Q&A: Jeanne G Harris, Sr Exec, Accenture's Institute for High Performance

'Analytics is the key to better business decisions'

How do business leaders make decisions? Based on gut feel or data, or perhaps a mix of both? According to research by Jeanne G Harris, an executive research fellow and senior executive at Accenture’s Institute for High Performance, and her colleagues, 40 per cent of the decisions that executives make are based not on facts but gut feel. Sometimes intuitive and experience-based decisions work out well, but often they fail. One can hardly be sure of the desired results. In her new book, Analytics at Work, Harris and her colleagues explain how companies can leverage the massive amounts of data at their disposal to make better business decisions that deliver impressive results. Recently in Delhi, she spoke to Amit Ranjan Rai on the importance of analytics at work.

 

Executives often rely on their gut feel to make decisions. In fact, some of the most well-known leaders are known for going with their gut feel. You don’t quite agree that intuition based on experience works very well...
Fifty years ago, executives had no choice but to make decisions based on their gut feel. There wasn’t the data that they needed, and no one had the information in a form that they could use it at the time of making a decision. They almost always had to go with their gut and instinct. That’s how someone became successful in business. What we are saying is that expert judgement is good, but expert judgement combined with data and rigorous analysis of that data is much better. In Harvard Business Review, several years ago, there was an article called “Go with your gut” which was followed by another article called “Don’t trust your gut” by an academic called Eric Bonabeau. In the article based on his research he said that having judgement is good, but judgement combined with data and data analysis is the best.

It is not about art versus science but about blending art and science together. Decisions based on prejudice or stereotype decisions — that’s not the best way to make decisions. It’s really a question of having the right information to use the judgement that you have developed over the years as an executive to make the right call. The most important thing an executive can do is to ask the right difficult questions. He needs to make sure he is out there not looking at the past but looking at the future, questioning the assumptions that everyone is making.

The challenge is that 75 per cent of the executives want to make decisions based on data and facts; but if you ask them what percentage of their decisions are actually made that way, they say 40 per cent of their decisions are made on gut feel. There is a disconnect between the desire and the reality. Companies want to compete on analytics but they are a long way away from that. That’s what the purpose of our new book is — to tell how any company can do a better job by building an analytical capability in the organisation.

What does your research say about successful companies? Has analytics played an important role?

Our research is global in nature. For over 10 years we researched extensively on what makes a company a high-performance business and the kind of decision-making that goes into building such a company. What makes a company a leader in its industry, not for the short term or, say, under a particular leader, but for a longer period of time? We found one of the key success factors is the use of data and information in decision-making. It actually contributes significantly to high performance. This was true for 19 different industries in 35 different countries. Every industry we looked at, we found a significant relationship between analytical competitors and high-performance businesses.

How do leaders make decisions most of the times? What does your research say?
One will have to look at the different types of decisions. I would like to think of them in terms of strategic, tactical and operational decisions. A strategic decision could be, say, acquiring a company or entering a new market. Those have more of a qualitative component to them. So there is always going to be more judgement in those questions. But you would not acquire a company without looking into its financials; that’s the quantitative fact-based piece. If you look at strategic decisions, some of them are more qualitative than others.

If you look at operational decisions in an organisation, some of them are already very statistically driven, for instance, supply chain optimisation. Having an efficient supply chain means you should be able to analyse to predict what your demand is going to be and to know exactly the raw materials that you’ll need. You need to have a good sales forecast, financial forecast and marketing forecast. Many organisations have pieces of these, but they haven’t put them all together; so they cannot predict with equal assurance across the lifespan of the product. But think about what you could accomplish if you have that kind of rigour and certainty.

