Imagine a simple customer interaction for an eCommerce business:
- A customer visits the website and browses various products;
- A few days later, they come back and place an order;
- The next day, the order is packed and dispatched to the customer;
- A few days later, the customer logs onto the site and leaves a negative review.
Each of these steps can be thought of as an event, and together they represent an event stream of things that happen over time.
Most businesses will record the fact that these events happened and use it after the fact in a highly summarised and aggregated way. For instance, the typical eCommerce company will know how many orders they have had for each product in the last month, what their average shipping times were, and what is happening to their reviews.
What they are not doing however is analysing and responding to individual events in real time, intelligently and at a low granularity. If they were to do this, the same eCommerce business could implement the following experiences:
- The customer could be prompted to complete their order 24 hours later;
- A push notification could be sent to the customer as the product is dispatched;
- Delayed order dispatches can be flagged for human review;
- An uptick in negative reviews for a particular product could be quickly identified and the product removed from sale immediately.
If businesses were to evolve towards responding to data in real-time, there is considerable opportunity to improve the customer experience or business metrics.
In a typical business, there are thousands of examples of this type of event based thinking and potential interventions based on what the event stream tells us, and we believe this is almost totally untapped potential.
Event streams like this are everywhere in business, and they have massive amounts of value if we can understand them and react to what they are telling us.
Unfortunately, most businesses today simply do not have this capability. Instead, they are using traditional delayed business intelligence where data is batch uploaded and then used by humans for strategic decision making at an aggregated level. The insights then sit on a dashboard, ready for some executive to log in and review them manually. These businesses are not thinking, modelling, or responding in terms of real-time individual event streams.
The technical approach to doing this involves moving away from existing batch based data model and aggregated "business intelligence", towards real time event stream processing. This approach allows us to continually process data high volumes of data as it is created and ingested in order to identify situations of interest.
Once this has happened, these events can be exposed into more operational analytics scenarios to tactically support employees, and ultimately incorporated into closed loop analytics for automated resolution before customers and KPIs are impacted.
To get there, businesses need to modernise their technology stacks and data platforms to become event based and incorporate streaming technologies. Events need to be captured at source, streamed through the technology stack, and processed using stream processing technologies. Then, the insights and analytics generated should be delivered to users and employees as part of their day-to-day experience. This is a complex technology journey for businesses, but one which can have considerable benefits and business value.