In a nutshell, Guavus is trying to make telcos — including “all tier-1 mobile operators” — smarter by letting them make sense of the data their networks are generating. Those companies are historically alright at using their demographic and billing data to improve marketing efforts, but they’ve been largely blind to what’s happening on their networks, Guavus Founder and CEO Anukool Lakhina told me during an interview a few months ago. However, he said: “The magic happens in marrying and infusing that network data with the demographic and billing data.”

To get a better sense of how Guavus does what it does, I suggest reading Stacey Higginbotham’s October 2012 interview with Lakhina. You can also watch his presentation from our Structure: Data event just last month.

Guavus has actually been busy lately. In January, it closed a $30 million funding round and bought mobile-analytics startup Neuralitic Systems less than two weeks later. When I spoke to Lakhina about that acquisition, he said the plan is to use Neuralitic’s marketing and application expertise to help customers automate business processes, promotions and other functions based on their newfound insights into what’s happening across the entire company.

Feature image courtesy of Shutterstock user Pavel Ignatov.

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If you visit the web site for Numenta, which was founded in 2005 but just recently emerged from stealth mode, you’ll see lots of images or neurons, synapses and dendrites, and lots of text explaining neurological processes. Don’t be intimidated. The long story short is that Numenta’s software, called Grok, is able to recognize patterns (e.g., temporal and spatial) from streaming data and then automatically build models that allow it to predict what will happen next.

The goal isn’t necessarily to be as intelligent as the human brain, but to be as fast as the human brain when it comes to processing data that Grok understands. People love to talk about “big data,” VP of Marketing Joe Hayashi explained, but “our mission is to help people act on fast data.” In Numenta’s largely machine-to-machine world, where the data half-life might be measured in seconds, the human-driven process of big data is just too slow.

“They can only go as fast as the data scientists can build models and really understand it,” Hayashi said.

The simplest explanation of Grok you’ll ever see

Grok, on the other hand, is continuously learning from every new data point that hits the system, and it’s always readjusting its models to account for any changes it sees in the patterns of data. Not only does this help it make predictions faster and more accurately, but it also helps Grok spot anomalies that could cause problems. Ideally, Hayashi said, the software will be part of a machine-to-machine system that makes decisions on its own, in real time, without human intervention.

For a customer such as EnerNOC, which helps energy suppliers operate more efficiently, Grok will help the company’s frequency-reserve service called DemandSMART optimally draw power from customers that are part of the program. Frequency reserve markets rely on a network of customers voluntarily (although for compensation) reducing power usage during peak times in order to ensure grid integrity. Grok could also help EnerNOC predict potential mechanical failures by identifying and flagging behavior it hasn’t seen before, or by discovering patterns that lead to failure.

Actually, Hayashi explained, EnerNOC is a really good example of where Numenta and Grok fit into the data-processing ecosystem. EnerNOC, like many Numenta users, already has a system in place for processing real-time data, but that system only lets the company see what’s happening now. Introducing Grok into the environment, will let them “know what’s going to happen,” he said.

All of Numenta’s detailed comparisons to how the brain works might be an impressive way to describe the technology, but it might also bury the importance of what the company is trying to do. As we’ll discuss at various sessions during our Structure: Data conference in March, the advent of ubiquitous sensors, webscale server farms and just an abundance of machines everywhere is generating more data, and at faster speeds, than human beings could ever hope to make sense of on their own. If we’re going to keep up, we’re going to have to learn to let software shoulder a lot of the analytical load.

Feature image courtesy of Shutterstock user pixeldreams.eu.

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In a world of streaming data and lots of it, Guavus’s strategy of analyze first, store later has been paying off. When I spoke with Guavus founder and CEO Anukool Lakhina in May, he said the company was already serving three of the top four mobile and wireline providers. They’re impressed with the platform’s ability to analyze data as it streams in off the network, meaning they can make insights closer to real time rather than waiting for data to write to a disk before querying it.

