Sept. 9 - 10, 2013
San Francisco, CA

Early Bird Tickets on Sale

Leading corporate performance management (CPM) company Adaptive Planning has raised a massive $45 million in its fourth and final round of funding with a goal to further dominate the CPM space, the business said Tuesday.

Adaptive Planning offers a suite of budgeting, planning, forecasting, and data discovery software in the cloud. Its software works for small businesses all the way up to large enterprises, which now represent 25 percent of its business. The company had 1,500 customers at the end of 2012 and it estimates it will have 2,000 customers at the end of this year. Big name customers come from all kinds of industries including Box, Coca-Cola, Johnson & Johnson, Jive Software, NBC News, Nissan, Red Hat, Toyota, and Zipcar.

“We address all of the CPM market — and it’s a huge market,” Adaptive Planning CEO John Herr told VentureBeat. “Most of our new customers move to us from Excel. We help customers upgrade from ‘Excel hell’ to Adaptive.”

Adaptive Planning competes with solutions from Oracle, IBM, and SAP, but its software-as-a-service has made it a leader in its space. And now the company will have more cash to hire more employees, further refine its software, and create more partnerships with resellers.

That last point is particularly important for the company, as it already has a strong reseller network of more than 400 “channel partners.” These partners help sell the product to more businesses and give it better access to international customers. One such partner is NetSuite, which offers Adaptive-based Financial Planning service that is integrated with NetSuite’s software.

The huge new funding round was led by new investor Bessemer Venture Partners (BVP), with participation by existing investors ONSET Ventures, Norwest Venture Partners, RBC Venture Partners, Cardinal Venture Capital, and Monitor Ventures. BVP partner Byron Deeter will join the company’s board.

“The business is on fire and this investment is a strong endorsement of what we’re about,” Herr said. “Many BVP companies are also customers of Adaptive.”

Including the new round, Adaptive Planning has raised about $100 million to date, including a $22 million round a little over a year ago.

Herr said this will be the last round of venture capital funds raised for the company and that it will explore the option of going public “in the next couple years.” He also indicated other “exit opportunities” were on the table. “We’re open to all positive outcomes,” Herr said.

Mountain View, Calif.-based Adaptive Planning was founded in 2003 and has 200 employees. Herr said the company aims to double its number of employees in the next 12 to 18 months.

Businessman with tablet via ollyy/Shutterstock

Google tricked out its entire I/O conference with hundreds of sensors this week. Today, we got a glimpse at some of the data and the work that went into making it happen.

Michael Manoochehri, with Google’s cloud engineering group, explained the project from Google’s perspective: It’s a big experiment designed to help Google figure out how to use its many cloud tools to collect, organize, and analyze large quantities of sensor-driven data.

To pull off the project, Google needed some hardware-hacking experts. So it called on Kipp Bradford, a professor of engineering design and entrepreneurship at Brown University; and Alasdair Allan, a hardware hacker with Babilim Light Industries in the U.K.

The pair designed a sensor module based on the Arduino Leonardo R3 chip, a popular microprocessor that many DIY hobbyists use. Each module, or “mote,” carries a mix of temperature, humidity, noise, and other sensors. Some have air quality monitors that measure the amount of particulates (dust) and volatile gases (from cleaning fluids or alcoholic drinks). Some are connected to floor mats that record footsteps.

The devices send their data via a Zigbee-based mesh network back to Google Data Store, a NoSQL database running on Google’s App Engine. BigQuery, a tool for doing analysis on large datasets that is based on Google’s own internal data analysis tool, “Dremel,” allows the team to perform rapid queries on the enormous dataset (see sidebar for some of the project’s metrics). The team even used the Google Maps API to display data from each sensor on a map of the conference center.

Tableau Software, which held an IPO this week and whose stock is already up 60 percent in its first day of trading, provided real-time visualizations of the data. That’s a Tableau-generated chart at the top.

Bradford and Allan are editors at Make magazine and have done similar work with O’Reilly’s Data Sensing Lab, albeit on a smaller scale. (O’Reilly is the publisher of Make and the organizer of the Maker Faire, coming up this weekend in San Mateo, Calif.) For O’Reilly’s Strata conferences, the Data Sensing Lab build networks of about 50 such devices.

Also helping Bradford and Allan were Julie Steele, a project manager for O’Reilly, and Harry Johnson, a Stanford engineering student who laid out the project’s first circuit board and acted as the project’s “sanity check,” Bradford said.

“When the first prototype came back and it worked, I breathed a huge sigh of relief,” Bradford told me.

Once the team had built several working prototypes and ironed out the kinks, Bradford send the circuit board design to Sunstone, a company in Oregon, which produced hundreds of custom circuit boards. He then arranged to have the boards and various components sent to VR Industries, a company in Rhode Island, which assembled all 525 devices. The per-unit cost was about $120, though that’s with volume discounts figured in (it would cost you more than that to build one or two of these things).

After that, the team spent a nearly-sleepless week configuring and setting up all the devices.

“We basically spent the last week locked in a room at Google Boston doing integration and testing,” Allan said.

For Google, it was a chance to get its hands dirty with real-world data.

“We wanted to understand how this kind of use case could work with the Google Cloud platform,” Manoochehri said.

Did they learn anything about the conference? So far, the takeaways seem pretty obvious. For instance, in the steps-per-minute graph above, there’s a big spike of activity as people lined up to enter the Google I/O keynote on the first day. Then activity settles down as they all sit to watch the presentation. There’s a flurry of activity as people attend the first evening’s party, and the noise level spikes during Billy Idol’s concert. Air quality tends to go down when people are milling about, as they raise dust, and it gradually improves after they go away. Also, a cleaning crew comes through the Moscone Center at 4 a.m. every morning.

The company is making all the hardware, code, and data from the project available freely to the public. The goal is to make it easy for others to use the same kinds of technologies in their own data-gathering projects.

There are already lots of sensors out there. From wristband health and fitness sensors to smart thermostats like Nest to automobile sensors, we are increasingly surrounded by connected sensors. These devices are already collecting a lot of data, and soon will be collecting orders of magnitude more, Manoochehri said.

“This is a really big deal,” Manoochehri said. “So we need to understand this use case, and test that our platform is good for this use case.

“People collect all this data, and they go, ‘Oh my god, what do I do?’”

This project provides an answer to that question — provided you’re willing to use Google’s many cloud services.

Also, if you are looking for the best place to take a nap, check the graph below, which shows Manoohchehri’s “serenity metric,” showing a combination of low average audio noise and low noise variance. Conference rooms in the far corners of the building are your best bet. The least serene location? Apart from Google’s radio-controlled blimps (which carried sensors over the crowd), the second floor escalator was the least serene location.

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HealthBeat 2013 is a new conference showcasing how technology is transforming health care. We'll explore how IT is driving out inefficiencies on the hospital, practice, and patient levels. Check out full event details here, and register here.

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Enterprise tech IPOs have seen big movement over the past few years. Now we can add big data and data visualization company Tableau Software to the list of successful enterprise IPOs, as its share price has popped as much as 60 percent in early trading today.

Last night, Tableau priced its IPO at $31 per share, raising $254 million with an offering of 8.2 million shares. Now that price has shot up to about $49 on the New York Stock Exchange, as of this writing.

Tableau offers products to help technical and nontechnical users create interactive charts and simulations based on raw data. Tableau generated $127.7 million in annual sales in 2012, more than double its 2011 revenue and more than quadruple its 2010 revenue. Back in November, we picked Tableau as one of 10 startups leading the way in big data.

“Our mission, to help people everywhere see and understand data, isn’t all that different from Google’s,” Tableau CEO and cofounder Christian Chabot told us in a recent interview.

Tableau originally filed for its IPO in early April. At that time, it was just looking to raise $150 million in its initial public offering.

Prior to the IPO, Seattle-based Tableau raised $15 million in funding over two rounds from New Enterprise Associates. NEA’s partners and investors have got to be pretty happy with this deal, as the fund was seeing a 20x return on its investment in Tableau, according to Fortune’s Dan Primack — and that was before today’s pop.

Screenshot via Tableau/YouTube

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HealthBeat 2013 is a new conference showcasing how technology is transforming health care. We'll explore how IT is driving out inefficiencies on the hospital, practice, and patient levels. Check out full event details here, and register here.

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Data visualization company Tableau Software has raised the price range of its initial public offering to $31 per share.

In a media release, Seattle-based Tableau announced that it has raised $254 million by offering 8.2 million shares at $31, above the revised range of $28 to $30. The shares are expected to begin trading on the New York Stock Exchange on May 17 under the symbol DATA.

The company originally filed for its IPO in early April. Back in November, we picked it as one of 10 startups leading the way in ‘big data.’

“Our mission is to help people see and understand data,” Tableau’s prospectus says. The company offers products to help technical and nontechnical users create interactive charts and simulations based on raw data. Tableau offers a variety of public, corporate and premium products, and caters to a range of customers.

Source: Tableau

Tableau CEO Christian Chabot

Tableau’s is the latest in a series of high-performing enterprise IPOs, including Workday, Splunk, and Palo Alto Networks, making 2013 the year for business software.

“Data visualization applications like Tableau are ushering in a post-spreadsheet era, threatening to kill Excel as the killer app for desktop analytics,” said data scientist Mike Driscoll, who is also the CEO of Metamarkets.

Still, Tableau faces strong competition from giants like IBM, Microsoft, Oracle, and SAP as well as startups like Tibco Spotfire. And maybe even Google.

“Our mission, to help people everywhere see and understand data, isn’t all that different from Google’s,” Christian Chabot, the company’s cofounder and chief executive, said in a recent interview with VentureBeat.

Goldman, Sachs & Co. and Morgan Stanley are acting as lead joint book-running managers for the offering.

This is a guest post by Jiff CEO Derek Newell

Digital health may be garnering all the glory for its promise to transform health care, but take a closer look and you’ll find a promising next wave of health care investments.

