Historically, businesses have used their data and analytics over strategic time horizons. For instance, collecting information about what happened over the last week, month, or year and using that to inform their next steps.
Operational analytics is about using data for more tactical and immediate purposes. For instance, choosing which customer order to work on next, reviewing live information from a website to optimise advertising spend, or deciding whether or not to reject an order for fraud risk.
Whilst strategic analytics are typically used by management and executives to support decisions, operational analytics are typically used by people closer to the day-to-day running of their business to support their work and help them choose their "next best action".
Operational analytics of this type have a lot of value for businesses because they can continually optimise and inform how they are working, potentially across a large pool of employees.
By guiding employees intelligently, and helping them to react to problems early, issues can be avoided entirely and the customer experience can be enhanced in a proactive way before they even impact a KPI.
The challenge is that real-time and operational analytics are harder to deliver technically.
Most businesses data infrastructure is still based around batch processing, which implies delays between when business events occur and when they can be analysed and reacted to. A delay of hours in data processing diminishes the potential for operational analytics drastically.
Operational analytics can also require higher volumes of data, and the requirements for more complex analytics over these data streams. Again, this is a technical challenge for data and infrastructure teams which are not set up for data of this nature.
The whole tooling ecosystem is also slow and batch based, right from how data is extracted and loaded into databases, through to how it is rendered on the screen. Even the modern data stack is built around hourly refreshes, in some cases due to the cost model of the tools.
We believe however that this is a problem worth solving. It has potential to be more valuable than building yet more dashboards for executives and managers, and has huge potential to improve the employee and customer experience and overall business efficiency. It is potentially a $multi-billion opportunity for many industries.
At Ensemble, we concentrate on building the technology infrastructure which enables real-time and operational analytics. Using ClickHouse and modern data architectures and tools, we can collect, process and store data in real-time, then analyse it and make it available to business users to support operational use cases. Please get in touch with us today for an informal discussion.