In the that old model, explained Lakhina — who used to work in the high-performance computing division at Sprint Labs (and who’ll be presenting at our Structure: Data conference in March) — companies would worry about storing the data first, but the queries never catch up to rate at which the data is flowing in. By analyzing the data as it inflows in off the network, carriers can do things like tell users their data usage in near real time or detect the source of traffic spikes. In the longer term, they could figure out customers’ activity around usage, or which apps (or types of phones) are responsible for the most data consumption, and try to build customized plans or pricing tiers.

And actually, Lakhina added, although Guavus is built to capture network data, ”most of our engagement takes place beyond the network.” By fusing that data with demographic and other user data, providers can do a good job segmenting customers groups around usage patterns.

Ultimately, mobile providers and anyone else generating streams of data off their networks, while also serving customers, has to come around on the idea that they need to get more focused on that data. “Our customers are using data to optimize their businesses and delight customers,” Lakhina said, but that means breaking the legacy mold of capturing data, transporting it to a central location and then finally worrying about analyzing it. Rather, they have to “collect [data] once, then analyze many, many times” at every step along the way.

Feature image courtesy of Shutterstock user PunyaFamily.

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DataSift is more than just a collector of Twitter data, however. It also takes in data streams from dozens of other web and social media sources, and can handle corporate data, as well. And the company’s real value comes in the analytics it applies to all that data, letting users filter and correlate across myriad different factors.

Here’s an interview I did with DataSift Founder and CTO Nick Halstead during our Structure: Europe conference in Amsterdam last month, in which he describes how DataSift built an infrastructure capable of handling so much real-time data and how companies can use such data effectively.

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Zoomdata Founder and CEO Justin Langseth started Zoomdata on the premise of building a company that envisioned BI free from decades of legacy baggage. In 2012, that means abandoning the desktop and designing for tablets, and taking advantage of the nearly unlimited computing power available in the cloud and even on our mobile devices. It also means designing a user experience so intuitive that users know how it works without ever really having to learn.

Just like someone can open up Google Earth and know they’re seeing the planet Earth, Langseth said Zoomdata users should open the app and say, “Oh, that’s my business.” And then they should be able to easily zoom in right where they want to go, using only their fingers. In a few swipes and pinches, Langseth said, users are soon uttering the business equivalent of “that’ my house, that’s my car, that’s my tree.”

It’s a pretty heady concept for a guy like Langseth who has been entrenched in the space for years, first at MicroStrategy in the 1990s and most recently doing a text-analysis startup, but he appears to have pulled it off thanks to the array of powerful components now floating around the web for free. Zoomdata is able to take data from anywhere — web apps, enterprise systems, Hadoop, email, you name it — and process it as it hits the system using an open source stream-processing engine called Storm. Once it’s processed, Zoomdata applies intelligence to figure out the best way to display that data visually and puts the result on the screen.

Data is like paint on a palette

That, Langseth said, is where the magic really comes in. “We’ve been thinking of data as kind of like paint,” he explained. The app takes many of the concepts from the D3.js project for creating HTML documents using data, but then makes them interactive and “lights them up with real-time data.”

The human interaction becomes a combination of watching a movie and finger painting. Combining data sources and sets by swiping your fingers is akin to blending colors from a palette. The interface comes with set of buttons for pausing, rewinding and fast-forwarding the visualization, too — because it’s a real-time engine, the data keeps coming and the visualization keeps changing until someone temporarily stops the flow.

Under the hood, of course, Zoomdata is a lot more complex than meets the eye. It’s all about scale, speed and huge amounts of data, Langseth explained.  The backend does all the work and only streams the data required at any given time, so as to save the processing load on the user’s device. If a user presses pause or rewinds, the system keeps processing new data while also letting the user interact with the older data unaffected. Zoomdata also supports historical data sitting inside databases and other data stores so that users can compare their real-time information against the past.

