Trusted Data To Deliver Significant Benefits To The Asian Energy Industry

Trusted Data To Deliver Significant Benefits To The Asian Energy Industry
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By embracing the power and capabilities of modern data technologies, energy companies can be well placed to meet future challenges and extract value from all of their assets.

Contributed by JJ Tan, Regional Director for Asia at Talend
Photo: JJ Tan. Photo credit: Talend.

Drop-in demand followed by a sustained rebound in a short period, the energy sector, including oil and gas, demonstrates that it is one of the most strategic industries for economic recovery in this post-pandemic period.

But while demand remains strong for now, energy companies will face multiple challenges in the future, including their energy transitions. According to Wood Mackenzie[1], “Before the end of the decade, Asia Pacific leaders must tackle some difficult decisions and balance economics, energy security, and policy goals when defining individual country pathways.”


However, while energy companies have been focusing on extracting value from their assets, many are becoming aware of a new asset, a potential source of value, or addressing their many challenges: their data.

Data is the new oil” has been repeated over and over again. For some organisations in the energy sector, this has been translated into a generating value, as for Petronas. The national oil company of Malaysia decided to create a data-driven organisation that democratises data access and creates self-discoverable data assets. Petronas accelerates its transformation journey by adopting a modern data platform and enables data to be a strategic asset with robust technology that enables excellence in energy, products, and solutions and unlocks new business frontiers.

Unlike other business sectors, such as retail or manufacturing — where data has been a focus for years — the energy sector must overcome sizable institutional, structural, and technical challenges in modernising existing data infrastructures.

According to the EY Oil and Gas Digital Transformation and the Workforce Survey 2020[2], “58 percent of respondents said the COVID-19 pandemic has made investing in digital technology more urgent, with a majority planning to invest a great deal (29 percent) or moderate amount (51 percent) relative to their total budget.”

It can seem impossible even to know where to start building a better data infrastructure with many logistical, production, and distribution hurdles to overcome. Fortunately, however, there is a solution. Though the energy sector has highly specialised needs for trusted data, it can still benefit from modern data technologies.

Liberating Legacy Data

One of the key areas that can benefit from introducing modern data technologies is the large, aging, legacy applications that underpin many activities. While they operate dependably, they were never designed to share the data they generate with other applications or organisations.

They are also challenging to use in conjunction with analytical tools to extract fresh value from the data. New cloud-based services are challenging to link, and accessing the data using mobile devices is often almost impossible.

By deploying a modern data infrastructure, it becomes possible to break away from these old-world constraints. Data can be replicated from source applications into a shared data repository, where data can be used for cutting-edge transformations and analytics.

Optimising The Supply Chain

Modern data technologies can also help energy companies better manage their large equipment fleets. The technologies can help interpret signals from IoT sensor devices in trucks, plants, infrastructure, and oil and mining rigs to confirm current locations and better map expected behavior against the reality on the ground.

Many solutions can comfortably correlate streaming data from connected devices and batch updates from vehicles coming back into range after a time in more remote, unconnected areas.

This capability can also help with achieving better predictive maintenance. Data collected from the field can provide critical detail about the degree and type of service that equipment requires. This allows the construction of a maintenance model that alerts operators of potential failures before they happen, saving both downtime and money.

Achieving Environmental Responsibility

Another area in which better data usage can deliver value is environmental responsibility. The emissions footprints of some of Asia’s largest energy companies show they are already having a significant impact on the environment. In the future, this could have profound financial implications.

Even operations specialising in non-energy resources will inevitably feel the pressure to demonstrate greener practices, which will be impossible without the appropriate data infrastructure. Modern data technology allows companies to become more transparent in their operations without sacrificing data security.

The technologies also provide flexibility and control when it comes to reporting. This is particularly important when new regulations are introduced that demand precise and specific reporting on the ecological footprint of operations and fleets.

Rather than waiting for financial and government institutions to change on them, energy companies should consider making the required investments now. This will also make building and sustaining a corporate reputation easier, which is a clear benefit.

Data As An Asset

At the end of the day, energy companies must treat their data as an asset and work to extract as much value from it as possible. By liberating data from the constraints of legacy systems, delivering healthy data across the organisation, and eliminating data bottlenecks, it can become an even more valuable asset than it has been in the past and change the way energy organisations are making decisions.

By embracing the power and capabilities of modern data technologies, energy companies can be well placed to meet future challenges and extract value from all of their assets.

In too many organisations there are still different lines of business capturing the same data for their own use. This is cost-inefficient, causes data silos which lead to data fragmentation and governance issues, and inevitably means there is variance in the accuracy and quality of data.

Which data set should the AI/ML project use? That’s the wrong question to ask. The right question to ask is “How do we ensure there is just one set of this data, shared across all lines of business?”

Ask the question, find the answer, then implement it and repeat for all silos. This will help the current AI/ML project immensely, should create cost efficiencies, and should support future AI/ML and other projects going forwards. It will also be beneficial for other data management processes such as backup, restore and archive.

Even in the era of digital transformation, Fortune 500 companies take weeks or, most often, months to deliver clean data to their teams, often mandating a carefully coordinated effort across multiple teams. Further, this has necessitated the use of ingenious, albeit often insufficient, methods such as the use of synthetic data sets or subsets of data.

The answer is to deploy zero-cost clones. When users can instantly provision clones of backup data, files, objects, or entire views, they can be presented to support a variety of use cases. Zero-cost clones are extremely efficient and can be instantly created without having to move data. This is in stark contrast to the inefficiency of the traditional DevTest paradigm, in which full copies of data are created between infrastructure silos. This is a dramatic shift to modernisation.

By decoupling data from the underlying infrastructure in this way, we enable organisations to automate data delivery, and provide data mobility. Zero-cost clones can be spun up in minutes rather than weeks. As a result, customers have been able to reduce their service level agreement (SLAs) for data delivery, accelerated application delivery and migration, and greatly simplified their data preparation.

With the right data flowing in, AI and ML projects can provide dashboards of insights that can be used by the organisation in the transformational ways it envisions. Focusing on the data from the start of an AI or ML project can help an organisation land on the right side of Gartner’s 50 percent, however, this focus must occur from the outset.


[1] Asia Pacific’s energy transition conundrum – is net zero possible?, Wood Mackenzie, 10 August 2021

[2] Oil and Gas Digital Transformation and the Workforce Survey 2020, EY, 30 September 2020


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