Grid Analytics can bring a lot of benefits to a utility. However, deploying Grid Analytics platform and products can become quite challenging. The following are some of the points to watch out for during deployment.

Use Cases: With many use cases possible, it is very critical that the utility deploying grid analytics identify and prioritize use cases based on company goals and focus on deploying a small number of use cases, to begin with. As the deployment progresses and the team develops expertise in deploying these use cases, more use cases can be implemented.

Integration: Grid analytics uses data from multiple data sources within the utility, such as GIS, meter interval data, OMS, CIS, and SCADA historian. Integration of these sources into analytics platform is a significant undertaking. Adopting standards based integration mechanisms for all the data sources can simplify and reduce integration efforts.

Data Quality: Analytics applications use data from multiple sources within the utility such as GIS, MDM, OMS, Data Historian and other databases or file stores. The quality of the data being imported is critical for analysis. Therefore, it is important to determine the quality of the data earlier during deployment by validation and then clean up any errors, preferably at the source systems.

It is important to determine the quality of the data earlier during deployment by validation and then clean up any errors, preferably at the source systems

Typically, errors exist to varying degrees inside GIS, and other data sources including the OMS. Typical errors include a meter to distribution transformer connectivity, asset connectivity, asset ratings, phasing information, and other characteristics. Different use cases require different levels of accuracy of the data. Therefore, the data validation and cleanup can be done in stages based on use cases being deployed. Once the data cleanup is completed, it is critical to introduce processes within the organization to maintain the quality. This ensures that the results obtained from the analytics are accurate.

Hosted or On-premise: Like analytics environments, grid analytics applications are installed on scalable server clusters, preferably a cloud environment. Such environments provide maximum flexibility and scalability. A utility deploying grid analytics must decide during deployment whether to deploy these applications in a public cloud, private cloud or in a more traditional in-premise data center. Data protection, privacy and security considerations also influence this decision significantly. Each solution has its own advantages and disadvantages that must be considered before deciding.

Change Management: To effectively utilize grid analytics solutions regular operational and planning processes within the utility must change. Therefore, it is critical to follow a change management process to define and establish new processes and procedures. Without proper change management, deployment of grid analytics, or any analytics for that matter, is destined to fail.

Pre-built apps or build your own: Grid analytics solutions provide an analytics platform on top of which vendors analytics applications are built. In addition, they may also provide facilities for the utilities to build their own applications. Building own applications by a utility will require significant IT and data science teams within the IT organization, whereas pre-built applications can be used directly by the users. Conversely, pre-built application functionalities are prebuilt and defined, may not provide the full flexibility to adapt to specific utility requirements that a self-built application may provide. Therefore, it is critical to evaluate the tradeoffs of using pre-built apps or to build own apps.