What is a Data Warehouse?
A Data Warehouse will help you to build accurate forecasting models and identify impactful trends. When building a Data Warehouse, it’s important to recognise the following steps and thoroughly address each one.
Defining Business Requirements
Since a Data Warehouse encompasses all areas of your business it’s vital that every department is involved with its design process. We recommend that the process of requirements gathering involves all stakeholders and helps every department understand the purpose of the data warehouse, how they will benefit, and what results they can expect.
Requirement gathering can happen as one-to-one or collective meetings. This phase is often one of the hardest parts of data warehousing implementation, as a data warehouse includes data from a variety of sources, spanning multiple departments. Beyond this, there will likely be negotiations over information sharing and prioritisation. At Ingentive, we have skilled Business Analysts that will act as external mediators to ensure all stakeholders are happy with the defined project goals.
The requirement gathering phase is vital in ensuring that department goals are aligned with the overall project and that buy-in from all the relevant stakeholders is achieved. It can also help to highlight current and future business needs from the deep dive into data which is used for analysis. During this, our clients have an opportunity to uncover where their data is and isn’t being used effectively.
Setting Up Physical Environments
Your Data Warehouse will typically have three environments which mimic software development best practice. These three environments are development, testing, and production and are used in tandem, to ensure changes are tested for integrity and security before they are pushed to live in the production environment. They allow for development and Quality Assurance to occur without affecting the productive environment.
Three separate environments are also needed to run test data, identify breakpoints that need to be rectified, and to reduce stress on server workloads. It is not enough to simply have different physical environments set up. The different processes (such as ETL, OLAP cube and reporting) also need to be set up properly for each environment.
Introducing Data Modelling
Data Modelling is the blueprint from which the Data Warehouse is built. It can help you visualise data relationships, standardise naming conventions and establish security process compliance.
This is known as the most complex phase of Data Warehouse design. A good data model will allow the data warehousing system to grow easily and perform well.
Data Modelling normally takes place at the data mart level and branches out into the data warehouse. We often find that the three most popular Data Models for warehouses are the Snowflake, Star, and Galaxy Schemas. The chosen model will impact the structure of your data warehouse and data marts and helps to guide the overall architecture within the warehouse.
Choosing Your ETL Solution
ETL stands for ‘Extract, Transform and Load’, and represents the collection and processing of data from various sources into one central data store where it can be later analysed. The likelihood is that your business has access to many data sources, but often these sources are presented in a way that is hard, and sometimes impossible to consume.
A good ETL process can be the difference between a slow, hard-to-use Data Warehouse and a sleek warehouse that adds value to every aspect of your organisation. For this reason, it’s vital that the right ETL solution is selected. This is why we ensure that our clients are supported and educated in choosing the best ETL process for their unique business needs.
Online Analytics Processing (OLAP) Cube
An OLAP cube helps you to analyse the data in your data warehouse or data mart. Since your warehouse will be sorting data from multiple sources, the OLAP cube helps you to organise all data in a multi-dimensional format that makes it easier to analyse.
Creating A Front End
This stage refers to the front-end visualisation, where users can understand and apply the results of data queries. If users cannot visualise the reports, the data warehouse is likely to provide little value to them, making front end development an important part of a data warehouse initiative. Another area of importance is the complexity of the reporting tool.
- How often do reports need to be published?
- Do they require specific formatting?
- Does the user require an interface that allows for customisable reports?
Optimising Queries
The more data returned from a query, the more resources the database needs to expand to process and store this data. This is why it’s important to only minimise data retrieval, especially if you’re paying for query power separately.
This stage is very specific to each organisation’s needs.
Rolling Out the End Product
Once the hard work is complete, and you’re close to getting value from your shiny, new Data Warehouse, team members will need to be trained in using it. Throughout the process, Quality Assurance and Testing have been ensuring there are no bugs or usability issues.
Although these are the standard steps in creating a Data Warehouse, it’s important to remember that every scenario is different. There may be additional steps that your business needs to take based on the requirements or complexity of your organisation’s needs. Ultimately, a successfully implemented Data Warehouse will deliver value at every level of your organisation.
If you have any questions about data or Data Warehousing, then please do get in touch, we’d love to hear from you.
Why Ingentive?
We stand as a strategic partner at the intersection of pro code and low code, empowering businesses to drive innovation, enhance collaboration, and achieve their strategic objectives. Whether it's the precision of custom coding or the agility of low-code solutions like Microsoft's Power Platform, we provide a strategic and adaptive approach to software development and data warehousing, unlocking the full potential of businesses in the ever-evolving digital era. Choose Ingentive for a transformative partnership that aligns seamlessly with your strategic business vision.
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