The Value of Data Analytics to Leaders and Managers

In the era of data science and data being “the new oil” what is the value of data analytics to today’s leaders and managers and how can they use this capability to derive sustained competitive advantage?

What are the Benefits to Firms Leveraging Data Analytics?

It would be worth beginning by asking whether firms that leverage data analytics have access to a capability that differentiates them and gives them an edge over their competitors.

It turns out that there is a wide body of evidence that confirms that the answer to this question is “yes” including a report from Wegener & Sinha showing that firms using data analytics are twice as likely to be top-quartile financial performers and another from McKinsey & Company showing that data-analytic firms increase their earnings before tax by 20% compared.

Beyond these two examples there is a wide body of research and evidence supporting the view that embracing data analytics will improve business outcomes and deliver competitive advantage.

What are the Strategic Benefits?

There is a traditional view that firms with good data analytics maturity start every meeting by reviewing the corporate data dashboard with every decision maker having immediate access to reams of spreadsheets detailing the minutiae of company performance.

There may be some truth in that but at a higher level leaders and managers of firms that have a mature data analytics capability know how to ask and answer the right questions giving them unique insight and an ability to make the right decisions to drive company performance.

Victor Kiam famously “liked the shaver so much, he bought the company” but in hindsight this decision was an example of something called “substitution”, a famous psychological effect that has ramifications for data-driven firms.

There are many questions that should be asked during a corporate acquisition but in asking the question “by how much is Remington company stock under-valued?” Victor appears to have answered the question “how much do I personally like just one of its products?”.

The substitution effect can be addressed in data analytics firms simply by seeking the data and information that will inform the decision. In the Remington example it is likely the main data required would have been on historical stock market performance and sales trends, not a single sample product survey, and this would have led to the correctly answering the right questions relating to the acquisition decision.

As well as avoiding substituting a different question to the one being asked, data analytic leaders and firms are far more likely to frame the right question in the first place.

During the “Cola Wars” one of the two leading firms produced a new, attractive glass bottle and raced ahead of its main competitor in terms of sales. The competitor swiftly formed a task force to design and produce an even more attractive bottle to win its customers back.

Having failed in this task the team stepped back from the problem to ask, “what is the right question?” and “what data do we need?”. It turns out the right question was “how do we sell more cola?”, the right data related to customer needs and the answer was to develop the plain old 2 litre plastic bottle which led to increased sales and profits.

This is a classic example of framing the right question and data analytic leaders and firms are much more likely to be aware of this and to frame business challenges in a way that improves their outcomes.

Data vs. Intuition

In “Thinking Fast and Slow” Nobel Prize winner Daniel Kahneman puts forward many arguments against purely intuitive decisions and makes a strong case for using data analytics to augment, inform and support important business decisions.

Roger Federer can hit a forehand in Tennis intuitively; he doesn’t have to think about the stroke or process any thoughts before or during the task, it just happens automatically. However, Roger can execute 1000’s of shot-related decisions quickly and get instant, accurate feedback as to whether he has got it right or wrong.

Now consider an advanced business decision like a corporate merger. It is likely that leaders will make a handful of decisions of this magnitude during their careers and even then, the feedback indicating success or failure will not be known for many years.

The first example is a good fit for intuition, the second example requires that intuition be combined with accurate, consistent, timely and applicable data to triangulate and verify the thoughts and views of the leadership team.

Data analytics does not mean that the intuition of leaders and managers should be ignored or is unimportant, but it does provide an invaluable way to check it and, if the two views do not triangulate, to ask searching questions about which view is right.