Making Data Work For You: 6 Steps To Limit Bias In Your Big Data

You can’t succeed in business today without the use of big data. In fact, I’ve long been a believer that data-backed decisions are essential for effective digital transformation. Not only does data help you learn a lot more about your customers—often in real time—it can also help speed up otherwise slow and taxing approval processes. Together, that creates a better customer experience as you’re able to deliver more of what your customers want, when they want it. But this only works when you use data correctly and limit bias in your big data.

Many legacy hangers-on refuse to believe that data could ever replace their experienced gut in terms of projecting sales trends and customer demands. But one group of people we don’t usually talk about is the data opportunists—those who use data to back a decision they’ve already made. And believe me, that’s a big problem.

We’ve all seen how scientists can aim their experiments toward specific results simply by strategically orchestrating the audience they choose or the questions they ask them. The same goes for big data. In this transitionary period between legacy and revolution, it can be tempting to use our gut to make a decision and set up data streams we know can prove it. That’s not only short-sighted, it’s setting your company up for a lot of lost time and revenue. So how do you prevent the human tendency to “use” data to their advantage? How do you limit bias in your big data? Consider the following.

• Know your purpose. What are you trying to solve or prove or find an answer for? Hint: it isn’t to prove how right you are about your industry! It’s to find the best solution to the problem you’re trying to solve, be it a new product or an improvement to customer service delivery. There’s no time for ego in purpose. Use the purpose you establish to guide your thinking throughout the data gathering process.
  If you want to know the who to target with your lead gen efforts, think about collecting data on the leads that have converted. If you want to know who to target for online marketing, think about collecting data on who has gone to your website. Limit bias in your big data by not letting it cloud your judgement. You might be surprised to learn who is actually using your brand.
• Develop your own scientific method. When scientists do experiments, the following specific steps known as the scientific method. What are your fail safes within your company that ensure people aren’t using data to their advantage? What are the standards you use to ensure that data is always used to further the company’s purpose, rather than an individual team’s agenda? Know these before you put a data plan in place.
• Know the resisters. As noted above, not everyone is in favor of data-backed decision making. If you haven’t yet built a data-backed culture, you may already know who your detractors in data-backed decision-making may be. Know them, understand them, and be sure to take their biases and agendas in mind when receiving their data-backed solutions.
  It’s important to work with your employees and help them integrate data into their decision-making process. Data is not scary and doesn’t have to be if you take the time to explain it. Limit bias in your big data by making sure everyone is on the same page.
• Create a hypothesis, but don’t cling to it. Yes, every good scientist starts with a problem and a potential hypothesis they believe will solve it. But the best scientists also admit when their hypothesis was wrong. Whatever you think you know, always be open to accepting otherwise. What’s more, if you’re in a position to create a culture where failure is never penalized, even better. It will go a long way to ensuring people aren’t afraid to admit when their hypothesis is wrong.
  Say you own a motorcycle dealership and think that your most likely customers are middle-aged men, but the data shows it’s millennials. Don’t be afraid to admit that the hypothesis was wrong. You should embrace the fact that it was wrong! You just learned something important for your marketing efforts that’s going to save you time and money and help your bottomline—who doesn’t like that?
• Don’t jump to conclusions. Many of us who have been working in certain industries for many years have an inner sense of how things “tend to go” in our market. Limit bias in your big data by putting these ideals on the back burner and brainstorm potential ways the situation could play out. Sometimes simply stepping back from the situation—or asking someone with a bit less experience in your lane—might yield some unexpected options.
• Accept the answers. Well—usually. While I do love big data, I know there is no substitute for good old-fashioned common sense. If the data really doesn’t fit, question it. Ask deeper questions. It’s possible the hypothesis was wrong, and it’s also possible the data wasn’t pulled correctly, or the correct data wasn’t pulled at all. In short: read the report. Understand how the answer was developed. A second pair of eyes never hurts anyone. It sounds like a catch-22 but to limit bias in your big data you have to be aware that bias is a possibility.

Big data is only getting bigger, and there will always be a tendency for humans to err on the side of ego, rather than algorithm. Now is the time to recognize where in your organization you are at risk for using data to support your already-made decisions—and to use some of the tips to turn that ship around.

The original version of this article was first published on Forbes.

Futurum Research provides industry research and analysis. These columns are for educational purposes only and should not be considered in any way investment advice. 

Author Information

Daniel Newman

Daniel is the CEO of The Futurum Group. Living his life at the intersection of people and technology, Daniel works with the world’s largest technology brands exploring Digital Transformation and how it is influencing the enterprise.

From the leading edge of AI to global technology policy, Daniel makes the connections between business, people and tech that are required for companies to benefit most from their technology investments. Daniel is a top 5 globally ranked industry analyst and his ideas are regularly cited or shared in television appearances by CNBC, Bloomberg, Wall Street Journal and hundreds of other sites around the world.

A 7x Best-Selling Author including his most recent book “Human/Machine.” Daniel is also a Forbes and MarketWatch (Dow Jones) contributor.

An MBA and Former Graduate Adjunct Faculty, Daniel is an Austin Texas transplant after 40 years in Chicago. His speaking takes him around the world each year as he shares his vision of the role technology will play in our future.