To make industrial data actionable, evolve your data historian with an AIoT strategy
In the rush to implement new technologies like AI, the cloud, and the Industrial IoT, organizations have found themselves with a technology stack packed with legacy, plumbed-together, on-premises solutions. The result is an environment with not only multiple siloed data sources, each storing, formatting, and securing data in their own unique ways, but also an equally siloed approach to understanding how to leverage that data into something actionable across the enterprise. Domain experts become not just go-to sources for understanding a certain process or workflow, but the only people with insight and meaningful context into different data sets tracked or generated by different sources.
In a rapidly digitizing organization, this is a poor way of maintaining and processing data across a site – but it’s especially counterproductive when you consider the generational churn happening in today’s industrial workforce. Veteran domain experts are increasingly retiring and being replaced with newer workers who neither academically trained to handle such specific, legacy technologies nor do they have the domain knowledge and operational expertise their predecessors had. This operational expertise gap leaves industrial organizations with not just more aggregated data than they know what to do with, but data that they don’t have any real visibility into.
Making industrial data useful and actionable in this scenario is a two-step process.
Step one involves leveraging next-generation data historians to democratize data access, ensuring that everyone within a plant and across the enterprise – regardless of skill, training, tenure, or expertise – has equal access and ability to tap into data that sits in any source, across the plant, from the edge to the cloud. Making data truly universal means using edge-to-cloud integrated data historians to eliminate silos, clean up data lakes, give structure to unstructured data, apply tags to make datasets easier to find, and make industrial data accessible in an AI-ready state to drive industrial intelligence.
Step two is then making that data actionable so that decision makers, from the production floor to the management level, are able to understand not just what the data is telling them, but what next steps to take.
To turn raw data into actionable insights, industrial organizations need to evolve their data historians to benefit from machine learning (ML) and AI algorithms, by leveraging an Industrial AI infrastructure that helps accelerate business value from industrial data. Data historians can’t just be used to collect process data; they have to be treated as the core of a greater industrial data management strategy, one that shifts gears from mass data accumulation to more thoughtful application, integration, and mobility of industrial data. Purposeful application of AI and ML are key to facilitating that evolution in the data historian’s function in an industrial organization, to tap previously undiscovered or unoptimized industrial data sets for new business value.
Many leading industrial organizations are adopting an AIoT strategy to accelerate time-to-value from their AI investments. An AIoT strategy provides integrated data management, edge and cloud infrastructure, and a production-grade AI environment to build, deploy, and host Industrial AI applications at enterprise speed and scale.
