Paulina Kondratowicz

How a graphical interface enables Machine Learning or Robotic Process Automation in your company?

How a graphical interface enables Machine Learning or Robotic Process Automation in your company?

Machine Learning and Robotic Process Automation (RPA) are booming technologies today. The new job positions are developing within the niche. They emerge because Machine Learning and Robotic Process Automation impose greater business’ involvement on the data feed creation, and thus the data provision. 

First of all, this phenomenon enables an easy starting point for understanding how to use business data in new tools. The mentioned emerging niche results in building new applications that cover data mapping visualization.  

The apps’ leading function is to extract a fragment of an extensive database, analyze it and automate this process. By providing the correct data by the business, you can reduce the time and costs due to fragmented data analysis, which, in turn, affects the acceleration of processes. 

What is Robotic Process Automation (RPA)? 

Robotic Process Automation (or RPA) is a form of business process automation technology based on metaphorical software robots (bots) or artificial intelligence (AI)/digital workers. 

For the traditional workflow automation tool, it is a software developer who produces a list of actions to automate a task and interface to the back-end system using internal application programming interfaces (APIs) or dedicated scripting language.  

Robotic Process Automation systems, on the other hand, develop the action list by watching the user perform that task in the application’s graphical user interface (GUI) and then perform the automation by repeating those tasks directly in the GUI.  

RPA tools are closely similar to graphical user interface testing tools in terms of graphics. These tools also automate interactions with the GUI and often do so by repeating a set of demonstration actions performed by a user.  

Robotic Process Automation tools differ from such systems. They allow data to be handled in and between multiple applications. 

Advantages of RPA 

The benefits of RPA are numerous and offer a wide array of competitive advantages. Robotic Process Automation software is a great deal for companies as it can interface with major business applications, accomplishing routine tasks faster and more efficiently than human operators. 

RPA’s are attracting large investment. IT research firms estimate that the RPA market will grow from an estimated $1 billion in 2020 to a predicted $5 billion by 2024. 

Though it is still an emerging technology, Robotic Process Automation interoperates closely with artificial intelligence, machine learning, and intelligent automation of all types. 

You can count on several advantages of Robotic Process Automation (RPA). These are:  

  • More Effective Use Of Staff Resources 

With your human staff freed from many tedious, low-value tasks, they can focus on higher value – and higher revenue-generating – tasks.  

  • Enhanced Customer Interactions 

RPA can move processes faster and more efficiently than human staff. Gain a better customer experience with many low-level requests handled automatically. 

  • Reduced Costs 

Certainly, an RPA solution is quite expensive. You must consider buying the application, installing it – and then ongoing costs for maintenance and upgrades.  

However, any given RPA robot is far less expensive than a human staffer.  Any chore performed by an RPA tool saves a business significantly.  

  • A Larger Virtual “Staff” 

The businesses have been able to get more done with fewer workers using RPA. 

In any case, a well-governed group of automated virtual workers will greatly expand your total workforce. 

  • Improved Analytics For Workflow Management 

A clear grid of activities and timelines can be established, and a Robotic Process Automation tool’s effectiveness can be tracked down to the most minor task and the smallest time increment. 

As Machine Learning and Artificial Intelligence are built into the automated process – as the RPA grows more intelligent – analytics will become more complex.  

  • Improved Scalability 

Robotic Process Automation solutions are designed for ease of scalability. 

A group of automated robotic processes can be duplicated and programmed to accomplish a similar but slightly different set of procedures. It can be performed numerous times if required.  

  • Better Security 

A Robotic Process Automation tool is always ready in terms of logging entry, logging out, or securing the password.  

  • A Clear Advantage Over Competitors 

Given the power of scalable, highly flexible automation that RPA offers, any business that deploys it will have a sizeable performance advantage over competitors that lack such a system. 

RPA Benefits By Industry 

Having known the most typical advantages of RPA implementation, it is crucial to point the industries it can be beneficial to. Robotic Process Automation can be used in:  

  1. Finance – RPA allows for performing tasks of many human workers and lower the costs by about one-third.  
  1. Insurance –  RPA tools can speed up plenty of mindless busy work. 
  1. Supply chains –  RPA can scale from a very small solution to a sprawling, multi-point solution. It is then well suited to scale with a large and often-changing logic system.  
  1. Healthcare – RPA enables to transfer data from one system to another, thus reducing massive amounts of human work. 

How can businesses prepare for the deployment of Machine Learning and Robotic Process Automation?  

Machine Learning and RPA (Robotic Process Automation) gain more popularity among businesses from various industries.  

Machine Learning, being an inevitable element of data analytics, allows increasing the business value by using appropriate automatically learning algorithms. It also enables information modeling in such a way as to increase effectiveness, efficiency, exclude emerging failures, and reduce costs.  

On the other hand, Robotic Process Automation Modeling allows you to configure computer software or “robot” to emulate and integrate a human interacting within digital systems to execute a business process.  

Both of these trends are based on data models. Through automation, but also learning through new experiences, organizations can predict the next beneficial business steps and focus manpower on other tasks than data extraction and process execution that brings more business value to the company. 

Using business data in new analytical and automation tools 

Machine Learning is perfect for preparing data for analysis, management, and modeling. Suppose you have several data sources like SAP, internal business platform, or volumes of data delivered by your suppliers. Perhaps you store some data on AWS or Azure. You would like your business team to use AWS data wrangler, but your distributed systems need one integration point. 

If your organization operates on a specialized data engine, but the visual layer of the application is not satisfactory, perhaps the tool does not meet all your requirements. Then you’ll need support in creating a visual GUI layer for the selected app.  

