An inside look into Retail Sales & Purchases

Refresh: (About US Census Bureau) Agency of the Federal Statistical System Accumulates and reports on American economic and social data Conducts nationwide Census every 10 years Uses Advance Monthly & Monthly Retail Trade Surveys (MARTS & MRTS) and Annual Retail Trade Surveys (ARTS) Produces comprehensive data estimates on retail economic activity (purchases, operating expenses, inventories etc.) Produced using samples from firms meeting certain criteria

Refresh: (Significance of This Data) Analyzing current state of economy and growth of retail categories Needed for making day to day, week to week, month to month business decisions Integral piece of GDP Investment (should we keep our money where it is) Economic Forecasting Analyzing trends in retail in certain categories and industries Predicting future growth areas Organizational opportunities for innovation, adaptation and expansion Investment (where and what should our money go into)

Current Data Challenges Data currently spread out and not consolidated << FIXED Spreadsheets and CSV files << FIXED No central location or datastore << FIXED Data structure not fully in place <<FIXED Lack of consistency, quality <<FIXED Dimensions somewhat defined, but not completely <<FIXED No reports No method for business users to run custom queries or reports Limited means for forecasting and observing historical facts Inability for companies to make strategic decisions regarding products and services

What Will Data Warehousing Provide? Pull data from multiple sources Consolidate into single database allowing for single query engine Allow for normalization in database Solid data structure within SQL Server Clean, comprehensible view for business end users Maintain sale & purchase history Spanning back 2 decades Creation of specialized queries and reports Singular data analysis Overview analysis High Return on Investment (ROI) In this case not for Census Bureau, but other companies and economists forecasting and reporting on past, present and future data

Original Business Dimensions Time ID (PK) Month Quarter Year Service/Product Line ID (PK) Description Sub_Category_ID Bus. Category ID (PK) Description Sub-Bus. Category ID (PK) Description Category_ID FACT TABLE RowID (PK) Service/Product (FK) SubCategory (FK) BusCategory (FK) Month Year

New Dimensions (STAR Schema) Created new dimension: Location Original source did not break data out into regions or states Removed the Category dimensions and instead included them as part of the Product dimension Fact Table attributes redistributed from original design

Dimensions & Fact Table in Access Database Location Table Fact Table Product Table Time Table

Fact Table Data Sample Over 130,000 rows of data Contains the Product ID Contains the Location ID Contains the Time ID Contains Amount Unique, auto-incrementing integer used for the Row ID of the table Amounts represent millions $99.76 = $99,760,000

Location Data Sample Due to time constraints and size limitations we did not break into individual states Would more than quadruple the number of rows in Fact Table Broken up into Divisions of each Region East North Central West North Central Mid-Atlantic Mountain New England Pacific South Atlantic West South Central East South Central

Time Data Sample Dates back to 1992 Extends up to 2013 Time only broken out by month Days would have proven to difficult to manage/break apart from original source Would have resulted in nearly 4,000,000 rows of data at the least (assuming one amount per product per region per day) Distinct identifier for each month of each year

Product Data Sample 56 different products More like Retail Store Lines than actual products 13 different main Categories including: Motor Vehicles Furniture & Furnishings Food & Beverage Stores Building Materials and Supplies Clothing Electronics and Appliances

Data Objectives Accomplished So Far… Gather, manage and examine data Consolidate data into database (normalized) Refine data structure and dimensions Perform extensive queries of data Extract key statistical figures Determine most profitable Bus. Category Sub-Bus. Category most primed for growth Service/Product line on the rise and fall within last 2 decades Estimate current economic growth/activity; predict future growth Consider financial crisis, and recovery of purchases and sales after such crisis Observe historical trends Predict future trends, spikes and drops

Tools Used So Far… Microsoft Excel Used to prepare data for importing into Access database Imported into Access using the External Data feature in access Microsoft Access Initial database platform to hold data SQL Server 2012 Will become the database for the data after migration from Access Allows for more data than Access SQL Server Analysis Services 2012 Online Analytical Processing (OLAP) configuration Reports Microsoft Excel Originating source of the data Will be used for displaying custom reports/dashboards

What’s Next…. Data Migration SQL Server 2012 Will become the database for the data after migration from Access SQL Server Analysis Services 2012 Online Analytical Processing (OLAP) configuration Reports

What’s Next Continued… Warehouse Outcomes Formulation of OLAP Cube Central Data Repository Location Means for Business End users to examine the data

Questions or Comments??