Big Data Blogs

From Hype to Reality, a Partner Ecosystem Perspective

Having spent most of my working life around technology I have seen my fair share of economic booms and busts. Apart from the meta-economic influences, the Silicon Valley cycle usually follows its own wave tied to “the next big thing”-- the wave of investment, over expectation, under delivery, and subsequent crash. This obesogenic hype environment means great ideas typically die before their time and are reborn years later when the foundational problems have been resolved.

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Leading Analyst Firm Weighs In On Industrial IoT (and that’s a good thing)

If you’re reading this, you know that one of the industrial sector’s current Holy Grails is data analysis. Industrial IoT generates huge amounts of operations data and so, the thinking goes, companies should move quickly to analyze that data. This, in turn, should pave the way to enhanced operational efficiency, smarter use of equipment, smaller environmental impacts, better worker safety and, of course, higher profits. Simple, right? Amazon, Google and Facebook profit mightily from sophisticated data analysis – so why not oil and gas, manufacturing and utility companies?

But if you’re reading this, you also know that processing, managing and integrating data from sensors, engineering systems, and transactional systems is really hard. The job generally requires deploying a battalion of people armed with spreadsheets. In the current era of algorithms and automation, managing industrial data is embarrassingly last-century.

Knowing this, it’s fair to ask if reality will ever catch up to the hype of industrial data analysis. Specifically, will a method emerge to efficiently manage and integrate industrial data so that sophisticated analytics can be performed?

We feel that Gartner, the world’s leading IT analyst firm, is increasingly focused on this question. We think that fact alone should tell you that something is up.

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Why Asset Operators Should Have Their Own Digital Twins

Digital Twins are a hot topic, landing on Tech analyst Gartner’s famed Hype Cycle and named a Top Ten Strategic Technology Trend for 2017.  Not a new idea (credit to Dr. Michael Grieves, 2002, Univ Michigan), Digital Twins are now at the forefront of Industrial /Internet of Things zeitgeist because they are essential to unlocking I/IoT value by enabling analysis, simulation and control of physical things and systems.

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Defining an Approach to Industrial Big Data


A few years back, in 2008, I was working at a digital advertising company. In measuring the audiences that together make up the internet population, and building a quantitative understanding of the people that make up those audiences, I learned the meaning of “big data”. An easy rule of thumb was: when you have enough data to fill a box of hard drives that you can’t pick up, you’ve got big data.

What we were building back then was really big data 1.0, developed for the Consumer Internet to support web search and ad targeting. The key challenges were to get the technology to work at all, and to scale cheaply enough to let us run our business. Now, internet-scale data processing is a commodity. Big data is getting ready for the Industrial Internet and with that, comes a whole new set of challenges.

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4 Steps to Data Readiness for Industrial Analytics

I recently attended a workshop in St. Louis—Big Data, Predictive Analytics and the Industrial Internet of Things—sponsored by our partner OSIsoft, Rockwell Automation and Anheuser-Busch (the makers of Budweiser beer for the uninitiated).  The event focused on how industrial companies can deploy new technologies like cloud, machine learning and mobile to turn raw data into analytical information that improve businesses outcomes.  

The presentations were informative, but mostly missing from the discussion was a deeper exploration of how poor data quality makes effective industrial analytics hard to achieve, especially at an enterprise scale.

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