or run it again now with four different companies, you’d probably gain additional insights or new perspectives as the field is developing so rapidly. That’s what makes it so exciting.

Elsevier and data literacy

Elsevier is one of four companies Bing chose for his thesis, based on their strong track record in data analytics.

Gaelle PertuisetGaelle Pertuiset, Elsevier’s Data and Analytics Head of Change and Process Management, said it was “really positive” to see the close alignment between Bing’s findings and the steps Elsevier has taken:

We are committed to becoming a data-centric organization, which involves much more than technology: it is about embedding analytics across every aspect of decision-making.

One of our priorities is to develop data literacy – that’s the ability to read, work with, analyze and argue with data regardless of your role, skill levels or the tools you use. We want all Elsevier employees to be able to ask the right questions, use data to answer those questions and interpret findings. There is not a one-size-fits-all approach to develop this skill within the company. Leading by example is one of the approaches we want to develop: we need to change the way we interact with our peers, stakeholders and leaders by “speaking” data in every-day interactions. We will also be working in the next few months on defining a learning and development plan.



Written by

Bing Windt

Written by

Bing Windt

Bing Windt is an MSc Digital Business student at the University of Amsterdam. He is currently specializing in Big Data and Business Intelligence as part of an international master’s program in Quantitative Techniques (QTEM). For his MSc thesis research, he studied data-driven transformations and the role leadership has played in four analytical organizations.


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