Terms like “data reporting” and “analytics” are sometimes used interchangeably. Because data reporting solutions help make critical information more accessible to decision-makers, it can be easy to mistake reporting for analytics. But these are distinct capabilities, and understanding the difference is not a matter of semantics. In fact, understanding the difference is a great way to conceptualize the full spectrum of potential use cases for utilities’ raw operational data.
Data Reporting vs. Operational Analytics
Both data reporting and operational analytics refer to processes for aggregating data from various source systems and processing it into a format that is digestible for end-users (whether that’s a chart or graph, a PDF report, an extensive regulatory reporting document, or a digital dashboard). Here’s the difference:
Data reporting focuses on aggregating data, organizing it, and translating it into a readily digestible format. But it provides reports that, while they may be updated at some regular interval, are fundamentally static. Reporting capabilities help utility professionals find the data they need, but reports do not analyze this data to answer deeper business questions.
Operational analytics leverage approaches like machine learning to not just gather data from source systems, but analyze the trends and relationships exhibited in this data to generate new layers of insight. This insight can help answer nuanced business questions that would be far beyond the scope of a simple “report.” Reporting answers the question “what happened?” and operational analytics answer the questions, “why?” and “what will happen?”
It’s important to understand that neither capability is necessarily better or worse. To understand the practical differences, it’s best to consider end-use cases. A relatively simple reporting process—collecting data from source systems and aggregating it into a static report—can certainly be valuable in many contexts. Robust, data-rich reports contain so much valuable information that it can be tempting for utility executives to think “Mission accomplished! We can check analytics off the list.”
But it’s crucial to understand that data reporting, however valuable, is only tapping into a small portion of the overall value that a utility’s data can generate. If “data analytics” refers to the broader, ongoing process of transforming data into assets usable by a variety of end-users, then static reports are only one of many potential outputs from this process. A data pipeline capable of processing data just-in-time to support real-time decision-making can deliver true operational analytics capabilities that go much further.
Consider the example of outage analytics. If a utility professional needs to ascertain, for example, the worst-performing assets for the last month, SAIDI/SAIFI ratings for specific assets, or a total number of outages, data reporting is the only analytics capability truly required. But what if the decision-maker needs the ability to quickly generate answers to questions requiring deeper analysis? Questions like:
Which assets will be in the path of an oncoming storm, and how many critical customers will be affected if they go down?
Which vendors provided the assets that have performed the worst historically?
Which preventive maintenance actions have provided the highest long-term ROI?
Which equipment is likely to fail in cold weather versus high winds?
Answering these questions requires capabilities that go beyond reporting, such as fully dimensionalized models and/or machine learning. By leveraging these capabilities, a true operational analytics platform should enable utility professionals to ask and answer specific, nuanced business questionsin a few clicks. In their absence, answering these same questions would require, at a minimum, writing ten different queries, cobbling together a spreadsheet, and diving into an exploratory analysis that could take days. By dramatically streamlining this process, a fully-realized operational analytics solution can provide actionable, real-time intelligence that is far beyond the scope of even the most ambitious data reports.
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