See a recording of the webinar here.
Following hydrocarbon oversupply for the past several years, the oil and gas industry has experienced sudden major shocks, entering into another downturn with the resultant considerable reduction in forces. The firms fairing best are data-centric – capable of capturing, ingesting, organizing, and utilizing data to drive efficiency and innovation. Nimble, entrepreneurial, and results-driven, these firms are expanding their footprint while their less sophisticated or willing counterparts recede.
This is the new industry, and the oil and gas workforce must adapt traditional skillsets to remain relevant. For SPE members in transition, for those preparing to enter the workforce, and for those concerned about the changing landscape, this is a time to invest in bolstering your data-centric skillset. Join the webinar to hear from the Digital Transformation Study Group on: Why should and how can energy workers become digital transformation change agents?
For individuals in the workforce, we will address the skillset below, and we will compare strategies and tactics for its acquisition and utilization.
1) Innovation Acculturation -- the drive to constantly brainstorm and consider alternative possibilities rather than rigidly adhering to tradition.
2) Data-Driven Decision Analysis -- the commitment and capacity to derive logical decisions from analysis of collected relevant data.
3) Digital Project Scoping -- the ability to assess the practicality and resource requirements to pursue data-driven innovation experiments that enable proper framing and evliery of digital projects and desired outcomes.
4) Personal Implementation -- the competency to execute light software scripting and data science methods directly.
For organizations, we will then explore how this skillset fits in to a bottom-up framework for optimizing performance, enabling process efficiency and data-informed decision-making across the firm. This progression moves from individuals, to teams, to P&L groups, and then to the organizational level. Examples will be shared.