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Does business have the patience for data science?

A data scientist call to arms, say hello to NFC jewelry, and the Magic Quadrant for mobile app dev platforms: The Data Mill reports.

Companies are learning how to collect all kinds of data, but they aren't learning how to do data science. That was the message from Kim Stedman, former analytics manager for the Seattle-based gaming company Meteor Entertainment. She recently gave an Ignite talk, a storytelling event for geeks sponsored by O'Reilly Media, where she called out data scientists everywhere: It's time to step up and take control of the data science terminology sprawl.

"[Companies] are looking for everything; they have no idea what they need," Stedman said.

Yes, companies have bought into the buzz and are willing to dole out big bucks to bring data science talent in-house, but ask them to outline the specific skills they require and it's more conceptual art than scientific blueprint. That lack of clarity doesn't help things much when a data science team, which can be a costly expense, is expected to produce immediate value and doesn't, Stedman said. Businesses may want the rigor of the academy, but they're rarely willing to pay for the time good science requires.

"Science is the assertion of a testable hypothesis followed by a cycle of validation and refinement in pursuit of a working theory. It challenges beliefs," Stedman said. That kind of new world order doesn't produce rich results overnight. "Data is fast, but science is slow."

One ring to rule all

A campaign on Kickstarter -- the platform that crowd-sources fundraising efforts for independent projects -- raised more than $300,000 to build near field communication (NFC) technology into jewelry. The campaign, launched by John McLear out of the United Kingdom, is specifically focused on producing NFC rings, which will be able to unlock smartphones, doors and even link people together when the ring touches another device. Two separate transmitters built into the ring separate private data from public data.

Prime time

Know what the biggest prime number is? Try 2 to the 57,885,161th power minus 1. It was discovered earlier this year by Dr. Curtis Cooper, a mathematician currently teaching at the University of Central Missouri, and was the culminating moment in Adam Spencer's recent TED Talk. The number is almost 17.5 million digits long.

"If you type it on paper and saved it as a text file, that's 22 [megabytes]," said Spencer, a comedian who happens to also love math.

Data begets data

Acxiom Corp. made the news this week with its site -- but it may not be the kind of news the data broker wanted. The portal allows visitors to find out the kinds of data Acxiom has collected on them for its corporate clients. But here's the kicker: In order to see the results, you have to -- what else? -- cough up your data. The login asksfor your name, address (physical and digital), birthdate and the last four digits of your Social Security number before granting access to the data they've collected through other means. As critics pointed out, so much for data transparency.

Magic Quadrant for mobile app dev platforms

Gartner Inc. released its Magic Quadrant for Mobile Application Development Platforms (MADP). SAP, jQuery Mobile, IBM, Kony, Adobe and Antenna were given the highest marks, followed by MicroStrategy and Salesforce.

The report also acknowledged the difficulty in choosing an MADP in today's market. It provided CIOs with several words of caution, including:

  • Native vs. Web vs. hybrid apps is still up for debate. And, although HTML5 is full of promise, "most enterprises find it is only a partial solution today," the report stated.
  • Finding one vendor for the entire organization isn't likely, at least not yet. The needs of the organization overwhelm what vendors can offer, at least right now.
  • The market is on fire with "new vendors and new products entering the market every month. At the same time, the market "continues to converge," making one vendor from the next almost indistinguishable.

BI refresh

Claudia Imhoff, president and founder of the Boulder, Colo.-based consultancy Intelligent Solutions, broke down business intelligence (BI) during a recent webinar with The Data Warehousing Institute.

1. Descriptive BI: The most prevalent form of BI and also the least valuable, she said. It describes what is happening for well-defined business problems or well-defined opportunities and relies on business reporting, dashboards, scorecards and data warehousing.

2. Predictive BI: A more proactive approach that tries to answer the questions, "What will happen and why." The technologies here are different from descriptive BI and more complex, including data mining, market basket analysis, text mining and forecasting.

3. Prescriptive BI: The ultimate goal of BI, the goal here is to identify what are the next best steps to take and why, she said. With this form of BI companies have to become adept at doing things like simulation and decision modeling.

Welcome to The Data Mill, a weekly column devoted to all things data. Heard something newsy (or gossipy)? Email me or find me on Twitter at @TT_Nicole.

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NFC rings: Good idea or compliance nightmare?
I think it’s a little too early to tell, but it shouldn’t be too much of an issue with compliance if handled correctly. After all, most compliance frameworks are really just ensuring that common sense is used.
I don’t know that it is so much that companies don’t have the patience for data science as it is the pace of business precludes the use of standard data science practices. I think we often associate the scientific method and the challenging of beliefs as slow and methodical processes, but they can be performed quickly and efficiently if the proper degree of rigor is applied. It’s just that, historically, we haven’t done it that way.
I'm not sure that its patience.  I think that perhaps some companies, don't see how data science can help them do business better.  They may see the up front cost to build some of the infrastructure, but do not weigh the great value it could bring if time is spent.

Business "leaders" have grown far too shortsighted. Unless data arrives with a guaranteed sales outlet, most seem hell-bent on trying to extract the greatest profits in the shortest time. Research, planning and infrastructure be damned. Quick profits may be great for today but they don't bode well for tomorrow.