Guidelines For Choosing A Data Warehouse

• Data sources that are operational, transactional and multi-channel from ERP, CRM, financial apps, IoT devices and more
• Staging area for data aggregation and cleaning
• Access or presentation area of data for analytics and sharing
• Data integration tools or APIs such as BI software, ETL and ingestion tools

When to Consider Using a Data Warehouse:-

• To analyze data from different sources - Combining data from various internal tools may become necessary to make better and more informed business decisions. It becomes easier if the data is stored in one central location rather than gathering data from different storage locations for analysis.
• To separate analytical and transactional data - If a company collects activity logs or other information in an app, storing it in the database may not be the best way to handle it. To ensure that the app performance is not affected by analytics, it's best to transfer or store this data in a data warehouse designed for complex querying.
• When source data is not suitable for querying - For example, a majority of business intelligence (BI) tools do not work with NoSQL data stores like MongoDB, which means that applications that use MongoDB on the backend need to have the analytical data to be transferred to a data warehouse for effective data analytics.
• To improve the performance of most used queries - For voluminous transactional data, it's a good idea to create summary tables that can aggregate this data into a queryable format, without which the questions can be prolonged and become a heavy unnecessary burden on the database.

Why Consider a Data Warehouse:-

• Putting data in one place: Pooling data into a single data warehouse enables businesses to access all the data from a single location, allowing for more insights across departments and reducing time spent accessing data from different backgrounds.
• Make better and faster decisions: Data warehousing helps improve the speed and eases accessing different data sets, making it easier for decision-making.
• Data integration: It helps analyze data from different sources like websites, apps, and other platforms. For example, linking Google Analytics with a data warehouse can be used to access information from the CRM, sales, and other media. Having data in one place can help run queries directly in the warehouse using tools like BI and others to automate and visualize the questions and outcomes.

Criteria or Factors For Choosing a Data Warehouse:-

 
• Business requirements - It is essential to determine the business needs and use cases while choosing a data warehouse. It may be challenging to understand the requirements of a data warehouse project, as enterprises have to deal with changing business conditions. Data warehouse evaluation can be done based on the capabilities that are and will be provided as businesses evolve. A data warehouse is not just a system for reporting requirements; it has to provide data analysis in meaningful ways to provide insights and make data-driven decisions.
• Cost estimation - A detailed estimation of data warehouse costs is essential to ensure a positive return on investment (ROI). Many costs associated with data warehousing are often ignored or understated, including deployment, data management, opportunity, procurement, maintenance and more. Total expenses during the warehousing lifecycle should be estimated to ensure that it adds value to the company, while the final cost may vary depending on what is used.
• Capabilities and technologies - Understanding the capabilities needed to get the best data warehouse solution is critical. Different providers offer different data warehousing tools and technologies. It is also essential that the data warehouse architecture provides built-in connectivity to other data sources, allowing a seamless integration since data warehousing aims to eliminate data silos and have a repository that supports business intelligence. To derive meaningful intelligence, data warehouse solutions offering advanced capabilities like data modeling, data mapping, data quality and profiling, ETL/ELT capabilities, job scheduling, connectivity with BI tools, and more can help. Features like data profiling and pushdown optimization can add immense value to businesses.
• Accessibility and speed - Accessibility and speed are crucial factors that can separate different warehouse solutions. Fast loading and data reporting can help users draw critical insights that can help them make timely decisions. Data warehouses offering parallel processing ETL/ELT capabilities and high processing power enable the loading of massive data records for faster analysis.
• Scalability - Scalability plays an essential role in warehouse consideration. It can significantly determine the financial feasibility and viability of the investment and the ability to continue as a data-driven company. Selecting a data warehouse that can meet the analytic needs for the foreseeable future is essential.

In conclusion, while many data warehousing tools are available in the market, companies must look for automated solutions that provide flexibility to customize. A cloud data warehouse consulting firm can help achieve faster time-to-value to support the company's analytics initiatives.