The seeds for the consumerization trend that is dramatically changing how enterprise employees work were planted far before the introduction of the iPhone in 2007.
Going back to the 1980s, companies such as Apple, Sony, and Microsoft were releasing computer products and personal devices that essentially trained a generation of consumers to be tech-savvy and tech-fearless, able to adapt quickly to different platforms, forms factors, and feature sets. As a consequence, millennials for years have been entering the workforce with the skills and knowledge to immediately use technology productively.
Is a similar process now occurring in the world of analytics? Thanks in part to the explosion of data from mobile devices, the Internet of Things, fitness and health wearables, and other digital data sources, along with the increasing availability of tools for regular people to collect and analyze all this information, big data is being consumerized.
Consider something as simple as a Fitbit (which my CITEworld colleague Ryan Faas writes about here): This wearable activity tracker measures steps taken, calories burned, miles walked and steps climbed, presenting the data to Fitbit owners in graphical form. Users also can alter the graphics by changing data points, thereby possibly gaining new insights into their physical activities.
I also own a Fitbit, and am convinced that it and other data-generating consumer tools I use regularly are giving me valuable data science skills.
Meta Brown, a data analyst and consultant, shatters my illusions.
“Looking at data is not the same as understanding and doing meaningful analysis,” says Brown.
OK, point taken. But c’mon, Meta, throw me a bone!
“In combination with some decent analytics training,” she says, “data toys like Fitbit might be useful as a persistent reminder of analytics.”
Which, if you’re an enterprise hoping to build an analytics culture, isn’t a bad place to start.
Tools for the masses
Further, more sophisticated data for consumers is coming. While the Fitbit is relatively primitive, the next generation of health- and fitness-related monitoring devices will be much more sophisticated, tracking heart rate, muscle activity, lactic acid levels, and other biodata.
More complex data naturally will require better analytical tools -- tools that many consumers will learn to use.
These types of “self-service” tools already are showing up in the enterprise world. Companies such as Alteryx, Platfora, and several others sell specialized analytics platforms designed not just for highly trained data scientists, but for regular line-of-business employees who want to quickly analyze and respond to actionable data.
“Over the past couple of years, I’ve seen a lot of technology companies recognize that there are only 3,000 true data scientists in the world,” says John Myers, research director at analyst firm Enterprise Management Associates. “So they say, ‘Let’s encapsulate the technological complexity and make it easier for people to do these things.'”
Myers says the pace of competition in the digital economy makes it imperative that people in lines of business are able to access, gather, and analyze data quickly.
While providing easy-to-use analytics tools to an analytics-aware workforce sure seems like smart business, Brown warns that it’s not enough if you’re serious about analytics.
“Without training,” she says, “most people won’t understand what can be done with the data, what’s realistic and what’s not, or how personal examples relate to the business.”
And that is where Fitbit comes in.
This story, "Soon, Everybody Will Be a Data Scientist" was originally published by CITEworld .