A business should create criteria specific to its processes and data environment, types of required analysis, output needs, and usability.
To ensure the toolset aligns with the business, the brand should begin by identifying criteria that maps specific needs to tool features. While expert analysts’ recommendations provide a starting point, these should be used as a narrowing tool before comparing brand criteria to each platform.
A BI tool should work with existing data sources, and any that are on the business roadmap. Equally important is the way the BI tool works with the data sources. Data import features often simultaneously perform cleaning and normalizing, for example, normalizing variants, or transforming text to numerical or binary data. If the process requires significant technical prep outside the system, the business team may not be resourced to support it. If possible, test an import to quantify any additional data preparation time added to analysis and reporting cycles.
The business should define the complexity and types of analysis expected. Combining data elements to review trends varies greatly from building “what if” models prior to implementing changes. Recent advances allow BI tools to analyze patterns using “black box” machine learning. However, expecting configurable machine learning to be performed by managers is probably beyond the capacity of a BI toolset.
Users of the output need to receive the data in multiple user-friendly formats, depending on use and learning style. The chosen BI toolset should provide numerous ways of presenting data that meet the needs of team members responsible for acting on analysis results.
Finally, determine usability requirements based on the end users. If managers and analysts find the tool cumbersome, low or uneven adoption will defeat the most powerful feature-laden tool.