An information management approach to data can help companies capture and structure institutional expertise
Like many others, the oil and gas industry is likely to be impacted by the impending departure of an aging workforce over the next few years. While there are ample warm bodies to replace them – Millennials are now the single largest generation in the workforce – it isn’t just what they do that will be missed. It’s what they know.
When long-time employees walk out the door for the final time, many will take decades of institutional knowledge with them. That’s because much of the knowledge they have accumulated (and their organizations take for granted) is unstructured, which means it is not readily available to others across the organization. This becomes quickly apparent when a question comes up and the statement, “We used to ask Bob, but he retired last month” becomes a familiar refrain.
The good news is that companies can hold onto Bob’s – or Jane’s or Alan’s – deep understanding of operations through a strategic approach known as EIM (Enterprise Information Management). By applying EIM to process and data management, organizations can retain hard-earned institutional knowledge while creating a foundation to deploy scalable solutions, such as work automation and business intelligence, that deliver a competitive advantage.
How to Replace a Seemingly Irreplaceable Worker?
Spending decades on a job would give any worker a hard-to-top knowledge set. Consider a longtime operations manager. Monitoring a facility’s assets has been this person’s life 12 hours a day for 30 years. They are charged with the care and feeding of this unit. They understand how it needs to run, and how even a small change could have an impact.
Now imagine trying to insert someone new in their place. Without somehow capturing their thought process and experiences, getting new workers up to speed as they assume their predecessor’s responsibilities will inevitably be painfully disruptive for the organization, and discouraging to the new employee.
Capturing and sharing knowledge within the workforce through EIM begins with creating a clear definition of the objectives, associated processes and information needed to get the work done (i.e., process mapping). Now new workers know where to go, where to look and what to look for—even in the absence of “Bob.”
Secondly, data must be recognized as an inherently valuable asset that, just like other assets, requires a business owner and maintenance or stewardship. Data, used as inputs and outputs to processes, is the supporting basis for communicating in a common language and capturing knowledge. It should be a primary responsibility for any data steward to apply standardization to this language. From there, the organization can better execute data mapping and lineage: determining what information is needed to perform different work processes, the current sources of this information, and number of steps it requires to get it. This distills data to the most accurate needed to perform the job right in the right amount of time.
Starting the (Incremental) Journey
Transforming into an EIM-driven organization, which demands trusted data and accurate information, is a marathon, not a sprint. The prospect is often overwhelming at first, as many leaders believe it will simply cost too much, take too long or add even more responsibilities to their already busy staff.
A simple exercise to discover where a company should begin the EIM journey is to examine what is involved in getting certain projects off the ground—such as putting together a work package. If planners must go to five different places for data, data management must first be improved.
In another test, ask operators how they ascertain the health of certain equipment, such as a pump. Will they all supply the same answer? Will they all go to the same source to get the answer? Any variation in the answers is a tell-tale sign of poor knowledge management (not to mention the operational risks it poses).
If answers and data sources are uniform, then measure how efficient the process of measuring the health of an asset is. This will identify if an issue exists in the processing of the data. If there are issues, the organization can begin defining and visualizing process and success metrics.
No matter size or type of an EIM initiative, the following best practices will help organizations realize the benefits of this approach sooner:
Engage stakeholders and identify sponsors. Build awareness and management support for structuring information as it relates to work or operating priorities.
Assess the current state. It’s imperative to know where you are, before you can figure out where you need to go and how to get there.
Set a roadmap. Sometimes the journey is a straight shot, and other times not so much. In either case, having a plan helps to get everyone there together.
Scope for success. Keep work deliverables and schedules manageable, so that you don’t have to sacrifice quality.
Deploy with rigor. Nothing can derail an EIM movement or Digital Transformation project like veering from the design, which causes undesirable delays, and re-work down the road.
Design for measures and feedback. Be able to produce reports and measures that allow you to see where improvement areas exist and when success has been achieved. Don’t forget to allow for feedback, a.k.a. contextual knowledge, as part of the process
Beyond knowledge transfer: automation and innovation
Once companies have a clear understanding of the work required, and data/information is structured to support completing that work, now attention can be turned to analyzing which processes are candidates for automation. Even areas that are consistently well-executed should be assessed to see if manual steps can be automated for increased efficiency and quality.
Essentially, the goal is to apply a digital solution to old problems. EIM is the structure that enables that solution—because when data is well ordered, structured and trusted, companies can blueprint how they work.
Of course, there will always be an impact when great workers leave an environment. But with EIM foundational principles in place, companies can maintain much of their institutional knowledge to both mitigate risk and maximize the opportunity for improvement.
Oil and gas companies are regularly faced with many industry-specific issues to overcome. Such issues, including exploration and drilling, are often complex and intricate processes with many unique challenges to overcome. Data analytics can play a massive part in streamlining some of the most fundamental operations that are involved in the oil and gas industry.