Case Study $(15$ Marks$)$

CenterPoint Energy is a Fortune $500$ energy delivery company based in Houston, Texas. Its primary business includes electric transmission and distribution, natural gas distribution, and natural gas sales and service. It has over five million metered customers in the United States.

CenterPoint Energy uses smart grids to collect real$-$time information about the health of various aspects of the grid like meters, transformers, and switches that are used in providing electricity. This real$-$time power usage information is analyzed with Big Data analytics and allows for a much quicker diagnosis and solution. For example, the data can predict and potentially help prevent a power outage.

In addition, the tool collects weather information allowing historical data to help predict the magnitude of an outage from a storm. This insight will act as a guide for putting the right resources out before a storm occurs to avoid an outage.

Second, to better understand their customers, CenterPoint Energy utilizes sentiment analysis, which examines a customer's opinion by way of emotion $($happiness$,$ anger, sadness, etc.$).$ The company segments their customers based on the sentiment and is able to market to these groups in a more personalized way, providing a more valuable customer service experience.

As a result of using Big Data analytics, CenterPoint Energy has saved $600,000$ gallons of fuel in the last $2$ years by resolving six million service requests remotely. In addition, they have saved $$24$ million for their customers in this process.

How can electric companies predict a possible outage at a location? $(2$ Maks$)$

What is customer sentiment analysis? $(2$ Maks$)$

How does customer sentiment analysis help companies provide a personalized service to their customers? $(2$ Maks$)$

Propose a BI architecture for this case. $(4$ Maks$)$

Explain the content of each component. $(5$ Maks$)$