With a proliferation of mobile apps and data being generated at a dizzying pace, few investments have yet to fulfill their financial promise. The real money will be made when companies build services around these applications, make the data actionable, and connect all this inbound patient data to the physical health care system.

Expect companies that find new, creative ways of connecting data to patients, determine what to do with the data when it’s generated, and figure out ways to creatively (and profitably) engage the health care system, to attract the attention of VCs and entrepreneurs alike. Here are three areas worth watching.

Smart sensors

What if a device could predict whether you were going to catch a cold, succumb to diabetes or even your risk for getting cancer? Sounds a little far-fetched, but a new generation of sensors holds the promise to do just that.

Companies developing smart sensors that can be placed on the body or in the home that can accurately, non-invasively predict your chances of becoming ill stand to attract investors’ attention. Component and connection costs are dramatically dropping, while at the same time the value of connecting to the Internet has dramatically increased. Just think of Tesla’s ability to collect data from its cars. Look for value in companies that develop sensors that collect, connect and predict high-cost events that can be prevented by timely intervention.

Software systems for data analysis

Data generated from new smart sensors holds little value if it can’t be analyzed and acted upon. New investment opportunities in the companies that develop software systems to analyze data and determine what to do with it will ultimately be very profitable for some investors. Single-use systems, where a single sensor talks to a single server designed to analyze its data (which is the way most are architected today), will be much less valuable than servers that can import and analyze data from dozens, hundreds or even thousands of sensors.

Businesses across the spectrum are developing algorithms to analyze, for example, whether a person’s blood pressure and weight require a fitness program or whether other symptoms merit immediate provider attention. The smarter the software, the better. Just imagine patient alerts being delivered to warn of an increased risk of illness before the illness is even detectable through symptoms. Watch for large companies, such as IBM, to jump into the fray while new companies emerge to help connect all the dots. It’s also worth keeping a close eye on artificial intelligence companies.

New service models for patients

Smart sensors and smart software, however, will only go so far. At some point, we’ll need good old-fashioned people power. A new crop of services companies will arise to give virtual care a healthy dose of checks and balances. Take, for instance, the smart sensor that detects a racing heartbeat or chest pain.

Software algorithms will take in the information, analyze it and determine, for example, that a person needs immediate attention. But what if the racing heartbeat was just a false positive because the person had just climbed stairs? Activating an expensive health care system will be a waste of money, so services companies that provide the human intelligence to validate and verify the information will be required. A second type of services company will also emerge to manage the care.

Everything that can be done digitally and virtually will be done digitally and virtually. This will dramatically improve access to and efficiency of the traditional health care system. Call centers will be staffed not just by the traditional nurse, but also by physicians, pharmacists and other professionals who can provide a higher level of care.

Derek Newell brings more than 20 years of experience growing and leading innovative health care technology and services companies to his role as CEO of Jiff Inc. He is passionate about exploring the intersection of health care and emerging technologies, and believes that health outcomes and wellness improve when mobile and digital health technologies connect consumers with the traditional health ecosystems. 

Prior to joining Jiff, Newell was president and CEO of Robert Bosch Healthcare. While at Bosch, Newell worked to build Bosch into the world’s largest remote patient monitoring company. Previously, he was the CEO of Health Hero Networks, a US-based remote-patient monitoring company, and the chief marketing officer of a leading disease management company. 

Derek is a leading voice in the areas of health care technology and finance. He has co-developed and taught health care finance classes at the Haas School of Business from his alma mater at the University of California, Berkeley, where he holds graduate degrees in business and public health.

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HealthBeat 2013 is a new conference showcasing how technology is transforming health care. We'll explore how IT is driving out inefficiencies on the hospital, practice, and patient levels. Check out full event details here, and register here.

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Sept. 9 - 10, 2013
San Francisco, CA

Early Bird Tickets on Sale

Hot database-as-a-service startup Cloudant has raised $12 million in its second round of funding, the company announced today.

Boston-based Cloudant was initially founded in Cambridge, Mas. in 2008 by three MIT physicists. The team struggled to move around multi-petabyte data sets and analyze those data sets wherever they went. So they ended up creating what would become Cloudant.

Cloudant offers a highly scalable database-as-a-service that makes it possible to store, access, and analyze your operational data in the cloud. In the layers of the cloud, Cloudant becomes a “data layer” that can be run on top of most infrastructure-as-a-service providers, including Amazon Web Services, Rackspace, SoftLayer, Microsoft Azure, and Joyent.

Cloudant CEO Derek Schoettle told VentureBeat that this “agnostic” approach to infrastructure deployment separates it from its biggest competitor — Amazon database services. “We believe in not having vendor lock in,” Schoettle said.

Cloudant has attracted some big-name customers with its approach, including Samsung, Microsoft, Adobe, Nokia, Salesforce, Expedia, Zynga, and Flurry.

The new funding was led by Devonshire Investors, Rackspace Hosting, and Toba Capital, with participation by current investors Avalon Ventures, In-Q-Tel, and Samsung Venture Investment Corporation. Including the new round, Cloudant has raised $16 million to date.

“One of the reasons we let Rackspace invest is because of their committment to customer service,” Schoettle said. “We also believe in that.”

In addition to funding, the company has opened up a new office in San Francisco, which joins the Boston HQ and offices in Seattle, the U.K., and South Korea. In the next 90 days, Schoettle said the company will open another office in Hong Kong.

Money in the clouds via Andy Dean Photography/Shutterstock

CEO Noah Pepper announced the acquisition this afternoon on the Lucky Sort’s website. The company has been building a visualization and data navigation engine called TopicWatch, which can discover real-time patterns in streams of data, and took in $500,000 in seed funding from Neu Venture Capital in early 2012.

The startup had operated in stealth mode, but it caught the attention of investors and the press when it raised seed funds from a roster of “big data” experts, including chaos theory physicist Dr. Norman Packard.

Pretty clearly, there’s a strong Twitter tie-in, as Twitter is nothing more than a live stream of data, continuously updated by millions of people. Where Twitter will use the technology was not fully disclosed, but Pepper did offer a hint.

Several of us will be moving to San Francisco to join Twitter’s revenue engineering department, so if you’re in the neighborhood and want to talk about text mining or data visualization, give us a shout.

Twitter has been beefing up its ad tools in the past few months, releasing new functionality for keyword targeting, self-service tools for interest, platform, fan, and gender targeting, and more. And as it ramps up for a possible IPO this year, it needs to give advertisers even better tools for placing, targeting, and evaluating their Twitter ad spend. It’s likely that Lucky Sort technologies will be helpful for that purpose.

In any case, the existing Lucky Sort toolset will be shut down, Pepper stated:

We’ll be helping current customers transition off our system in the coming months such that we can focus fully on our future at Twitter.

Twitter has been acquiring small companies at a fairly rapid pace lately, having just acquired big data startup Ubalo. And in 2012, Twitter was the fourth-most acquisitive company, buying a total of 10 startups and ranking just behind Facebook, Google, and Groupon.

Here is Lucky Sort’s full announcement:

Lucky Sort acquired by Twitter!

Two years ago I started Lucky Sort with several friends. Our goal was to make huge document sets easier to analyze, summarize and visualize by building elegant and user friendly tools for text analysis.

Today I’m very excited to announce that our journey has entered a new phase: Lucky Sort has been acquired by Twitter!

Several of us will be moving to San Francisco to join Twitter’s revenue engineering department, so if you’re in the neighborhood and want to talk about text mining or data visualization give us a shout.

We’ll be helping current customers transition off our system in the coming months such that we can focus fully on our future at Twitter.

In building Lucky Sort we had an enormous amount of support from friends, employees, advisors and investors. It has been uplifting to have so many people help us and it highlighted just how much business is a social endeavour.

Best,

Noah Pepper
Chief Executive Officer
Lucky Sort

And like in all big rushes, the companies providing the picks and shovels can make a killing.

Lately, a number of enterprising startups that sell big data analysis tools to large companies are seeing explosive revenue growth — the latest sign the big data gold rush is fully on.

One of them is Datameer, a Silicon Valley (San Mateo, Calif.) company that offers a tool to help other companies analyze and visualize their big data quickly and easily — by providing it in a spreadsheet-like user interface that most employees can understand.

Datameer has tripled revenues over the past 12 months and looks on track to hit a $10 million run rate by early Q3, according to back-of-the-envelope math. Datameer CEO Stefan Groschupf says the company will reach 100 enterprise contracts by that time, and the average contract is $100,000 in value. The company offers annual software subscriptions, including an enterprise version can cost $100,000 or more, depending on data throughput.

Groschupf says many of his customers like to keep the details of their contracts confidential. But if you check Datameer’s web site and other announcements, you’ll see it serves companies like Sears and Visa, as well as emerging companies like hot gaming firms Kabam and Kixeye. Datameer says it also has four of the five largest global banks as paying customers as well as three of the four largest credit card companies and the U.S. government, though Groschupf is mum on specifics, citing confidentiality agreements.

Datameer’s traction comes as news emerges about other big data companies with similar growth. GoodData, a San Francisco company that provides business intelligence from data, said it too has tripled revenues over the past year. GoodData has been around since 2007. Datameer was founded in 2009. The coinciding explosion of growth this year suggests we’re seeing the “hockey stick” growth part of the big data cycle.

Datameer has raised $17.8 million from firms like Kleiner Perkins and Redpoint, but it may raise an expansion round to ensure it can grab more market share even as other competitors pile in. It can reach profitability based on the money it has already raised, said Groschupf.

Datameer’s value comes in offering an easy-to-use application layer on top of Hadoop, the popular open source software framework that allows companies to store and organize data on the fly. While companies can try to hire hard-to-find and expensive Hadoop experts to analyze the data themselves, Datameer’s app layer dumbs things down. Just about any manager in any unit can integrate, analyze, and visualize any data for their own needs. Does an HR manager want to track the performance of sales executives in new ways? Well, they can query that.

