Leadership and big data: friend or foe?

Source: Management Team (04/12/2017); Author: Karlien Vanderheyden

Research from the MIT Center for Digital Business has shown that the 30% of companies that use data-driven decision-making most are an average of 5% more productive than their competitors and 6% more profitable.

Leaders who take the right approach to big data know their business better and can transform those insights into better decision-making and results. That is the conclusion reached by researchers at the American Kellogg School of Management: ‘Big data isn’t a data science problem. It’s a leadership problem.’

Let’s take another look at Michael Lewis’ bestseller, Moneyball. The book tells the story of Billy Beane, the manager of the Oakland A's baseball team. With only a small budget for buying new players, he didn’t have the market on his side. But he used data analysis to discover certain factors that could predict who might turn into a promising baseball player. Buying apparently underrated players thus led to victory after victory for his team.

In itself, there is nothing new about using data analysis in baseball. What was special about Billy Beane was that he was a leader who understood its potential. And he also had the courage to change direction based on these insights.

What are your challenges as a leader in a data-driven company?

1. Stop giving the right answers and start asking the right questions

Is data in your company scarce, expensive or not available digitally? If so, trust your experience to make decisions. However, if you have an excess of data, you would do better to use it to find answers. In that case, your expertise will help you to define the right problems and ask the right questions. ‘Which data set shall we use?’, ‘Where does the data come from?’, ‘What does the data tell us?’ or ‘Which analyses shall we conduct?’
Be careful: powerful data can never completely replace human judgement. As a leader, you still need to be able to detect opportunities, think creatively, present an attractive vision and convince your team members to embrace this vision.

2. Attract the right talents - and develop them as well

In a data-driven environment, it is clearly crucial to deal with large quantities of information in the right way. Your employees need to be able to select and structure that data. They must be able to visualise the results of data analysis in a comprehensible way and experiment with it. On the other hand, they need to speak the language of the business. So it may help to get a ‘translator’ on board, who can facilitate discussions between managers and data analysts. For example, a person like this can help the sales manager to formulate a business problem in a way that makes sense for big data, then support the data specialist in conducting the right analyses, and after that interpret the results with the sales manager and communicate them to the team members.

3. Stimulate maximum cooperation between job roles

Regardless of the department where someone works or the job they do, you need to be capable of bringing together the people who have the right problem-solving skills. Create a flexible environment and avoid ‘not invented here’ syndrome.

4. Build up your own basic knowledge of data sciences

You certainly don’t need to be an expert in conducting (predictive) analyses or setting up experiments. But you do need to be able to ask relevant questions and understand which analyses make sense. For example, when you are faced with contradictory conclusions, you need to be able to solve the problem using your big data knowledge.

What are the pitfalls for leaders in a data-driven company?

1. Too much - or too little - trust in data

Data is not the Holy Grail and will not provide a concrete solution to all your business challenges. However, you do need to believe in the power of data and put your intuition aside for a moment.

2. Data is not a crystal ball

Nobody can predict the future precisely. Data analysis can indicate a trend, though, which helps to make future-based decisions.

3. ‘More data’ is not the same as ‘better data’

Over-analysing wastes time and may also slow down or obstruct decision-making. Know when to stop and don’t let things get out of hand.

4. Make sure you can always see the wood through the trees

Focus your time and energy on the data that will have a real impact on your business results.

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