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Week Five – Historical Data

Marlon R. Evans

DBM/502

4/17/17

Mark Paxton

HistoricalData

Now that we have collected an adequate amount of data over a satisfactory period of time, it is of great importance that Estations, Inc. now has the appropriate tools in place to study historical trends of the collected information. We will explore the technology and star model that will assist Estations, Inc. in successfully completing this task, along with an explanation to Business Management detailing the benefits of the associated technology to be used and what information will be gained as a result.

Analytical Technology

Though a database has already been created to gather and store the information that will be studied, it is at this point that we must identify a new technology solution that will enable the organization to determine if they have been successful in achieving its goal of being able to adequately supply its clients with their Estation charging needs. To ensure that this data can be appropriately analyzed, we have decided to implement the SAS Analytics 9.4, due to its partnership with Intel to create an in-memory analytical solution that can provide results in both real-time, as well as in a predictive manner.

Not only is this solution made to scale, but it will also enable Estations, Inc. to store massive amounts of data off-site with lightning-fast accessibility. Additionally, the provided software that interfaces with this cloud service is easy to read and extremely user-friendly.

Star Model

Because of Estation’s need for quantifiable data to determine its success, the organization will need to know the following information:

  • Number of locations
  • Number of charging stations at each location
  • Number of vehicles that have charged per day/month/year
  • Battery size of each vehicle
  • Average charge time per battery size
  • Busiest time for charging
  • How often Estations are at maximum capacity

In order to find the answers to this information, we must have several pieces of information including Date, Time, Battery Size, Location, Dock number, Year, Charge Time, Customer, Paid Total, as well as Referral.

Conclusion

In conclusion, with the implementation of the SAS/Intel Analytics cloud service and the ability to find out the above information needed, it is our belief that Estations, Inc. will be able to not only appropriately study its historical data, but will also minimize the need for many physical components leading to reduced costs, while gaining speed and efficiency.

References

Accelerated Insight Through Analytics.(2015).Retrieved from