The advantage of the Hadoop-spreadsheet approach is that it allows companies to interact directly with new or existing data sources, without actually changing the underlying data. And it lets them run new types of queries on-the-fly. This contrasts to the cumbersome technology that has dominated until now, where data queries are constrained by slow and expensive data manipulation process: Generally, ETL (extract transform load) technology is used to feed data into enterprise warehouses for subsequent manipulation by business intelligence apps. This process can take up to 18 months to set up, and requires preconceived data modeling. In other words, if your intended queries aren’t thought up beforehand, you’re out of luck. No peering into your data with new sorts of queries.

Datameer’s CEO Stefan Groschupf recently shared evidence of his company’s traction during a panel discussion I moderated at Interop Tuesday (see a good summary here).

Here are five interesting ways Datameer, or services like it, are creating value:

Making money

Datameer helped a large anti-virus software company double the conversion rate of some of its marketing campaigns, netting it more than an additional $20 million in revenue within six months. Before using Datameer, the company had sunk more than $1 million into advertising campaigns that weren’t converting very well: It had used Google ads and tried to market through weblogs, its download logs, and sales accounts. Datameer helped it optimize across all of these areas, with no additional investment in infrastructure or advertising outlays. Similarly, Datameer is helping fast-growing gaming companies like Kabam and Kixeye respond more dynamically to data collected by their users, giving them a leg up on more mature companies like Zynga, which isn’t using Hadoop and is reliant on the more traditional, less flexible ETL/warehouse technology. By encouraging Kabam and Kixeye game developers to write to log files that can be instantly analyzed, Datameer can run 1,000 reports a day on them, checking to see say, what color — red or blue — is best used on game buttons for enticing gamers to click. Similarly, hundreds of other variables can be tracked on the fly, about how gamers are clicking, buying virtual goods, and then leaving.

Identify fraud

Datameer worked with a credit card company to help it save billions by identifying fraud before it happened. Groschupf wouldn’t identify his customer. However, the WSJ recently reported how Visa has used big data to save “billions” by identifying fraud that could have cost it $2 billion; it mentions Visa is using Hadoop but didn’t mention the app running on top of it. The technology allows credit card companies to view data at the individual merchant terminal level and across hundreds of attributes from average authorization volumes to the frequency of purchases that turn out to be fraudulent.

Prediction

Datameer helped an unnamed “hardware company” analyze server usage among its customers to predict when those customers will need a new server. Instead of waiting for the customer to realize servers are at full capacity, Datameer’s data knows capacity will be hit in, say, two weeks, and ships a new server to the customer before they realize they need it. That has helped the hardware company improve sales by more than $100 million “over a few years,” according to Groschupf. Datameer can perform a range of other needs for customers based on trends. One example is price optimization. While online retailer Amazon is using price optimization techniques to update prices up to 12 times a day, traditional retailers have typically optimized pricing once every 12 weeks. Datameer recently helped Sears shorten that time to three days.

Matching people

Datameer’s principal competition is not other providers, says Groschupf, but when companies decide to do the job themselves. Relationship site eHarmony, for example, was an early adopter of Hadoop, and claims this has helped eHarmony allow 550,000 U.S. marriages to happen. eHarmony matches people by running Hadoop every night on the questionnaires filled out by its customer base.

Another big step for the big data industry occurred late last year, when Datameer announced an OEM deal with online human resource software vendor Workday to embed the Datameer analytics platform as part of their big data analytics application for customers. “This marks one of the first deals to integrate Hadoop analytics into the enterprise packaged application ecosystem,” said Tony Baer, an analyst at research firm Ovum at the time. The firm called it a “key milestone in the process for big data securing mainstream enterprise acceptance.”

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HealthBeat 2013 is a new conference showcasing how technology is transforming health care. We'll explore how IT is driving out inefficiencies on the hospital, practice, and patient levels. Check out full event details here, and register here.

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Debbie Landa is the CEO of Dealmaker Media/Under the Radar.

This is part three of a five-part series sponsored by Under the Radar aimed at helping innovative startups attract the attention of C-level executives. Meet Suaad and hear how he manages technology at Under the Radar 2013, May 22-23, in San Francisco. Register today using special discount code VentureBeatVIP.

Debbie Landa: We live in Silicon Valley, where there must be thousands of companies and VCs pitching you to consider their products and services. How do you manage this?

Suaad Sait: I regularly get pitched by startups and VCs because I live in Austin, Texas, another hub of startup activity, and also because Rackspace is known for mentoring and supporting early stage startups in the Bay Area and in other markets.

These days, there are quite a few social media and Big Data companies pushing to get their products in front of chief marketing officers (CMOs) and marketing departments.

My criteria for taking a pitch are simple: If it’s a referral from one my VC friends or a Rackspace customer, I make time. For example, a friend at Mayfield recommended that I speak with a portfolio company working on a Big Data and predictive analytics product for B2B marketers. The technology was relevant to our immediate needs and it was a referral, so I took the meeting.

Landa: What are your big pain points?

Suaad: My number one pain point right now is mining the massive amount of data we are accumulating each day. As a CMO, I want to use objective metrics to make effective marketing decisions, but the data dimensions are staggering. All of the data — which shows what kind of companies are finding value in Rackspace and how they are buying and consuming our services — creates a very interesting analysis opportunity.

Interpreting this data and forming a thesis about the things we want to find out about our customers and prospects is problem number one.

The second problem is creating the right kind of reach — as in reaching the right people in the right business with our messages. By that I mean the industry constantly goes through cycles of email fatigue. One day, email is a good vehicle for marketing, and then suddenly it’s not a good vehicle for marketing.  Similarly, we go on again, off again with industry trade events. So one dimension I think about a lot is how to identify the most effective, modern method of communicating and how to reach the audience types that we want to talk to on an ongoing basis.

It’s content marketing, social media, PR and direct marketing — a variety of things.  But what is relevant where and to whom? How do we tailor our programs and messages to buyers? This is a problem we are constantly trying to solve.

Landa: What is your vetting process?

Suaad: First, they must pass the “referral test” so that I know they’ve been vetted — there’s nothing more valuable than a trusted referral.

Second: they must be top of mind and address a business problem we’re actively trying to solve. The predictive analytics company I referenced earlier is a good example; we’re a cloud-based company with over 200,000 customers and lots of data, and I’d like to be able to examine that data in a variety of ways to optimize our marketing. So it made sense for me to engage in a dialogue with this particular company.

I always take calls from Rackspace Startup Program customers that are building (or have built) their applications on the Rackspace open cloud when they reach out. It’s our duty as good tech citizens to be part of the vetting and mentoring process for startups that are using our very own infrastructure.

Landa: What is the last company you bought and implemented?

Suaad: The most recent marketing-focused vendor we selected is Spredfast, a social media marketing platform.

We spend a lot of time and effort trying to understand the influence and impact that social media has on Rackspace brand awareness and our sales funnel, and Spredfast provides metrics and measurements around our social media campaigns to help us evaluate and optimize our programs. Getting this visibility has been in the back of my mind for some time.

When we originally spoke with Spredfast, we weren’t ready as a company to digest this technology because we didn’t have resources to devote to social media marketing in a decisive, measurable way.  It’s become higher priority because social media is one of the top ways our prospects and customers interact with us.

Landa: What are your challenges in adopting early stage companies and what’s the sales cycle?

Suaad: This is an interesting question that hits close to home for me. I personally have been an entrepreneur and have launched early stage startups. Rackspace itself was a startup not too long ago, so we absolutely want to give startups an opportunity.

If a startup is part of a well-known accelerator or backed by a well-known VC firm, the probability of us doing business with them is higher because other smart people have screened the team and the technology.  We do worry once in a while about some of these startups not being around for the long haul.  If we’re experimenting with their technology then it’s not a concern.  If we’re going to bring these solutions to market from a technology perspective, we think about that.

There is no typical sales cycle. It depends on several factors: Whether the product addresses an immediate need, how quickly we can test pilot, and if we have resources to adopt the new product.  It can be 30 days, while others have lingered for 6-12 months.

Landa: Advice for startups pitching you?

Suaad: Nail your positioning and messaging.  Make it simple and elegant, but tell me what makes you unique and valuable to me. I talk to a lot of startups and it often takes way too long for them to explain what they really do and the problems they’re solving.

Landa: Worst pitch you received?

Suaad: I’ve seen a lot of bad presentations, so I won’t single anyone out. I would have to say the biggest mistake I see is around lack of focus and specificity.

I’ve worked at enough startups to know that every startup is only going to be able to do one thing really well. However, I get a bunch of pitches that include a couple of paragraphs of messaging, which basically claim that using their technology will accelerate revenue, improve productivity, reduce operating costs, and lead to total world domination. Sometimes the messaging is just so generic that I can’t determine what’s special about the company.

Any pitch that fails to provide a clear one-liner about what the company does, I’d classify as a bad pitch.

Landa: Best pitch?

Suaad: The best pitches are always the ones that are laser-focused on Rackspace and our markets, delivered by someone who is a whole lot smarter than I am about how to address my problems. That’s what makes the information relevant and engaging to the person or company you’re pitching.

The best way to get my attention is to tell me something I don’t know about my own company. Recently, someone asked me whether I knew that there were 6,348 social media conversations about Rackspace across these three channels in the last 30 days. Immediately, I was engaged and giving my full attention to trying to learn more about what these conversations entailed.

Landa: What value do you get out of attending conferences like Under the Radar?

Suaad: Under the Radar is a valuable face-to-face vetting mechanism or filter. It’s a condensed opportunity to see the best of the best emerging products and speak to the entrepreneurs who are so passionate about solving the big problems we struggle with every day. It gives me a good lens to find the most promising solutions to our challenges. Rackspace actually ended up acquiring a company that recently presented at Under the Radar.

