Melbourne Business School News What companies get wrong about data-driven decision making

What companies get wrong about data-driven decision making

There are several common myths that leaders fall prey to when embracing data, says Professor Oded Netzer. Here's how to spot them.

Professor Oded Netzer in front of coloured wall

"In today’s world using data is not a choice anymore. It's a reality," says Prof Netzer, Vice Dean for Research at Columbia Business School.

"If in the past using data was reserved for tech or finance industries, today we see data in all almost all facets of business and public policy, from B2B to healthcare and retail, and across various roles within the organisation.

"On one hand, businesses today observe a lot of data and I think this is great – it's a huge opportunity, but I think it has also blindfolded a lot of organisations, leading them to overly rely on data and misuse it in their decision-making process."

Prof Netzer is one of the keynote speakers at this year's Melbourne Business Analytics Conference on 11 October and co-author of the new book Decisions Over Decimals, Striking the Balance Between Intuition and Information with American Express Vice President Christopher Frank and Google's Head of Global Strategic Alliances, Paul Magnone.

Ahead of the conference, Prof Netzer outlines the top misconceptions that hinder business leaders from using data effectively – and explains how to avoid them.

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Myth #1: Data will remove all uncertainty 

Big data created what Prof Netzer likes to call a "certainty myth" – that data would finally get leaders to a decision-making nirvana where uncertainty ceases to exist.

"Every decision we make involves uncertainty," Prof Netzer says.

"Data can be very useful in reducing the level of uncertainty involved in the decision, but in most cases eventually humans need to make the decision itself. Facts are black and white but decisions are grey.

"Understanding this, leaders can focus on what humans do very well, which is defining the problem, interrogating the data and synthesising the information to make better decisions."

Myth #2: To use data effectively, you need to be good at math

You don't need to be an Excel whiz or top of the class in math to use data effectively, Prof Netzer says.

"There are skills you need to make decisions with data and being very good at math is rarely one of them," he says.

Instead, what's needed is the ability to observe a business problem with a clear mind and to break down what information you will need to help you solve it.

"It does not require knowing super fancy math or engineering, but something that we call 'Quantitative Intuition', which is the ability to combine data with business acumen and human judgment. The people and organisations that can strike this balance are those that will benefit from using data."

Myth #3: A data-centric culture is driven by fancy tools

There's more to data-driven decision-making than just having good software and a dedicated data science team, Prof Netzer says.

"Sometimes I see companies applying fancy tools just for the sake of saying 'I'm using machine learning' or 'I'm using artificial intelligence'.

"Instead, leaders should ask themselves: 'What are the specific data and analytics tools I need in order to try and solve the problem I am facing? As opposed to chasing the shiniest data source or data analytics tool.

"It is often true that in order to process big data we need data scientists, data engineers and computing power. But these alone are not sufficient conditions for your organisation to become data-driven."

"What would make an organisation data-driven is leaders who embrace the use of the data and analytics all the way from the top and say: 'These are the problems. These are the decisions to be made and here is the data that we need to be able to make decisions.'"

Myth #4: We have tons of data, there has to be some good insights in here

"Companies often suffer from 'the streetlight effect', where they tend to look for the problems where data exist, as opposed to looking for data where the problems exist," Prof Netzer says.

The term 'streetlight effect' comes from a story that goes like this: A drunk man loses his keys in an alley late at night. A passer-by offers to help him look for them, but after half an hour of fruitless searching asks if he's sure that he lost his keys here.

The drunk man responds: 'No, no, no. I most likely lost them up in the dark part of the alley, but there's no way we're going to find them there so I'm searching here under the streetlight.'

Companies suffer from the streetlight effect when they focus on the most readily available data to define the problem and identify the answers as opposed to spending the effort in finding the right data to address the problem.

For example, the focus on internal CRM data often leads companies to underestimate or ignore competitive reactions. Another example is when marketing teams assess customer engagement based on less relevant but observable metrics like the number of followers or likes on social media.

"Are these really the metrics that we should care about? No, but companies go and evaluate themselves based on them because they are available," Prof Netzer says.

"So many of these data-driven journeys look exactly like that, where companies tell the analyst: 'Oh, we have all of this data. Can you tell us if there is anything interesting in it?'

"Well, if the analysts crunch enough numbers, they're going to find some insights. But the chance that these insights are going to help the company make decisions is close to zero because you haven't guided the process."

A data-driven approach: Quantitative Intuition

So what can companies do to effectively enhance data-driven decision making? Rather than looking through the terabytes of data they already have, Prof Netzer suggests that leaders should think about what data they will need to help solve a particular problem or make a decision.

"What we are preaching in our book, if you will, is to guide the process," says Prof Netzer.

"The data you need won't always be available to you or may be buried in the firehose of data pointed at you. Make sure to spend the time analysing the data you need to make the decision, as opposed to sifting through the troves of data available to you and the infinite ways to analyse it.

"One of the tools that we advocate as part of the book is IWIK – I Wish I Knew. To guide the discussion all the way from top leadership ask them: 'What is it that I wish I knew in order to make the decision.'"

This and other tools are part of a framework and a set of tools that, Prof Netzer calls Quantitative Intuition. This means combining intuition together with information and striking the right balance between the two

"Combining quantitative skills with intuition may sounds like an oxymoron at first, but in today's data-rich environment it is the right way to make efficient and effective decisions."

Prof Netzer will be one of the keynote speakers at this year's Melbourne Business Analytics Conference on 11 October. To see the full list of speakers and register, visit the conference webpage.

Prof Netzer's latest book Decisions Over Decimals: Striking the Balance Between Information and Intuition is available now, published by Wiley.

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