In all economic conditions, advanced analytics solutions help companies increase operational and business efficiency, empowering their people to get more out of existing assets.
MORGAN BOWLING, Seeq
Despite recent record profits in the oil and gas industry, many companies are exercising restraint when it comes to new spending, due to turbulent economic conditions and geopolitical concerns. However, this does not negate shareholders’ expectations of increased returns on previous capital expenditures, even while enterprises cautiously keep spending at moderate rates, due to market conditions. One way that oil and gas companies can ensure these returns is by driving digital transformation initiatives with existing equipment and assets. These initiatives can improve operational and business workflow efficiency with minimal upfront capital cost by empowering the workforce with data and insights, helping them solve age-old problems in new and better ways. In this article, we will describe digital transformation at a high-level, then explore its three key phases—evaluation, implementation, and scale and optimization.
What is digital transformation? According to Deloitte, “digital transformation is all about becoming a digital enterprise—an organization that uses technology to continuously evolve all aspects of its business models (what it offers, how it interacts with customers and how it operates).”1 When it comes to analytics, this often means taking tasks that require significant manual efforts, such as data aggregation and cleansing, and automating them. This empowers teams to spend more time actually digging into data and identifying operational issues and areas for improvement.
A recent survey conducted by the ARC Advisory Group2 found most manufacturing companies have made progress on their digital transformation journeys, but only about a third report substantial progress—i.e., are at a point at which they are focused on optimization and business-level improvements. This translates to great opportunity for increased revenue from assets, driven by digital transformation projects. Companies often make the mistake of believing they need a fully designed and implemented IT/OT architecture to get started on these projects, but modern applications can deliver near-immediate value, regardless of where an organization digitally stands. These types of self-service advanced analytics solutions provide access to data where it natively resides, significantly decreasing the time to value by helping teams form valuable insights that aid in decision-making, which ultimately drives business outcomes, Fig. 1.
Evaluation: move fast, build teams, gain trust. Many oil and gas operators often fall into one of two traps when they are in the evaluation phase of a digital transformation project. The first mistake is letting prior failures cloud future decisions. Many companies have been jaded in the past by technology implementations that failed to provide the value promised, which can slow down or halt future adoption.
The second mistake is getting caught in pilot purgatory, failing to ever make a decision that enables teams to move from test-drive to run mode, where true business value becomes apparent. Fortunately, there are many ways to combat these common mistakes and move projects effectively from evaluation to implementation.
To start, it is crucial to involve the correct cross-functional stakeholders from the outset, including a mix of technical and administrative personnel, which helps ensure the project adequately addresses specific business needs with a workforce at its back. By breaking out of organizational silos, evaluation teams can identify the highest-value use cases, resulting in quick wins for the team, while building confidence throughout the organization. Next, teams should look for a software solution that provides efficient access to data, is easily integrated into existing homogenous enterprise architectures, and can be implemented quickly, as in days instead of months.
Pilot provides ROI assurance. A recent article in Forbes described Chevron Phillips Chemical’s experience in the evaluation phase of a digital transformation project, where they chose Seeq to provide self-service advanced analytics for the organization, Fig. 2. The article states, “Upon completion of the proof of concept, users were able to learn the potential value of using Seeq from trusted colleagues. Seeq was easy to use and solved a massive number of problems quickly. The time to return on investment (ROI) was weeks rather than months or years. The problems that they were able to solve using the technology were problems that the workers did not previously know how to solve.”3 This confidence provided during the evaluation phase lowered adoption friction in subsequent phases of the project.
Implementation: look for quick wins and identify internal changemakers. Once a decision is made in the evaluation phase, it is time to implement a digital solution. To do this successfully, leaders should focus on quick wins that can be broadly deployed for business value, and identify changemakers who can champion progress in various parts of the organization.
As companies move from evaluation to implementation, many attempt to advance along the maturity scale too quickly and solve the most difficult problems first. Instead, teams should focus on the “low-hanging fruit” to not only accelerate the time to ROI, but also build confidence throughout the organization. After demonstrating through a series of quick wins that progress can enhance the ease with which employees do their jobs, the resistance to change should rapidly decrease.
Additionally, changemakers—certain individuals at varying levels of the organization who influence others—become apparent, and leaders must lean on these folks to garner support for digital transformation efforts among their teams. Because changemakers have pre-established rapport in their areas of the business, they can help create confidence in, and show colleagues the value of, the new solution. When changemakers’ energy is amplified appropriately, it can rapidly accelerate organization-wide buy-in, increasing speed to the scale and optimization project phase.
Scale and optimization: pairing technology with teams’ ability to use it. When teams enter the scale and optimization phase, they must remember that while digital transformation projects have defined end conditions, digital transformation, in general, is a journey with no final destination. As records are made to be broken, digital improvements are laid as building blocks for future efforts.
Therefore, a continuous improvement and sustainment plan should always be a key deliverable of transformation projects. This helps teams remain focused on value delivery while continuously evolving to meet the ever-changing needs of the business for overall efficiency improvement.
On top of ensuring a path for continuous improvement and sustainment, the scale and optimization phase is all about making sure those tasked with adopting new technology in their daily workflows are given the right tools to be successful. This includes not only support from project leaders and changemakers, but proper training, use case support, internal knowledge sharing, and a feedback mechanism to the project team.
The project team must be agile enough to take feedback and make adjustments to ensure underlying issues are addressed. Additionally, this group must remain focused on end-users and business units, defining appropriate measures for success and quickly addressing potential issues, even identifying them before they occur. When scale and optimization are completed successfully, it eases technological adoption and speeds up the time to value of digital transformation projects.
Scaling with care. Marathon Oil, where teams are tasked with monitoring nearly 4,000 wells, recently implemented an enterprise advanced analytics solution to ease this task. The company delivered workflows during the scale and optimization phase that reduced the time required to create a new alert from months to hours. Implementing alerts and staying up to date is essential to the business, because it helps keep wells online and limits deferred production.
“Using Seeq improves scalability for Marathon Oil by connecting production data from across all its wells. The company has over 50 employees using the solution with 170 workbenches in Seeq Workbench. It generates 1,500 tasks and over 1,500 notifications a month. What was being manually identified in the past is now automatically generated. Overall, by using Seeq, Marathon Oil is looking to increase production by proactively identifying issues to increase uptime.”4
The company increased production and achieved this scale by placing curated technology in the hands of its personnel, empowering them with notifications and insights to operate efficiently.
Don’t forget the big picture: people and business value. While many companies are in the evaluation and implementation phases of their digital transformation journeys, rapid acceleration towards the scale and optimize phase is inevitable over the coming years, aided by digital tools. Regardless of which phase you find yourself in, it’s important to look up and around every now and then to remember the reason for the journey. There are two essential questions to ask during these times:
Getting back to the definition of digital transformation, two key words to remember are “continuously evolve.” It is not too early or too late to begin such initiatives; the time is now. Whether an enterprise has yet to begin evaluation, or is wrapping up a project with scaling efforts, companies must work on continuous improvement to maintain value-driven cultures, remain competitive, and get the most out of their data and assets. WO
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MORGAN BOWLING is an industry principal at Seeq, where she monitors the rapidly changing trends surrounding digital transformation in the process industries and translates them into product requirements. Ms. Bowling has nearly a decade of experience working at independent and integrated major oil and gas companies to solve high-value business problems, leveraging time series data. She has a process engineering background and holds a BS degree in chemical engineering from the University of Toledo.