Not only UX / UI designers can help in extending the functionality. Also, back-end developers would introduce validation functions, data processing, or building new data engines. 

When considering the use of business data in analytical and process automation tools, we distinguish individual functionalities. 

  • The deploy data preparation workflow into production with a single click function allows for simple data sending through a tool integrated with the website. All you need to do is prepare the tool once, and the result will be no need to use built-in mappers from Cloud providers. 
  • With the quickly estimate Machine Learning model feed accuracy, you get hints about what actions it can perform and how to combine the data. This feature also supports mapping data from different databases, allowing you to perform validation and receive one report at once. In this case, the visual layer helps to perform the above actions, with know-how being on the client’s side.  
  • The understanding data visually function makes you, as a user, see the exact context of the selection among the data provided. Grouping data into smaller and larger clusters makes your understanding of data connections almost intuitive. 
  • The customize data transformations in PySpark, SQL, or Pandas function allow for easy data transformation. Each piece of information is saved in a specific format, and thanks to mappers you can easily reformat it according to your needs. 
  • The quick select and data query function allow you to operate on databases. With this feature, you can enter the project effectively. It means that you do not need to know how the database works. By entering a query, you can get the data you are interested in. All you need to know is how to write the query, and the tool can validate it for you. 
  • The data preparation function is to combine resources from different databases, and thus create the appropriate flow for a better process understanding. 

Data mapping with GoJS library 

The GoJS library is an advanced source for building interactive diagrams. It can be used as a visual guide for data mapping. GoJS isn’t equipped with any mapping engine, even in very complex processes. However, mapping is possible thanks to the implementation of appropriate algorithms for business needs. 

Mind, however, that thanks to a graphical interface introduction for data validation, you get hints on what actions you can perform on the objects. These are dragging, adding, exporting pieces of information, etc. Moreover, developers working with the GoJS library support data parsing. It is nothing else than translating data language from one programming language to another.  

Both of these issues allow you to understand what interactive diagrams are. They are not only used to create an image, but most of all, interactive diagrams operate in a specific, designed way. They work well in simulations by applying an appropriate scenario. They also support cooperation in the project between its participants.  

GoJS can therefore reduce the entry point to the system by adding interactive functions when manipulating data. How to understand this statement? 

  • GoJS enables so-called templating, which could be reused for different data models. Once created, templates can be copied freely. You can study changes in data after their use. 
This code presents differences in environments for business modeling and highlights the changes.
This code presents differences in environments for business modeling and highlights the changes.  
Data modeling with the use of different data sources and business modeling environments
Data modeling with the use of different data sources and business modeling environments 
  • GoJS delivers a layout direction, which will help you understand all dependencies correlations. The use of the path direction function allows you to make one decision without reversing the process. Thanks to this, the user has to follow a particular flow
Adding artificial linking lead to erasing the chaos on the canvas.
Adding artificial linking lead to erasing the chaos on the canvas. By clicking on the group of objects you can see the exact linking to other objects on the canvas. 
  • GoJS highlights the crucial objects and blurs out less relevant data. It’s for you to focus on an extract. It makes you understand part of a process or sequence of events. 
Greying or blurring the objects supports better diagram’s view
 Greying or blurring the objects supports better diagram’s view 
  • GoJS enables grouping. Here you can create data preprocessors and set output from multiple tables in one system. 
Grouping objects from the implemented data table to indicate the relationships between different data sources.
Grouping objects from the implemented data table to indicate the relationships between different data sources. 
  • GoJS showcases canvas on one screen. Automation of actions happens by introducing the diagrams’ cooperation. It means that when something changes on one diagram, you can see the same manipulations on the other one. 

Advanced actions on the diagrams indicate the possibility of extending the system by using GoJS, which will serve as a visual data processor for your team. The above solutions are great for addressing distributed data sources and lack collaboration tools when modeling Machine Learning or Robotic Process Automation feeds. As you know, creating a data model might be a team effort. To keep on track the governance of data feed, you need good tools.  

 Simplifying the entry point to the project, also through interactive libraries like GoJS, helps the business team to create an appropriate feed for the Machine Learning model by dedicated developers. 

Pros of using GoJS in mapping 

While using Machine Learning or Robotic Process Automation, the user becomes familiar with the visible effects of his work with databases. It indeed simplifies entering the project by releasing you from the need to learn how the tools work. 

When using databases, their sources can be very different. Thanks to the GoJS library, you obtain a graphic layer that is more accessible to the user. Aggregation of all data is embedded in the application logic. What happens through user action is performing operations on specific data schemas, e.g., grouping, drawing common points, and other operations on sets. The user must know the principles of operation of a given function, but only sees the effect of the work already done through the appropriate algorithms and functions built into the tool. 

With the graphical interface usage, which is created for a tool operating based on the GoJS library, the user can replace complex programming methods with more business-friendly ones. A set of rules on data allowing for the creation of a simple visualization is written inside the function. It allows for high accessibility to use. 

GoJS also means that you don’t need to have expert knowledge of creating variables. These objects contain all the definitions, thanks to which you can perform even the most complex actions on the diagram. 

The final words  

We can see that the expanding branches of RPA and Machine Learning are giving rise to new positions by combining business with new tools.  

Knowledge of the principles of creating a GUI layer for Machine Learning or Robotic Process Automation applications allows you to visualize using diagrams, lowering the starting point for such positions. Functionalities such as graying out the background and focusing on the merits, allow the user to erase the information noise. 

Thanks to UX and programming expertise, it is possible to properly sew up complex programming methods (aggregation or filtering) using transparent visual objects. This is where the GoJS library comes in handy, as it allows you to present in a simple but visually attractive way the complex functions inside the diagram.