As automation veers towards measurable results or outcome-driven innovation, automation software has rapidly evolved to provide near real-time monitoring of solutions.
It is with measurable results in mind that companies approach automation of business processes. For e.g., the reasons for automating business process could be.
- Visibility & Accountability
- Efficiency
- Decrease time consuming manual activities
- Meet customer demands
In each of these cases the reason for automation can be brought down to specific outcomes that need to be achieved. For a customer-facing process, it is measuring Net Promoter Score (NPS) at various milestones. It could be reducing the Average Handling Time (AHT) of operations so that customer requests can be resolved quicker or the measurement and improvement for lead qualification and conversion percentages which are the drivers for a lead capture and engagement processes.
To achieve the desired business outcomes, we often look at technology and the tool-sets available. Technology since its inception has been at the forefront of automation both as an outcome as well as a cause. And with any development in technology there has been a need to monitor the cost and efficiency of the tools used.
With that in mind, technology over time has built a wide array of monitoring tools to measure efficiency and usage. Continuous tuning of hardware and computational power has ensured pay per usage models for infrastructure. Software solutions also have assimilated the same principles towards business outcomes aiding in a comprehensive ability to meter solutions.
To measure or meter solutions, aggregations and data analytics have been used to collate data on tools such as the following
- Data Lakes: System and business events are funneled through streaming analytical solutions to enable edge analytics or post processing of events for insights.
- Data Warehousing: Data warehousing solutions and BI (Business Intelligence) solutions to have post analysis of temporal data and insights that can be derived.
- Operational data stores: Data stores having the most relevant information across systems to provide data and dashboards of what is currently happening in the system.
- Reporting databases: Data persisted separately from transactional databases allowing aggregation queries to run in real time along with processing.
In each case technology focuses on events occurring in the solutions to measure outcomes. The events are more generically aimed towards capturing the following data:
This data persistence allows metering both in terms of solution to define business outcomes as well as infrastructure to meter usage.
AutomataPi piStack uses a combination of a data lake and an operational data store to capture the above events as they occur in its platform. The combination of the events captured allows AutomataPi to display real time analytics and results. Using BI tools, it allows for configuration of data so that it can provide near real time dashboards and reports in parallel with the implementation of business processes.
A few examples are.
- Capture of processing time for incentive processing of a leading hospitality firm resulting in a reduction from 14 days to under 30 Minutes
- STP (Straight Through Processing) / Non STP processing of customer email processing resulting in automated handling of 53% of emails for a leading broking firm
- TAT (Turn Around Time) of processes resulting in reduction of media planning time from 2 business weeks to under 5 Minutes.
- AHT for claims logging in call centre process reduction from 20 Minutes to 10 Minutes for a mobile insurance company.
To summarise process automation platforms needs to invest in the right set of tools to meter solutions just as to automate tasks. The platforms with the right set of event data, scalable querying and business intelligence layers will enable business leaders to get a real time view of the returns on their investments.
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