Sales were booming and bonus time was fast approaching. Who would have thought that the Portugese would love the app so much?
Every project builds up information inside it.
This could be the result of post purchase analysis, running a comprehensive survey, or merely from logging your users. It’s easy to build up enough information to make finding the simplest item akin to finding a needle in a haystack.
In the broadest possible terms, an Analytics Engine is a method of viewing and filtering this information. You could even call Microsoft Excel an Analytics Engine! An Analytics Engine can be just a simple set of filters on a list, so that you can select whether you want to see just the sales, just the item views, or even a list of every time someone logged into their account.
It can get significantly more complicated than that however. When dealing with large datasets, graphs will make your life much easier, allowing you to see important details about the distribution of the data at a glance. Smart Filtering will allow you to drill down into the data in detail, to get precise information about the demographic you are trying to analyse.
Pros:
- Accessible: With an analytics engine, the data has never been easier to visualise. You can plot trends, check out information between dates, select only users looking for a particular product...whatever your business is most interested in can be examined in great detail.
- Extensible: You can think about an Analytics Engine as a way of filtering data. This means that if you want to extend it, it's just adding a new filter.
Cons:
- Complex: When you are first planning out a product, it should be the simplest possible thing that fits your needs. You don't want to put anything into it that you don't explicitly require. For some applications, using an Analytics Engine can be feature bloat, as just reading the logs produced can be sufficient to get started.
