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Data Warehousing for improved analytics

The majority of websites that use some sort of analytics tool, whether it be Google Analytics or Adobe Analytics, allow webmasters and business owners to get an understanding of how their website is performing. But, what if you have external systems such as from e-fulfilment vendors or other systems that don’t directly integrate into your analytics platform? How can you get even more value from those tools?

Data warehouses, such as Google Big Query, Amazon Redshift, and Azure allow you to not only store data from analytics platforms, but also blend data to report on multiple data streams.

What is the difference between a data lake, data warehouse, and a database?


A database is any collection of data stored electronically in tables. In business, databases are often used for online transaction processing, which captures and records detailed information in real-time, such as sales transactions, and then stores them for later reference.

Data Warehouse

A data warehouse, meanwhile, is a centralized repository and information system that is used to develop insights and guide decision-making through business intelligence. A data warehouse stores summarised data from multiple sources, such as databases, and employs Online Analytical Processing to analyse data.

Data Lake

A data lake, finally, is a large repository designed to capture and store structured, semi-structured, and unstructured raw data. This data can be used for machine learning or AI in its raw state and data analytics, advanced analytics, or databases and data warehouses after being processed.

Advantages of using a Data Warehouse


Your data is backed up to at least 2 places; the source and with the data warehouse. This safeguards against policy changes, tools lifespans, and other potentially unforeseen issues. An example of this is Google Analytics. In July of 2023, Google will be sunsetting Universal Analytics for an the newer GA4, when it will stop processing new data. Six months from then data will no longer be available to users. What is you need to compare data from last year to this year’s data? A data warehouse means that you can keep records for as long as you like, even once a service is deprecated.


The majority of systems and platforms tend to have connectors for the major data warehouses, and if not there is usually a third-party company that will offer a solution to getting data from your platform to the warehouse. If your software is extremely niche, Data warehouses can utilise API requests to pull data into them.

Enhanced Reporting

If your fulfilment provider’s systems integrate into a data warehouse, and your ecommerce systems also integrate into the same warehouse, you can get a better understanding of your users and fulfilment. Such as, pairing specific sales with the distribution centre that fulfilled the order. The possibilities are endless.

Easy Dashboarding

There are an array of drag-and-drop reporting tools that integrate into the major data warehouses. Tools such as, Looker Studio and Power BI offer easy to use interfaces to build amazing, and flexible dashboards that can be shared throughout your organisation. The data warehouse can include data on who can access specific sets of data, so the flow of information can be controlled.