Big Data

Big data investors: 'The challenge is to make simpler & easier tools'

Matt Marshall, CEO of VentureBeat, Ping Li, Accel Partners
Mike Dauber, Battery Ventures
Adam Ghobarah, Google Ventures
Tim Guleri, Sierra Ventures

Above: Matt Marshall, CEO of VentureBeat, Ping Li, Accel Partners Mike Dauber, Battery Ventures Adam Ghobarah, Google Ventures Tim Guleri, Sierra Ventures

Image Credit: Michael O'Donnell

In recent months, we’ve seen a rising backlash against the term “big data,” with analysts complaining that it’s overused to the point of seeming meaningless.

But Silicon Valley’s self-proclaimed big data investors don’t particularly care what you call it. To them, what matters most is that these new tools are proving to be big business. The leading companies in the space are currently valued at billions of dollars. A number of companies that help customers manage mountains of data, like Splunk and Tableau, recently experienced killer initial public offerings.

At VentureBeat’s DataBeat/Data Summit conference, investors from Silicon Valley’s top VC firms stressed that it’s a trend that warrants the hype. So moderator Matt Marshall, VentureBeat’s founder and chief executive, asked for their predictions on the next big trends in enterprise tech.

How will these big data investors spend their dollars?

Across the board, investors are funding technologies that make it easier for anyone to build predictive models.

“The next generation of software will make the user of that software intelligent,” said Ping Li, a big data investor at Accel Partners, a Silicon Valley-based venture firm.

What this means is that you won’t need a Ph.D in statistics to use the new data visualization and analytics tools. They are designed for junior-level employees at marketing and sales teams who can benefit from real-time information about potential customers.

Colleges like the University of California Berkeley are teaching data science. However, Li believes that the supply of data scientists can never meet the demand, so it’s important that the next generation of tools are accessible.

Tim Guleri, a partner at Sierra Ventures, said that tools have moved to a visual representation, which he views as a “tremendous opportunity,” as this makes data far easier to interpret. He believes that business intelligence software is on its way out. “SAS is a $2.5 billion revenue company — that has not been challenged; they need to go.”

Most of the investors expressed interest in vertical and horizontal plays, meaning startups with a narrow and broad industry focus. In the past, most technology companies adopted a horizontal approach, meaning that it broadly marketed products to a variety of sectors. Salesforce.com, for instance, aims to provide customer relationship management (CRM) tools for sales people in every industry, including education, financial services and health care.

It’s a portfolio company, but Li and Mike Dauber, an investment partner at Battery Ventures, both predicted that a startup called RelateIQ will be the next Salesforce.

Investors also see value in big data startups like Ayasdi, for instance, that have focused their energies on building a product for one major market. Ayasdi has found a niche in health care, and customers include the Mt. Sinai School of Medicine.

Google Ventures‘ investor Adam Ghobarah said he typically sees visualization technologies, tools to build predictive models, and data integration services. He’s particularly excited about startups that are applying data to large industries, like Climate Corporation, which Monsanto recently acquired for close to $1 billion, or Foundation Medicine, which recommends a personalized treatment option to cancer patients.

What skills do you need to become a data scientist?

At our DevBeat conference last month, Pivotal data scientist Hulya Farinas stressed that it’s a very “tiny jump” from developer to data scientist.

Not everyone in the industry agrees with Farinas, and this has provoked some interesting discussion about the core capabilities you need to land a data science job.

“The three competencies you need are math, programming, and business,” said Guleri, who stresses that the business ability should not be underestimated. It’s not just about finding trends in the data; the job of a data scientist is to apply this information to drive the business forward.

Another investment opportunity is data-science education tools, which can help students and employees can gain a base level of understanding. Last month, Guleri invested in a startup called Alpine Data Labs, which instructs its customers in how to best use the tools. Guleri believes that we’ll see the equivalent of a Khan Academy or Codecademy for data science.

With technology consulting firm McKinsey forecasting that the U.S. will face a shortage of up to 190,000 data scientists by 2018, it’s certainly a hot career opportunity. “We definitely need to keep promoting education at colleges and universities,” said Li.


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