To optimize the outcome of any business transaction, operational analytics are needed.
But what do we mean by operational analytics?
Operational analytics applies to the day-to-day activities of a business and relates to real-time decision-making at the moment of risk, or moment of opportunity. Throughout a business transaction there are multiple times when an important decision needs to be made.
For example, when creating a purchase requisition, a business user needs to select a reliable supplier to deliver goods on time, or choose the optimal quantity to order. These are the moments when a business user needs the support of analytics to help make real-time decisions in the flow.
Operational analytics is not just for data analysts. Operational analytics supports all roles in an organization and at all levels, from the C-suite to the shop-floor workers.
Launch the video for an example of operational analytics:
In the past, analytics were separated from operational transactional processing. This was largely due to the limits of the technology at that time. Key transactional data would be collected throughout the day and then copied to a dedicated analytics system, usually during the night when the transactional tables were not being accessed by business users. The next day, business users would have access to analytics. For strategic analytics, where long-term decision making on highly-aggregated data is needed, a delay of one day is not usually an issue. But for operational analytics, where we are making real-time decisions within individual transactions, a delay in the provision of analytics has a negative impact. For example, it might mean we have to delay the confirmation of an order, or the raising of an invoice until we have access to the analytical data that supports decision-making in these tasks.
The latest developments in technology have enabled operational analytics. One of the most impactful developments is the in-memory database. In-memory databases can process complex analytics using huge amounts of data in real-time, and present them to the business user inside a transaction. Data for analytics needs preparation. This means aggregating, filtering, sorting, calculating and converting data. These tasks are usually performed in a separate, dedicated analytical system. This means data needs to be copied from the transaction system to an analytical system. In-memory databases are able to perform these analytical preparation tasks in real-time using the same database tables that are used for transactional processing. This means that the data used in operational analytics is always live and there is no need to first copy data to a separate system.
Another significant development that help to support operational analytics is the availability of analytics on mobile devices. Mobile devices allow analytics to be in the hands of any user wherever they need to make decisions, and to reach far more users who previously did not have access to real-time analytics who may not work from desktops.
For example, real-time analytics can be in the hands of an account manager who is visiting a customer to discuss and showcase volume discount scenarios. Another example might be where a maintenance engineer, working in a production facility, spots a failing piece of machinery used in a production process. They immediately perform an up-to-date analysis, without returning to their desk, on the finished-product rejection rates to determine the urgency of the repair.
Today, transactional process steps and analytics are no longer two separate stages. The lines between them are blurred. Transactional processing often contains analytics, and analytics often contains transaction processing. It might not be obvious to the business user who doesn't - and shouldn't - see the difference between the analytical parts of the process and the transactional parts.
To summarize, modern, operational analytics includes these key requirements:
- Access to real-time business data
- Fast response, even on large and complex data sets
- Access anywhere from any device
- Seamless movement between analytics and transactional processing without disrupting the workflow
- Ability to act immediately on insight