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Big Data and Mobility: How Well Will They Work Together? It Could Depend on Your Mobile Device

In 2012, the NY Times reported on Big Data efforts at the retailer Target. Their analysis was able to determine which of their shoppers was pregnant, based only in changes to items purchased. These were not obvious changes like the addition of baby wipes, but subtle changes in shoppers’ baskets. The article (“ How Companies Learn Your Secrets” ) documented one case where Target knew that a teen shopper was pregnant before her parents knew. This just shows that there can be a wealth of information hidden in a forest of data.

Which is why we are seeing Big Data “mining” techniques applied in industries like manufacturing where highly automated factories could use the uncovered insights to support customization and optimize precise assemblies, things like jet engines, communications equipment, and premium automobiles to name a few. It’s also why this trend to use data to analyze, adjust and maintain equipment is such a central part of the Industry 4.0 initiative, of which we’ve written about recently (in blog links below).

Blogs8 Ways Rugged Mobility Will Change – or Be Changed By – Global Industry, Government in 2017 Part I and Part II

However, it’s important to note that we are now seeing Big Data-type techniques appear in Utilities. Though their potential applications are interesting, especially considering the extreme mobilization of the industry’s workforce.

Power Generation Data Analysis

In December, I attended the Power-Gen International conference in Orlando. About 20,000 attendees viewed 1400 exhibitors’ booths focused on the equipment, tools and techniques involved with power generation. Renewables, Nuclear Power, Coal Generation, and many other categories of electrical power generation were on display. So it was interesting to learn about how data analysis is used.

Scott Affelt of XMPLR Energy LLC wrote in Power Engineering :

“Advancements in data analytics … can now detect anomalies earlier and more accurately than before, diagnose the cause of the anomaly and predict the remaining useful life (RUL) of the asset.”

And

“Furthermore, cause diagnosis and RUL prediction optimizes the performance and reliability of these assets and allows the move to condition-based maintenance programs.”

I agree with these views and I am highlighting them here for two reasons. One, significant trends impacting our key industries should be understood so that IT can make intelligent decisions. Secondly, this supports our analysis that field service technicians in these industries need to now be treated as “Information Workers”. These technicians must be masters of everything: mechanical skills, diagnosis abilities, and managing real-time data. As Big Data analysis delivers insights such as condition-based maintenance, the technician on the scene needs access to that data, and often needs the ability to connect with the equipment directly for diagnostics or maintenance. Therefore, utilities increasingly need a force of information workers who can also use that information to intelligently deliver maintenance or repairs.

Indeed, Big Data can deliver keen insights to increase the level of performance. It also raises the “floor” for performance, setting a new minimally-acceptable standard. Baseball’s version of this is Sabermetrics, which changed the way teams value players, with less emphasis on home run hitters and more on players who can get on base often. And thanks to Big Data, race drivers in Formula One all now know the best way to get their car around the track. There are no more secrets or tricks that one driver may have as an advantage. Now, driving teams have the data to know who is driving the best – and why – as well as specifics like which braking point and turn-in spot is the best. Similarly, we are seeing the rise in “exactness” and complete visibility introduce new advantages within Utilities, Telecoms and Manufacturing.

But though data is increasingly being collected in these industries – and being analyzed at a surface level – via existing systems, the next step of analyzing this data at more micro and macros levels has yet to hit its stride – though it’s close. This new phase that many in these three industries are moving into in 2017 will deliver insights long needed to make more precise decisions and take real-time action, which will ultimately increase the performance expectations for operations and support. That just further validates our view at Xplore that more field service workers will become information/service workers this year as well.

Big Data Analyzing

I must note, though: To perform well in this new role, service workers increasingly need mobile devices which not only support the multitude of jobs they’re completing every day – often in challenging work conditions, might I add – but also the multitude of data that they will need to send and receive to really take advantage of the Big Data model. At a minimum, they’re going to require devices that are mobile and connected to reliable communications networks and to equipment, most likely via RFID, barcode, serial ports, etc. The devices must also be built to survive the workday (think battery run-time, ruggedness) and have flexibility to display and access data from disparate sources.

Remember: Big Data analysis is still new for the Manufacturing and Utility spaces. The software companies that are addressing this dynamic market have different approaches, styles and methods. The methods by which organizations will be able to access the results of data analysis and deliver inputs into the system will vary across software providers.

As this data analysis market shakes out, Utilities, Telecoms and Manufacturers should create an environment to allow flexibility, and not be locked in to one company’s iPad or smartphone app. Use a browser-based interface at first and let the winners emerge. The mobile devices deployed in the field today should be flexible, and not lock you in to one data analysis company in this emerging market. That means you need large screens and open Operating Systems, such as Microsoft Windows and Android, along with the other capabilities noted earlier.

And though data analysis is changing everything – from Formula One to power generation – from less art to more science, one thing remains constant: The need for Big Data and mobile systems to complement one another .

The only mobile computing systems that I believe will have the flexibility and long-term capability to extend the benefits of Big Data to the mobile workforce are rugged tablets. After all, what good is an “information worker” if they don’t have the right tools to take fast, meaningful action on the insights derived from such impressive systems?

Blog Author: Bob Ashenbrenner
President of Durable Mobility Technologies, LLC.