How can you use RPA analytics and insights to drive business value?

Process Optimization: Identify and improve inefficiencies. Performance Monitoring: Track and enhance bot performance. Cost Savings: Quantify savings through reduced manual effort. Compliance and Audit Trails: Ensure regulatory compliance and transparency. Predictive Maintenance: Proactively address potential bot issues. User Experience Improvement: Enhance interactions for users. Data-Driven Decision Making: Make informed decisions based on analytics. Scalability Assessment: Evaluate adaptability to changing business needs

RPA analytics provide valuable insights by tracking and analyzing robotic process automation performance. Businesses can leverage these insights to optimize processes, identify bottlenecks, and enhance overall efficiency. By monitoring key metrics like cycle time, error rates, and resource utilization, organizations can make data-driven decisions to improve automation workflows, reduce costs, and increase productivity, ultimately driving significant business value through streamlined operations and enhanced decision-making.

IMO, below are the few most important business value that can be achieved using RPA. - Performance Monitoring and Optimisation - Process Efficiency Improvement - Error Analysis and Resolution - Customer Experience Enhancement - Predictive Analytics for Proactive Measures

Define you goals is one of the most important thing to achieve before you choose the technology or method to collect information. Ishikawa diagram is very useful to understand base problems that generate others more and complex problems.

Creating a project plan, project charter, and defining what problem you are solving is critically important for any successful automation project. Make sure the project solves a problem; all too often automating the process can become the objective, rather than solving the problem.

If you are looking for a tool that can easily be replicated across different geographies/business models, perhaps RPA will not be your first choice. No use case is identical and most of the data is not structured in the same way hence you will end up creating different versions of the same thing. There is no learning or self improvement capability hence you will continue to work in silos, just in a faster rule-based way. Not only that but by automating the inefficiencies you make it even harder to streamline and obtain an global standardized way of working. If however the aim is to step away from excel macros and/or process more data faster, then RPA can help immensely. In the end, depends on what is it you are trying to achieve.

One of the best tools on the market right now is Microsoft Power Automate and Microsoft Power Automate Desktop. With these tools, coupled with Dataverse, Power Query, and Dataflows, you can provide a wide range of automation projects regardless of the data source and platform. Additionally, Microsoft Power BI is a nice tool to help document and share analytics and insights.

Digital landscape assessment must be highly considered on this. Your organization's Core Systems, Databases, ERP, etc. Intuitively most orgs. will go for Microsoft native RPA such as Power Automate for MS based core systems and DB e.g. SQL DB sources. However there could present some limitations such things as scalability, ease of use and maybe on the interfaces itself. Choosing the best based on the Digital landscape and Available talent on your RPA tools.

Everyone needs measuring - Bots touch almost all the system/pages/apps/tabs that a common employees does - It helps you to give more visibility about the gaps in the processes in the form of business exceptions - it helps in identifying how stable your apps as if more the selector breaking issues the apps needs a engineering team visit at the earliest - it helps in tracking the health of bots lesser the downtime better the performance

Data is key to everything in RPA, you can't automate a process without detailed data (most RPA processes that fail do so because of unexpected scenarios from small datasets). Additionally without comparable data it's not possible to track how successful the solution was.

Microsoft Power Automate, Dataflows, and Power Query are great products that work together to collect and analyze data to monitor your automation projects. With these tools, you have the ability to collect data from nearly any data source. With Power Query applied steps, your data can be cleaned and formatted in a consistent format, ensuring your dataset is accurate and complete. These tools also help with data privacy and regulations as needed.

Once objectives are defined. Time to design how your "Bots" will behave to meet this objectives based on the sources, data transformation (for sure this will be required). and frequencies as well. Mapping this streams of data to its right direction and purpose is the key here.

It is important to keep in mind that these "Bots" behave on GiGo (Garbage in - Garbage out). It is how you define the timing, data transformation, data-joining, data-cleansing will yield a "Report" tasks to this "Bots"'s Data Integrity outcome. It could be and army of "Bots" in Supervised or Unsupervised behavior to ultimately yield a good Business Intelligence.

Not all data are subjective. It sometimes could be a case by case even with automation, special case or abnormal conditions exists. Definitely focus on the outcome with more potential while keeping tap of those abnormalities and dont worry much about it first since doing that would add immediate values into the process and generate fund mentally and physically to drive forward

Communication for all projects, not just RPA is key. Celebrating success helps motivate the team and encourage adoption. Sharing failures and learnings is just as important, business have short memories and are often cursed to make the same mistakes again and again

Best practice to any Digital Transformation initiatives/projects is communication to key stakeholders right off the beginning. This way we understand key components of the requirement (Dashboard views, KPI/KSF, etc.) Failing this might require extra steps to sell your outcome or worst wasting the efforts to a very beautiful dashboard without consumers/users. Have the right level of communication from start to finish by having cadence and having MVPs, proto-types with proper consultations on varieties of channels (E-mails, reports, etc.).

Really , no one care about the data let be honest . What they might prefer would be “statistically based on these data” on the screen would be charts and circle with number and percentage ratio . Communicate becomes effective to tailor your outcome with the right keywords to the specific target . Example could be finance cares about money hence dollar per day, per year total current, existing projection based on so and so then IT would care about numbers of users coming in per sec per minutes and enhance their works to get what needed to support the efforts

Automation field is not only restricted to the tasks mentioned here but in broader sense it covers robotics and processes as well. What is being discussed here is just a faction of what automation is about.

It can be used for calculating the bot efficiency , handling of licenses for user. Data is required for monitoring. It helps our management and support to understand the saves and productivity .

Basically RPA analytics and insights provides these clarification -Identify and prioritize RPA opportunities -Identify compliance risks and opportunities Organizations that effectively use RPA can gain a competitive advantage by improving their efficiency, productivity, and customer satisfaction.