More and more utilities are realizing that they have a problem of too much data and not enough ways to use it all.
Some executives are wondering if they shouldn’t just disconnect their new sensors and data-gathering devices and walk away. Others, however, are searching for solutions, and have found their answers in AI technologies. Machines can compile, organize, and analyze data at large scale and great complexity. AI technologies provide utilities with the insights they’ve been searching for from the piles of data they already have.
Now these utility companies are faced with yet another question: How do they obtain machine learning technology for themselves? What is the best method for implementing AI in utilities?
Internal Implementation
The temptation for many utilities is to try implementing a machine learning solution internally, without using an outside partner or consultant, since most utilities now have their own data scientists. Whether or not this is a viable strategy depends heavily on the company. If a utility has a large and specialized enough data science team, this may actually be possible. But any executives attempting this need to take the time to fully understand the infrastructure needed for a successful machine learning implementation. Only then can they properly assess whether an in-house implementation is the right course of action for their utility.
External Implementation
For many utilities, the best option proves to be external implementation. There are many advantages to this route. A carefully vetted partner with deep AI expertise brings solid experience that most companies will have difficulty attracting. They also can dedicate far more resources and personnel to the project, as these implementations are their primary function. An AI partner provides ready-made solutions to a company’s specific issues, while using their understanding of the technology to tailor where needed. Depending on the project and the partner company, working with a partner may offer a significant return on investment within three to six months. With most internal implementations, it takes years to see this same level of progress.
This is not to say that internal data science teams have no place in this process. Machine learning companies still need people inside the client utility who understand the analytics at hand, and who they can work with to deliver solutions. An external machine learning company is not going to replace in-house data scientists, but augment them.
Some utility executives are hesitant to work with an outside company for other reasons. Will an external partner take the same models originally built for their own utility and distribute them to competitors as well?
This is an unfounded fear. While the precise approach may vary from company to company, often about 70% of what a machine learning company does is using their own IP and platform, and the other 30% is customization to meet each client’s needs, such as plugging in the client’s data and creating a custom user interface. This 30%, which is strikingly different for each client, is where the real work of the project is. The first 70%, while replicable, is a framework that the machine learning company leverages. The end result is unique to each client.
The Need for Collaboration
Of course, this all depends on selecting a good company to work with. A high quality machine learning company will collaborate with all personnel within a utility to ensure smooth implementation. The vendor must know the importance of fostering a cooperative relationship with all levels of their client’s organization.
Whether they choose the external or internal route, top executives need to understand this as well. Machine learning brings huge alterations to utilities, and strong change management is needed to guide the whole company through the transition. Buy-in from top management is essential, as those managers help spread the vision of machine learning—and the benefits it brings—to the utility as a whole. Implementing AI in utilities requires good leadership regardless of the method used.
Utilities have already started to make the changes that are critical to their continued success. Now they need to take it one step further, and find the right way to move forward as a company and as an industry.
SparkCognition is an advanced machine learning analytics company experienced in collaborating with and augmenting utilities and their data science teams. For more information, contact us at sales@sparkcognition.com.
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