The Case for E&P Data Consortiums

The Case for E&P Data Consortiums

Preston Cody, Head of Analytics Lab, Wood Mackenzie

Preston Cody, Head of Analytics Lab, Wood Mackenzie

Over the past decade, the E&P industry has undergone a dramatic transformation both in terms digital technology advancement and adoption. Thanks to information technology (IT), data has become increasingly available –in real time and from diverse sources – and workflows and cycle times of decisions makers have become more streamlined.

As a result, E&P companies today are operating more efficiently and safely than ever before, and they have the architects, engineers, and developers from IT departments to thank. An enormous debt of gratitude is especially owed to CIOs for their role in leading and orchestrating all the technologies as well as people, operational processes and policies necessary to bring the industry into the Digital Age.

Despite this progress, however, many E&P CIOs face an uphill battle demonstrating the bottom-line results stakeholders expect. Impressive proof of concept demos and results from pilot programs for technologies—while raising awareness of the value of digitalisation—have quickly escalated the expectations of investors and executive leadership. However, these technologies are often siloed one-offs, unable to be operationalized at scale.

We are also increasingly seeing digitalization programs given hard targets, such as 5-10x return on investment (ROI) targets or nine digit cost reduction goals. But new technologies or data, in themselves, will not deliver that kind of value. While most gains thus far have enabled organizations to run leaner and faster, there are fewer examples in the industry where technology, data, or analytics actually moves the needle on a decision with "real world" impacts.

Drowning in a Sea of Data

With more data than ever before, leadership at many E&P companies turn to data science to help them mine business value from this endless sea of digital information.

This has resulted in partnerships with external purveyors of "analytics solutions" and internally adopted the workflows and methods of Machine Learning, Artificial Intelligence, and Predictive Analytics. But without context and domain knowledge, black box algorithms and statistics leave stakeholders with more questions than answers.

To solve this problem many E&P companies are "democratizing" data science and training subject-matter experts in these new techniques to make them "citizen data scientists." This approach however, has not remedied the problem. Instead, companies are seeing subject-matter experts complain that 80-90 percent of their time is being spent wrangling data.

"We are also increasingly seeing digitalization programs given hard targets, such as 5-10x return on investment (ROI) targets or nine digit cost reduction goals"

Does this mean the onus is back on CIOs and data management groups to get subject-matter experts the data and tools they need so they can focus on geoscience and engineering? This is the constant dilemma CIOs find themselves in, where the business thinks data is an IT problem to solve, while IT is giving the business better solutions than ever but they don't know how to derive value from them. But this is not a technology, process or people problem. The real problem is the data itself.

Historical vs. Analytical Data

Data is the foundation upon which any value from analytics will be found. There’s no question that—when deployed correctly—data and analytics have great potential, and that machine learning, artificial intelligence, and other technologies will deliver new value, but this can only happen if that value can be found in the data that’s been analyzed.

That said, even if a company manages to assemble a world class enterprise data solution, and the domain experts steward that raw information into rich and tidy datasets, it will still not be good enough to answer the most impactful questions. That is because it will represent a towering edifice to the past and chronicle in exquisite detail everything the company has already done. Which only helps companies do what they've already been doing faster, cheaper and better.

True breakthroughs using big data technologies and analytics will come only through the bringing together of disparate, cross-functional datasets and using the algorithms (appropriately) to find patterns across domains, the kinds of patterns that the human brain is not capable of identifying when working within its own functional silos. To accomplish this, companies must turn outward to include the widest possible dataset and take their digitalization efforts to the next level.

The Need for E&P Data Consortiums

Ultimately, industry consortiums will prove to be the most effective way to develop the kind of robust datasets that can transform the industry by unlocking new ways of creating value and new modes of operation. Those companies that are open to pooling data and collaborating on solutions will find themselves collectively outcompeting their larger, but more insular, competitors.

We’ve heard every objection there is to an E&P data consortium—like a company’s data is too valuable, competitive, or complex—but there is a growing recognition that we need to do things differently. Our parent company, Verisk Analytics, serves two of the most digitally evolved industries today –insurance and financial services. These industries had the same argument at the start of their digital transformation, but today appreciate, and benefit from, the power of data consortiums.

For insurance companies, pooling data—centrally managed and prepared by a data analytics group—has allowed them to conduct actuarial science on practically the whole population being insured, not just their slice of the market. In consumer finance, banks have been able to analyze profitability and default risks from those they extend credit to, even when they are but one of many credit cards in any given wallet.

In both cases, insurers and banks contribute their data to one data analytics company, a far more effective and economical way to consistently prepare and protect data than multilateral, self-organized data trades.With that central, "analytics ready" dataset, companies can get straight into analysis to find and optimize the value in their portfolios. Over time, having all of this data in one place leads to new ways of adding value that are only possible with that combined dataset, such as fraud detection and predictive analytics.

There are lessons to be learned by the E&P industry frominsurers and consumer finance companies who are now enjoying the ROI they’ve gained from analyzing data in industry consortiums.

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