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Big Data jumps to the cloud

Big Data jumps to the cloud

Big Data-as-a-Service offers quick, inexpensive, targeted analytics

Big Data-as-a-Service offers quick, inexpensive, targeted analytics. The information is coming in fast, at high volumes, in a variety of formats. It's too much for human beings to handle – it's a job for big data analytics.

Kevin Walker's sales team was buried under information. Internal records, news reports, third-party data sources.

Take a simple question like "Which customer should I call first?" The sales team might want to reach out to a customer who has just hired a hundred new employees, or the one whose equipment is ready to be replaced – but that requires going through all those information sources and then prioritising.

The information is coming in fast, at high volumes, in a variety of formats. It's too much for human beings to handle – it's a job for big data analytics.

Walker could have opted for an in-house analytics solution. He's the director of global vertical marketing at Dell, and the company has plenty of computing resources at hand.

But instead he decided to get his big data analytics from SaaS vendor, Lattice Engines. Going with the cloud, he says, allowed him to get up and running faster and cheaper. The entire set-up process took just two weeks, he says. In addition, he says, Lattice Engines specialises in just this one area.

"The fact that you're doing something within a vendor's core competency is going to have a better outcome," he says. "It allows us to scale globally, as well, and aggregate data from other cloud-based services."

Today, sales people can prioritise their work based on how likely it is that a customer is ready to make a purchase. The system can even make suggestions about what, specifically, the customer may be looking for. Or what a customer might need and not even know.

"Maybe other customers like them have purchased a certain security tool," Walker says. "The customer might not even know that they have that security weakness."

And the analytics began paying off quickly. According to Walker, for every dollar he spent on the tool and associated expenses, he's gotten US$30 back. "I expect to see even better returns when I roll out globally," he adds.

These kinds of results are making a lot of companies look at big data analytics. Cloud-based big data services can work well for small companies and startups that don't have the budgets for building their own systems, says Joe Pignatello, senior manager of business information management at CapGemini. But they also be a good fit for larger companies, especially those who need to move quickly to take advantage of business opportunities.

There are some downsides, however. As with any cloud service, companies have to do their due diligence for security and compliance. In addition, they have to be careful not to end up with a collection of Big Data silos from different vendors that don't play well together.

A thousand point solutions of light

Most large enterprise vendors have general-purpose big data analytics, but specialised vendors are springing up with focused solutions that address just one part of the big data puzzle.

They tend to tackle the lowest-hanging big data fruit, are fast and inexpensive to deploy, and have easy-to-use dashboards or fit themselves easily into existing corporate workflows.

Take MaintenanceNet, a cloud-based analytics vendor that serves a very specific audience – technology companies that want to get more revenue out of their maintenance and support plan sales. Comstor, a networking equipment distributor, saw a 30 per cent gain in its overall service business last year from higher renewal rates and overall growth as a result of its use of MaintenanceNet's analytics platform.

"Until the metrics and data can be accessible, a lot of your intuition and assumptions prove to be inaccurate," says Chris Fender, Comstor's director of service sales. What made the cloud-based service particularly attractive for Comstor was the ability to expand the tool to the hundreds of resellers who are its partners.

"If we needed to deploy technology, hardware or software, to the partners' premises, it would be very costly and challenging to manage from an IT perspective," Fender says. "It's a type of application that is really only feasible in a cloud-based environment."

One fast-growing segment is that of website analytics. Google Analytics provides a set of traffic data points for free, but it won't tell you, say, how old visitors' kids are. That's one of the answers that analytics vendor Sailthru provided to Totsy, an online retailer specialising in sales to women with young children.

"Sailthru's big data approach meant that we could use her browsing behavior to infer what ages she tended to shop for and in what categories," says Totsy CEO Lisa Kennedy. "We've seen this provide meaningful lift in both click-through and conversion while being operationally simple to execute."

Deployment is easy because all customers have to do is add a little snippet of code to their websites.

Another easy-to-deploy tool is marketing analysis from US-based Convertro. It takes a few minutes to install the code. The company doesn't just look at website behavior, but also pulls in information from media companies and other channels to find out how effective a particular advertisement or marketing channel actually was.

Online men's clothing retailer Indochino used Convertro to figure out when a mobile visitor was the same person who had earlier come in via a browser on a personal computer.

"The big piece is being able to associate that individual as one individual," says Antonio Guzman, the company's manager of digital marketing. "We're also interested in understanding which channels really drive influence and purchase behaviors or other behaviours we're interested in."

In the past, performance reporting would be based on the first click on an ad, he says. Now, Indochino can look at a transaction and see all the touch-points that led up to the customer making the final purchase decision.

"The big question is which marketing channels are contributing how much to revenues," says Tom Cole, CEO of Beau-coup, an online party retailer that is also a Convertro customer.

"We are now able to very precisely allocate our marketing budget because we now have a very good view of what each channel is contributing," he says. Cole says the increase in marketing ROI has been "significant" as a result, but declined to provide specific numbers.

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Sales and marketing is a major target of many big data as a service vendors. Like Dell, other companies have salespeople wondering who they should call first, and what they should talk about when the other guy picks up the phone.