Companies tend to be very good in one or two functional areas initially, and, as they get success in those areas, it expands over time. Perhaps they start with supply chain optimisation using predictive models to figure out what kind of raw material they need, but they quickly realise that in order to know that, they need to know the demand for the product. So then they go to the demand side. So the application of analytics might start with marketing, finance or operations but as companies get more successful, there is momentum amongst management to apply the same kind of discipline, and think of it as a new management discipline. Analytics is a very powerful tool to make the science of management more rigorous, and as executives see its power in one area, they tend to apply it to others.

How should companies go about building analytical capabilities?
We surveyed over a thousand companies and did in-depth interviews with hundreds more. Based on that we realised that there are five factors that you need to have to be successful in any analytical initiative. And that’s what we termed as the DELTA model. The Greek letter D (delta) signifies “change” in an equation, and it certainly can change your business equation. D here stands for accessible, high-quality data. Data is the prerequisite for everything analytical. You need to have the technology to get it and capabilities to analyse it. Innovative companies will go out to collect new data that they didn’t had before. But, of course, a company has a lot of data in its own systems. No one has the information about a company as it itself has. It is really a proprietary asset.

E in the DELTA model is for taking an enterprise-wide perspective. You can do good things with analytics in supply chain, finance or marketing, but to create an analytical organisation, you need to think how you are going to use analytics across the entire business. What marketing does affects sales, operations and so on. And, of course, you need to have the right technology and software to get your data out of silos and integrate it with a more enterprise perspective.

L, the next letter, stands for leadership. Leadership is at the heart of any analytical organisation. Leaders can lead their organisations to become more analytical. We found that analytical leaders are much more innovative and experimental.

T is for targeting. Once you start to see success with analytics in one part of the business, it tends to spread. That’s a good thing and a challenge because fundamentally every organisation has finite resources, and you have to identify where you are going to do the most good. It’s all about picking your spots.

A is for analysts. Though you have computers and data-driven analytical decision-making, they are not nearly as vital as people. An analytically-oriented company needs to have lots of analytically-oriented people, or analysts. Finding, developing and deploying such analysts are critical for success.

How is an analytical leader different?
They don’t think that data analysis is for somebody else to do. Often you see executives who say everybody in my organisation should use data and facts, but they don’t necessarily do so in their own decision-making. An analytical leader has certain behaviours — the expression is that they walk the talk. They don’t do one thing and expect their subordinates to do another. So they use data and they challenge people if they make a statement in a meeting and don’t back it up with data and facts.

Any example…
A good example would be Barry Beracha, the former CEO of Earth Grains which is one of the biggest bakeries in the US. Beracha joined Earth Grains after working at Anheuser-Busch (maker of Budweiser beer) which is a very analytical company. Earth Grains, of course, was not at all analytical. So Beracha put a sign on his desk which said: “In God we trust, all others must bring data.” Any time, anyone came into his office saying they need to open a new plant or invest in new equipment, Beracha would say, do you think so or do you know, show the data.

So an analytical leader makes decisions based on data and challenges people if they make decisions based on assumptions. He questions assumptions. It’s like applying the scientific method to management. Recently, you wrote an article in Harvard Business Review on analytics in talent management. How can companies use analytics on the HR side?
Human resource and talent management are areas where very few people have used mathematics or predictive analytics. If you think about it, statistics relating to people have been around for a long time, but HR or companies never did anything with that. Consider sports; in the last five or six years professional sports has transformed significantly as there is a huge movement to use analytics to make decisions.

Take, for instance, a professional football team like AC Milan. A few years ago, AC Milan brought in a player in the team paying ¤70 million. Within a couple of weeks he injured his knee and was worthless. The management thus concluded that it is no good to have good players if they were not fit and healthy. And it decided the critical success factor will depend on analysis around how to select players who are not injury prone, how to keep its players healthy and playing, and then go out and find the best talent. AC Milan created a laboratory that analyses 200,000 data points around how someone jumps and moves when playing football and that helps predict whether a player is injury prone or not. It films them and analyses whether they are getting into bad habits. This is one of the main reasons why you see AC Milan has such great older players because they know the management is going to keep them healthy and playing, and make them successful.