What are data domains? When it comes to building a data analytics infrastructure and organising your data, domains are vital. But what are they and what do they do?
Domains are built in a data warehouse and are integral to the structuring of data for analytics. Without a data warehouse, multiple sources of data cannot be organised in a structured and logical manner.
Many scaling businesses first start accessing their data for analysis and reporting directly through a BI tool, pulling raw data directly from multiple sources, without a data warehouse.
Whilst this activity can work in some cases, for example, when you want to explore some data without having to worry about connecting it in the warehouse, it is not a long term solution.
Most BI tools provide connectors to connect data sources directly to the visualisation tool. However, this skips an integral part of the data puzzle.
There are several problems that can arise from this approach, namely:
The implementation of a warehouse ensures up-to-date data is pulled through to the BI tool. Domains on the other hand, ensure the logical structuring of data. Without a warehouse, the process of pulling data into a BI tool becomes increasingly laborious and slow. Eventually, a transform layer will be required to make the data usable and ensure that it is presented in the correct format.
Data warehouses democratise data by structuring it for reporting and applying the business logic, organising data into domains. This step prepares the data for the chosen BI tool. By building domains in the warehouse, you’ll generate value to the business which can be accessed by users via pre-built reports and dashboards.
Establishing a data warehouse as the single source of the truth eliminates silos and brings all of the data in an organisation together. Furthermore, warehouses reduce the risk of generating inaccurate and inconsistent information and its distribution around the business. This is prevented by the organising and cleaning of the data before entering the chosen BI tool.
Domains are integral to using data for BI and analytics. Creating a domain involves bringing together all of the same “types” of data from disparate sources.
Domains can be broad or more granular. For example, a D2C business might require a customer data domain, or a more specific domain, such as customer name. Ultimately, the domains built and used by a business depend on the organisation. They will therefore be decided in the process of building the warehouse.
Take for example a bookshop wanting to understand all transaction data. There will be data from transactions at the till, transactions from their online store and perhaps transactions via a third party merchant. A union of all transaction data sources into one general transaction domain is vital to understand transaction data.
Examples of common data domains include:
In short, establishing data domains ensures that the information remains consistent and organised within the established parameters.
Furthermore, foreign keys allow the joining of domains to link different types of data. For example, you can link order data to customer data via a customer_id attached to each order.
At kleene, we want to make the process of accessing your data and using it to drive your business forward simple. Our SaaS ELT tool automates the extraction and load into the data warehouse, so that it can be transformed and neatly housed in domains. What’s more, if you don’t yet have a data warehouse, we can help!
The kleene Build Your Warehouse service establishes your data domains by combining all data from fragmented data sources into useful groups of data. Built bespoke to your business, the domains ensure your warehouse is organised to suit your data requirements. As a result, a BI tool can be connected to the warehouse to build reports and dashboards, directly from the automatically updated domains, ready for business users.
Thinking about establishing a data warehouse and using your data for BI? Talk to us to see how we can support your data journey!
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