10 industries that are changing because of data

The migration to big data is quickly becoming a race: keep up or shut down. Whether you’re a standalone brick-and-mortar shop or the next Silicon Valley unicorn, there’s an emerging urgency to adopt big data for all of your major business decisions. But it’s not just because the information is valuable, rather the information is literally changing the way industries operate.

Last October, Accenture published a groundbreaking study in partnership with GE that set much of this urgency into motion. Among the key takeaways, this report found 89% of respondents agreeing that if companies don’t adopt big data as part of their strategies within the next year, they risk losing their market share, and their momentum.

While this study focused mostly on the Industrial Internet and healthcare, the conclusions were not limited in their application.

Almost a year later, here are ten industries that are being radically transformed by the adoption of big data. Even if yours isn’t on this list, take heed of companies within your circles who resist this very necessary change.

The fact is: 84% of enterprises see big data analytics changing their industries’ competitive landscapes in the next year.

So even if your industry isn’t on this list, take heed of companies within your circles who resist this very necessary change.


10. Energy

Technology and the Internet of Things are already totally revolutionizing the energy industry. On a micro level, look at an appliance like Quirky’s Aros. It uses smart data to learn your habits, and keep your home cool and regulated at a temperature you prefer, without wasting nearly as much energy.

On a macro level, there are smart grids. Pilot programs popping up across the country are aggregating massive amounts of data on how we use energy, which can help us to produce smarter habits, reduce carbon emissions, and develop ways to reduce energy where its use is unnecessary.   

9. Real Estate


Real estate drives the market. In 2008, we saw the very negative side effects of that, but it also works on a very small, personal scale: real estate is a major personal investment that can be extremely lucrative, or a devastating blow—and much of that is just calculated risk. If only there were a way to curb that risk…

Enter big data. There are three different levels at which big data in real estate can both improve quality control and reduce risk. First, it changes finance. Using data, you can assess the quality of the building and longevity its structural integrity. Is it financially reliable? Do the required loans make sense?

Second, it produce smarter sales. You can gauge a larger percentage of customer interest at the same time, making more intelligent offers and closing deals faster. And third, it improves building management. Find and make repairs faster, incorporate smart-home technology, and reduce liability for accidents.

8. Insurance

The insurance industry has been more in the spotlight in the past five years than almost ever before. That’s due in large part to a presidential piece of legislation that you avoid bringing up at parties—but good or bad, it’s brought attention to the system’s inherent flaws and complexities.

Insurers have to look at policies from almost every angle: what’s best for the provider, what makes sense for the consumer, how to appeal to the broad consumer market, and how to reduce overall risk. In that regard, the insurance industry is the perfect candidate for a big data overhaul.

And it’s already happening: big data is being used to provide faster analysis for claims, discovering ways to provide more targeted programs for individuals, reduce fraud, and even provide methods to keep patients healthier.

One auto insurance startup, MetroMile, offers “pay as you go” insurance, where drivers pay by the mile. The company claims that this saves low-mileage drivers an average of $500 a year.

All in all, big data analysis in insurance can radically change a system that’s already being radically changed.

7. Music


You’ve probably read a lot about the gains and failures of companies like Spotfiy and Tidal in recent weeks. While music sales have plummeted in the last decade, artists and record companies are scrambling to find ways to make music profitable for everyone—including artists—without just jacking up the price of concerts and forcing musicians to go on global stadium tours 364 days per year.

The problem is they haven’t quite figured it out yet. Taylor Swift refuses to be a part of Spotify until they can figure out how to pay artists a substantial wage for their streamed music—and nobody wants to pay Jay Z $20/month to listen to “Hi-Fi” music they could listen to on Spotify for free.

One potential solution lies in advertising partnerships. Big data collected from studies on social media—specifically instagram—show how branded partnerships between brands and artists, namely photographers, can produce lucrative results for both while still keeping the artistry intact.

