Namrata Nath interned as an Operations Data Analyst at Extra Space Storage. Her responsibilities included extracting operational data from the company database using SQL Server, comparing metrics across sites, and generating reports for decision making. She also worked on a predictive analytics project to develop a new model for forecasting rental numbers using historical vacates data, which she analyzed using correlation, regression, and time series analysis in Excel and VBA. The experience helped her gain analytical skills and knowledge of tools while enhancing her communication and presentation abilities.

1. Operations Data Analyst Intern
Namrata Nath
Extra Space Storage
Salt Lake City, Utah
Name: Namrata Nath
Major: Management Information Systems
Email: nxn141230@utdallas.edu
Phone: 4698038846
Contact
Extra Space Storage is a US provider of self-storage units, with over 1,071
facilities across 35 states plus Washington, D.C. and Puerto Rico,
Headquartered in Cottonwood Heights, Utah, Extra Space Storage is the
second largest owner and/or operator of self-storage properties in the
United States, as well as the largest self-storage management company.
The company is revolutionizing the self-storage industry by using vast data
collection and analysis techniques to expand the business and provide
better services to the customers. In the past two years, the company have
grown several folds and opened more than 250 sites all over the country.
Data analysis has been a major part of the expansion. The company
wishes to put the huge data at hand to better use by analyzing it and
extracting valuable information that can be used for important business
decision making process.
About the Company
The first part of the project was to extract historical data for past four years
for all sites based on which the predictions were going to be made. I used
SQL Server 2012 for pulling up data from the company database.
Introduction
Daily responsibilities:
1. Extract operational data from the company data base using SQL server.
2. Compare the data for various sites for different metrics on either a month
over month or year over year basis.
3. Generate reports and present them with deductions that can be used for
decision making purposes.
Project Responsibilities:
1. Run correlation and regression analysis to come up with the various
predicting factors.
2. Extract the historical data from database.
3. Formulate a model that would take in the historical data and provide with the
prediction for the next quarter.
4. Run the model and compare the predictions of new model against the
prediction of the existing model.
Learning Objectives:
1. Develop analytical skills and knowledge of various analytical tools.
2. Improve on communication and presentation skills.
3. Learn new tools like SAP Business objects, PowerPivot, SQL Query
Analyzer.
Working as an intern at Extra Space storage was a great learning
experience. I gained so much of knowledge and enhanced on my
analytical and presentation skills. Apart from that I had the opportunity to
learn a handful of new tools. Also working on an important project of the
company, I was given the opportunity to work with different departments
other than just mine. All of this has not only given an experience of working
in the industry, but has also helped a lot in my overall development of
personality. This experience will definitely prove to be of great value in my
future career.
Learning & Take Away
For Fall 2015 internship, I joined Extra Space Storage as an Operations
Data Analyst Intern working with the Operations department and reporting
to David Decker. My daily responsibilities included analyzing operations
related data, deducing interesting and unknown patterns, generating
comparison report and prepare an overall report including various metrics
for every month.
Apart from these responsibilities, I worked on a project for development of a
predictive model for rentals (i.e new customers renting storage units with
us) for the next quarter based on historical data.
The company already uses a predictive model for forecasting the rentals.
However the current model fails to provide a close to accurate prediction.
Hence the company decided to come up with a different model that would
use different set of predictors to forecast rentals. After analysis a bunch of
predictive factors, it was deduced that vacates (i.e customers leaving their
storage units) has a strong correlation with rentals and hence can be used
to predict the same.
Materials & Methods
Objectives & Responsibilities The extracted data was analyzed using correlation and regression analysis
in MS Excel. The analysis result was used to develop and formulate a
model that would predict the rentals using a series of regressions and time
series analysis.
VBA in Excel was used to run the regression analysis for all the store and
then predict the numbers for the next quarters.