CIO Survey Reveals Challenges, Opportunities and Potential of Industrial Big Data

Guest post by Jeremiah Stone, GM of Asset Performance Management at GE Digital. 

Bit Stew Systems recently commissioned a survey by IDG Research of senior IT executives to better understand how organizations are being impacted by the Industrial Internet of Things (IIoT) – the steps being taken to prepare for it, the potential benefits the IIoT offers, and the challenges encountered along the way.

Jeremiah Stone, General Manager of Asset Performance Management, at GE Digital, shares his insights on how the research findings match up with his experience at GE.

Industrial companies are in the midst of an exciting and transformational digital journey. At the heart of this transformation is the power of real-time and predictive data analytics to unlock new sources of value. However, challenges of big data, unique to the Industrial world, and the threat of digital disruption and changing workforce dynamics are real.

In order to maximize the fast-moving technology wave of the Industrial Internet, companies need to think strategically about the foundational elements of their data architecture, starting with industrial data management.

Abundant Data by Itself Solves Nothing
Despite the promise of big data, industrial enterprises are struggling to maximize its value. Why? Abundant data by itself solves nothing. Its unstructured nature, sheer volume, and variety exceed human capacity and traditional tools to organize it efficiently and at a cost which supports return on investment requirements. Inherent challenges tied to evolution and integration of industrial information and operational technology, make it difficult to glean intelligence from operational data, compromising projects underway and promise for further investment and value.

Research Confirms Data Integration is Slowing IIoT Adoption
We have seen first-hand, how data integration has challenged IT and OT teams for decades. The advent of IIoT adoption is compounding the problem. The insights from the IDG survey match up well with our experience. Senior IT executives are echoing the sentiment that data integration is the #1 barrier inhibiting IIoT adoption in their organizations. 64% of senior IT executives surveyed said that integrating data from disparate sources/formats and extracting business value from that data is the single biggest challenge of big data. As we go forward, driving technology advances and best practices to integrate disparate data sets is critical.

Lack of Preparedness will Cost your Business
According to the survey, senior IT executives are saying the biggest risk of not having an IIoT strategy in place is losing valuable data insights which can significantly cost their business. 87% state the most concerning risks of not have a data management strategy is they will be overwhelmed by the volume and veracity of data being generated, and they will lose valuable business insights as a result. In addition, 33% say they are afraid that businesses that don’t adopt a data management strategy will become marginalized, obsolete or disappear.

Finding a Better Way: Maximizing Value from Machines and Enterprise Data
At GE, we are experiencing first-hand a better way—a better way to manage industrial big data that triggers insights. We are in the early stages of a long journey
of discovery and invention, taking a longer-term view to strategic data management and its technologies that translate to business advantage. Our businesses, customers, and partners are committing their business success by transforming to become data-driven businesses. At GE Digital, we are investing in our capabilities and the ecosystem to deliver the right solution to help them get there.

To extract meaning and value from industrial data, new systems are required to handle the challenges posed by the volume, velocity and variety of these data sets. Many industrial companies have already started their digital journeys towards Industrial Internet maturity. Technologies including automated integration and empirical data model management, machine learning and physics-based analytics, that we have been deploying for our customers, are
now seeing double-digit performance gains across the following sectors: power generation, oil and gas, transportation and mining.

Learn More About This Topic

IDG Research White Paper | Download the in-depth report here.

This blog post originally appeared on Bit Stew Systems’ blog page, Bit View. 

Solving the Data Integration Problem with Bit Stew Systems

This guest blog post by Mike Varney originally appeared on Bit Stew Systems’ blog page, Bit View. 

Data integration is proving to be the Achilles heel of the Industrial Internet of Things (IIoT) and is blocking progress on
the transformations and ROI that industrial enterprises had originally envisioned.

Typical Big Data analytics projects that employ traditional ETL or Business Intelligence tools often falter under the complexity and scale of industrial environments. The rigid architecture and manual process associated with these solutions make them less than ideal for an industrial customer.

So why are so many industrial customers still using these clunky, brittle, and slow solutions?

ETL: Compounding Your Data Problem?
ETL or Extract, Transform, and Load is a traditional IT methodology whereby data systems architects tasked with Machine Intelligenceproviding data intelligence from multiple systems will first extract the data and place it all into a common location, then apply transformations to normalize or cleanse the data and then place it back in this common container for analysis. It may not seem laborious to the untrained eye but ask any data wrangler, enterprise architect, or IT manager and they will tell you that ETL can take several professionals months.

So why do it? ETL is attractive to IT departments because it usually leverages existing software investments and does not require teams to come up to speed on any new technology. In fact, it has been a tried and true method for decades.

IIoT Amplifies the Data Integration Challenge
Those who opt for traditional ETL are forgetting that the Industrial IoT is set to connect billions of more devices to the Internet by 2020. That explosion of data will most certainly be too rapid, and too large of a change for traditional systems to handle.

The risk for those who lag behind the curve on Industrial IoT is that they will cease to be competitive in the global industrial markets. Almost all industries will be affected by this change, from oil and gas to manufacturing and all those in between.

The technologies behind IIoT have brought significant advancements to industries such as Manufacturing, Transportation, Oil & Gas, Aviation, Energy, Automotive and others.  These technologies have allowed industry to remotely monitor and control assets to optimize production and improve yields.

However, these same technologies have exacerbated a long standing data integration problem by massively increasing the volume, velocity and diversity of data required by the business.

A New Way of ThinkingMachine Intelligence
Solving the data integration challenge requires a new way of thinking and traditional data architectures must be reimagined to support the rapid proliferation of data from an exponentially expanding set of data types. So what’s the solution? The key to solving the data integration challenge is semantics.

Bit Stew’s integration technology is designed to rapidly ingest and integrate data to provide a semantic understanding of information across disparate systems. Deeper analytics can then be applied intelligently through analysis methods and workbenches.

Download the infographic to get a deeper understanding of the steps required to create a semantic model.

Download the White Paper

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