• The key to unlocking the power of data in heavy industries
    Geir Engdahl, Co-Founder & Chief Technology Office, Cognite

Fuel for Thought

The key to unlocking the power of data in heavy industries

Jul 13 2022

Inside nearly any type of business is a treasure trove of data. It’s the companies that understand how to maximise the value of that data and use it to improve decision making, accelerate innovation, enhance the customer experience and drive operational efficiency that will have the competitive advantage. However, it’s easier said than done and companies may find extracting this data value to be challenging. 

Siloed data, outdated tools and shadow IT are the most common hurdles faced by industrial businesses. These are the barriers that companies need to overcome if they aim to democratise data and analytics, streamline collaboration and accelerate time-to-insight. The global skills shortage represents another barrier and it’s clearly one that must be addressed if companies are to have access to the right talent pool to tap into that data. According to a DNV study, 91% of energy companies say digital skills training is needed in the oil and gas industry.

The energy industry is also facing an aging workforce, with 43% of workers over age 50, according to the UK’s Department for Business & Energy Strategy. That makes the optimisation and contextualisation of data essential to ensure maintenance and operations teams have access to insights that might otherwise be lost as a critical part of the workforce enters retirement. This is yet another reason why driving business continuity and operational resilience through the power of industrial data is crucial. 

Tackling proprietary data protocols
When looking at process-heavy industries, focusing on core operational technologies is key. Systems from multiple vendors, each paired with proprietary protocols, can lock down data, and these systems have an average lifespan of around 20 years. The impact of this mix of legacy kit, disparate control systems, non-compatible data models and communication interfaces can limit a company’s ability to collect and contextualise its data.

Cognite experienced this challenge first hand when it supported an oil and gas company that had 30 oil platforms with more than 300 wells. The operator lacked a unified overview of maintenance activities within and between all assets – ultimately a costly and ineffective way of working. As the data team coming in to fix this challenge, the Cognite focus was on ensuring that this business didn’t have too many disparate control systems using proprietary data models and communication. By bringing these systems together into a shared platform, this oil and gas operator could consequently optimise scheduling, improve communication across organisational silos and make data-driven decisions. 

Concentrating on user needs
The secret recipe for many successful companies is to maintain a laser focus on their users and on improving their operational efficiency and their ability to make rapid and higher confidence decisions. Data plays a role here, and the work to structure an organisation’s data can bring value to multiple users. The key is understanding how people interact with data across the operation and be aware of how the data needs to be presented to the various roles in the company. By maintaining a user-centric focus and having a solid foundation of scalable data, companies can accelerate time to value.

Across industrial operations there is also a major focus on data analytics to support optimised decision making and to enhance operational efficiencies. In the future, this could lead to the adoption of AI and machine learning to intercede in the operation of industrial facilities in complex use cases, such as where Distributed Energy Resources (localised energy generation) is deployed. 

Environmental impact is also something increasingly important for users. One example of this is from another Cognite customer, Aker BP. This oil and gas company used machine learning smart monitoring systems to visualise all data relevant for troubleshooting water contamination and identify factors related to high oil-in-water concentrations. This helped the company decrease its time spent on mitigating actions, a savings equivalent to an annual revenue potential of $6 million. So, concentrating on user needs not only helps to unlock the power of data, it also to drive operational resilience. 

Using trusted data sources
Industrial data empowers everyone who engages with it but the analytics and applications that leverage this data will come from the end users, software providers and equipment manufacturers. When you have a trusted data source with common assets you have a very strong basis for using low code to develop in-house applications, as well as AI to enhance decision accuracy. Given the current industrial landscape, as well as greater market requirements, such as data-intensive carbon reporting and business model disruption from digital technology adoption, companies that do not focus on data as a key asset will face a significant competitive disadvantage. 

Fortunately, most companies realise that if they were starting operations today, given the tech we currently have available, their processes and teams would look very different relative to today’s teams and processes built around legacy technology. The businesses that can adjust their people and processes will have a first-mover advantage in this new data-driven era. Those that remain wedded to past investments will eventually have to shoulder twice the technology debt.

At this point in the industrial space, there is a lot of focus on analysis to support optimised decision making and making operations more efficient.  In the future, there may be adoption of AI and machine learning to operate industrial facilities for more complex use cases such as smart city concepts.  Unlocking the power of data will be key to ensuring companies can maintain business continuity, drive operational resilience and grab on to all the benefits they can from emerging technologies.


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