What's the best way to evaluate data warehouse tools?

Start with a clear articulation of your business requirements. Assess your objectives, the types of data you will be handling (including sources, formats, volumes, and velocities), and your requirements for data quality, security, and governance. Understanding your analytical needs and decision-making processes is also crucial. This step ensures that you focus on tools capable of meeting your specific business needs, thereby narrowing down the options to those most relevant to your situation.

Before selection of any Data Warehousing Tools we need to put together an evaluation framework that will help us evaluate tools objectively 1. List out Business Requirements that we are looking to support and provide weightage. 2. List out Technical Requirements that the tool need to meet like (Interoperability with other tools, Fitment within ecosystem, Performance, Tools support, Feature upgrades) 3. Evaluate each tool on the above points and provide a score. 4. Meet with Vendor personnel to understand the costing, support model and future plans to make an informed decision.

It is also very important to understand business pain points to know what the driver behind the tool change or new tool acquisition is. While it is important to understand current business requirements, it is equally important that the selected tools are futuristic and also in alignment of organization's vision. Market availability of skillset is another aspect to keep things in check while doing the evaluation.

Consider how different tools support various data warehouse designs. Whether your preference is for a dimensional model, a relational model, or a hybrid approach, the tool you choose should facilitate the creation of a data warehouse that is scalable, flexible, and efficient. Assess each tool’s capabilities in terms of data modeling, schema design, storage options, and partitioning features to ensure compatibility with your design methodology.

Define the schema structure of your data warehouse, including tables, columns, relationships, and constraints. Choose a schema design that balances data integrity, query performance, and ease of maintenance. Consider factors such as denormalization for performance optimization and normalization for data consistency.

While assessing a tool, data transformation activities and their availability along with ease of use should be evaluated as well. Performance of these data integration/transformation activities with a larger volume is equally important. Not all business problems need all types of integrations, features, it is also important to derive a weighted average of the features while performing the evaluation. This puts the right focus on desired features.

I will coin it differently not visualization, but adding value to business is the goal. Why have a human to visualize if from the analysis itself the action is known ? I want the tool to tell my action options and the ROI on each option - a good visual may help but is not the goal

ROI, ease of usage, a tool/technology's customer support system or documentation, market adoption, training availability, skilled resource availability, migration from existing tool (if any) etc. should also be considered while doing a tool evaluation.