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10/31/2014
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Big Data Software For Mainstream Users

Not everyone needs to be a data scientist, Red Lambda argues.

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When a task involves sifting through petabytes or more of streaming data to find insights, anomalies, or other kernels of actionable information, software can do what humans can't. But can software also lessen the dependence on human experts who guide big data strategies?

This question matters because a projected shortage of data scientists -- individuals with a mélange of tech-oriented skills, including business analyst, machine learning expert, and data engineer -- may hinder big data's potential within a few years. On the other hand, these fears may be overblown, particularly if technological advances render the data scientist less essential, if still vital, to organizations' data-driven plans.

Executives from Red Lambda, a big data security and analytics startup based in Longwood, Fla., say that increasingly sophisticated software tools are simplifying the complexities of big data and making it more accessible to mainstream business users.

[For more on making big data useful to more people, see Analytics For All, No Data Scientists Needed.]

In a phone interview with InformationWeek, Red Lambda founder and executive chairman Bahram Yusefzadeh and CEO Iain Kerr commented on how the role of the data scientist is evolving as streaming analytics software, such as Red Lambda's own offering designed to find security threats in vast streams of moving data, takes hold in organizations.

Detecting anomalies such as security threats, for instance, requires near-instantaneous analysis.

"In the world that Red Lambda functions in, we don't need a data scientist for solving the security problem and identifying anomalous behavior," said Yusefzadeh. "We don't have time for a data scientist to come and analyze, as this data is in motion. You're not going to find anybody who can do that."

Solving security problems, he added, is analogous to finding a needle in a haystack -- but in very short time.

"The bad guys are so smart, and are doing so much so quickly, that you don't have time for a data scientist to come in," Yusefzadeh added.

(Source: Hebi65)
(Source: Hebi65)

The notion that ongoing refinements will help make complex technologies accessible to mainstream users is nothing new, of course. But given the apparent shortage of data scientists needed to make today's complex big data software run smoothly, enhancements in analytics tools are particularly welcome.

"We're not saying you don't need data scientists in your organization," said Kerr. "There are many reasons why these folks exist."

But, he added, sophisticated software deployed effectively, such as Red Lambda's security apps that highlight anomalies, can enable business users with dashboard-style visualization tools to pinpoint potential threats very quickly.

Not every organization uses big data for near-real-time analysis, of course, and data scientists play an important role in many areas, including regulatory compliance and forensic analysis.

Red Lambda, for instance, has worked with a social media company that wanted to "slice and dice" its big data for purposes of selling the information, bringing in advertising, and myriad other uses that weren't particularly time-sensitive, unlike, say, security threats.

In addition, the concept of the "hired gun" data scientist is misleading, said Yusefzadeh, as it's difficult, if not impossible, for an outsider to parachute into a business and immediately understand its strategy and the relevance of its data.

"There's no way you're going to be flying in data analysts... [who] look at your data and tell you what you want to do," Yusefzadeh said. Rather, people who are "already inside your company," those on the executive, strategy, and marketing teams, will provide the real actionable insights.

Considering how prevalent third-party attacks are, we need to ask hard questions about how partners and suppliers are safeguarding systems and data. In the Partners' Role In Perimeter Security report, we'll discuss concrete strategies such as setting standards that third-party providers must meet to keep your business, conducting in-depth risk assessments -- and ensuring that your network has controls in place to protect data in case these defenses fail. (Free registration required.)

Jeff Bertolucci is a technology journalist in Los Angeles who writes mostly for Kiplinger's Personal Finance, The Saturday Evening Post, and InformationWeek. View Full Bio

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Li Tan
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Li Tan,
User Rank: Ninja
11/2/2014 | 11:19:54 PM
Re: Since when is security-threat detection a "mainstream business user" issue?
There are still gaps here - from technical professional's point of view, the analysis environment is already simple enough for mainstream business users. But for business users, it's still over-complicated and hard to manage. In other words, business users need to be more technology aware and technical professionals need to design the tool from layman's perspective.
fjones67
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fjones67,
User Rank: Apprentice
11/1/2014 | 12:31:52 PM
Re: Since when is security-threat detection a "mainstream business user" issue?
I agree with D. Henschen. This company seems confused about who they are selling to. Why would "mainstream business users" buy enterprise security tools? Outside of some niche areas (like brand analytics where SaaS offerings seem popular), all the streaming data analysis applications start with IT too.

It's not clear to me what differentiates them from Splunk or SIEMs?
D. Henschen
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D. Henschen,
User Rank: Author
10/31/2014 | 1:34:30 PM
Since when is security-threat detection a "mainstream business user" issue?
I'm not connecting security-threat detection or, indeed, most streaming-data analysis applications with "mainstream business users." At the very least, I would think we're talking about having security analysts or business analysts working alongside IT to tap into streaming sources.

Once IT is involved, I'm guessing the likes of Splunk or Sumo Logic would give Red Lambda a run for its money on supporting such analyses without having to turn to data scientists. Amazon Web Services Kinesis also seems to offer a comparatively easy stream-analysis environment, but, here too, I would doubt it would be something for "mainstream business users."
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