Welcome to GetGround's Data Task! We hope you find it fun.
We've included some data from SQL tables in CSV format. The goal will be to insert this data into a SQL database on your local machine; run some SQL queries and analysis; document, explain and visualize your response to the questions asked.
GetGround currently has end-customers referred to us by partners, such as lettings agents and mortgage brokers. The customer then signs up for our service, and we pay the partner a small commission per referrals. Referrals are on a company level: a customer who signs up for five companies counts as five referrals. Five customers in one company count as one referral.
Partners each have consultants, such as Joe Smith working at Lettings Agent A. The referrals are attributed to the specific consultant at a partner.
The data tables provided are as follows:
partners
id
created_at
updated_at
partner_type
lead_sales_contact
sales people
name
country
referrals
id
created_at
updated_at
company_id
partner_id
consultant_id
status
is_outbound
For referrals, the updated_at
field essentially says when the status went from pending
to either disinterested
or successful
. Timestamps are in Unix Nano format.
is_outbound
is true
when we refer a customer to a partner, i.e. "upsell". In this case we send them the customer, and they pay us a commission. We haven't done this very thoroughly yet, so most referrals are inbound.
Our sales people work in a "key account" model. Referrals come from partners, and a sales person typically manages partner accounts.
We currently have sales people in the UK, Singapore and Hong Kong.
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Please insert the data provided as CSV into tables in an SQL database. Please include SQL queries used throughout the assignment.
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Use dbt to pre-precess the data and output dbt models for analysis. Include appropriate data quality tests and documentation.
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Analyse the data using SQL. Be sure to include your investigative thought process, findings, limitations, and assumptions.
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Based on your analysis, how would you reccomend GG improve the quality of the analyses we can deliver.