Here's the coolest tech we saw at DataBeat 2014

Yes, we’re all exhausted after our DataBeat 2014 conference wrapped up Tuesday night. But we’re also excited.

A few startups launched impressive tools. And a new integration startup announced hints at the future of cloud software.

[aditude-amp id="flyingcarpet" targeting='{"env":"staging","page_type":"article","post_id":1478051,"post_type":"story","post_chan":"none","tags":null,"ai":false,"category":"none","all_categories":"big-data,business,cloud,enterprise,","session":"B"}']

Perhaps most important, DataBeat showed how the conversation about big data has evolved. We’re no longer talking so much about how much data companies have and what cool stuff they can do over time. Now, it’s more about how companies can achieve business goals — like preventing fraudulent money transfers, making sure companies finish filing their taxes, using humans sparingly in the customer-support process. Ultimately, moves like that can help companies can cut down on expenses, bring in more revenue, and keep customers (both businesses and consumers) satisfied while not getting overwhelmed with an increasingly wide variety of data.

Few companies have arrived at that point, but we think that’s the future — or at least companies should start asking about growth potential when they evaluate big data software.

Get me analysis, stat!

Tools that startups debuted at the conference fall in with trends we’ve written about in recent months: getting data ready for analysis in less time (and by extension leaving more time to analyze data) and providing easy-to-use services that enable more people inside a company explore data.

Tamr can do the former. It uses algorithms that start out with good intuition for how to combine data sets and get smarter over time. But it pairs that computational intelligence with input from people at a company who know the most about specific data sets.

Analytics expert Tom Davenport believes such technology could enable big-data analytics that draw on lots of sources.

When it comes to actually taking a look at data and charting it out, people now have a nifty new tool out.

Wes McKinney, the creator of the popular Python-based Pandas open-source library for data analysis, debuted software for visualizing data from his startup, DataPad. It can generate graphs based on large data sets, and it’s possible to change the graphs quickly with a few clicks. Then again, power users can assemble more complex visualizations of data. In other words, different people at a company can get different things out of DataPad.

Building on existing tools

Some startups have devised whole new tools to help businesspeople answer questions. Others have decided to improve existing software.

[aditude-amp id="medium1" targeting='{"env":"staging","page_type":"article","post_id":1478051,"post_type":"story","post_chan":"none","tags":null,"ai":false,"category":"none","all_categories":"big-data,business,cloud,enterprise,","session":"B"}']

That’s the case with machine learning startup Wise.io. It’s stuck its artificial brains inside service-desk software from Zendesk in such a way that customer support can focus on the problems that require assistance from people. That way, people can answer emails or jump on the phone to provide nuance and detail, and they can resolve more basic issues by pushing some buttons in software. The addition could save companies time and money.

Now with Wise’s technology, Zendesk makes predictions based on previous support tickets. But if predictions are wrong, Zendesk users can make manual changes, smartening up the software for future use.

Wise cofounder and chief technology officer Josh Bloom unveiled the startup’s Zendesk integration during VentureBeat’s Innovation Showdown competition.

[aditude-amp id="medium2" targeting='{"env":"staging","page_type":"article","post_id":1478051,"post_type":"story","post_chan":"none","tags":null,"ai":false,"category":"none","all_categories":"big-data,business,cloud,enterprise,","session":"B"}']

Startups like Context Relevant have been getting funding to provide predictive analytics to help salespeople focus on the best sales leads, and such tools should become more popular in the years to come. At the same time, integrations of smart-data analysis and text-analytics tools — like Wise.io.’s pairing with Zendesk — only make sense, too.

Growth from Hadoop

Meanwhile, several hot consumer-focused companies and longstanding financial-services companies spoke at DataBeat about the advantages of augmenting their IT architectures with tools for storing and processing big and messy data sets.

Representatives of Airbnb and Pinterest talked about how their companies have found ways to increase how much people use them — a major achievement for consumer web favorites like them.

Pinterest data infrastructure team member Jie Li said that by analyzing data in Amazon Web Services’ RedShift data warehousing service, the social bulletin-board company boosted sharing rates by 150 percent and increased Pinterest use outside the U.S. with content it thinks would pair well with people.

Airbnb head of data science Riley Newman explained how the home-renting company invested time in cleaning up data for analysis and later managed to cut down on customer support inquiries by turning input into product enhancements that addressed those concerns. Ultimately, that means customers are more satisfied — and might use the site again and again.

[aditude-amp id="medium3" targeting='{"env":"staging","page_type":"article","post_id":1478051,"post_type":"story","post_chan":"none","tags":null,"ai":false,"category":"none","all_categories":"big-data,business,cloud,enterprise,","session":"B"}']

Publicly traded enterprises also have seen perks from analyzing data.

Money-transferring company Western Union combines a wide variety of data and processes it all in real time. As a result, Western Union can increase the accuracy of its fraud-prevention efforts, weeding out the acceptable transfers from the scams. Information like location from a mobile device and social-media connections can come in handy, said David Thompson, Western Union’s chief information officer and executive vice president of global operations and technology.

As a tool for storing lots of different kinds of data, Hadoop open-source software will become more popular inside data centers worldwide. That’s according to Ron Kasabian, Intel’s general manager of big data solutions. Hadoop can help in many industries, including advertising, whereby businesses can predict consumers’ preferences after considering many data sources.

[aditude-amp id="medium4" targeting='{"env":"staging","page_type":"article","post_id":1478051,"post_type":"story","post_chan":"none","tags":null,"ai":false,"category":"none","all_categories":"big-data,business,cloud,enterprise,","session":"B"}']

In financial services, for instance, more companies are showing interest in analyzing large quantities of data — not just Western Union. Charles Schwab, for instance, has begun using data to identify its customers and then get them to spend more money with its services, said John Carter, the company’s big-data senior vice president.

Finance and tax software provider Intuit has registered revenue growth as a result of using Hadoop, too. Engineers noticed that use dropped off at one particular spot in the process of filing for tax returns in the company’s online TurboTax software. They came up with a system for providing information to customers rather than asking them to stick in a tedious string of letters and numbers, and the change increased conversions, Intuit’s Chris Chapo said.

As cases of data coming to the rescue proliferate, open-source software like Hadoop as well as proprietary tools like Wise stand to become more popular. And as that happens, growth could become more common — leading companies to look for the next set of technologies that can enable growth.

To read all of our DataBeat coverage, as well as posts we’ve published in the run-up to the event, click here.