A: You start with storage and infrastructure. Whatever data you collect has to be stored somewhere. The infrastructure component needs to be as robust as possible, so whatever different types of data that you're going to ingest, it can handle it. That's the first level.
Once you have the storage component, then you're going to fill it with data, but you have to have the data connected together and integrated. So the next thing that you need is connectivity — being able to connect disparate data sets together through some kind of identifier, some kind of deterministic or probabilistic way. And integrated data is different than mashing up data in something like Excel where you still take two separate data sets. That’s not something you can base analysis on. Integrated data is the important note there.
Once you have that, the next piece is processing or what we call “ETL” — extract, transform, and load. Ultimately, what you're doing is you're giving that data shape and meaning. You're turning the raw data into a usable format, whether you're putting it in cubes or sessionizing that data. You're taking the raw stuff and putting it together in a usable format.
Then you've got the analysis or the modeling layer on top of that, where you're actually using some kind of UI. Potentially, you could be using some kind of language like R or Python. But now that you've collected it, you've integrated it, and you've transformed it, you're analyzing it. And then the next portion is visualization and interpretation.
Even when you look into Adobe Analytics, you can go in and break tables of data down, then put them into visuals so that they're more digestible, understandable, and shareable. You're taking data and you're turning it into information, which in and of itself still isn't necessarily valuable. But once you get it to data visualization, to the insight level, that's when it turns into knowledge.
Ultimately, there needs to be an output of analytics. It could be a segment or an audience that you've identified that you can send into a tool like Adobe Target and run different A/B tests with. It could be a predictive score. When you acquire new customers on the site, based on their traits or behavior, you can assign a score to them. And then that lets the system know how much time or effort to invest in each individual.
Connecting the outputs of analytics into systems of action, whether it's for content optimization or ad optimization, is really the last piece. Just being able to slice and dice data and have something pop up in the UI is not useful. You have to take some kind of action on it. An insight in and of itself has a shelf life because data just gives you a snapshot in time. Your data and insights will expire and after that point it’s no longer beneficial to pursue the opportunity that you found.