MEMBER NEWS: A Recipe for Industrial Data Innovation

True to its namesake, big data analytics has been evaluated inside and out, up and down. In fact, as time has passed, analytics has become the salt and pepper of business—it’s essential for every strategic business recipe, but we don’t talk about the ingredient independently. The role salt and pepper plays in industrial recipes, however, is worthy of more conversation. With the entrance of the Internet of Things (IoT) over a decade ago, industry has been working diligently to build platforms to better manage data from wearables, connected cars, and “smart” homes.

Industrial use cases bring a massive volume and variety of data that increasingly requires timely and complex analyses. Beyond volume and near real-time analysis, there is the challenge of protecting the data throughout its journey from collection, aggregation, modeling, and onwards thru its transformation into valuable insights. Finally, once it’s securely analyzed and contextualized, how can you action decisions that could lead to billion dollar outcomes?

In this era of digital transformation, industrial organizations are changing the way they organize, operate, and innovate. Technological advances enable new capabilities by sensing and delivering more data, faster. This means industrial organizations will not only have the agility to improve efficiencies, but they are laying a foundation to enable innovation in ways these traditionally hardware-focused organizations never imagined possible. As we continue down this digital path, there are areas of big data analytics which become increasingly important in industrial recipes:

  1. Streaming enterprise data – Nearly every piece of industrial equipment, whether on-premise or remote, will soon be equipped with sensors reporting real-time data to support improved operations. The terabytes and petabytes of data created from hundreds and thousands of sensors collecting a multitude of data can all be analyzed in real-time with stream processing. To that end, companies like DataTorrent and Equalum have created interesting solutions to stream this high volume of Industrial IoT data from disparate sources (sensors, ERP systems, etc).  With zero coding to create fault tolerant maps, Equalum centralizes and simplifies data ingestion management from legacy enterprise apps. DataTorrent’s pre-built data pipelines enable high-volume streaming data ingestion and complex event processing within milliseconds with built-in transformations to cleanse and analyze data while in motion. Turning this data into real-time insights and actions, these solutions raise the bar for operational efficiency.
  2. Edge computing – Although extensive IoT analytics are available in the cloud, moving data at an industrial scale to the cloud can be quite expensive and raise security concerns. FogHorn, has created “edge intelligence” software to bring the power of advanced analytics and machine learning to the on-premise edge. Spurring efficiencies, this solution enables closed-loop device optimization with heterogeneous applications at the edge.
  1. Data integration and modeling – For industrial organizations with end-to-end IoT environments, true big data analytics can’t focus on just a few data sources. To gain the full benefit of asset performance management (APM), solutions must combine data silos, ingest, normalize and model the unique nature of industrial assets within their operational context. In an industrial environment, knee-deep in a digital transformation, integration of many inputs from legacy infrastructure is essential. Bit Stew, a recent GE Digital acquisition, addresses this unique problem. Their solution’s machine learning data integration functions collect billions of data points and quickly organizes them into a rich data model.
  2. Comprehensive asset and data security – Protecting industrial asset information is crucial to business continuity for asset operators. Security solutions that protect distributed data at rest, data in motion, and data at use are paramount to ensuring comprehensive protection of industrial controls and critical infrastructure networks. A startup in our portfolio, IoTium, enables industrial organizations to securely connect legacy onsite systems to cloud-based applications with zero-touch provisioning and connectivity. By isolating every single data stream and deploying encryption and compression at scales industrial organizations require, IoTium shapes the traffic to enable secure utilization of cloud-based applications and provide quality of service.
  3. Augmenting human expertise – We’ve all seen the beautiful charts and graphs produced by enterprise data analysis. In contrast, complex industrial assets stream volumes of mind-numbing, real-time data which makes discerning actionable insights a challenge. This difficulty is due, in part, to assumptions that data, data lakes, algorithms, and analytics are solutions unto themselves. What’s further required to unlock the human expertise of critical decision makers is a way to encode and apply human expertise so it can be applied across the silos of incoming data, to digitize insights and drive them into relevant decision flows. Maana helps encode an organization’s human expertise and data-driven decision making methods, enabling subject matter experts to make better and faster decisions that improve operations.

Regardless of the industrial recipe, big data analytics can enable automation and your work force to create better outputs. GE Digital provides a cookbook of these recipes with achievable and valuable outcomes in focus. With  Predix, the platform for the Industrial Internet, we are seeing intelligence applied to data spur efficiencies and reduce downtime.

As GE Ventures, we’re looking for partners with strong industrial domain experience who can collaborate with us on our mission to enable our customers to be more adaptive and responsive to change in the uncertain world. We are eager to find companies which can do unique things with data. Are you ready to join us?

CTA: https://www.geventures.com/


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