Texas-based truck parts manufacturer FleetPride recently deployed SalesMax, a predictive sales application from Zilliant, in Texas. "When we initially rolled out the program the sales people didn't believe the data," says Rick Turner, FleetPride's national sales director. "We had to put the analysis in relatable terminology and once we got buy in and the field began to use the data, they were pleasantly surprised about how accurate and helpful it was."

For example, the tool can predict when customers are thinking of defecting by analyding historical transactions, and data from CRM platforms, internal data warehouses, social sources and third-party databases.

Business magazine publisher SourceMedia uses a big data vendor, Scout Analytics, to tell which trial subscription users would be most likely to convert to a paid subscription, and which current customers are not getting much out of their subscriptions and may be thinking of canceling.

"We'll send them an email with stories relevant to them, remind them of the site," says SourceMedia's Adam Reinebach, executive vice president of marketing solutions and circulation.

Avoiding the silo effect

Big data as a service can offer fast and inexpensive tools that businesses can sometimes deploy with little or no input from IT, and can offer immediate benefits.

"Individually, all the different point applications that are running in the cloud can be adding value," says Ron Bodkin, founder and CEO of Think Big Analytics, a big data consulting firm. "But collectively, it can be a nightmare."

Companies that don't plan ahead could wind up with problems integrating data from different big data systems, and end up with a lot of duplication, he says.

But according to Dell's Walker, getting big data analytics as a service from a vendor actually allows for more connectivity than otherwise possible. For example, if Dell wants to partner with, say, VMware, Lattice Engines enables the companies to aggregate certain types of data.

"I don't want to give them all my data, and they certainly don't want to share all their data," Walker says. "We can let Lattice act as a kind of escrow account. You can imagine the amount of data we have internally about our customers and the products and services they've purchased. It's rich and valuable. But you take a partner like VMware and running its data in tandem with ours makes our data much richer."

Lattice Engine always plays well with other vendors, such as, he says, and Lattice was also able to feed results into other tools.

"I wouldn't say it was automatic and truly standards-based," he adds. "Lattice is not quite a turnkey, flip-the-switch offering yet. Interoperability and open standards would always help."

Complicated integration projects can sometimes slow deployments, however. It took SourceMedia about three months to complete its integration with Scout Analytics, for example.

"It was slightly more complicated than a regular integration in that we have multiple systems that Scout had to worry about," says SourceMedia's Reinebach.

Scout can pull data from billing systems, internal CRM platforms and outside vendors like Scout Analytics also pulls in third-party information, such as databases that can link visitors' IP addresses to the companies where they work, or from vendors that track social media sentiment. The results can also be exported both by individual users, and programmatically through a query language. is at the centre of many cloud-based analytics projects, because of its dominance in the CRM space.

Rembrandt M&A is the largest adviser for people looking to buy or sell companies in the Benelux region, part of the Netherlands-based Rabobank Group, and a customer.

"We use CRM in the way it is meant to be used," says Gerrard Snippe, Rembrandt's IT manager. "We attach every email. We log every meeting. We log every phone call. We really try to build a good 360-degree view of our contacts."

Then there are all the knowledge documents, best practices, and templates, plus a third-party database that collects information on all the small firms in the Netherlands.

And where Salesforce falls short, there are many vendors in the Salesforce ecosystem that can help fill in the gaps. For example, for Rembrandt, the challenge was to search through all these documents to create easy-to-use, holistic views of companies, and the solution was a tool from Quebec-based Coveo, which makes indexing technology that can correlate large amounts of data from different channels. has been actively expanding its reach lately. It moved into the social analytics space in 2011 when it acquired Radian6. Last fall, the company built on this with an announcement of its Marketing Cloud, with an ecosystem of 20 social analytics vendors. also offers access to third-party data sets, like Experian's business credit data, and company information from D&B.

Though primarily focused on sales and marketing, is rapidly growing into a more general-purpose business platform in the cloud, and the availability of APIs allows for interoperability with both internal systems and other vendors, reducing the silo effect.

Spooky action in an instant

Most big data analytics projects aggregate data and then answer questions based on that data. Some automatically send answers to employees who can do something with those insights. But the latest evolution of this technology is to go one step further, and act on those recommendations.

"That's one trend I'm seeing," says Fern Halper, director of TDWI Research, a research and education institute focusing on data analytics. "A lot of vendors are talking about 'insight to action' and providing the actionable part."

For example, an analysis platform can track real-time Tweets about a company, decide which ones need immediate attention, and forward them along with a recommendation for specific action to the right person to deal with them.

And some vendors are cutting out the human component altogether. For example, Sailthru's platform can be used to change website pages on the fly, to show visitors content that they haven't seen yet, or special offers tailored specifically to their tastes.

"If you look at T-shirts and buy the red version, we know that the first image of a piece of clothing should be the red option if we have one," says Sailthru CEO Neil Capel.

There is still a role for humans in the big data picture, however. "You still need to apply critical thinking to what is coming out of this thing," says TDWI's Halper. "Does it make sense, what it's telling you, does it make sense for the business?"

Say, for example, a company is looking for information about social sentiment. The vendor could be processing trillions of data points, but only a handful might be relevant to that company – and even fewer of them have information about gender or geographic location associated with them, not enough to reach a meaningful conclusion about how men and women in particular locations view the company.

"With a lot of these tools, you only have a 50-50 chance of getting the sentiment right," she says. "If also depends on who you're going with – if you pay peanuts, you get monkeys."

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