Dos and Don'ts

The Big Data Gray Area: Dos and Don’ts

5 minute read
Joanna Schloss avatar

I’ll be the first to admit it: If you’re looking for a perfect answer to the big data ethics dilemma, you’re not going to find it in this article — or any other article, for that matter. That’s because there is no perfect answer. If there were a perfect answer, an obvious answer, or an easy answer, we wouldn’t be facing a dilemma — there would be no gray area.

Perfect solution or not, in business, as in life, there are certain things we should and shouldn’t do. There are certain general standards to which we should hold ourselves and our companies, regardless of the fact that there may be no laws requiring us to do so, or that the principles behind them may not apply 100 percent of the time. There may be no perfect solution, but it is our responsibility to try nonetheless.

Big Data Ethics Dos and Don'ts

With that in mind, and with the full understanding that these recommendations are anything but perfect, here are what I believe to be the dos and don’ts of big data ethics:

Do work hard to become a data-driven organization. Considering the technological advances that have made it both possible and (perhaps more importantly) affordable to more easily and efficiently manage, integrate and analyze data, there’s really no reason for any organization not to embrace a data-driven culture. Large or small, organizations that wish to remain competitive in the modern business world must have a willingness to share information and to allow decision-making to be guided by the insights and behavioral patterns hidden within big data.

Don’t always assume data is right. As we just established, there’s no question that it’s time for businesses to start embracing the power of big data analytics. But that doesn’t mean that logic and intuition should be cast aside completely, and it most certainly doesn’t mean that companies no longer need to exercise common sense about what is and isn’t ethical when it comes to big data. Just because you have collected and analyzed data, you needn’t follow those findings blindly. Any number of issues, including poor data quality, a lack of metadata or an unreliable data model, can cause data to steer you in the wrong direction. To ensure no ethical lines are crossed, develop a culture in which analysis and intuition both have a place at the table.

Do secure explicit consent before adding people to your prospect database and delivering them content or promotional offers. The good news is that most companies that actively market to prospects with content and promotional offers are already doing this, if only because they’re usually required to by law.

Don’t assume that having customer consent means there’s no longer an ethical line in the sand. Ignoring the fine print for a second, when people give you consent to contact them they’re giving you consent to do so with relevant and appropriate content and offers. The next person who consents to being spammed or offered products or services that are completely irrelevant to her will be the first. In other words, just because you’ve received consent by way of an opt-in, doesn’t mean you don’t have to exercise good, ethical judgment about how often and with what offers to approach customers. Consent or not, if it feels as if you’re reaching out to them too often or with too wide a range of offers, you probably are.

Do try to offer personalized experiences to your customers. This, at its core, is really what big data is all about — the opportunity to better understand customers and their behaviors so that you can better interact with them and better meet their needs. This is what modern-day consumers want and expect from the companies with which they do business.

Learning Opportunities

Don’t assume that personalization is a science. You cannot automate personalization. Just because you have snippets of data about someone doesn’t mean you know them. The fact that someone asked for a car insurance rate quote one time, three years ago, doesn’t mean they are perpetually in the market for car insurance. Focusing on all data rather than just snippets of data will help you better personalize your offerings.

Do be aware of the laws and requirements around big data that govern your industry vertical or business sector. No elaboration needed here: If there are laws governing the use of data in your industry, learn them and follow them.

Don’t act as though your responsibility stops with the law. For starters, not every industry even has rules governing the use of data. And even when they do, what’s legal and what’s ethical are more often than not different things. Don’t rely on regulations alone, and don’t assume that your customers won’t be upset by ethical breaches as long as the law wasn’t broken. Put your own rules in place to govern your organization’s use of data based on what’s in the best interest of your customers. In this case, they dictate they law of the land, and if they’re not happy with the way their data has been used, you shouldn’t be happy with it either.

Do be diligent about collecting, integrating and understanding data. Nothing is more important to the current and future success of your company, and none of the aforementioned cautions are meant to imply otherwise.

Don’t act on information just because you have it. This is an edict that should be passed down to everyone in any organization who in some form or fashion interacts with data. Not all data is meant to be acted on. Companies should only use data for the explicit purpose for which it was provided. In other words, if you know something about a customer, but that customer never intended for you to know it, then don’t act on it. No gray area required.

Creative Commons Creative Commons Attribution 2.0 Generic License Title image by  *_Abhi_* 

About the author

Joanna Schloss

Joanna Schloss is Senior VP of Product Marketing at SmartBear and has more than 20 years of experience successfully transforming and evolving both global 500 companies and startups. She has extensive knowledge in big data analytics and business intelligence and has launched a variety of tools and applications for various companies, including Confluent, IBM, and Oracle, among others.