I also look at the Under the Rader event as a really good way for me to network with others in the industry and to learn about what’s on top of their minds.  I think of this in multiple dimensions.  One is technology I can use in the business. But from a macro level, I’m also thinking about Rackspace and our go-to-market strategy.  Specifically, what things are my peers seeing? Are there disproportionate growth inflections in markets that they may be observing?  Overall, the event is an opportunity to network with peers that are thinking about markets in maybe the same or in different ways than I am.

Suaad Sait became the first Chief Marketing Officer for Rackspace Hosting in January 2012, having been a consultant to the company since May 2011. In this role, Sait leads the company’s global marketing and branding efforts. Prior to joining Rackspace, Suaad was the founder and Chief Executive Officer of Reachforce, a venture-backed B2B marketing-data services startup, where he continues to serve as chairman of the board.

Top photo credit: Rackspace

Sept. 9 - 10, 2013
San Francisco, CA

Early Bird Tickets on Sale

Cameron Peron is VP Marketing at Newvem, a cloud operations optimization service.

Amazon has launched a series of local Amazon Web Services summits across in key cities across the world. Capitalizing on the re:Invent conference in November of last year, the AWS summits are a great forum for local AWS users to learn about featured AWS services and meet partners exhibiting at the event itself.

The AWS Summit in San Francisco a number of days ago lived up to this expectation. Here are 5 insights from Amazon senior VP of web services Andy Jassy’s keynote, and the exhibition itself.

It’s all about the enterprise

Adoption of the public cloud by the enterprise was a key message through the introductory keynote.  In sharp contrast to the keynotes delivered in re:Invent in November, Andy Jassy emphasized the public cloud as part of an enterprise’s IT and cloud strategy as opposed to a complete alternative to on-premise and virtual private cloud.

Andy highlighted use cases of AWS services that the enterprise can use to both move workloads to the AWS cloud as well cooperate between on-premise and AWS environments.

Security = priority #1

Jassy stated that AWS is committed to providing a secure public cloud, highlighting the addition of advanced security controls, certifications and accreditations.

No doubt this was a direct message to enterprise level CIOs that are considering moving small variable workloads to the public cloud, but need to deal with security and compliance risks that run deep into their respective organizations.

Redshift, redshift, redshift

The keynote contained many use cases and examples of using AWS RedShift, a data warehousing and data analysis solution.

Based on an hourly pricing model, RedShift enables AWS customers to analyze large volumes of data with their existing business intelligence tools.  The RedShift use case was a common theme throughout Andy Jassy’s address, use cases delivered throughout the keynote, and breakout sessions. RedShift follows in the footsteps of enriched AWS services such as OpsWorks and Trusted Advisor.

Cost is still the driver for onboarding new business

Throughout the keynote Jassy championed many organic AWS services, as well as solutions provided through the AWS Partnership Network that enable companies to scale once on the AWS cloud.  Despite this, low cost is still king.

Just as Werner Vogel discussed cost savings in the beginning of the New York City keynote, Jassy emphasized that AWS lowered prices 31 times in the absence of competitive pressure to do so.  In line with the success of the Amazon.com model, Jassy implied that that AWS will continue to reduce prices.

Jassy also offered examples of customers reducing costs by using solutions beyond EC2, highlighting that Foursquare reduced their analytical cost by 50 percent with AWS.

Launch of the AWS Certification Program

Jassy also shared the launch of an AWS program that certifies solutions architects, SysOps Admin, and developers.

To qualify, applicants must complete an exam that covers both proficiency in AWS as well as general IT knowledge and experience.  The program should complement and reward AWS users who have championed both onboarding and scaling AWS within their organizations by mandating and regulating their skill sets throughout the career.

In other words DevOps and other AWS users can add AWS certification alongside experience and proficiency in code, such as Ruby and Python.

Cameron Peron is VP Marketing at Newvem, a cloud operations optimization service designed for cloud users. Offering a business view into a company’s public cloud operations, Newvem actively tracks cloud health in order to help reveal and solve cloud irregularities related to cost, security, utilization and availability.  Follow Cameron at @cameronperon

Ubalo announced the acquisition on its website, saying that the company was founded to focus on making large-scale computing easier and that after fruitful collaborations with Twitter’s infrastructure team, the two agreed to come together.

The startup was working on simple ways of scaling code beyond single machines. Using the Ubalo infrastructure, developers could write code for multiple machines with no additional overhead, using the same tools, languages, and libraries that they would ordinarily use for single-computers applications. Case studies that the team published include reducing image processing tasks from eight hours to five minutes using 100 processor cores on Amazon S3 and using Ubalo to generate 8GBs of data by drawing one billion samples from a set of data and computing sample averages, all in just .7 seconds.

Ubalo achieved this by creating modular “pods” that run calculations in replicatable environs you set up once and can run anywhere as well as APIs and messaging protocols to keep all the calculations in sync, and data management techniques to access, manage, and store gigabyte-sized data files.

The startup’s name, Ubalo, means “counting,” according to the site, and it had just four employees including the founders.

Here’s the team’s announcement:

We’ve got some exciting news: The Ubalo team is joining Twitter. In early 2011, we started Ubalo to make large-scale computing easier and more accessible to a technical audience, and we’ve had a great time working with our partners and customers on a number of interesting products. When we met the infrastructure folks at Twitter, we realized that it’s a company with brilliant people, strong momentum, exciting challenges and a promising future. We quickly became enthusiastic about the possibility of collaborating with them and the impact we could have there.

A few days ago, Twitter agreed to acquire our technology and we agreed to join their staff. We look forward to working with Twitter in the years to come.

Thanks for your support and interest in Ubalo!

— Jacob Mattingley (@jem_nz) and Ian Downes (@ndwns), May 9, 2013

It seems to be more than simply an acqui-hire, as Jacob Mattingly, whose Twitter account says “I like making complex things simpler,” tweeted that his startup’s intellectual property was acquired. Cofounder Ian Downes apparently just started using Twitter today and has only one tweet — a retweet of Twitter Engineering’s tweet — to his credit.

Twitter, of course, can always use top-notch expertise on its infrastructure teams. The company’s days of fail-whale adventures seem to be in the past, but with a continuously growing network of users and new services like Twitter #Music, among others, there’s a strong need for ongoing talent acquisition.

Ubalo was based in Palo Alto, Calif., and was funded by Harrison Metal.

Sept. 9 - 10, 2013
San Francisco, CA

Early Bird Tickets on Sale

Enterprise software giant SAP has been throwing its hands in the air for years, exclaiming that it is indeed a cloud company. But yesterday, SAP took a big step that shows where the it and its customers are at by offering its “HANA” in-memory database technology from its own cloud.

HANA (High-Performance Analytic Appliance) is an appliance that stores terabytes worth of data and can move through that data at high speeds. As of this week, SAP and its clients are storing more than 750TBs of data in the system. HANA is an expensive solution not many companies can offer, and it’s clearly important to SAP’s future. “We expect [HANA] to have a billion-dollar future on its own,” SAP mobility head Sanjay Poonen told us in November.

SAP previously only offered HANA via the cloud through Amazon Web Services. There are many potential reasons why SAP would want to offer HANA from its own cloud rather than AWS. For example, it gives SAP more control over its product, lets SAP allocate the right high-performance hardware for HANA’s monumental tasks, and lets SAP offer HANA in the cloud at a lower overall cost.

Another reason SAP has moved HANA to its own cloud is that HANA has reportedly underperformed with current customers. SAP has even given away HANA for free to some tech startups in order to seed interest and maybe gain more big customers if those startups grow big.

But after talking with several “big data” experts, one final reason particularity sticks out: SAP needed to move HANA to its own cloud to make it easier to deploy the damn thing to businesses.

“In the last 10 years, the speed of business has significantly increased,” Stefan Groschupf, the CEO of Hadoop-based big data analytics startup Datameer, told VentureBeat. “No one has time to wait 18 months anymore.”

Essentially, many businesses have given up on overly long deployment cycles — it bogs down other processes, and the software is outdated once it’s ready to be deployed. Cloud software pioneers like Workday have shown enterprises how handy the cloud can be and they like what they’ve seen.

SAP and chief competitor Oracle have been watching this trend carefully during the past few years. Both companies have acquired smaller companies and launched new cloud-focused products to help speed up deployments and stay relevant. SAP’s biggest move in recent history was acquiring SuccessFactors for $3.4 billion back in December 2011. Similarly, Oracle has purchased a boatload of companies.

In the case of HANA, this is SAP tapping two huge trends (big data and cloud) and trying to tie the biggest companies in the world to its solution. Offering it via the cloud means companies might bring in those companies that see how the cloud can speed up their workflow.

That said, lean startups focused on big data solutions could gobble up some of the market SAP wants to attack with HANA. One such startup is SiSense, a big data company that has seen a 520 percent growth in subscription revenues in the past year. SiSense’s offering is different than HANA but it still thinks it can solve many businesses’ big data qualms.

“We can run on any hardware out there; many companies don’t want to buy new hardware,” SiSense marketing VP Bruno Aziza told VentureBeat. “There’s been a shift in the market for how people procure their big data solutions.”

For example, SiSense’s CTO recently crunched 10TBs of data in 10 seconds using an off-the-shelf $10,000 server as a conference stunt.

Another thing on SiSense’s side: it only takes “hours” to deploy.

Clouds image via PhotoAtelier/Flickr

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Big Data and Predictive/Real-time Analytics startups: Are you looking to jumpstart development & accelerate market traction? Sign up for the SAP Startup Focus program to receive technology, support, resources and community to help you develop new applications on SAP HANA, a cutting edge database platform. Get started here, and enter promo code “VB2013″ on the form.

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Sept. 9 - 10, 2013
San Francisco, CA

Early Bird Tickets on Sale

Box‘s Aaron Levie and GoodData‘s Roman Stanek [above] have already joined forces in making business software sexy. The young CEOs are often lumped into articles about the rise of the “enterprise cool kids.”

So it would seem somewhat inevitable that the chief executives would form a partnership. And today, the companies released a new product dubbed “GoodBox.” Box customers can now visualize relevant data to better understand the effectiveness of their teams.

