Robotic Process Automation BI Facts And Myth

According to a recent study from Harvard Business Review, there's an immense appetite for learning from your company data. Regardless of industry, decision-makers worldwide know they can harness data to make the right strategic calls, whether they are corporate leaders, salespeople, nurses, or technicians.

The only problem is that many can't extract the correct data from their complex systems. Either they lack the technical skills required or the systems aren't suitable for reliable data gathering and extraction.

The latter is mostly due to the age of the systems, making them harder to connect to others. This makes it difficult to extract and compare data quickly, resulting in data silos. Robotic Process Automation (RPA) and Business Intelligence (BI) solutions are the answer here. RPA BI provides many benefits and allows more instant access to data.

While RPA and BI may be the future solution to all many problems, there is still a great deal of confusion around this topic. When it comes to robotics and AI people have misconceptions about the technology, its potential, and its limitations. Here are some common myths around RPA BI.

RPA and business intelligence tools allow companies to gain deep insights into their operations, as well as automate processes through robotic means. While RPA may be able to carry out certain jobs more efficiently than a human worker, they will never be able to actually replace the human element.

RPA is not actually intelligent, and it cannot make decisions based on common sense. Instead, robots are restricted to the rule-based operations in which they are coded.

Digital workers are great for getting certain tasks done in order to help humans and increase efficiency. However, RPA will never actually be able to fully replace the human element.

Automating tasks through RPA aims for total efficiency. However, just like humans, robots can make mistakes too. In fact, these mistakes can become even worse with RPA BI, because the technology will not know that it made a mistake, and could repeat the error.

Certain process management tools and technology can help to monitor this. There is plenty of sophisticated process intelligence available to keep an eye on this. Although RPA can create incredibly successful repetition tasks, there is always the risk of an error. Just like humans, this needs monitoring.

One of the major benefits of implementing RPA is saving money over the long run. However, this is not always the primary reason that businesses choose to follow this route. RPA can improve all levels of business processes. This can include task efficiency, time-saving, and often improved quality in the outcome.

For example, RPA BI systems can help users to retrieve data from a database using only Natural Language (as opposed to technical SQL API). This means that end-users don’t require specialized training and that the business process becomes faster and easier for all. Here, RPA is not just working to reduce costs, but to make processes easier for humans.

Upfront costs for RPA can be pretty large - something that can scare away many businesses. However, implementing RPA can offer substantial increases in ROI. Automatic back-office tasks can result in huge savings. RPA is also easy to implement, and commonly produces massive investment returns.

A big problem with older systems is noisy data. Modern software is more or less built around centralizing data entry. Let's say, for example, you enter a new account in your online CRM. This is immediately visible to every other user, regardless of where they are or what device they happen to use. While it's still possible to input faulty values, mistakes are easier to spot and fix.

Compare this to using systems that aren't connected, either online or with each other—say, your CRM with your customer support and accounting systems. The worst-case scenario? Individual, error-prone files like spreadsheets.

This opens the door to duplicate data entries spread over multiple databases or hard drives, which makes it very hard to know with absolute certainty that your data is reliable. And who wants to make decisions based on incomplete data?

But even if you use modern software, it can still be complex to find or churn out the right data. That's where automation for Business Intelligence (BI) comes in, in the form of Robotic Process Automation or RPA. Anyone with RPA BI can drill down into data using only Natural Language for getting basic details instantly in order to double-check and make sure about data accuracy. 

There are two approaches to RPA BI. Files and other content get the index-based approach, while the NLSQL (Natural Language to SQL) engine is used for numbers.

By adding an intuitive Natural Language Interface to your data and using this in combination with SQL transformation logic, anyone can find everything they need instantly via apps like Slack, Microsoft Teams or webchat.

Finding information becomes as easy as Googling for it. This helps to increase productivity and faster decision-making. Instead of waiting hours for colleagues to respond, you can find all the data you need through the NLSQL engine.

Depending on the use case, the approaches can be combined or used separately. In practice, we’ve observed that users prefer to work with a Google Search-like web page for finding files and other content within corporate systems.

When it comes down to the numbers, (AI) webchat rules the user experience charts. The system can ask for additional input from the user, making sure everything that needs to be in the output is there. You can have all the numbers and figures you need to seal a deal right at your fingertips.

All in all, NLSQL is a practical upgrade that increases both productivity and usability and helps you make decisions faster than ever.

Upgrading your BI with RPA may seem like a daunting task to some. However, this process focuses on automation and the smart use of strictly coded technology to help maximize your business.

RPA can provide plenty of benefits across various industries. In particular, with NLSQL, information is more accessible allowing for processes to become more efficient.