As we ramp in to 2016, it’s worth looking at how people are using big data to do what they do. It’s exciting. From these real-life cases you can learn a lot about what big data is up to—you’re getting a peek into the workings of companies who are using this stuff in real-time, in different ways. From this peek, you can draw some conclusions about trends for 2016, about how industries are going to be using big data.

But first, let’s talk about data use cases. According to Appnovation, you can use big data for:

  • Recommendation engines, which provide personalized product suggestions based on user behavioral data and profiles (Netflix, Amazon, Facebook, and LinkedIn all famously do this)
  • Risk modeling, which uses transactional data to tell financial institutions about vulnerabilities, and prepares them for potential problem scenarios
  • Fraud detection, which analyzes customer behavior and transactional data to determine the likelihood of fraud
  • Marketing analysis, which tells enterprises which customers are likely to leave for a competitor, as well as different ways to market to consumers based on behavior
  • Network monitoring, which analysts employ to determine the efficiency of networks, such as cloud servers and Virtual Private Networks

It’ll be fascinating to see the ways in which company practices align with the above uses. It’ll also be fascinating to see how they don’t align—how creative and innovative these different companies are with their uses of big data.

1. UX (User Experience) Marketing

TSheets is a cloud-based time-tracking software startup. If you look at the TSheets Time Tracking site, you’ll see something interesting. Scrolling across the screen, there are continual updates on how users are employing the software. We’re getting to view data a company could look at in terms of marketing analysis. But TSheets is doing network monitoring to enhance the UX of their website.

What’s the intent here? How does this enhance UX? According to Intelliconnection author Sandra Donovan, UX is a balance between context, user needs and behavior, as well as content. Good UX makes the site valuable to the user. By showing the time tracking data, TSheets intends to increase value, to show how people are constantly using their software, to provide context, and to connect the website user to the software user through content.

2. Brand Recognition

This is an oldie but a goody. Morton’s The Steakhouse is a Chicago-based restaurant. A regular customer was flying into Newark and (jokingly) tweeted he’d love to have a meal from Morton’s at the airport when he arrived.

Morton’s used their database to find out he was a regular and to see what he normally orders. They figured out what flight he was on and sent a waiter in a tuxedo to deliver the meal. By doing so, they made the news, but they wouldn’t have been able to pull this off if it weren’t for the type of behavioral data that companies use for recommendation engines. Talk about personalization!

3. Predictive Human Resources

Fomerly Evolv, Cornerstone OnDemand is a configurable cloud platform that uses predictive analytics to help companies decrease turnover. According to CEO Max Simkoff, Cornerstone “takes hundreds of millions of disparate data points on potential or current employees and utilizes these to make recommendations that ensure better job fit and progression.”

Cornerstone OnDemand analyzes data points on things like gas prices, social media usage, and unemployment to help predict who will be the best fit for a job. This also helps companies predict turnover and solve morale issues. Companies such as Xerox and AT&T have seen a $10 million impact on Profit and Loss with the software.

Cornerstone’s approach to big data is a combination of the recommendation engine and marketing analysis. Employers receive recommendations on potential employee job fit, along with analysis of current employee behavior, which helps determine job satisfaction and the likelihood of turnover.

4. Health Risk Analysis

Of course an insurance company such as Aetna would have a lot to gain from using big data to help patients be healthier. But so do the patients. Aetna does something akin to risk modeling, in which it looks at data regarding “ineffective treatment plans or harmful suggestions.” It also looks at what has worked for treating specific types of cancer.

Doctors can use Aetna’s data to do analysis, based on population groups and results. They can then use this analysis to inform treatment, whether it be the likelihood of a medication working or the likelihood of it clashing with another medication.

For patients with metabolic syndrome, Aetna compared one patient’s metabolic data with that of 36,000 other patients. Then, doctors were able to personalize treatment, to the extent that, “90 percent of patients who didn’t have a previous visit with their doctor would benefit from a screening.” Aetna showcases an innovative combination of risk modeling and the recommendation engine.

5. Ecommerce management and marketing

Urbio is an ecommerce startup that didn’t have an IT department to help them figure out what to do with data. They had their shop going and had plenty of data available from social media, Shopify, the Paypal sales app, and Google analytics. What they needed was a marketing analysis solution, one that could present the data in such a way as to facilitate actionable insights.

Based on a recommendation from Shopify, Urbio’s VP of Operations Blair Stewart plugged in two solutions at once: DeepMine and SumAll. DeepMine provides valuable ecommerce information on SKU and sales rates. SumAll does the magic of combining all the data from diverse platforms, everything from social media engagements to Google analytics information to sales data, enabling Stewart to visualize activity and takeaway insights.

By combining two platforms that specialize in curating data, Urbio was able to do marketing analysis without an IT department. Unfair for IT folks? Well, for Urbio, hiring more people wasn’t an option. Their use of cloud solutions aligns with a trend, as more and more startups turn to SaaS to do big data jobs entire departments used to do.

Originally published on Smart Data Collective