Big Data. Big Decisions
InformationWeek
Special Coverage Series


Wanted: Apps With Data Science Baked In

Data scientists too hard to find? Business-oriented applications with data science capabilities might be the solution.

 Big Data Analytics Masters Degrees: 20 Top Programs
Big Data Analytics Masters Degrees: 20 Top Programs
(click image for larger view and for slideshow)

Many organizations launching big data platforms are finding that data scientists are in short supply. How bad is it? The McKinsey Global Institute predicts the United States alone could face a shortage of 140,000 to 190,000 data analytics experts just five years from now.

More Insights

Webcasts

More >>

White Papers

More >>

Reports

More >>

Granted, the rarity of the elusive big data guru might be a short-term problem. More business schools, for instance, are offering analytics courses that give students a basic set of data science skills, a trend that might enable highly trained data analysts to focus on more complex matters.

Still, democratizing big data is a worthy goal. But how do you go about it? According to Pros, Inc., a pricing and revenue management software company, the answer is to create apps with the data science cooked in.

"Data scientists are hard to find, there's no question," said Pros chief marketing officer Tim Girgenti in a phone interview with InformationWeek. One way to close that gap, he noted, is with "an application that has the data science embedded in it."

[ Big data has value that's often not reflected in the books. Read What's Your Big Data Worth? ]

"In the future, you'll buy business-oriented applications that understand your industry segment," said Pros CEO Andres Reiner. "And you'll have already configured the data science capabilities that most matter in your business."

The idea of big data apps is growing increasingly popular, and the Pros executives aren't the first to pitch custom applications as a potential solution to the data scientist shortage.

Laura Teller, chief strategy officer for predictive analytics firm Opera Solutions told InformationWeek in January that big data applications could help automate many data scientist tasks. These apps could help with simple, basic analytics, enabling non-techies to make data-driven decisions without consulting the staff big-data guru.

More sophisticated analysis, however, would likely require experts trained in a variety of technical disciplines, including computer science, analytics, math, modeling and statistics. Pros sells big data applications to help companies drive sales growth. The company started 27 years ago with a focus in the airline industry, and has since expanded to other industries such as manufacturing, distribution, services and travel.

For a big data app to succeed, it must meet the needs of a specific industry and address real-world problems, the Pros executives said.

"Take a wide variety of data, whether it's structured or unstructured, pull it together, and apply data science to predict a business outcome," said Girgenti. And then, "understand what the likelihood of the future is going to be, and prescribe actions around it."

Companies are now expanding beyond relational databases, and are leveraging technologies that offer real-time processing and analysis, said Reiner.

The move from "disconnected to connected data" is another emerging development, and Reiner used an automotive analogy to explain the trend. "Cars have become much more sophisticated over time," he said. "We started with collision detection, for example, with sensors that beep when you're approaching an object."

But newer, more advanced car safety features combine multiple components. If sensors determine other vehicles are too close, for instance, the vehicle can automatically activate the brakes to avoid an accident. And Google's driverless car takes things a step further by adding GPS to the mix to drive the vehicle automatically.

"These components are starting to be used in conjunction in the car. They're not decoupled, they're connected," said Reiner. "In the future, data must be connected and processed in real time too. And it should give simple prescriptive action."

Attend Interop Las Vegas May 6-10, and attend the most thorough training on Apple deployment at the NEW Mac & iOS IT Conference. Join us in Las Vegas for access to 125+ workshops and conference classes, 350+ exhibiting companies, and the latest technology. Use Priority Code DIPR02 by Feb. 9 to save up to $500 off the price of Conference Passes. Register for Interop today!



Related Reading




Currently we allow the following HTML tags in comments:

Single tags

These tags can be used alone and don't need an ending tag.

