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This blog contributed by: Mike Varney, Executive Director, Product Management & Strategic Initiatives, Bit Stew Systems
Pervasive across industrial environments is a sleeping giant that threatens to derail almost half of all well-meaning analytics projects. That giant is data integration. In this two part series, we will explore why data integration is a major problem for industrial environments and why typical ETL methods simply won’t work.
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 or Extract, Transform, and Load is a traditional IT methodology whereby data systems architects tasked with providing data intelligence from multiple sy stems will first extract the data and place it all into a common location, then apply transformations to normalize or cleanse the data a nd then place it back in this common container for analy sis. 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 Thinking
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 Bitstew semantic model infographic to get a deeper understanding of the steps required to create a semantic model.
Leveraging Machine Intelligence – Download Bitstew's New White Paper
Download the white paper to learn how to leverage machine intelligence with a purpose-built IIoT platform to solve the data integration problem.