Why a Datawarehouse? Data-warehouses are the centre of the modern data set up. It allows the reconciliation of different data sources. As a company grows and the need for data increases. Differences appear from data source to data source. eg Google Analytics (event tracking) and Marketing data fails to align with backend or CRM data (Salesforce, Mail-chimp, Hubspot etc). Without a Datawarehouse (or a correctly organised one) a number of problems arise for companies - Data is spread across Spreadsheets (Excel, google Sheets): theses are increasingly difficult to maintain and debug - Numbers change historically - Departments disagree about the source of truth and the definition of KPIs - Data quality issues go unnoticed or are hard to track down - Building reports is slow and labour intensive My Motivation I believe in there being one reconcilable truth, and that using this knowledge is the best way to move forward for any company. A correctly functioning Data-warehouse is the best way to reach this truth and transparency. I specialise in designing or repairing data-warehouse solutions as well as training the Analytics or BI to be more effective at their job moving forward. Data-warehousing is typically an expensive project with a high risk of project failure. With modern technology and my approach this risk and cost can be reduced. Outputs - Datawarehouse set up: AWS Redshift, BigQuery, Snowflake, Postgres (not recommend) - Data imported from core data sources: -- Production Databases -- CRM systems: Salesforce etc -- Call Systems: New Voice Media etc -- Marketing Platforms: Google Ads, Criteo, Facebook etc -- GSheets -- Event and Product Data: Snowplow, Google Analytics etc - Data Modelling according to Kimball Method -- In SQL Modeling layer -- Core Fact & Dims tables - Transformation and Cleaning of Raw data -- Business Logic applied -- Data reconciliation -- Data quality control -- Data quality testing - Training and Upskilling -- Training regarding best practices for inhouse BI Analysts or Engineers -- Assistance in hiring as required - Budget control and Prioritisation Planning -- Choosing the best products and the correct scale point for businesses -- Suggesting and implementing bespoke in-house implementations where appropriate - Demo Dashboards as required Timeline - 1.5 to 6months depending on company size and desired scope -- example: Company starting fresh Datawarehouse for marketing purposes with a gross revenue of less than €2million per month, (or Marketing spend less than €4million per month) seeking initial design and 1 core fact table = 1.5months Example Downstream Value Adds from improved Datawarehouse - Correct data across the company - Improved Marketing spend (ROI) - In-house Multitouch attribution - Improved Customer lifetime value and cohort analysis - Allows Datascience (data-science performed before a functioning datawarehouse is ineffective and often flawed) - Product Feeds - Providing Data as a product to end customers
Data-warehouse Choice and Setup (Redshift, BigQuery, Snowflake)
Extract & Loading of Raw data into Data-warehouse
Modelling of Core business Dims and Facts Tables
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