Because social media and music streaming sites are already so closely linked, the data for listening demographics is readily available, and record labels could—and have—begin to use it to make strategic partnerships with brands that could pay their artists for branded music and music videos.

6. Aviation

Big data is busting at the seems with information on the aviation industry—specifically commercial air travel. With the amount of data collected on commercial flights annually—even daily—there is plenty to be utilized that could is radically transforming the way airlines can make itineraries, create incentives, and even increase sales.

To start, there’s quality control. The massive amounts of data collected on flights, a study from IBM argues, can radically reduce the costs that airlines spend on equipment and repairs, which will undoubtedly make them more competitive, helping to reduce ticket costs, ultimately driving sales. Furthermore, flight data can help to reduce time and delays, as well as improve baggage handling, and even generate smarter recommendation for future travel and customer retention.

5. Telecom

If you watched the documentary Citizen Four about the NSA whistleblower Edward Snowden, you probably already have some idea of how much data is associated with the telecom industry. Using what’s called metadata, it’s plausible that a simple Instagram post can give away your location. But in this case, that’s not why data is transforming the telecom industry.

T-Mobile combined all of their customer datasets, broken up into six categories or zones, to analyze the full customer experience. Ultimately, their analysis lead to a 50% decrease in churn. In short, big data helped T-Mobile figure out what influences a customer’s decision to stay with or leave their telecom service—and they adapted.

4. Consumer Goods

It doesn’t take much to show how big data can radically transform the consumer goods industry—in fact, there’s just two words: “Supply,” and “Demand.”

You’ve probably noticed that most coffee shops are converting their POS systems from clunky old-timey registers to those suave looking iPad POS systems like Square. Square is a small-scale credit card processing system, which means its aggregating huge amounts of consumer data for brick and mortar shops—and that data is easily available for the standalone shops themselves.

This data translates first and foremost into smarter buying for fluctuating goods and supplies. It can also prepare stores more for influxes of buying, produce a better understanding of consumer demographics, and help shops to run more efficiently. Bottom line, big data produces analytics that are better for everyone’s bottom line.

3. Hospitality

The hospitality industry will almost never die—no matter what shape it takes. People will always travel, people will always want to go on some kind of vacation, it’s just a matter of how, where and when. Some companies rely too heavily on this notion, ignoring changes that the sharing economy, namely AirBnB, brings. But then there are companies like Duetto, who are really starting to make it competitive again.

For hotels using Duetto, they get a massive amount of data on consumer habits that tell them manage distribution, adapt their pricing, and even forecast demand. People are constantly traveling, so there naturally should be a very heavy bank of data available to hotels, and Duetto makes it extremely accessible and easy to analyze.

2. Gaming


The gaming industry has absolutely exploded in the last decade. As Halo 5 gets released this fall, it joins a series that has produced $3.5 Billion in global sales for Microsoft. The series alone is largely accounted for the Xbox One’s marketability as it struggles to compete with Sony’s PlayStation 4.

But it’s not just these two consoles that hold the gaming world captive. Everything from World of Warcraft to Steam has produced a vibrant, lucrative industry that has billions of loyal fans. Now, the gaming industry has turned to big data to further improve the experience. We’ve come a long way since NES came out a little more than 30 years ago.

Social connectivity and massive multiplayer online experiences leave incredible amounts of data that the industry can use to improve the overall customer experience. With expansions and instantly downloadable content and updates, its easy to take feedback and turn it into almost immediate changes for an better in-game experience.

1. Data Storage

Finally, there’s data storage. With the amount of data out there, and the way that it’s so dramatically changing nearly every industry, there needs to be a way to store and warehouse data that doesn’t require massive servers or clunky CRM programs.

And that’s where a program like Box comes in.

They’re hoping to completely overhaul the way that businesses store and use data, making it much easier for companies of all sizes to access and benefit from their newly found data.

Whatever industry you’re in, the landscape has changed. Find ways to break down the barriers between your industry and its valuable data, or find yourself becoming one of the companies that falls behind.

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