KPI:
In terms of the project deliverables, we need a “dashboard app” to display the data we have collected and worked on for this project. In terms of that, the KPI’s following is related to this:
- Number of Leads
- Cost per Lead
- Cost per Acquisition
- Social Media Traffic
- Social Media Conversion Rate
- App Store Download Number
- Sales Revenue
DATA SOURCES:
- NYC Open Data Restaurant Dataset:
- NYC Open Data Rodent Population Dataset:
DIMENSIONAL MODEL (Draft):
- Requirements – MD
- Document overall Enterprise business processes (According to Gartner)
- Strategy to execution: Delivering organizational goals in the form of tangible projects
- Business process analysis: Optimizing specific process to gain efficiency and effectiveness
- Enterprise architecture (EA): Holistically analyzing, planning and modeling business structures
- DigitalOps: Technologically planning, modeling, coordinating, governing and monitoring the processes and resources associated
- Identify ONE core business function to model
- Integrate the functions and new data coming from the data sources
- Identify source OLTP systems that support/feed this core business function
- OLTP systems provide source data to us.
- UPDATE and DELETE data once a week when we’re updating the data warehouse.
- Gather business user requirements in terms of KPIs
- Need to create a social media integration to the dashboard to share the analytics
- Ability to handle analytical errors
- Document overall Enterprise business processes (According to Gartner)
- Grain – Pete
- Identify the basic unit of fact data (grain) for the data warehouse
- Restaurant_name,cuisine_description, Inspection_Date, inspection_type, Job_ticket or work_order_id, violation code, location
- Identify the summary grains for the data warehouse
- Rodent population by violation code and location
- Identify preliminary dimensions to support the grain
- Inspection date, job_ticket_or_work_order_IF
- Identify the basic unit of fact data (grain) for the data warehouse
- Dimensions – Luan
- Identify all data warehouse dimensions:
- Location, Date, Grade, Restaurant, Inspection, Restaurant would each be a dimension set up for a star schema around one or two fact tables
- Identify slow-changing and fast-changing dimensional attributes
- Type 4 SCD, because although inspections happen at a low frequency, and restaurants rarely change locations, they will happen frequently enough so that creating a dimension for historical data would benefit the analytics processes.
- Level of granularity
- These fact tables will have a low granularity because we have the specific longitude and latitude as well as daily dates.
- Identify all data warehouse dimensions:
- Facts – Steven
- Identify facts
- Restaurant Name
- Cuisine Type
- Inspection Date
- Inspection Type
- Description
- Job Tickets
- Food Orders
- Violation Code
- Location
- Critical Flag
- Grades
- Identify fact types (additive, etc.)
- Semi-Additive
- Job Tickets
- Inspections Types
- Violation Code
- Inspection Dates
- Semi-Additive
- Identify accumulative facts
- Design composite keys for fact tables
- Estimation of fact table size and growth
- Identify facts