In Analytics

Organizations often wait until they believe they have complete and perfect data before undertaking data analytics projects. This hesitation is understandable. No one wants to waste effort analyzing the wrong data. But the day of complete and perfect data may never arrive.

On the other hand, a situational intelligence solution can actually improve data quality.

By visualizing data in its spatial, temporal and network context, situational intelligence solutions quickly highlight data problems that might otherwise be hard to spot among millions of rows of data. Likewise, by correlating data from multiple siloed systems into a single display, situational intelligence solutions quickly highlight inconsistent data across your organization.

For instance, if you visualize all your utility’s substations and feeders, you can easily see if your data has incorrect relationships between substations and feeders. This would be laborious to discern by crawling through tables of data to double check associations. Having the correct asset relationships in your data is critical to analyzing network impacts related to failing equipment, crisis situations, capital expenditure planning and more.

Situational intelligence solutions also speed the process of colleagues working together to fix data quality issues, by bringing multiple sources of data together into single, unified display. Continuing with the substation and feeder example, having colleagues from operations, GIS, asset planning and other departments all working with a single display is a faster way to resolve bad or missing network model data compared with multiple people trying to manually correlate disparate data to pinpoint the problem.

Waiting for perfect data postpones getting value from the data that you currently have and perpetuates existing data quality issues. Beginning to implement situational intelligence solutions helps you realize value from your data today, while also improving the quality of your data for tomorrow.

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