Big Data and WIIFM

by | Oct 12, 2016 | Industrial IoT | 0 comments

The hype-scale for the phrase “Big Data” has been with us and pegged for many years. A quick Google search yields 264 million search results. Wikipedia defines this concept as:

…a term for data sets that are so large or complex that traditional data processing applications are inadequate to deal with them. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying, updating and information privacy. The term “big data” often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set.[2] “There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem.”[3]

big-data-controlengeuropeA Control Engineering Europe magazine article, Big data: what’s in it for you?, asks the essential “what’s in it for me” (WIIFM) question. Many automation suppliers were asked for their perspectives.

Emerson's Mike Boudreaux

Emerson’s Mike Boudreaux provided insights. Mike described the need to focus on unsolved industry problems with the use of big data and analytical tools. This data:

…is made possible through the use of pervasive sensing technologies which combine innovative sensing and analytics to deliver actionable information.

These additional measurements along with data from other sources:

…can add context to measurements or offer additional insights. However, it is a tedious and complex task without the right tools. The datasets needed for effective analytics can also be difficult to access because they are distributed across multiple storage platforms and trapped in proprietary systems.

Analytics technologies for Big Data have advanced to:

…simplify the process of collecting, aggregating, and cleansing the data from disparate sources. This enriched dataset feeds analytical models that can more accurately predict process upsets or degrading performance such as heat exchanger fouling and pump cavitation.

These tools can be quite complex and highly focused for specialists such as data scientists. Custom analytics can address specialized problems.

In some cases, new and improved algorithms based on big data are embedded in Emerson’s application solutions.

We’ve touched on several examples of this pervasive sensing and embedded analytics in posts such as:

Read the article for additional perspectives from Mike as well as from other suppliers. The article’s author concludes:

Tools are continuing to be developed to make it easier to get value out of existing data. It is certainly worth taking a look at what is available. The data already exists and the more knowledge that you can gain from this the more you will be able to improve process safety, reliability, security and sustainability, ensuring that you stay competitive.

If you’ll be joining us at the October 24-28 Emerson Exchange conference here in Austin, make sure to catch Mike and his co-presenters presentation on the Industrial Internet of Things. You can also connect and interact with other process improvement experts in the Improve & Modernize group in the Emerson Exchange 365 community.

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The opinions expressed here are the personal opinions of the authors. Content published here is not read or approved by Emerson before it is posted and does not necessarily represent the views and opinions of Emerson.

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