GoodData claims that it will also be easier for customers to monetize their content. GoodBox can be used by execs to identify behavior common to high-performing individuals, and apply the insights to optimize the whole sales or marketing team.

“Eventually, we expect to be able to provide predictive solutions based on the information GoodBox is gathering for us,” said Damian Fasciani, a technology services manager at REA Group, and one of the first customers of the new product.

Source: Sean Ludwig/VentureBeat

Box CEO Aaron Levie

“It’s a win for Box as their customers will be engaged, and it fits into our vision to organize an enterprise’s data,” said Stanek, GoodData’s CEO.

Stanek told me in a recent interview at the VentureBeat office that the company makes about 50 percent of its revenues through white-labeling its products. Rather than build their own “big data” analytics tools, software as a service vendors like Zendesk can opt to embed GoodData. “This is the new cloud integration model,” he explained.

Indeed, rather than engage in the so-called “cloud wars,” Stanek said his strategy is to forge partnerships with vendors in related categories. ”I am playing nicely with Box and Amazon Web Services,” he said. “This is how you succeed in the new world of enterprise software.”

That’s going back to the future, according to Monetate: going back to a time when all commerce was personal.

But there is a yin and a yang here.

While we may want personalized experiences, and we want websites to be smart — to know us, essentially, and act as an intelligent, solicitous person might — privacy is part of the picture. A good third of us don’t want our website activity tracked, and a quarter of us don’t want the websites we shop to personalize our experience at all.

Monetate has four tips for online retailers:

1. Use marketing automation technology and big data to assist with personalization
2. Target segments with relevant content based on what you know about them
3. Don’t think of channels, think of customers first
4. Be in it for the long haul, not the quick win

All the data, in visual form:

photo credit: crsan via photopin cc

Enigma launched out of beta today to shed light on this hidden world. This “big data” startup focuses on data in the public domain, such as those published by governments, NGOs, and the media. According to founder Marc DaCosta, this data is “totally in the dark” and “scattered across a dizzying array of data silos.” Enigma is building infrastructure to connect all of it together and make it searchable and accessible through a web platform and API.

“Currently, the world of public data is much like the world that existed on the Internet before search engines became available in the 1990s,” said DaCosta. “Because there is no infrastructure to search and discover public data, huge sources of real important insight and knowledge about how companies, people, and places interact in the world is hidden from view. By surfacing this data in a usable and intuitive way, Enigma empowers a factual, data-driven view of the world that currently is not possible.”

The company describes itself as “Google for public data.” Using a combination of automated web crawlers and directly reaching out to government agencies, Engima’s database contains billions of public records across more than 100,000 datasets. Pulling them all together breaks down the barriers that exist between various local, state, federal, and institutional search portals. On top of this information is an “entity graph” which searches through the data to discover relevant results. Furthermore, once the information is broken out of the silos, users can filter, reshape, and connect various datasets to find correlations.

DaCosta said that while there are plenty of notable players in public data, such as Factual, Socrata, Bloomberg, Thomson Reuters, and LexisNexis, Enigma is distinguished by its “holistic approach” to data acquisition and interface which supports organic discovery of data. He was working on an interactive cartography project funded by Intel and grew frustrated with how difficult it was to easily access and use public data. His former classmate at Columbia University Hicham Oudghiri was working as a currency trader in New York and felt the same pain point. So the two founded Enigma in 2011 to map the “huge terra incognito” that is public data.

The technology has a wide range of applications, including professional services, finance, news media, big data, and academia. Engima has formed strategic partnerships in each of these verticals with Deloitte, Gerson Lehrman Group, The New York Times, S&P Capital IQ, and Harvard Business School, respectively.

For example, someone looking for a correlation between student loans and unemployment could connect Sallie Mae default data with Bureau of Labor statistics on unemployment. Or a journalist could dive into whether the plant that exploded in Texas had any safety or environmental violations with the EPA or OSHA.

Enigma launched in beta in February 2013. Today, the company is also announcing a strategic investment from The New York Times which brings its total seed funding to $1.45 million, with investment from Crosslink Capital, Tripplepoint Capital, Brent Hurley, and Strauss Zelnick. The company is headquartered in New York City and has 13 employees.

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Makes some sense, no?

So the ratings site checked all the credits on the all-time top 500 movies. And then checked where those actors, directors, and producers went to school to learn their trade.

The result?

A ranked list of the top 25 film schools in the world — or, at least, the English-speaking world:

1. New York University (208 credits)
2. University of Southern California (186 credits)
3. University of California – Los Angeles (165 credits)
4. Yale University (110 credits)
5. Julliard School (106 credits)
6. Columbia University (100 credits)
7. Harvard University (90 credits)
8. Royal Academy of Dramatic Art (86 credits)
9. Fiorello H. Laguardia High School of Music & Art (64 credits)
10. American Academy of Dramatic Arts (51 credits)
11. London Academy of Music and Dramatic Art (51 credits)
12. Stanford University (50 credits)
13. HB Studio (49 credits)
14. Northwestern University (47 credits)
15. The Actors Studio (44 credits)
16. Brown University (43 credits)
17. University of Texas – Austin (40 credits)
18. Central School of Speech and Drama (39 credits)
19. Cornell University (39 credits)
20. Guildhall School of Music and Drama (38 credits)
21. University of California – Berkeley (38 credits)
22. California Institute of the Arts (38 credits)
23. University of Michigan (37 credits)
24. Beverly Hills High School (36 credits)
25. Boston University (35 credits)

Using publicly available data on movie quality was critical, according to Ranker data scientist Ravi Iyer. Ranker used data from Freebase, DBPedia, IMDB, and its own rankings in order to arrive at a list of quality movies from which the source data was gathered.

Otherwise, of course, movie-mill film companies that produce reams of B-quality films would skew the rankings.

When the company mashed the data, it turns out that even though USC produced the most film credits by graduates overall, NYU produces more credits on the top 500 movies. Interestingly, some high schools made the list, such as New York’s Fiorello H. Laguardia High School, at number nine, and L.A.’s Beverly Hills High School, at number 24.

It’s perhaps not exactly big data, with 500 movies and perhaps 100-250 contributors to each, but it is a big data approach to solving a problem.

photo credit: silkolive via photopin cc

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Sept. 9 - 10, 2013
San Francisco, CA

Early Bird Tickets on Sale

The hottest enterprise startups will take investment from In-Q-Tel, regardless of whether or not they need the cash.

Today, a well-funded enterprise company called Apigee revealed it has taken a highly-strategic investment from the firm. But the details of the deal have deliberately been kept under wraps. It’s a similar story to Huddle, the cloud collaboration startup that had just closed a sizable funding round when In-Q-Tel offered to invest. For the founders and board of directors, it was a no brainer to take the additional check.

Why is In-Q-Tel treated with such reverence by business software providers? It is the investment arm of the CIA and specializes in funding technology that is secure enough for government agencies. It was founded during the dotcom boom when the agency was drowning in data that it needed a secure technology to manage.

Huddle’s exec team took “purely strategic” investment from In-Q-Tel.

“In-Q-Tel is a great stamp of approval on any company, and opens up a huge market,” said Alistair Mitchell, Huddle’s CEO, in an interview. As a direct result of the In-Q-Tel investment, Huddle gained two large customers: the Department of Homeland Security and the National Geospatial-Intelligence Agency.

It’s an ego boost to get a phone call from In-Q-Tel, but more importantly, it’s a direct path to major government customers. In-Q-Tel has had its hands in virtually every enterprise success story, and has invested in a lot of the technology we use in our daily lives.

“Much of the touch-screen technology used now in iPads and other things came out of various companies that In-Q-Tel identified,” said Jeffrey Smith, the former general counsel of the CIA, in a rare interview with NPR. Google Maps is another example of In-Q-Tel-backed tech.

Don’t miss the shortlist of In-Q-Tel’s portfolio investments in the gallery below.

Government customers are also a strong indicator of a company’s commitment to security. According to Mitchell, Huddle won new clients in the health and financial sectors, who viewed the In-Q-Tel investment as proof the product was sufficiently “vetted and accepted by the best.”

In-Q-Tel has invested in cool tech, like this pen that can expedite data entry.

When pressed, Apigee wouldn’t reveal much about its relationship with In-Q-Tel, or the amount of funding it received. The company’s head of marketing Dave Jordan said little more in an interview than they were “thrilled” about the investment and “looked forward to expanding our relationship with In-Q-Tel and its government partners.”

Jordan and the Apigee team view this investment as a sign that it’s core product, an API management platform, will become more of a priority in Washington D.C. Developers can use the platform to build applications that will prove useful to the various government agencies.

Curious about In-Q-Tel’s current portfolio? Here are some of the firm’s most cutting edge investments.

Top image via Adapx

SAN FRANCISCO, Ca.: The dirty word that no one would address at Dell‘s press event this afternoon? “Privatization.”

The computer maker held the press event to introduce the press to its new software group. But the meeting was overshadowed by news this morning that private equity firm Blackstone has dropped its bid for Dell, bringing an abrupt halt to the company’s plans to go private. The New York Times reported that Dell’s board has no one but itself to blame for the debacle.

Dell execs would not respond to requests for comment on the failing deal, preferring to remain tight lipped about the whole business.

With a media firestorm swirling around them (and press hungry for answers), Dell execs hope to settle customer and partner anxiety with talk of their “big data” and cloud software products. The company is pushing on with plans to provide hardware and software to existing customers, primarily mid-market companies.

“Dell has been thinking about this transition to software for a while,” said David Kloba, a general manager for Dell Software, who joined the company by way of acquisition. But Kloba admitted that even two years ago, his team met with the sounds of “crickets” when he broached the topic of software at Dell. Now it’s considered the future, especially given that PC sales are in decline.

Dell Software CIO Carol Fawcett

Dell’s software practice is a essentially a melting pot of products it has acquired over the years. Carol Fawcett, the chief information officer for the group, got the job after Dell acquired her company, Quest Software.

