Interana

Fig 01 Interana architectural elements

Figure 1 shows a lot of the functionality of Interana, which we do not need to repeat. However, one point in particular is worth commenting upon. Firstly, note that Interana includes columnar data storage. This is sensible as time-series data is well-suited to columnar storage. However, this does make the product more than just your average analytics tool, most of which rely on third party database storage. Conversely, it means that you are less likely to use Interana with a database that has its own time-series capabilities.

Fig 02 Query results using Interana

The major feature of Interana not illustrated in Figure 1 and introduced in the most recent release of the product, is the graphical query builder. This is shown in Figure 2. The most notable feature of this is that it does not require any knowledge of SQL or any other programming skills, with queries being compiled on the fly and under the covers, while the product will automatically discover relevant dimensions for query purposes. The resulting dashboards allow ad hoc exploration of the data – following a train of thought – as well as drill-down. Moreover, the platform has machine learning built into it (leveraging patterns of events over time) so that the software can make recommendations as to what you might do next. In other words, Interana is not just about discovery but also has prescriptive capabilities. It treats the underlying data (which may be sampled or used in toto) in terms of events, actors and flows and this allows you to customise queries so that you can, for example, query across (marketing) channels within a defined lifecycle. Users also have the ability to enrich their queries, for example, by classifying customers into relevant buckets, for instance by frequency of engagement.

Finally, we should mention performance and scalability, both of which are foci for the company and of which the company is especially proud. Several features that lend themselves to this – for example, parallelism, compression and the use of streaming data (no ETL) – are mentioned in Figure 1. The company uses SSDs rather than spinning disk for storage, for exactly the same reason. The company claims the ability to process trillions of data points per day and cites one user that had previously been running a Spark-based query that took three days to run, reduced to
25 seconds with Interana.

Interana is pretty much a unique product. While there are other products in this space that were built from the ground up to support time-series, these tend to be focused on particular use cases, for example process manufacturing. Indeed, we know of only one other general-purpose product and that is focused specifically on predictive analytics and we would not regard it as a competitor to Interana. Other tools in the market either don’t have time-series capabilities (Tableau et al), don’t support the sort of train of thought analyses that Interana offers (clickstream and mobile analytics) or are designed for IT specialists and developers (log analytics and so forth) rather than business users.

As we have mentioned, behavioural characteristics are not limited to customers, but also apply to suppliers, personnel and IoT-based sensors. However, it does appear that Interana’s focus is on customer environments rather than anything else. This is understandable but we would like to see more features within the platform to support these other use cases.

The Bottom Line

We are very impressed with Interana: it is visually appealing, easy to use, will support complex and large-scale environments, and claims impressive performance and scalability. If you are interested in understanding (customer) behaviour over time (not only what but why) then Interana should be one of the first products to look at.