Paul Caffery, Senior Director, Pacific Gas and Electric Company
It is becoming more and more apparent that utilities need to have a strong asset management strategy if they intend on becoming a “digital utility” in the future. As current infrastructure ages and demand increases for lower customer bills, utilities must have the ability to know where and when to make investments in the infrastructure that will deliver the highest return while reducing the most risk to the system. This raises some interesting questions that must be answered. How is our data handled? Where is it stored? Who is accountable for its accuracy? Is our data scalable? Answering these questions with a heightened sense of urgency will be paramount to the success of driving the company’s investment strategy.
“Data needs to be treated as a critical asset and not just an afterthought or something that is a requirement to be captured. Most utilities define their assets in the traditional manner such as tangible items such as pipe, poles, wires, buildings and vehicles, but not their data.”
What this equates to is that data needs its own identity and focus. Too many times how data is managed is left up to each individual line of business resulting in disparate processes and tools to manage and keep up to date. This results in not only increased expense for managing multiple sources of data, but also conflicting views on the accuracy and scalability of data.
"Data needs to be treated as a critical asset and not just an afterthought or something that is a requirement to be captured"
When data is treated as an asset, it will attract many of the process and procedures that other assets naturally receive. These include strong governance, identified data ownership, and a simplified technology structure to house the data and retrieve it when required. Many companies have adopted an Asset Family Owner structure to ensure that the critical assets in each family is getting the correct attention they need and are driving risk reduction.
One of the challenges that I see that gets in the way of creating a good data asset strategy is that too many times lines of business look to and put the responsibility of data in the IT organization. This can drive a company to adopt a technology solution(s) that don’t meet the needs of the business resulting in the business creating a different solution or copying the data and making business decisions on data that may not be from the source system. When this happens, it leads to a miss trust of the data from the end user.
When a decision is made to make data a critical success factor to the asset management process, the company must then put structure in place that can make this change possible. There are numerous organizational models that one can choose, centralized at the corporate level, centralized at the line of business, etc. Regardless of which one is chosen, they all rely on strict governance, data cataloging and modeling, consistent policy and procedures and accountability. This future organization must include key, strong personnel from all areas of operations and the IT organization working together as one entity with common data principles.
Data principles may vary from company to company but at minimum include the following:
• Data is treated as an asset
• Data is open, accessible, transparent and shared
• Data quality is acceptable and meets its intended need
• Data is secure and compliant with regulations
• Data has a common vocabulary and definition
• Data is not duplicated, it is collected once and used many times
• Data is used to maximize decisions
The current trend is that technology is progressing at a much faster rate than the condition of the data available. Numerous programs and projects are delayed because of the quality of the data, the need for human intervention for system updating or the costs of the interfaces required to utilize it from multiple systems.
Data is at the core of everything we do as utilities. If the long-term strategy is to become a digital utility and utilize advanced analytics including AI and Machine Learning, data must be at the forefront of that strategy. Implementing tools to develop advance analytics on poor data will only result in faster, inaccurate, prettier reports. The use of poor or inconsistent data can result in additional costs through required human intervention or outputs that may not be accurate. Although machine learning has the capability to clean and categorize data, the cost of this is related to the condition of the data being evaluated.
In conclusion, data needs to be one if not the most important information element that is addressed in any Utility that has a desire to become a “Digital Utility” in the future. Treating Data as Asset is critical to ensure it gets the appropriate level of governance and attention needed. Companies need to prioritize the condition, source and governance of their asset data to ensure investments in quality and technology are being spent on reducing risk and increasing safety and reliability. The creation of a strong set of data principles will ensure that everyone in the company understands and is following the same processes and procedures as it pertains to data. When this is accomplished, Asset Management can truly trust that the data being used is traceable, verifiable and complete