“Dell wants to be an end-to-end solutions provider, a one stop shop,” said Fawcett in an interview. “But we don’t want to be another Oracle.”

Fawcett said her team is focused on expanding the current software suite, which already includes units dedicated to mobility, data protection, and security. Fawcett said Dell’s products can meet each customer’s IT needs — whether it’s a single product or a full solution.

But just like its privatization plans, Dell is in a precarious spot. Joshua Greenbaum, an IT analyst with Enterprise Applications Consulting, said it needs to make a choice between offering the ”integrated portfolio … the soup to nuts” and offering software that “fills a gap.” “It’s hard to do both,” he said.

Related: Michael Dell also sees a future in storage — Dell launched a $60M fund to support startups.

For now, Dell’s vision is expansive. Both Fawcett and Kloba welcomed a comparison to consumer device giant Apple, albeit with more of an enterprise focus.

Fawcett also referenced another competitor in the small- to medium-sized business space: Intuit, the maker of QuickBooks software.

“Mid market is our bread and butter,” she said. When asked about whether Intuit and Dell will be the two companies associated with software for SMB’s, Fawcett immediately shot back, “I’d say Dell.”

Carol Fawcett image via Quest Software’s website 

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DataHero, the startup that would do away with Excel, has just launched its free product to the public.

DataHero’s tool can spin up a data visualization in a matter of minutes. Anyone who’s been tasked with creating a pesky pivot table for work will breathe a sigh of relief.

“We have a lot of intellectual property around making sense of the data before the user has to do anything — way before they ask a technical person for help,” cofounder Chris Neumann (pictured above, left) explained in a phone interview.

Register for free, and you’ll be invited to peruse sample data sets, or upload your own data set. The technology integrates with Excel — so you can upload a CSV file. Then you can use the tool to create nifty visualizations that will knock the socks off your colleagues and boss.

DataHero will initially offer a free service to consumers. However, businesses that require integration with existing business tools, such as Stripe, Dropbox, and Salesforce, will need to pay a fee. The payment model is not yet concrete — Neumann said the company will soon be introducing an enterprise subscription service.

DataHero’s competition includes Tableau Public, Microsoft’s Excel, and business dashboard Chartio, but Neumann believes it is one of the few startups designed for individuals with little or no technical expertise. “We see ourselves as data visualization for the 99 percent of people who don’t work with statistical queries everyday,” he said.

Thousands of people have used the beta since it was released last year. The Palo Alto, Calif. startup has grown to a team of five (all designers and engineers) and has raised a seed round of less than a million dollars in funding. DataHero intends to raise an institutional round of funding this year and is hiring engineers as it preps for growth.

Still in stealth mode, DataHero made our 2012 list of the hottest “big data” startups. Check it out here.

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Alvaro Fernandez is the CEO of SharpBrains.com.

This month, President Obama launched the BRAIN (Brain Research through Advancing Innovative Neurotechnologies) Initiative, which the administration calls “a bold new research effort to revolutionize our understanding of the human mind.” The president announced $100 million in funding to kick off the research project, likening the effort to previous “grand challenges” like the human genome mapping initiative and the Apollo space program.

As the president noted, we can identify galaxies light years away and study particles smaller than an atom, but “we still haven’t unlocked the mystery of the three pounds of matter that sits between our ears.” For that reason, the BRAIN Initiative is welcome news.

However, it’s unlikely to move the needle because, unlike previous national projects, it lacks adequate funding and actionable objectives that can capture the imagination of innovators and the public at large.

The U.S. needs more commitment to brain science

To put the BRAIN Initiative’s $100 million in funding into perspective, consider that the European Union has allocated $1.5 billion for its Human Brain Project, and China’s Brainnetome initiative has been operational for almost a decade.

In the U.S., the National Institutes of Health, one of the primary beneficiaries of the BRAIN Initiative, is taking a $1.6 billion dollar hit due to current budget battles.

So while the $100 million in BRAIN Initiative funding is a good start, it’s not nearly enough. And the need for a broad, concerted effort to tackle brain-based challenges couldn’t be more clear or urgent: According to a recent RAND Corporation study, the cost of caring for patients with dementia in the US is already at least as expensive as treating cancer or heart disease, and the cost is expected to double by 2040. Yet, the attention and resources allocated to brain health and mental well-being today pale in comparison to those devoted elsewhere.

The BRAIN Initiative needs actionable goals

When President John F. Kennedy articulated his vision for the US space program in 1961, he outlined a clear objective: “I believe that this nation should commit itself to achieving the goal, before this decade is out, of landing a man on the moon and returning him safely to earth.”

The BRAIN Initiative could benefit from a similar clarity of purpose. In his speech, President Obama alluded to new insights into Alzheimer’s disease, autism, strokes and traumatic brain injury that research could produce. But bold initiatives call for audacious objectives: To capture the public’s imagination and inspire cutting-edge scientific innovation, the BRAIN Initiative should “go big,” embracing clear goals and working backwards to achieve measurable progress towards those goals.

As it has been articulated so far, though, it comes across as one more interesting basic research project disconnected from those pressing societal priorities.

Digital health entrepreneurs have a role to play

Is there an alternative approach? Yes. Start with the societal goal in mind, and chart the most likely path to make a groundbreaking difference there. For example, relatively inexpensive assessment tools distributed over the Internet can generate valuable data to expand understanding of cognitive functioning, and Big Data analysis can yield amazing insights that have immediate applications in health, education and productivity. Public-private partnerships could help advance the BRAIN Initiative’s goals.

Other countries have already successfully tried these approaches:

• In Canada, a brain health check-up application is providing valuable data for individuals, while gathering information that will provide insights for public health policy decisions.
• The United Kingdom’s National Health Service has rolled out computerized therapies as a first-line treatment for anxiety and depression.
• In Singapore, quantitative EEG monitoring and training are building students’ concentration capacity.

To inspire progress, focus on fitness

Although the BRAIN Initiative is often compared to the moon project, perhaps the better opportunity would be for it to draw inspiration from JFK’s fitness initiative. In announcing that program, President Kennedy said, “The strength of our democracy and our country is really no greater in the final analysis than the well-being of our citizens.”

The truth of that observation applies to brain health as well as physical fitness. Practical goals related to brain health and a call to action that inspires all Americans — including innovators in the digital health space — would make for a smarter BRAIN Initiative.

I wish the best for the new BRAIN Initiative. It’s always good to have more research dollars and new research tools. But this small initiative is no substitute for the concerted effort we’ll need to plan and implement in order to optimize the brain health and performance of over 300 million Americans and over seven billion brain-owners worldwide.

Alvaro Fernandez, named a Young Global Leader in 2012 by the World Economic Forum, is the CEO of SharpBrains.com, a leading independent market research and think tank tracking health and productivity applications of neuroscience. He has been quoted by The New York Times, The Wall Street Journal, CNN, Reuters, and Associated Press, among others.

Image credit: Liz Henry/Flickr

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Paypal cofounder Peter Thiel doesn’t shy away from making risky bets in disruptive technology and research.

Today, the Thiel Foundation announced it would grant $350,000 to two of Breakout Labs’ teams: SkyPhrase and Stealth Biosciences. Both startups are working to bridge the gap between technology and the biological sciences.

SkyPhrase is working on making computers understand human language with greater precision. It’s creating a platform to search, access, and monitor data that relies on natural language processing. The company is already viewed as a potential competitor to Apple’s Siri, which struggles to understand complex-language questions.

Stealth Biosciences is developing a device that can exert control over biological processes at the level of single cells. The technology is the brainchild of Stanford University professors Nick Melosh and Craig Garner. These devices could be a new toolset for research and diagnostic applications; it will initially use the funding to develop a “Nanostraw” device that can reprogram human T-cells for the purposes of cancer therapy.

Thiel is funneling the millions he made through early investments in Facebook, Yammer, and Yelp into potentially life-saving research. Through the nonprofit Breakout Labs, which was founded in 2011 and received financing through the Thiel Foundation, he kick-starts ideas that most traditional venture capitalists wouldn’t touch.

Last year, Breakout Labs’ funded a dozen startups, including a venture to produce bioengineered animal products — lab-grown meat — with about $4.5 million. Breakout Labs awards grants from $50 to 350,000 on a rolling basis, but the funded companies retain their intellectual property.

Many of the companies admitted into Breakout Labs go on to win government and academic grants, and many of the founders are university professors. This reflects “a growing and much welcome interest from the academic community in commercialization,” said Breakout Labs executive director Lindy Fishburne.

Peter Thiel image via Kenneth Yeung/Flickr

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Could you explain your enterprise technology company to a 13-year-old?

We’re all a bit sick of the “big data” trend, and founders making abstract claims about “answering unknown knowns.” And don’t even get me started on “SaaS,” “Dbass,” and “PaaS” (frankly, they are all a pain in the ass).

So VentureBeat has hatched a crazy plan to banish buzzwords and marketing speak, at least for an afternoon.

At our San Francisco office, VentureBeat invited four CEO’s from various cloud and “big data” companies to talk tech in an on-camera interview.

The catch? The execs would face a grilling from … a group of kids flown in from around the country. We didn’t just want Bay Area natives on the panel, as many of them grew up surrounded by tech. So we contacted an ed-tech startup called Rocket21, which helps young students explore their interests and passions, and they assembled a group.

The kids proved to be straight-talkin’ interview machines (I’d better watch my back; I could be out of a job one of these days) with a keen understanding of consumer technology and mobile apps.

We were gobsmacked when Ella Gillmor, 13, asked Huddle founder Andy McLoughlin about his data center (pictured above, center, with Gillmor and Alexa Stephenson) and if the cloud collaboration tool was “similar to Apple iCloud.” Her dad, Steve Gillmor, works in a senior role at Salesforce.com, so she’s clearly versed in tech. Likewise, 13-year-old Tommy Whiteley asked some tough questions about the Software-as-a-Service business model (“Do you make money by selling to people or companies?”)

