Leveraging the power of data has become synonymous with growth and development in the modern business ecosystem. Managers, analysts, and consultants depend on quality data and visualizations to make informed decisions and form business strategies. In this regard, the concept of developing an enterprise data warehouse (EDW), albeit new, has quickly gained traction among data professionals worldwide.
A data warehouse is a centralized and highly denormalized body of data that contains aggregations of tables from across an enterprise’s various databases. It is used with business intelligence tools such as PowerBI and SQL clients to generate analytics reports, visualizations, and dashboards. Data might enter the data warehouse through various sources, and updates are usually automated by defining preset intervals. Data warehouses thus minimize the time and effort needed for analysis by providing quick access to key figures and facts.
This blog will look at some of the main benefits of modern data warehouse solutions for businesses, how they can be implemented, and some considerations that solutions engineers need to be mindful of when setting up their data warehouse architecture.
Key Benefits of a Data Warehouse
Data warehouses allow organizations to keep a consolidated store of data from various structured and unstructured sources and perform analysis on it in real-time. In addition, the highly denormalized nature of data and the integration of slowly changing dimensions (SCDs) in the data warehouse architecture helps users keep track of historical data and past trends.
An efficient and well-designed data warehousing solution can give an organization a competitive edge over its business rivals. Business users can maximize the potential of their data for effective decision-making by implementing a data warehouse that is suited to their needs. Some of the main benefits of a data warehouse are:
- Subject Oriented: Data warehouses can quickly give all the relevant information, facts and figures concerning one variable in the dataset. For example, managers can quickly visualize how many sales one particular salesman had, the products he sold, and the revenue he brought in.
- Integration and Standardization: Data warehousing solutions are based on highly standardized and denormalized tables that are consolidated into simple fact and dimension tables and arranged in the form of a star or snowflake schema. This enables managers and business users to perform analyses from a more holistic approach as they do not have to examine sources separately.
- Security: Whether the data warehouse is stored on cloud or on-premises, once it ingests data, it will remain there in a stable, non-volatile form. Data warehouses also maintain a log of all activity, and users have the liberty to access data whenever they need it.
- Time variant: Data warehouse solutions are built to track changes of data over time intervals. Most data warehouses come with the date dimension feature that allows users to look at data on specific months, days, hours etc. This is especially important when the business model of the organization is cyclical.
- Cloud integration: Most modern data warehouses come with cloud integration features that eliminate the need to be present on-premises and accessing servers to utilize the data warehouse. Cloud compatibility allows business users to access all their data from any geographic location, quickly and securely. In addition, this also saves the business costs on maintaining servers and hiring an IT team to maintain the on-prem data warehouse.
The different components of a data warehouse are arranged to provide agile insights and maintain a single source of truth for all analytical processes. Businesses sometimes use data warehouse assessment templates to collect in-depth information about the needs of the organization and its exact use case. These are then used to identify specific pain points and the extent to which the enterprise might need a flexible and scalable solution.
Implementation Strategies
Once a thorough evaluation of the organization’s needs for a data warehouse solution has been performed, solution engineers and architects must look for the most effective way to implement the data warehouse across the business. Generally, data warehouse architects tend to choose dynamic designs that can easily cater to denormalized, dimensional or hybrid data warehouses. In addition, they make it a priority to ensure data is cleaned before it enters the data warehouse and often automates the cleansing process through ETL tools.
The following data warehouse architectures are widely practiced across different industries and provide value to the business use cases that are relevant to them:
- Basic Data Warehouse: Consists of raw data, metadata and denormalized tables from the central database. This can be designed using the top-down or the bottom-up approach. Usually has clean and structured data coming in and does not need to be extensively transformed and cleaned before the loading stage. This type of data warehouse provides quick and easy access to the end-user and is relatively simple to set up and maintain.
- Data Warehouse with a Staging Area: This is similar to a simple data warehouse except for a separate table or area dedicated to the consolidation and standardization of incoming data sources. Data stored in different structures and file formats are first reorganized in the staging area before passing it to the data warehouse for analysis. The staging area simplifies the data preparation processes and eliminates the need for extensive coding.
- Hub and Spoke: Involves data marts as information is divided and propagated to different departments within the organization according to their requirements. This improves efficiency and saves time as business users do not have to navigate the entire data warehouse to look for relevant data.
- Sandboxes: Although these are relatively unconventional, they might be effective in industries where secrecy and privacy are essential to operations. Sandboxes allow users to quickly access and preview new datasets without going through the formal setting up of a data warehouse.
Conclusion
The choice of data warehouse architecture and implementation strategy depends on the exact use case of the business. However, solutions engineers need to be mindful of the best practices and incorporate them into their data warehouse solutions to achieve significant results.
The benefits of a data warehouse are not limited to those that have been mentioned in this blog. With increasing innovation and advances in Artificial Intelligence, the modern enterprise data warehouse promises to be a self-appreciating investment for all businesses looking to expand and develop.
