The solution to the Great Data Scientist Shortage of 2014 is not more data scientists

This sponsored post is produced in association with New Relic.

Data is no use without a helping hand to make sense of it all.

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Companies of all sizes are starting to drown in the sea of big data, and it’s not hard to see why: The questions you would traditionally have to field with your neighborhood data scientist, like “How many people signed up for our newsletter on its launch date?” are going unanswered.

However, you may be having trouble getting your hands on a capable data scientist these days. The demand for data scientists is so staggering that universities are scrambling to create training programs to put the next generation of data scientists to work. There are even startups like Kaggle whose entire mission is to match data scientists with companies who need them.

In the meantime, to deal with the chronic shortage of data scientists in the market, a lot of startups have created comprehensive solutions that help non-data scientists — especially marketers, product people, and others on the business side of a company — extract actionable answers from their databases.

But one of the most comprehensive data analysis solutions on the market right now comes from New Relic, a company that specializes in tracking application data and analysis for the data-obsessed. Its powerhouse data analytics have already attracted many big-name clients like Nike and Groupon.

The company recently launched New Relic Insights, a real-time data analytics platform that fetches data from your own software using queries like revenue by geography, unique visitors, and new signups with a few clicks. To boil that down into plain English, Insights lets business people quickly visualize and sort through data, all the better to make money-making and money-saving decisions.

The platform runs on a customized version of the SQL language called NRQL (pronounced “Nerkel”) that lets its customers “ask” their massive piles of data meaningful questions. Instead of looking at a list of visit logs that are essentially data points, NRQL users can ask specific questions about data in almost-English.

For instance, if you’re a publisher trying to get traction on a piece of content, you can ask how many page views there have been since the last hour with this SQL-esque command: SELECT * FROM PageView SINCE 1 HOUR AGO

You can also ask for targeted, specific data points — like which potential customers have been on the checkout page more than twice in the past week without making a purchase — and you can even ask for your data points to be visualized in a histogram. The key is that it’s easy to create and share dashboard, and easily modify existing queries to get to the precise information you really need.

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Here are some examples of how to use Insights to drill down into the key factors affecting your business:

Let’s say you want to find out how many customers your ecommerce site has in China. To single out this piece of data, you’d use this query:

SELECTuniqueCount(customerID)FROMPageViewWHEREcountryCode=’CN’

SELECTuniqueCount(customerID)FROMPageViewWHEREcountryCode=’CN’FACETfeatureName

SELECThistogram(leadCount)FROMPageViewWHERECountryCode=’CN’ANDleadCreatedDateLIKE’March%’SINCE1 weekAGO

SELECTaverage(revenue)FROMPageViewWHEREcountryCode=’CN’TIMESERIES1 daySINCE1 monthAGO

For more on how to get those kinds of specific data points out of a massive dataset, New Relic has information on its NRQL documentation page.

New Relic Insights has been in public beta since March 19. New Relic automatically collects data directly from real software/websites run by real businesses and stores it in its cloud-hosted platform for immediate analysis.

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The past couple of years have presented a real land grab in the big data analysis market. We’ve seen all-purpose companies emerge, like Scalable Performance Monitoring, as well as specialist players like mobile analytics company Countly. (This list of 42 big data companies “to watch” speaks for itself).

For companies outside Silicon Valley who don’t know where to look for their data analysis, there’s even talk of cobbling together a team of people who might not individually have the requisite skills to be a data scientist but who could work together with others as part of a “team” data scientist.

Then again, better, smarter big-data tools seem like an immediate and intelligent option when you’re faced with a mountain of data and not enough nerds to mine it.

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