I’ve felt DataOps was destined to be an important data management tool since I was introduced to it a few years back. Hitachi Vantara is one of two companies I follow specifically bringing this technology to industrial applications. Here it introduces a new portfolio bringing IIoT specifically into the core working with digital twins, machine learning (ML), and user interface.

Background. Ultimately, most industrial IoT difficulties are rooted in data management shortcomings. However, these challenges are not the same as those faced in a purely IT setting. For example, operational technology (OT) data is high-velocity time series and event information that many times lacks the detailed metadata descriptors and features needed to leverage it outside of the operations organization. In comparison, business IT data comes across in batches or transaction records with different metadata descriptors where time-stamp references are not always correlated. Merging these datasets, in context, is not trivial work, but, if done right, yields new operational insights that can provide a competitive advantage.

Lumada Industrial DataOps automates the process of abstracting, tagging, and rationalizing IT and OT data and organizes it in the data lake or data warehouse so it is usable for analysis and building AI and ML solutions. Data pipelines are established, and multiple transformations and inferences can be calculated and orchestrated as part of the workflow. Industrial process engineers can work with data scientists, analysts, and applications consultants to unlock the combined value and make major operations improvements.

But reality hasn’t lived up to the promise, and industrial operations have had a mixed relationship with IoT technologies. While there has been considerable success at the project level, broad IIoT deployments and the resulting analytics capabilities have progressed in fits and starts. Enterprises will need to leverage IIoT as well as AI and ML technology across far more use cases to better support their existing workforce and overcome supply chain issues. It turns out that it is more complicated than developers anticipated to scale their IIoT proofs of concept to stretch across a company.

The Lumada Industrial DataOps portfolio adds IIoT Core software with IIoT platform framework capabilities. The new toolkit is delivered as IIoT Analytics to accelerate the convergence of traditional IT with expanding IIoT data sources and bring powerful new software-based capabilities to life. IIoT Analytics offers prepackaged modules that provide data integration and preconfigured functions that give you a faster start on your application so you can focus on fine-tuning it for your specific requirements. A typical IIoT Analytics toolkit includes:

  • Digital twins for data and asset organization
  • ML models for faster assembly
  • Simulation software interfaces for greater accuracy
  • ML services framework for deploying AI/ML applications

Lumada Industrial DataOps directly addresses the four key challenges that hinder the enterprise-wide expansion of IIoT applications.

Challenge 1: The Need for High-Level Data Management Organizations need solutions that make it possible to access data in motion and at rest from the widest array of sources, integrate all that data, transform the data, and perform analysis. While all that happens, data security must be maintained and policies enforced to adhere to compliance and governance requirements.

Challenge 2: Automating Data Organization To create an efficient production pipeline for AI models, data scientists and analysts need an environment within which they can organize data and build models to detect events. This requires a system that automates the data analysis function, rejecting noise and providing people with a rich data signal that can be predictive or prescriptive in context.

Challenge 3: Accelerate the Training of AI Models Starting every model from scratch is not practical, as this approach may introduce delays and costs that get in the way of meeting business objectives. Data science personnel instead need templates that provide a proven foundation that they can then refine and adapt to meet specific requirements in a timely manner.

Challenge 4: Shorten Application Delivery Time Engineers and developers also need ready-made application components that provide a starting point.

Using Lumada Industrial DataOps, organizations can accelerate their development of digital twins, which can be further combined with new AI and ML analytic templates that address a variety of critical industrial activities. These analytics include anomaly detection and prediction capabilities for maintenance and operations effectiveness. These data management and application building blocks support the many industry-specific solutions offered by Hitachi to speed cooperative deployment efforts for Hitachi clients and partner organizations.

Lumada Industrial DataOps embraces the synergistic srelationships between data management, AI applications, and the next-level decision-making required in modern industrial environments. With Lumada Industrial DataOps, Hitachi empowers industrial enterprises to move their IIoT-driven AI applications out of the endless pilot phase and more quickly develop and scale for enterprise-wide deployment.

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