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If the utility standardizes and normalizes all enterprise data by pushing it into a single version of the truth (such as the data warehouse), the ability to find the remarkable signals in the data is lost. Data scientists are information anarchists of sorts, and they need the latitude to consider chaos and decide what is useful to them to help solve the problems at hand. This is a sticky wicket for the utility: For transactional systems, governance, security, and predictable business process flow, unified versions of the truth may indeed be a worthy goal.
And where there are so 26 Big Data Analytics Strategies for the Smart Grid many functions within the enterprise, enabling interdependence through data integration has significant traction. It is logical to think that enabling the datadriven enterprise is dependent on the veracity of the data. The utility sector has always been pragmatic, and there is nothing more sensible than wanting a trustworthy view of the business. As you’ll see, when it comes to data management, the truth is a matter of perspective.
Pdf. Putting the Smarts in the Smart Grid 5 While other developed nations fare somewhat better than the US at keeping the lights on, especially those that rebuilt their infrastructure after the widespread devastation of the Second World War, decay and resiliency aren’t the only important issues. Other pressing problems motivate the smart grid: There are more than a billion people in the world who do not have electricity, and as the impact of anthropogenic climate change grows more alarming, nations are coming together to mitigate the threat with greater conservation, efficiency, and renewable forms of generation.