The trouble with data is that it is big, dense, and difficult to understand.
If you want anything contextual or relevant from data to use for your marketing, it requires the services of a data scientist or analyst. They can pan for nuggets of data gold and melt it down and turn it into something valuable for other organizational departments to use.
Doing all that is time-consuming, complicated, and prohibitive to almost anyone without the correct skills and patience.
Though data experts are undoubtedly able to uncover additional (and treasured) insights, the process often lacks the promptness and responsiveness that marketers need when they want to query and explore their customer data. If an organization were to lean on the IT department to report on data, the results would probably be static and lack the sort of interactivity needed to break their data down to a granular level.
Thankfully, it doesn't have to be this way.
Modern train-of-thought analytics facilitate data for use by non-technical people, making datasets easier to access and manipulate and to see results far quicker.
The key to the user-friendliness of these new analytics tools is visualization, which gives context to understanding the data. Those platforms enable marketers to drill deep down into customer data with easy-to-comprehend diagrams, statistics, and other reporting graphics to understand behaviors, segments, trends, and demographics.
This type of data analysis plays well with marketers, who enjoy an iterative approach of asking questions to extract insight. An initial query may not tell you much, but with train-of-thought analytics, the results could spark a second or third question (and more) to really dig down and unearth useful knowledge.