It became apparent during the interviews that these tech natives had little patience for jargon. Gillmor prompted Chris Saad, the cofounder of Echo, to explain the meaning of a widget (which Saad aptly described as a “piece of a website that gets put into other websites”).

Alexa Stephenson, a high schooler from Tennessee, asked the founders whether big data is like the movie Fight Club because no one could give her a straight answer, which prompted nervous giggles from the room.

“Um, I don’t know where to start,” Kaggle CEO Anthony Goldbloom responded. “I knew it. It’s the first rule,” Stephenson said.

It’s not easy to explain business intelligence and analytics in such simple terms. Here were some of our founders and CEO’s explanations:

1. On business intelligence and analytics: Birst CEO Brad Peters made a useful analogy to driving a car. ”The dashboard measures how fast you’re moving … [otherwise] it would be hard to drive a car,” he said. “Analytics is the dashboard for a lot of people and business to understand things that are more complex than what’s right in front of you.”
2. On big data: Goldbloom gave it a stab: “Big data is mostly about taking numbers and using those numbers to make predictions about the future. The bigger the data set you have, the more accurate the predictions about the future will be.”
3. On the cloud: “The dirty secret in this industry is that nobody even really knows what the cloud is,” said McLoughlin. “What it means is it’s put up in the Internet and it’s gone to our servers and it’s held somewhere centrally. All it means is it’s a place up there in the sky that is available from all of your devices no matter where you are.”

For further jargon-free (we promise!) discussion, register for DataBeat 2013, the conference about putting data to work.

Kids from VentureBeat on Vimeo.

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IBM sees a substantial future in Flash storage, announcing plans for a $1 billion investment in Flash research and development.

The surge in data use from smartphones and tablets has taken its toll on corporate servers. IBM sees a solution in Flash memory, which has the potential to process the data faster, more reliably, and with less energy than a hard disk drive full of moving parts. IBM’s investment will go toward designing and building servers, storage systems, and middleware that better integrate Flash storage.

“The economics and performance of Flash are at a point where the technology can have a revolutionary impact on enterprises, especially for transaction-intensive applications,” IBM systems & technology group general manager Ambuj Goyal said. “The confluence of ‘big data,’ social, mobile, and cloud technologies is creating an environment in the enterprise that demands faster, more efficient, access to business insights, and Flash can provide that access quickly.”

Along with investing in future Flash-based hardware, IBM announced that its new FlashSystem line of appliances are available to businesses. Based on technology acquired from Texas Memory Systems, the FlashSystem line only uses Flash memory, which according to IBM enables it to be 20 times faster than a standard spinning hard drive and store up to 24 terabytes of data.

It also boasts an energy reduction of up to 85 percent for business analytics applications, or up to 80 percent in cloud-based data centers.

Sprint Nextel is one of the first companies to adopt IBM’s Flash-based vision, cutting a deal to install nine of these storage systems at its data center.

To help convince other companies to jump on board, IBM is establishing 12 of what it calls Centers of Competency around the world, places where potential clients can run tests using IBM’s Flash servers to see how they handle the data. Centers of Competency are being built and will be up and running by the end of 2013 in China, France, Germany, India, Japan, Singapore, South America, the U.K., and the U.S.

The San Francisco-based company was founded by Dan Siroker, an engineer who was inspired to launch the startup when working at Obama for America.

Siroker told me he first heard President Barack Obama speak in 2007 when he was working as a product manager at Google. Obama had made the pilgrimage to Silicon Valley to root out analytics tools that would prove beneficial for the campaign.

Siroker was so inspired that he quit Google, and went to work for the campaign as a volunteer. He would later rise up the ranks to become the director of analytics, and would lead a team of analysts using new technology to maximise voter registration and donations. “Even back in ’08, we viewed data as a means to make better decisions,” he explained by phone.

It was during his time working in D.C. that Siroker grew frustrated with the current suite of web optimization tools, such as Adobe’s Test & Target. So Siroker set out to create his own technology, Optimizely, which is billed as “A/B testing software you’ll actually use.”

The Obama and Romney campaigns used Optimizely in 2012, alongside 4,000 other customers. This quarter, it will launch the product in nine languages in 36 countries. The company says its revenue growth rate from March 2012 to March 2013 is 400 percent.

Optimizely’s team claims to be the third highest valued startup to graduate from the prestigious accelerator program Y Combinator — after Dropbox and AirBnB.

As part of the funding, Benchmark’s Peter Fenton will join the company’s board. Bain Capital Ventures, InterWest Partners, and Google Ventures participated in the round.

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This is a guest post by Christian Fritz, a researcher at PARC.

Over the past 10 years or so, many organizations have recognized the conceptual value of data and have started recording and retaining more and more of it. But after doing this for a while, they’re asking, “What the heck do we do with it?”

Google, Netflix, and other big companies have taught us that data is valuable for insights that can be obtained from it, so others have started exploring their own data and want to do more with it. Some companies have been doing this in a way that sets no expectation as to what can be learned from it.

Rather than starting with a question and looking for an answer, people started finding the questions data was already answering. The idea has been to find the hidden potential of data, and we’ve already seen benefits to doing this type of analysis.

Fixed data or fixed problem?

What if you are really committed to solving a specific prediction problem or have a specific question in mind? Project managers know that they need to decide which of their time, budget, and schedule are fixed and which are more flexible, and that this decision can fundamentally change the nature of a project.

We need to make that same kind of decision in data analytics projects: The common opportunistic nature of “big data” implies that the question is more flexible than the data that can be used, which is fixed. If you reverse this — fix the question and accept flexibility in the data — then it now defines “infinite data.”

Predicting suicide

A recent example of an infinite-data problem is the Defense Advanced Research Projects Agency’s (DARPA’s) interest in predicting suicide.

Consistent with DARPA’s mission statement, this is an extremely ambitious endeavor, a “DARPA-hard-problem,” as DARPA would say. Also consistent with the way DARPA operates, there is not one prescribed way of approaching the problem or a defined set of data sources to be used. Hence, the playing field is wide open for approaches that rely solely on analyzing existing data on suicide in more depth and using more sophisticated machine-learning algorithms following the big-data mindset.

However, we can also consider a reversed approach and focus on incrementally identifying and collecting the right data (e.g. better understand the specific problems of at-risk personnel and how these problems manifest in measurable data). Once the object of interest is being studied, the possibilities are endless regarding the kinds of data to be collected and analyzed as long as privacy concerns are met. In the context of suicide, data can come, for instance, from clinic notes, interviews, social media behavior observed by friends, shopping behavior, and location data.

But it does not have to end there — there is always more data. Your smartphone already has access to tons of interesting data, and companies are already using it to target ads and provide buying recommendations. It seems more than appropriate to use this data to extract some value for users as well. After all, you are the ultimate owner of your behavioral data.

Infinite data: Why now?

The time is ripe for infinite data for two reasons:

• Big data has laid the computational and economic foundation for dealing with vast amounts of a wide variety of data.
• There are many possibilities for instrumenting environments where predictions can be made, even if the environment is physical. The possibilities exist in the form of very cheap sensors, which are already all around us.

Smartphones already capture such a slew of data about us that it now seems reasonable to even try and use this data to make difficult predictions about medical conditions such as the progression of Parkinson’s Disease.

New challenges

Given the different nature of infinite-data projects from big-data projects, how do we go about executing such a project, and what is required for it to be successful? There are three big challenges with infinite data:

• The streams of data never end.
• There are infinite ways of pre-processing data to carve out relevant features, including simple combinations of individual data points or more complicated ones such as change detection in the frequency domain of temporally recurring events.
• There are always more kinds of data that can be obtained and additional models that can be applied to infer additional data.

In practice, even though data may be infinite, our available computational power and our budget for acquiring new data sources is not. So we need to identify the most relevant and significant features obtainable.

Succeeding with infinite data

The team that will succeed with infinite-data projects in practice needs to be multidisciplinary. It needs to include subject matter experts as well as a set of experts in a broad set of disciplines of computer science including feature extraction, signal processing, computer vision, natural language processing, design of experiments, automated diagnosis, spatial and temporal analysis, modeling, and statistical machine learning.

Such a team will develop new methodologies for going about the data identification exercise in a principled fashion that makes the process repeatable and generalizable. Such a methodology might be based on inferring from data that is already available to guide further data acquisition decisions. Or, the team might deploy “cheap” experiments on sub-populations or at low resolution to estimate the value of information that can be obtained from a higher resolution and larger data collection effort.

Whatever the optimal methodology ends up being, two things are clear: challenging prediction problems will get solved and tons of new data will be collected that will set off a new wave of yet more opportunities that come along with it, closing the loop with big data as we know it.

Christian Fritz, Ph.D., is a PARC researcher working on real-world applications of artificial intelligence with particular interest in the combination of symbolic knowledge representation and machine learning; behavior recognition; planning; execution monitoring; modeling; and diagnosis.

Big data image via Toria/Shutterstock

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SiSense closed a $10 million funding round today for its “big data” technology that competes with giants like SAP and Oracle.

“We are seeing most success when we compete against the bigger guys who are too expensive or can’t scale to the terabyte [data] range we are going after,” said Bruno Aziza, SiSense’s marketing lead, by phone.

SiSense’s VP of Marketing, Bruno Aziza

Every company in the business intelligence and “big data” space will tell you that they analyze mountains of data and infer actionable insights that can inform a customer’s business strategy. SiSense’s messaging is no different. But according to Aziza, the competitive advantage is that SiSense won’t just ask for a sample of data — they want it all.

“We can use the entire data set,” said Aziza, meaning that SiSense will analyze all manner of structured and unstructured information, including emails, documents, and tweets. “That is really difficult to beat from a competitive standpoint,” he added.

In the past year, the company says it’s experienced a 520 percent growth in subscription revenues. Customers include household name brands like Target and Merck, as well as hot startups with a mass of customer data, such as Wix and Uber.

