Managing power infrastructure assets is increasingly challenging, especially as the amount of data sources expand and the volume of data increases. It may appear attractive to build your own utility analytics system for customization and control, but it’s a good idea to take a step back and consider the pros and cons of a do-it-yourself (DIY) approach versus partnering with a company specializing in power system software solutions.
Pros of a DIY Utility Analytic System
1) Promotes in-house knowledge
Leadership may find comfort in knowing that the system was developed in-house because that means there is an internal team that thoroughly understand the system. That in-house knowledge will be beneficial if a regulator requires a review of the code to ensure transparency and accuracy. With a DIY system, code can be tested to understand how it works, rather than having to trust the intellectual property of a software vendor which you cannot test thoroughly in most products on the market.
As a bonus, it may be an excellent opportunity for team members to develop their existing skills and gain more experience – increasing job satisfaction.
2) No third-party reliance
A DIY utility analytics system will prevent the need for a third-party to provide support, which may cost more than originally promised. In recent years, the asset management sector has promised greater insight with the purchase of systems such as ERP and GIS, yet has failed to deliver on these promises even with the most advanced, and costly, customizations.
3) DIY utility analytics system can save money
A DIY approach can save money on expensive software and help businesses avoid paying another business for doing the work that an internal team is capable of.
The price of software is increasingly costly, largely due to outdated architecture and technology that require extensive overhead support. Pricing can often be too prohibitive to justify. A DIY system may be less expensive in the long run.
Cons of a DIY Utility Analytic System
1) Building, operating, and maintaining modelling software is not a utilities core business
Building a system from the ground up is a significant amount of work, while using a hybrid system with generic business intelligence and data science tools requires the same management effort as a software business. Entering this space requires deep niche knowledge which is often underestimated by many utilities. This can distract a utility from its core business and providing boots on the ground services to the grid and customers.
2) Lack of data and processes
Data is the backbone of all analytics. Utilities may find themselves with limited data, or with data that is not shared or documented properly. Without the proper data a DIY utility analytics system isn’t going to provide what is needed.
While developing models in-house may seem attractive, the average utility lacks data and the possible inconsistency of data across multiple sites will inhibit the quality of insights. The best data sources are with utility service providers, however they cannot match that up with other data required beyond their limited-service information. Utilities need a system that can provide complete data.
3) Lack of software operations knowledge
No matter how brilliant, a team will struggle if they have a lack of software business experience leading to more time and money in development costs.
Utilities may provide products from a utility space, but they do not have the same expertise in product management, user experience and user interface (UX/UI), specialized data science, and software operations as the private sector. Additionally, creating a sophisticated platform requires highly sought-after and highly compensated resources even for basic tasks such as cleaning advanced metering infrastructure data.
4) Knowledge loss during employee turnover
It'll be a challenge when someone leaves the organization, the work that they were doing may need to be scrapped because no one else can understand or maintain it. This can create complications from a regulatory perspective and hinder the ability to measure the utility’s performance year over year. Countless businesses have invested millions into their own teams and then had to restart when the team inevitably left.
What Does This Mean for Utility Businesses?
Building an asset management system in-house can be extremely beneficial, but make sure the organization is set up for managing an internal software product and has rigorous management of change process and succession planning in place to mitigate the risks of a failed investment.
While the temptation to create your own utility analytics or asset management system is understandable, creating a successful system requires an immense amount of effort and resources, which will divert attention away from the utility’s core business. Generally, very large utilities (over 1 M customers) potentially have the economies of scale to undertake such an investment, most utilities fall below that threshold. It is important to consider the true cost before making this decision - a DIY system may seem cost-effective until you’re left pouring money into a project that isn’t sustainable, requires your team to spend time developing skills they don’t have, and the utility ends up running an internal software business unit well outside of their core business.
Ted Zalucki is the CEO and co-founder of Engineered Intelligence Inc, an infrastructure analytics technology company. Ted has 10+ years of hands-on experience in the T&D sector working within several utilities and as a consultant across North America. His expertise includes advanced analytics, investment planning, asset management, risk modelling, productivity, process optimization, construction management, design supervision, operations, and regulatory filings and defense. Ted’s background is in Industrial Engineering and Financial Engineering, he holds an ELITE certificate from the University of Toronto and is a practicing Professional Engineer.