Turn industrial data into your competitive superpower

Industrial companies today are facing foundational challenges, both in meeting their operational objectives, and leveraging the vast volumes of data available to them to decide how best they can meet those objectives. For example, 55% of Middle Eastern capital-intensive companies highlighted in recent research commissioned by AspenTech say they are balancing global demand for resources with sustainability targets and a need to meet profitability goals.

To have the best chance in meeting these objectives it is important to have a holistic industrial data management strategy that leverages existing assets and technologies to unlock the full potential of the plant.  This means an integrated solution that enables connectivity to all data sources, provides access to real-time monitoring, and inherent security and governance. This solution will enable digital initiatives such as improving asset reliability with predictive and prescriptive maintenance, digital twins, scheduling optimisation, and many, many others.

Delivering on this vision is challenging today for many asset-intensive organisations across the Middle East and North Africa. Of course, there is no lack of available data.  Companies across these industries have been recording and capturing large amounts of it for decades. This data has incredible potential but putting it to good use is far easier said than done.

Unlocking high value use cases that leverage this data, in areas such as production optimisation, machine learning, or emissions tracking, requires a data management strategy that is different from what has been done in the past. Industrial data and systems have, after all, traditionally been located in organisational siloes, rendering most of the data not actionable at scale.

Mired in the data swamp

To address the challenges outlined above, organisations across MENA will often build data lakes in which data from disparate sources is aggregated. These data lakes are capturing data at an accelerating rate and rely on a highly skilled workforce to put that data to good use. At the same time, these highly skilled personnel are difficult to cultivate, and the issue is further exacerbated by a rapidly evolving workforce.

The data lake that previously had so much potential becomes an expensive data swamp with low visibility and high complexity, wasting the potential of the captured data.

The emergence of real-time data platforms fit for commercial use

So, what’s the solution? Traditional data historians continue to have a mission-critical role in collecting, merging, and storing large volumes of data and making that data accessible. Today, operators and engineers within plants across asset-intensive organisations use historians to monitor operations, analyse process efficiency and investigate opportunities. These are mission-critical systems, customised for the operation teams’ use.

But over time, there has been a growing need for a new breed of solutions to support advanced analytics and to quickly scale horizontally across an organisation. These new breeds of solutions are sometimes called a data hub or data fabric. At the same time, on the IT side of the equation, digitalisation teams and their initiatives require clean, structured and contextualised data to generate usable insights and grow use case volumes. While the various data sources, including historians, provide at-a-glance analyses, the customised and disparate nature of these sources makes it difficult for consistency in how data is contextualised and structured.

Moving ahead with a new solution

By combining plant-level historian solutions with enterprise data integration and management technology, providers can streamline IT/OT convergence.  In line with this, we are now witnessing the emergence of next-generation real-time data platforms or hubs, helping industrial organisations collect, cleanse, consolidate, contextualise, and analyse data from their operations.

An enterprise data hub solves the biggest data challenge organisations face: scale. Scaling value from data and scaling digitalisation initiatives is being held back by the complex web of technologies, integrations, and connectors. A data hub properly implemented will decrease system integration efforts, accelerate greenfield integration projects, add value to brownfield installations, and increase an organisations data I.Q.  This enterprise data hub now becomes the starting point for industrial organisations to optimise processes using a myriad of methods:  machine learning and AI, advanced analytics, and enabling new opportunities.

A positive route map forward

Industrial companies today face numerous challenges, including the need to meet operational objectives, understand vast amounts of data, and improve asset reliability. To address these challenges, industrial companies need a data management strategy that leverages existing assets and systems. This strategy should include an integrated solution that enables companies to connect all data sources, access real-time monitoring, improve asset reliability, and increase overall plant efficiency.

The good news for industrial companies is that they don’t have to start from scratch. The correct, sustainable approach is to build on top of the technologies and process that exist today. Traditional data historians should not be replaced but augmented with this next generation of enterprise data integration and management technology.  This can be done as a limited implementation that quickly scales as value becomes apparent.

The use of real-time data platforms is becoming increasingly common in the Middle East and North Africa, as companies across these regions look for ways to improve their operational efficiency, decision-making, and pace of digitalisation initiative implementation.  Industrial data can be a competitive superpower only when it is leveraged; put your data to work today with a data management strategy built for the needs of tomorrow.