We reported on the Israeli company last month when its chief technology officer crunched 10 terabytes of data in 10 seconds using an off-the-shelf $10,000 machine. The team pulled off the stunt at the O’Reilly Strata Conference, which focused on “big data.”

Marketing lead Bruno Aziza said the company plans to use the funding to open an office in New York. SiSense will also ramp up its sales efforts, and it recently brought on a sales lead from networking giant Cisco.

The funding was led by Battery Ventures with participation from Opus Capital and Genesis Partners.

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“Big data” startup Deep Information Sciences has raised $10 million in its first round of funding to help its customers with a “new approach” to database architectures.

Deep was previously known as CloudTree, but the company took on the Deep name and a new focus on big data today. Its flagship product is a database appropriately called DeepDB, and it provides simultaneous transactions and analytics in the same data set in real time. Companies can access DeepDB from on-premise or via the cloud, depending on company needs. The big three things it aims to solve are “minimizing disk space, maximizing cache effectiveness, and optimizing CPU and core concurrency.”

“We are bringing to the market an innovative way to manage big data in real time,” Deep cofounder and CEO Kurt Dobbins said in a statement. “Our powerful technology, combined with our leadership bench strength, positions us for a fast path to market leadership in big data. We look forward to the opportunity to help our customers overcome the challenges of big data and seize competitive advantages through a next-generation approach to data management.”

The new funding was provided by Robert Davoli, Stage1 Ventures, Cabletron cofounder Robert Levine, Chamberlain & Steward, Alessandro Piol, and other angel investors.

“They are addressing the fundamental issues around performance and scale that have challenged databases for decades, and they’re coming at them from a unique perspective,” Davoli said in a statement.

Deep is based in Portsmouth, N.H., and was founded under the CloudTree name in 2010 and it claims to have spent “thousands of hours” developing DeepDB.

Top photo via Deep Information Sciences

It’s no secret that a cadre of data analysts helped President Barack Obama secure a second term: While on campaign trail, the campaign rigorously assessed and measure the electorate with a batter of new tracking tools.

Political candidates are harnessing this proliferation of data in new ways. But how can you use this information to hold politicians more accountable?

A startup called ElectNext has developed a database that can determine where politicians really stand on the issues. It gleans its information from organizations like The Sunlight Foundation, GovTrack, and Follow the Money, and it also works with open data-friendly cities like Philadelphia.

And the company just closed a $1.3 million funding round so it can bring its data to where it’s needed most: the media.

“If ElectNext can be the vehicle that helps us deliver open data to the public, that’s a perfect partnership,” said Sunlight Labs director Tom Lee, the data arm of the Sunlight Foundation.

When ElectNext launched at the DEMO conference in October, it was focused on consumers. In the run-up to the presidential election, the survey would help you decide who to vote for.

“‘Big data’ is the biggest trend in politics today. Yet all the benefit of that big data is going to industry, not individuals. We’re here to change that,” Dannenbaum said at DEMO.

The mission struck a nerve with the audience. Andreessen Horowitz partner Frank Chen wrapped up the reviews by calling ElectNext the most compelling presentation of the day.

Since then, the Philadelphia and New York-based company has been building technology that can better integrate with its set of media partners.

So if you’re on a political news site and come across an issue, a person or a political event, click on an ElectNext embedded profile. Each profile will contain relevant biographical, campaign finance and legislative information. ElectNext is currently working with The PBS NewsHour, Hearst, the Washington Post, and the Philadelphia Inquirer, among others.

The new focus on the media will lead to new revenue opportunities. Dannenbaum told me she envisions a subscription service for politicians and political groups who want to monitor their earned media across ElectNext’s publishing partners.

The seed round was led by Brooklyn Bridge Ventures along with Liberty City Ventures, Digital News Ventures, Gabriel Investments, The John S. and James L. Knight Foundation, Comcast Ventures and Investor’s Circle.

Top image via DEMO Conference // Stephen Brashear 

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From healthcare and financial services, to government and academia, “big data” has gotten unprecedented attention as industries across the board struggle to answer a key question about their data: what does it all mean?

Now, more than ever, companies are seeking out talent with deep analytical skills who can find the value in their data. And while machine-based learning algorithms and advanced math is a starting point for breaking down data sets, finding meaningful relationships and connections in data points requires human skill and intuition to extract the most value. Enter the latest prized commodity — data scientists.

Demand for data scientists is heating up

Demand for data scientists is sharply on the rise. The U.S. alone will need 140,000 to 190,000 people with deep analytical skills by 2018 just to keep up with the pace of innovation, according to the McKinsey Institute. Though it’s clear that companies are hungry for this kind of talent, there simply aren’t enough data scientists to go around.

We’ve already begun to see academia capitalize on this opportunity, like the data science and statistics initiative that New York University just launched. While a person can certainly get a leg up by taking these courses, data science doesn’t lend itself to a defined career path. Granted, there are real technical requirements needed for a successful career in data science, but knowing how to manipulate data is arguably one of the most critical skills.

Algorithms will only get you so far

The important part of the term data scientist isn’t “data”, but “scientist.” Scientists work to find results that are correct, compelling, and convincing. That’s what data scientists do, too — they just do it with bits. They need to be knowledgeable about machine learning and statistics, but beyond analytical talent and computational ability, they must be observant and persuasive.

There’s no doubt that computer algorithms are getting more powerful. Already, they are being used to predict outcomes like customer retention, average patient hospital visits, and whether or not an individual will default on a loan. However, computers have their limits. As one New York Times columnist recently observed, “capable as these machines are, they are not always up to deciphering the ambiguity of human language and the mystery of reasoning.”

Computers can’t recognize context or nuance, but people can. Whereas a machine will do exactly what you tell it to do — no more and no less — the role of data scientists is more complex. It’s the data scientist’s job to raise objections and to question any result that the machine yields. That is the real value that they bring to the table. This kind of creative approach to analyzing data is an art itself. And when you marry science (machine learning models) with art (human inference), you’ll get more accurate relationships and conclusions you can trust.

What to look for in a data scientist

I work with a team of several modelers and, interestingly, no one on our team has a core background in machine learning. We have team members with degrees in physics, psychology, and even pure math; most, but not all have doctorates. That’s because data science is a performance-based field: a person’s creative approach to a problem is the key skill, more important than simply another PhD. That may be counterintuitive to those making the hiring decisions, who might typically assess a candidate based on paper, and it can certainly complicate the hiring process, but it’s a critical fact.

Whether a company is looking to build a more robust internal data science team or to outsource its analytical talent, where’s the best place to look? Though you might be tempted, I advise that you avoid looking through the traditional channels for fresh hires. The computer science major from that Ivy League school may look good on paper, but there are also a lot of high-performing computer science majors that don’t necessarily attend the best universities. I guarantee that you’ll end up with a more well-rounded person and end up paying much less for someone with equal, if not greater, talent.

And if you’re going to cherry pick candidates working in academia, you’ll want to grow your team with a biologist or psychologist-type — someone who is not only familiar working with computers, but also with running data tests. In some cases, enterprises might already have the talent at their fingertips — they just haven’t realized it yet. I’ve found that forecasters and business analysts are often equally good working with data sets, but they’re often underutilized internally.

Capitalize on your data scientists

It used to be that company executives made business decisions based on their own intuition and hunches. Today, with the help of data scientists, these decisions are now being made on the basis of data.

It’s not that big data is new — a treasure trove of database information has been lying untouched for years. Only now, companies recognize the importance of data. As data science continues to evolve into a highly prized industry, the most valuable thing that enterprises can do is to capitalize on your analytical talent and do it before the competition.

John Merrill is a software engineer and modeler at ZestFinance, a Los Angeles-based financial services technology company that uses big data to help make better credit underwriting decisions in order to provide credit alternatives to the underbanked.

Dog on computer via Annette Shaff/Shutterstock

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Big data may be cool, but it’s not pretty. Visual.ly, a startup focused on data visualizations and infographics, has raised a new $2 million round of funding, the company has confirmed to VentureBeat today.

Visual.ly’s platform is like a “basecamp” for design projects that want to utilize big data from multiple sources. It’s clients (brands, businesses, and news organizations) subscribe for access to the platform, which can match them with Visual.ly’s community of over 55,000 designers. Once matched,the designer and the client work together using the platform, with Visual.ly’s team acting as a project manager. Essentially, it answer the question of what do to for companies after they’ve hired a designer and don’t quite know what to do next.

“Right now existing ad agencies are our biggest competitors,” Visual.ly CEO Stew Langille told me in a phone interview. He explained that, in addition to building useful, interactive graphics for clients, Visual.ly is becoming more attractive because it allows designers to earn more money on a project and advertisers to spend less.

Depending on the level of project management, Visual.ly takes anywhere from 10 to 30 percent cut of each project’s total cost. The goal, Langille said, is to improve the collaboration platform for designers and clients to work on their own. And while he didn’t disclose financial figures, Langille did say that Visual.ly’s revenue is increasing 300 percent each quarter, and the number of projects are increasing 200 percent per quarter.

The new round came from previous investors, such as SoftTech VC, 500 Startups, and Giza Venture Capital, as well as strategic investors that include Yammer chief product officer James Patterson and several top executives from the advertising industry that will help Visual.ly build out its presence in New York City.

The additional capital will primarily be used to advance the startup’s data visualization /collaboration technology and hire more talent over the next year. Langille said Visual.ly has held off on raising a larger round, but does plan on raising additional funding in the future.

Langille also said he’d like to use the funding to produce more free data visualization tools that showcase the Visual.ly platform’s potential. For example, the startup’s recently released tool that interfaces with Google Analytics to produce a weekly traffic report, which you can send via email automatically. Also, the startup’s Twitter user battle infographic tool.

Founded in 2011, the San Francisco, Calif.-based startup has raised a total of $4.4 million in funding to date. Visual.ly currently has 25 employees, with plans to hire another 10 to 15 people before the end of 2013.