<br> Defines a single line break

<hr> Defines a horizontal line

Matching tags

These require an ending tag - e.g. <i>italic text</i>

<a> Defines an anchor

<b> Defines bold text

<big> Defines big text

<blockquote> Defines a long quotation

<caption> Defines a table caption

<cite> Defines a citation

<code> Defines computer code text

<em> Defines emphasized text

<fieldset> Defines a border around elements in a form

<h1> This is heading 1

<h2> This is heading 2

<h3> This is heading 3

<h4> This is heading 4

<h5> This is heading 5

<h6> This is heading 6

<i> Defines italic text

<p> Defines a paragraph

<pre> Defines preformatted text

<q> Defines a short quotation

<samp> Defines sample computer code text

<small> Defines small text

<span> Defines a section in a document

<s> Defines strikethrough text

<strike> Defines strikethrough text

<strong> Defines strong text

<sub> Defines subscripted text

<sup> Defines superscripted text

<u> Defines underlined text

BYTE encourages readers to engage in spirited, healthy debate, including taking us to task. However, BYTE moderates all comments posted to our site, and reserves the right to modify or remove any content that it determines to be derogatory, offensive, inflammatory, vulgar, irrelevant/off-topic, racist or obvious marketing/SPAM. BYTE further reserves the right to disable the profile of any commenter participating in said activities.

Disqus Tips To upload an avatar photo, first complete your Disqus profile. | View the list of supported HTML tags you can use to style comments. | Please read our commenting policy.

Follow InformationWeek

By The Numbers

What Are Your Primary Concerns About Using Big Data Software?

Base: 417 respondents at organizations using or planning to deploy data analytics, BI or statistical analysis software
Data: InformationWeek 2013 Analytics, Business Intelligence and Information Management Survey of 541 business technology professionals, October 2012

What Do You Think?

What's your attitude about SQL analysis on top of Hadoop?
We want fast, standard SQL analysis capabilities on Hadoop ASAP
Hadoop is for unstructured data; SQL is for relational databases
We'll give SQL on Hadoop a try, but relational DBs will remain the mainstay
Given strong SQL support on Hadoop, we'd nix the data warehouse
We're not interested in Hadoop
No opinion



Related Content

From Our Sponsor

Five Big Data Challenges and How to Overcome Them with Visual Analytics

Five Big Data Challenges and How to Overcome Them with Visual Analytics

Business leaders often need a visual snapshot of data to quickly grasp and use it. This paper identifies five challenges in presenting data and how visual analytics can resolve them. Solutions are suggested to overcome the challenges of: speed, data clarity, data quality, displaying meaningful results, and dealing with outliers.

Game-Changing Analytics: How IT Executives Can Use Analytics to Create Innovation and Business Success

Game-Changing Analytics: How IT Executives Can Use Analytics to Create Innovation and Business Success

Today's competitive advantage requires a deeper understanding of your business, your market and your customers. As an IT executive, you can drive that knowledge transformation. In this white paper, learn how to make decisions as a strategic business leader and three steps to begin an analytics initiative within your enterprise.

Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics

Data Visualization Techniques: From Basics to Big Data with SAS Visual Analytics

High-performance data visualization turns sophisticated analyses into meaningful graphics, leading to faster and smarter decision making. In this white paper, learn how visual analytics can transform big data, with additional features such as real-time functionality, mobile compatibility, robust applications for technical groups and accessibility for nontechnical users.

Big Data: Lessons from the Leaders

Big Data: Lessons from the Leaders

Financial performance, competitive advantage, operational efficiency, strategic decision making - every business goal can extract value from big data, and the time for doubt or inaction has long passed. In this Economist Intelligence Unit report, in-depth interviews with data pioneers reveal the link between the effective use of big data and the bottom line among other results.

Decision-Driven Data Management: A Strategy for Better Decisions with Better Data

Decision-Driven Data Management: A Strategy for Better Decisions with Better Data

Which came first, the data or the decision? This white paper makes the case for having a decision in mind, then tailoring big data's volume, variety and velocity to achieve business results such as overcoming customer dissatisfaction or creating well-informed strategies in real time.

Informationweek Reports

Research: The Big Data Management Challenge

Research: The Big Data Management Challenge

The challenge of big data is real, but most organizations don't differentiate 'big data' from traditional data, and nearly 90% of respondents to our survey use conventional databases as the primary means of handling data. We'll help you understand what constitutes big data (it's not just size) and the numerous management challenges it poses.