Analytics: A Key Skill for Oil and Gas YPs To Get Into the Driving Seat
We are witnessing a structural change in the energy landscape. The days of “if-you-build-it, they-will-come” are gone. Exploration and production (E&P) companies have stopped earning economic rent from merely “finding” new oil and gas resources. The market is now rewarding them for efficient monetization of their hydrocarbon reserves. Therefore, E&P companies will need to be hyper-focused on producing the highest-margin barrels.
The traditional cost-cutting playbook employed by leadership will yield minimal to no incremental benefits. The industry now needs to make fundamental changes to the way it operates. Analytics is a key untapped transformational lever that will drive a meaningful impact in the short-term and a core competitive advantage for companies in the long term.
The oil and gas industry will no longer be a place where professionals should expect careers centered only on petrotechnical expertise. As analytics moves further into the mainstream, successfully delivering value will require hands-on working knowledge of relevant analytics tools. Given the strong quantitative background that most oil and gas professionals come from, upskilling for analytics is an achievable goal.
Upskilling for Analytics Needs To Happen in a Few Related Domains
As the analytics landscape continues to evolve, most teams and individual professionals find it overwhelming to choose a starting point for their upskilling journey from the vast array of options (visualization, data engineering, machine learning, etc.). The key to success is realizing that it is a team effort delivering analytics initiatives and the team as a whole should have fit-for-purpose skillsets for the objectives it is looking to achieve.
To this end, establishing a common language and definitions with respect to different elements of analytics is a must. Organizations do not need to build this from the ground up; multiple external frameworks exist and it is worthwhile for the organization to select one. Below is a high-level starting point that anchors on ingredients that need to come together for a typical analytics-backed initiative to succeed. The team should use this (or a similar framework) to define and evaluate which skill sets members need to solve the problems at hand.
The teams should complement technical competencies with digital leadership behaviors such as creativity and empathy for nurturing an innovative mindset.
How Can Young Professionals Develop as Analytics Leaders?
Make learning an integral part of your career: With energy landscape changing quickly on multiple fronts, young professionals should constantly look to upskill themselves in analytics. Learning skills in school and applying them during long, stable careers with companies is not guaranteed to be a viable model.
Some Commonly Used Analytics Tools
Domain knowledge coupled with analytics quotient will be a successful recipe: Domain knowledge will continue to be important as the industry takes on the analytics transformation journey. We advise young professionals to keep building those skill sets and develop data science skills in parallel.
Strong analytics basics go a long way: Fundamental skills such as developing coherent hypotheses for analytics experts to test and interpreting the analytics results (e.g. when they are conclusive and when they are not) are often under-appreciated but critical to both creating compelling analytics use-cases and explaining the results to management. No matter how much you decide to upskill yourselves, make sure you have a strong grasp on the basics.
Take advantage of the ecosystem of learning opportunities: Almost all organizations have learning and engagement opportunities in this area (courses, hackathons, and workshops). Make full use of these and leverage the broader ecosystem (e.g., Massive Open Online Courses) to accelerate your learning.
Leverage the in-depth knowledge in your area to come up with and implement analytics ideas, even if they are small: True transformation happens from the bottom up. We encourage young professionals to take leadership within their teams to identify ideas for creating impact through analytics and following through on their execution. The ideas can be simple and small e.g., automating the data dashboard for Daily Production Meetings, thus eliminating the morning ritual of compiling data from disparate sources.
Adopt an agile mindset: An agile mindset as described here, goes much beyond solution development in “sprints” and “MVPs.” If you are the end-user of an analytics product that is being developed, make sure to be actively involved in different iterations of the solution. Successful analytics require multiple pivots on several fronts ranging from operating model and technology architecture; embrace these frequent changes. Experimentation is a key part of analytics; successful companies do multiple experiments, and some of them do not work out.
Vikram Mukhi (left) is a manager at Accenture's Strategy Energy practice. He has more than 8 years of experience working for exploration and production and oilfield equipment and services companies. At Accenture, he focuses on advanced analytics, digital transformation, and operating model themes.
Manas Satapathy is a managing director in Accenture's Strategy Energy practice. With more than 20 years of experience, he has worked with many of the largest oil and gas, mining, and oilfield services companies to formulate and implement strategies and investment initiatives. He has authored several thought pieces in Petroleum Economist and Energy Perspectives on evolution of global crude oil and natural gas landscapes.
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28 September 2020
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