The key is to understand the difference between data and information. Data is just that: data, which can tell you absolutely nothing without applying some sort of focus or filter. Once that focus or filter is applied, then you MAY be able to turn that data into insights: That is, information about what users do and when.
Now that you I have stated the difference, let’s look at a simplified process.
With modern web sites and systems, you have a dearth of data at your disposal: Traffic logs aplenty, and beyond that you also have outside sources of data that tell you site popularity, and general traffic patterns and usage. This telemetry is great stuff – IF it is used correctly. Hence, the data is less important than the filter you apply to it. And this is the tricky part.
Sidebar: I worked with a customer to analyze their web site traffic logs, and the insights I came up with – well, she completely disagreed with them. The reason was simple: She disagreed with HOW my team analyzed the data. She came to the table with an assumption that was wrong, and because she did not “come up with” the process we applied (which was a super simple one – where were users going, how long were they there, and when life ever did they come back) she refuted the results that disagreed with her preconceived bias. Lesson: Bias is a real danger in any analysis, especially that devil “Confirmation Bias.”
So, my recommendations: Start with a simple “filter”, such as stated above, to take the data and convert it into insights. What are users doing? What are they NOT doing (harder to “prove” but if there is a big banner ad that no one is clicking, you can dig deeper into that situation). What are the standard “pathways” that users take through the site – and how can these “pathways” be made simpler or streamlined? What are the “blockers”/dead ends that the data tells you. Hence you are converting the data into useful information.
But don’t stop there.
You can do a tremendous amount of quantitative analysis of a site and define a lot of information, but you also need to observe usage – ask people to let you watch them use the site or system, applying guerrilla usability testing techniques. If it is a mobile app, ask them to log a “diary” of how and when they use the app. Interview users to ask them what they do and think. This helps you flesh out the insights the data gives you and you can then start to have evidence of the HOW. This understanding of users’ motivations and drives can REALLY help you improve the whole experience.
And do this same activity every six months, so you can adjust to new usage patterns and mental models.
Finally, remember that people often tell you they do one thing and then do another – so observation of usage is crucial.