Operational Analytics RPA

The best organisations find a way to create automatic and regular processes that extract information to "distil" a set of performance indicators
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More and more it seems that everyone is always talking about Analytics and KPIs. Maybe it’s because Artificial Intelligence with Deep Learning has given a new impetus to data analytics, while at the same time pushing Data Science to the point of becoming a fashionable career choice. Or perhaps it’s because organisations have gone through the hype of good old decision support systems, and finally reached the plateau of productivity when it comes to using and visualising data. Or even because everyone has websites where Google Analytics is omnipresent.

There is so much communication on the subject, that the lines become blurred, making it difficult to keep track of what Analytics really means. It could be Data Science (ad hoc exploration of data, using very sophisticated statistics, using Neural Networks or not), or it could just be a bunch of indicators, calculated occasionally, manually extracting data from systems, at the operational level, and doing some basic statistics with them in spreadsheets, without any regular and automatic process in place. It could even be the conversion rate of the website, by talking to a web designer.

In any case, every company wants to have “Analytics”, and every software company provides it. We all feel the need to extract structured knowledge from raw data, because that is the only way to detect patterns, manage by exception, optimise processes, analyse trends and even predict the future. Mastery of all these things is essential in order to manage and grow healthy organisations. The best of these organisations find a way to create automatic and regular processes that extract information to ‘distil’ a set of indicators. They also choose a few key indicators, the ones that really impact performance and regularly act on it.

Acting on the information is the main purpose of all this, and it closes the loop: we measure things, compare metrics to what we want, take some actions to change the way we work, so that the next cycle shows metrics that are closer to what we want. Since we don’t have time to look at everything, and some things are much more important than others, we focus on Key Performance Indicators, or KPIs.

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Verne Harnish, the author of “Scaling Up”, goes one step further and revises the KPIs as Kept Promises Indicators. All companies make promises to their customers, even when they are not fully aware of it. Top companies are perfectly aware of these promises, and relentlessly track how they deliver on them by defining, monitoring, and acting on a few well-chosen KPIs.

Digital Transformation processes also need to be measured and managed, namely the last step of the chain: automation of tasks, sub-processes and end-to-end processes, usually the mission of RPA.

Fortunately, it is easy to collect data from robots (after all, we program them, so we can include automatic steps to collect data about data, i.e. metadata), gather them all in one place, process them and present these insights to the people running the process.

This is even more true when robots are run under the supervision of an orchestrator, such as ARPA’s iMAESTRO. Using an advanced orchestrator alleviates a lot of the robots’ work in collecting data, while contributing more data on its own, as ARPA iMAESTRO is aware of the end-to-end processes, while the robots focus on their specific task.

In addition, ARPA iMAESTRO inserts the human collaborator into the loop, coordinating the contributions of humans and robots. This means that ARPA iMAESTRO can also collect metadata about how things move on the human side, allowing ARPA’s customers to see a part of the big picture. Although robots are becoming increasingly sophisticated, when tackling tasks that would have required human intervention a few years ago, they still can’t do creative work like humans do.

The best solutions are usually hybrids.

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RPA Analytics can provide insights at all levels that relate to successful implementation, from monitoring to optimising both resources and processes through error analysis and actual Return On Investment (ROI) calculations based on actual robot usage.

While RPA has many more benefits than cost reduction, the truth is that project ROI is an important consideration for most clients and prospects.

Being able to deliver on promises is crucial to building trust, and KPIs help achieve this. Our direct clients love just being able to show their staff that the project is delivering on its promises, so it’s time to trust the system and feed the automation factory a few more processes.

One of our clients, AMORIM CORK, was able to recoup their investment in automating 8 processes, via RPA, in less than 10 months, and they can verify this because they also use ARPA KPIs Dashboard, ARPA’s platform that collects, processes and presents a cockpit, in an actionable way (meaning: it’s easy to look at and act to correct problems).

Developed on top of Elastic’s suite, available in cloud or on-premise, it provides our customers with a complete view on how the overall RPA initiative is going (money savings, processes already implemented, records processed), helping them focus on operational issues, which usually become relevant when scaling up (resource-intensive processes, underutilized robots, etc.) or even evaluating the quality of processes (error percentage, with and without reprocessing, statistics, etc.). And, of course, seeing in real time how much money they are saving just by running the robots.

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The ARPA KPIs Dashboard has been in use for a long time, as it was developed over our previous version of our orchestrator (ARPA RSO). In the meantime, we have collected a lot of data from users, regarding new KPIs, flexibility in their exploitation and how to select and display data. We also launched ARPA iMAESTRO, our advanced orchestrator, which allows for more detailed data collection while broadening the scope of collection, especially for robots that do not explicitly record metadata.

Finally, several of our customers have asked us to also support the Microsoft stack: ELK, which is a great technology and most of our customers live on a Microsoft base, so it’s very important for them to integrate things seamlessly.

So, to address these three vectors, we are developing a new version of the ARPA KPIs Dashboard, which should be 100% ready by the end of the first half of 2022.

Please keep in mind, KPI stands for Kept Promises Indicators.

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