What you Need to Know
About RPA Analytics
RPA for an Innovative Workforce
Any business process must be monitored, measured and managed to achieve effectiveness and efficiency. RPA is a process enabling technology. As such, bots that automate parts of your business processes must be monitored, measured and analyzed. Here is what you need to know about automation analytics.
RPA and Data
Today, 92% of global businesses are data driven in some form or fashion. The majority of businesses are still exercising manual avenues of analyzing and exploring data from given processes. However, if a business process is manual, making decisions from this data is hard to accomplish.
Placing your analytical data in the hands of bots can be a challenging task. After all, data speaks volumes in the workforce and must be analyzed with goals in mind. RPA both automates and digitizes the process of data collecting and analysis. This enables big data collection from those previously manual processes. Here are 4 key benefits of taking the automation approach towards analytics.
#1: Process Mining
Process mining technologies can be deployed to visualize the entire process on the basis of data generated by RPA, which enables a more insightful understanding of the process. This also creates a cadence of unscrewed data that was once manually overseen.
Take for example the accounts payable process in a given finance department. Before RPA is integrated, data from this process would come in multiple forms and live in different places in the system (emails, online banking, procurement, hard files). Data collected from these sources proves to house errors, and often cannot be accessed 24 hours a day. Through RPA, data is automatically and efficiently generated for every step in the process, giving you a seamless, detailed data trail that can be visually structured.
#2: The Digital Twin
Gartner placed digital twin technology among the top 10 strategic technology dynamics in business today. This concept merges the physical and digital world of the workplace, where every product has a digital replica of itself. From design to development, a digital footprint is made to fully represent a company’s tangible product. Thus, the lifeblood of the organization has a fully digital replica of itself, which accesses data in real time. Through digital twins, analyzing data is instantaneous, efficient, and goes much further than manual data stimulation. It can help detect early problems in the product life cycle, generate warnings to the user, and develop new opportunities through simulations.
Leveraging AI and big data, the digital duplicates of the physical aspects of the business are created using sensor technology. Engineers and development teams creating these digital replicas access data from various sources of the business. Paired with AI algorithms, a virtual representation of a given asset is created, stored, and can be accessed even before a physical product is manufactured. Applying analytics to these digital replicas will give deeper insight to the physical product, and provide consistent access to new data as it comes. Industries where digital twins are prominent are:
- Manufacturing of sorts
- Automobile creation
- Merchandise & product stores
- Technology infrastructure
#3: Simulating Standard Processes
Predicting the impact of an RPA integration can be especially useful when you are planning a shift in your current processes, whatever this shift may entail. By modifying specific variables in the simulation model, you can effectively simulate a prediction to see the outcome of a more automated integration. In other words, you get to “try before you buy” in a sense. This concept pairs with digital twin technology discussed above.
This can also impact the moral of your personnel who may be hesitant about “robots” working alongside them. In manual business processes, it is extremely difficult to predict how a new (but still manual) integration will affect a process downstream. Attempting to make a simulated change can also result in wasted manpower.
#4: Machine Learning and AI
As it pertains to being data driven, machine learning can be used to answer open ended questions, like how to increase the speed of a data driven process. Various algorithms are available that serve the purpose of explaining how certain elements in a system effect the overall outcome. If you can feed audit data trails into a machine learning algorithm, you can get specific solutions to improve the process at hand.
For example, a machine learning system may present the solution that ordering a certain piece of material from a given supplier during a major holiday will result in a “60% increase” in order fulfillment. If your staff is out on vacation during this week (like a Christmas holiday week), you could tweak the algorithm accordingly to see how this task would perform without manual interference.
How can we Help?
Smartbridge implementation architecture calls for tracking, recording and analysis of bots and the processes they automate. Our team of business intelligence specialists have over 15 years of experience in building analytical solutions for clients across diverse industries. We implement automation into your processes to achieve the following:
- Data tracking
- Data management architecture and models
- Reports and dashboards
- Predictive and prescriptive analytics
Through analytics, we establish the best practices to help you analyze and improve your RPA enabled business processes.
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