14 Myths That Can Derail Your Business’ Data Analytics Efforts

In today’s digital marketplace, data has taken its place as a valuable asset of companies across industries. With the information gleaned from collected data, businesses can improve their products and services, enhance customer service, adjust strategies and more. 

While data analytics is an important function, it’s not necessarily an easy one. Complicating matters are the many myths that have arisen around the collection and analysis of data, which may lead companies down the wrong path. Below, 14 industry experts from Forbes Technology Council discuss some of the most common data analytics myths business leaders need to be aware of.

1. The data will confirm what I already know.

If we truly want to understand how our business is performing, then we have to allow the data to do the talking and be willing to let go of our emotions (or ego). It’s even more helpful to believe our assumptions are wrong until the data proves them right. – Kathy Keating, TextUs

2. We can’t do this without a data scientist.

When implementing a data analytics program, many business leaders assume you need to have a data scientist on your team. In this day and age, that’s not the case. There are plenty of tools and software systems that enable anyone with beginner or intermediate IT skills to plug and play their own data platform so they can visualize KPIs in one central dashboard. – Marc Fischer, Dogtown Media LLC

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3. Following where the data leads is scary.

The myth is that data analytics is scary. Few leaders are bold enough to truly go where the data leads them. Instead, they act on gut instinct and look for data to justify their decisions. This is a learned behavior that stems from dealing with static dashboards, while the sacred language of SQL is available only to a select few. To democratize insights, data use must be ubiquitous and easy. – Sudheesh Nair, ThoughtSpot

4. Getting insights from data is simple.

It is a cliché that “data is the new oil.” While that’s true, getting value out of data analytics can be harder than finding and producing oil—unless one is focused on what to solve for. Data analytics is best done in the context of a business problem, and insights from the data need to drive a business outcome. – Çağlayan Arkan, Microsoft Corporation

5. The more data, the better.

More data is not always better data. Too much data can create signal-to-noise ratio issues. Almost every organization has a handful of vital metrics that are tightly aligned to performance. Successfully leveraging data analytics is finding, surfacing, managing and drawing insight from those vital metrics—that’s what ultimately sets apart a good business from a great one. – Denis Whelan, Projector PSA

6. Data equals knowledge.

Too many companies are pulling together data without knowing the business questions they need to have answered. Data teams are creating many metrics every month, but those who receive them often don’t know what the trends mean. – Laureen Knudsen, Broadcom

7. Survey responses are fully reliable data.

“Survey responses are all you need” is a common myth. Too often I have seen business leaders take survey responses as gold in analytics. However, just because a customer says they want to take an action or buy a product doesn’t mean they will do so. The same is true for most other fields. – Kevin Korte, Univention

8. All data is good data.

To trust the data you receive you have to understand where it comes from and why. Good data tells the truth about a particular area of your business—for better or for worse. That data is unimpeachable because it cannot be manipulated to fit a narrative or modified in any way. – Endre Walls, Customers Bancorp

9. Data must be 100% accurate to be useful.

One myth to address is that data must be 100% accurate or else it’s useless. While this is true for life-or-death use cases such as self-driving cars, in many cases we can glean useful insights even when the data is only 75% accurate. There are degrees of accuracy in data, and for many companies, it’s better to do something with “good enough” data than nothing at all. – Nicola Morini Bianzino, EY

10. Once you’ve set up the model, you’re done.

Some people believe that once you have deployed and tuned a machine learning model, your work is done. In reality, data continually changes. The world is dynamic; markets, behaviors and interactions change all the time, and some of this is reflected in data changes by the day, hour or even the second. You must feed analytic models with the most current and pristine data possible and continually tune them to changing data. – Dale Renner, RedPoint Global Inc.

11. The questions to be answered must be settled up front.

If you ask the right questions data will tell you everything. The industry tends to ask for specific types of algorithms in order to answer specific types of questions. To do proper data analytics you need to develop it as a science and have a method. Make sure to understand and evolve along with your data, and it will eventually show you the information behind it. – Edgar Escobar, Grupo ALTO

12. Data analytics is a destination.

People should remember that data analytics is a journey and not a destination. Data changes, and so do predictions and prescriptions. It is important to understand that having a continuous feedback loop built in will refine your analytics program. – Selva Pandian, DemandBlue

13. Knowing ‘the numbers’ is enough.

Some leaders lack context about their data—they focus on measuring instead of translating the data into something that’s meaningful to the company. Done right, data analytics includes installing a proper data culture and understanding why you have the numbers that you do. This helps a leader be more resourceful and deliberately guide the team—and ultimately, the company—toward having better numbers. – Diana Xhumari, Tegeria

14. Our data is unique.

One thing we’ve learned in working with major enterprise organizations is that ultimately, the vast majority of your data isn’t unique. There are knowable trends and patterns, and you should be leveraging them instead of wasting time trying to optimize things that don’t offer real value-add for your business. Figure out where you differ and focus your energies on those pieces of data. – Jason Cottrell, Myplanet