Having a data warehouse is considered a core component of business intelligence and will exponentially improve your organisation’s reporting and data analysis capability. Data warehouses is a big project for many organisation and the size will ultimately be dependent on the amount of data you have. As such, it’s important you have confidence in your data warehouse supplier, and have the ability to understand and question the processes they use.
What is the role of a data warehouse?
A data warehouse is every business analyst’s dream! It gathers information about almost every one of organisation’s activities into one place, allowing it to be analysed by a single set of tools.
The industry term for this is the ‘single source of truth’. This may sound rather cryptic, but in actuality the ‘SSOT’ is just a state of being for your company’s data. As it’s been aggregated from many systems into one location, the data is the most accurate it can be – providing decision makers with timely information that they need to make the right choices.
In practice this might mean bringing together the data extracted from your accounting system, ERP, marketing database, and customer portals into one easy-to-use system. It should also offer a straightforward and succinct display by eliminating data that may not be useful for decision-makers.
What are the stages of designing a data warehouse?
Before a data warehouse is built and ready for use, there are 8 steps that need to be taken.
In summary these steps are:
- Defining business requirements
- Setting up physical environments
- Introducing data modelling
- Choosing your ETL solution
- Online analytics processing cube (OLAP cube)
- Creating a front end
- Optimising queries
- Rolling out the end product
What are the benefits of a data warehouse?
Building a data warehouse can represent a significant investment in both time and money. So what are the tangible benefits your company will be able to see:
- Saves time
- Enhances data quality and consistency
- Gives you a competitive advantage
- Enables forecasting with confidence
- Generates a higher ROI
- Improves the decision-making process
What are the components of data warehouse architecture?
Data sources: Taken from either flat files or operational systems. This could be transactional data and can take the form of structured, semi-structured or unstructured data.
ETL: This represents the staging area where data, extracted from external sources and following different formats needs to be validated and standardised. ETL stands for Extract, Transform and Load and is used when data needs to be prepared for a new location.
Warehouse: The data has now been cleansed and is ready to be stored within the central repository that is the data warehouse.
Data marts: The data mart is also part of the storage component, and there can be multiple data marts each containing a subset of data relating to a different function of an organisation (ie Purchasing, Sales or Inventory).
Users: Will access data marts via analytics, reporting or data mining. Data mining is the process used to find hidden patterns that are present within a database with the help of algorithms.
Who uses a data warehouse?
A key aspect of working with data warehouses is their ability to integrate with other key systems for the purposes of data visualisation. A great use-case for data warehousing is to integrate it with data services such as Power BI, allowing you to build custom reports and dashboards to find insights more quickly.
Whilst it’s easy to assume that the main users of a data warehouse will be senior management who are responsible for making key decisions, the beauty of a data warehouse is that it makes information more easily digest-able for people at all levels of an organisation.
We’d love to discuss your data challenges with you. Please get in contact and a member of our team will get back to you shortly.