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A means to an end

by Ruben Ugarte
Indian Management June 2021

Busting the following myths:
Myth 1: Data is the most important resource
Myth 2: Collecting data is the hardest problem
Myth 3: Everyone wants to be data-driven
Myth 4: Machine learning is the future for all companies
Myth 5: Technology is the trickiest part of any data strategy
Myth 6: Facts are clear, and everyone can see that
Myth 7: Opinions without facts are not welcome here

Sometime in the last 100 years, Edwards Deming uttered a quote that will be used constantly when talking about data: “In God we trust. All others must bring data.” Companies and executives use it to justify why they must keep investing in data. People use it to argue against opinions stated without any evidence to back them.

I think Edwards Deming had the right intention with his statement, but the essence has been lost in today’s world. Data is important, but it is not the most important thing for a company to focus on. Data is a lever that can make other things easier, not the end goal in itself.

Our data-driven world has swung too far to one end of the pendulum, and we need to correct it. I want to bust seven myths that companies cannot seem to let go of when it comes to data.

Myth 1: Data is the most important resource
The Economist dedicated an entire issue to describing how data is the new oil. It was, of course, talking about companies like Google and Facebook who have turned data into multibillion-dollar empires. For most companies, these are not role models. Almost everyone else is more about creating great products/services and delivering those to happy customers.

Data can help, but it is not your product. For Google and Facebook, data is their product. They want to capture as much as possible and then sell it through advertising. For other companies, data is merely meant to help support decisions. Strive to build datasupported cultures, not data-driven ones. Give yourself room to make decisions without data; it will not be the end of the world.

Myth 2: Collecting data is the hardest problem
After years of technology evolution, data collection is, in fact, the easiest challenge for companies. Everyone is drowning in data, and the onslaught isn’t stopping. Knowing about your customers is easier than ever. This is why the conversation has shifted from “How do I collect more data?” to “How do I store it and access it rapidly?”

Conferences now focus more on data warehouse, data lakes, and anything that could help companies obtain more insights from their ever-increasing pile of data points. The ease of collection has made other problems worse: we cannot process data fast enough, we get easily overwhelmed, and we cannot separate the truth from the noise.

In your company, but the focus should be on how many insights you’re regularly learning. Whether those insights come from billions of data points or a handful, it does not matter. How is your data helping you change your behaviour?

Myth 3: Everyone wants to be data-driven
There is an idea that everyone wants to be datadriven. Just give them the ability to make their own reports and dashboards, and everything else will take care of itself. In my experience, this has not been the case, though, perhaps I operate in a different world.

I come across many people who are not comfortable with basic statistics, probabilities, and numbers, in general. It reminds them too much of math class back in school, and they do not want to revisit those times. Some people want to have the insights and not have to show their work to the teacher.

Your company should be planning for all these use cases. Make it easy for people to export data into CSV and other raw formats and make it easy for people to get insights without running complex formulas. Data democratisation is about reducing the barriers for people to interact with the data, but nothing says that everyone needs to become a math whiz.

Myth 4: Machine learning is the future for all companies
There is a lot to be said about machine learning and the world of AI. For starters, the world is not as new as we think. We have been working on AI since the 1950s, and even then, we were apparently only 10 years away from self-driving cars.



The world of machine learning did go through a significant shift in the last 20 years as cloud computing became accessible at scale. The real use cases are hiding behind all the hype that follows whenever anyone talks about AI. Generally speaking, anything to do with pattern recognition, fraud, and analysis is a good challenge for machine learning.

Focus on how these use cases will help you achieve strategic goals. Why go through all the effort of creating models if they are not going to help with revenue, market position, reputation, or something else that is tangible? Save your research energy for problems worth tackling.

Myth 5: Technology is the trickiest part of any data strategy
Most of the prospects who reach out to me come because of specific technical questions. Should we use vendor X? What if vendor Y does not work for us? I understand their frustrations. There are hundreds of options in any given category, and they are all quite similar. Making the wrong choice seems like a huge waste of time and resources.

However, do not confuse volume with priority. Sorting through vendors can be tricky, but it is also the last step in any data strategy. Start by figuring out your people's role with data and how you will convert data into insights (process). Then, you can see what role technology can play in helping you with the first two items.

Going through this process actually makes technology selection easier because it narrows down space. The choices left will match your company’s unique makeup and approach.

Myth 6: Facts are clear, and everyone can see that
“Show them the facts” seems to be the rallying point during the COVID-19 pandemic. Look at the case numbers and you will see everything you need to know. As it turns out, everyone interprets facts differently, and the same happens within companies. I have been in board meetings where facts are presented, and executives extract different lessons than expected.

Any fact will go through a psychological filter based on our experiences, biases, and desires. Your data strategy should take into account how the human components change how facts are perceived. Do not assume that everything is black and white.

Myth 7: Opinions without facts are not welcome here
I respectfully disagree with Edwards Deming. I don’t think you can discount opinions that do not have as much evidence behind them. If an executive with 30 years of industry experience suggests an idea, are you going to dismiss it because of poor evidence? Isn’t his/her experience the data?

There is a pushback against things like intuition and gut feeling, but I think they play a role in how we make decisions. In fact, I think one of the biggest benefits of data is our ability to train our intuition. Ensure that your company can allow opinions, or you will lose a big chunk of experience and ‘hidden data’.

Data is playing a huge role in how companies innovate; continue to invest in it but do it delicately. Do not get caught up in the hype and try to put the wrong expectations on the data. Remember that data is just one part of how we make decisions, and it is not the end-all for most companies. Perhaps it is time for us to increase our trust in ourselves and our opinions.

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