Assessing the quality of data tools is crucial for effective analysis, particularly in fields like media and conflict analysis where accuracy can significantly impact decision-making. A robust evaluation framework should include criteria such as data integrity, usability, and adaptability to emerging technologies like artificial intelligence. By prioritizing these factors, organizations can enhance their analytical capabilities, ensuring that insights derived from data are both reliable and actionable in a rapidly evolving landscape.

If you're evaluating the quality of a data tool, then I suggest looking at the following features: 1) Connectivity to data sources to make sure you can directly connect. 2) Custom scripting to implement pre- and post-processing steps. 3) Integration within the suite of tools already in use. 4) Performance to make sure it can perform tasks in the time required to complete them (e.g., transform 1M rows in < 2 minutes). 5) Exception handling to catch errors and fix issues in flight. Generally speaking, it's how the tools are used that play a more important role in the quality of the output than the tools themselves.