The future: Bigger screens, smarter visualization and stiff competition

As if all this doesn’t sound futuristic enough, Langseth’s plans to take the technology further. “There’s a whole bunch more intelligence we can add to the system,” he explained, referencing his plans to incorporate machine-learning algorithms that will make the system even better at choosing how to visualize the raw, often schemaless data it’s receiving.

He also likes the idea of big touch screens, like CNN Big Board big. Sometimes when he has his iPad display showing on this 50-inch office television to play music, people come in and just assume they can start interacting with it like a touch screen. “Not just being able to see it, but to touch it, really excites people,” Langseth said.

Of course, as with all companies trying to carve out their space in the lucrative BI market — including the newly cash-rich Platfora — Zoomdata will have to prove itself a worthwhile alternative to big, expensive legacy technologies. Langseth thinks the real-time, mobile nature of his company’s product will at least make it a nice complement to existing desktop-based BI tools for historical data. And like pretty much everything powered by the cloud and rendered on a mobile device, its simplicity might appeal to a lot of users who don’t need the price or complexity that comes along with much legacy software.

“Some people need 10,000 features,” he said, “but most people need 5 features.”

We’ll see if Langseth is right soon enough. Zoomdata is currently in private beta after development began in March, and the company hopes to keep refining the user experience and open it up for broader consumption next year.

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Deepak Advani, VP of predictive analytics at IBM, explained to me recently how such a vision might play out. Basically, it’s a matter of putting analytics tools to work for customer service reps without them ever having to learn a thing about data analysis. In such a scenario, customer service isn’t left trying to deal with a potentially upset customer while simultaneously trying to find a resolution to his problem because the analytics system has done much of the legwork already.

Knowing you’ve done, predicting what you’ll do

Once a call is initiated, the rep will have a screen full of charts and dashboards showing a customer’s history, preferences and propensities, based on what the system has been able to determine from previous calls and general activity on his account. Depending on the reason for the call, the system might offer a variety of possible offers specifically tailored to that customer. A customer likely to cancel service, or one likely to upgrade to an even more-expensive plan, will be offered whatever it is the system thinks will make them happy.

But predetermining propensities and offers is only the starting point; Advani says real-time analyis is a very real possibility, too. Whereas the information in front of a customer service rep going into a call would be based on historical data that has undergone some predictive analysis, reps could actually type what a customer is saying, which would let the big data system perform sentiment analysis in near real time and adjust accordingly. Such a system could be self-learning, too, Advani explained, learning as it goes what keywords are correlated with what actions and constantly rescoring customers’ propensity models.

Better analytics are good for business

Advani thinks wireless providers might be among the first to embrace such a data-driven customer service model because they finally realized that keeping their own customers might be more profitable than focusing poaching customers from rivals. Additionally, he said, having accurate models of likely customer behavior will let providers even more accurately target and time advertising and special offers.

And before anyone gets any bright ideas about trying to get determine what keywords will trigger beneficial results, Advani also said that the great thing about predictive analytics is that things change so fast. Keywords will likely always be changing as the system learns what’s what, he told me, so that strategy might not work even if someone actually knew how the algorithm looked just a few days ago. It might be a bit more useful to occassionally call a service provider and threaten to quit just to keep the system honest, but I have to think a track record of never having actually quit, and of actually always moving to bigger and better plans, might outweigh a few empty threats.

However, while a call center example is beneficial for explaining how such a system might operate, it’s far from the limits of what’s possible. Advani says IBM is already working on generating propensity scores in about 10 milliseconds to enable real-time experiences, and he said a next step is to start connecting time-and-space data with propensity models. Beyond determining what people might do, Advani said, the ability to tie predictive analytics to streaming data could have far-reaching effects, including helping energy providers analyze sensor data to more optimally balance load across their networks and energy types or maybe refining IBM’s existing work around predicting criminal activity.

Image courtesy of Flickr user pasukaru76.

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