How to integrate robot automation in big data projects

Robotic process automation requires repetitive data. Discover which tools can help you structure and read your data.

Image: Olivier Le Moal, Getty Images / iStockphoto

Information Services Group (ISG) reported in 2018 that 92% of companies were aiming to use robotics process automation (RPA) by 2020 because they wanted to increase operational efficiency. This large number shows how eager companies have to automate their routine business processes.

One of the easiest places to use RPA is in very simple, very repetitive business processes that depend on transaction data with fixed record lengths, with data fields always in the same locations. This data is very predictable and automation tools such as RPA that depend on recognizing repetitive data patterns are in strong positions to excel.

SEE: 16 books that you should read about the impact of AI, robotics and automation (TechRepublic Premium)

However, even the most routine business process consists of unstructured and semi-structured big data, as well as the more traditional fixed record data. For example, RPA is often used for invoices.

Invoices are usually presented by suppliers in a PDF format. An invoice can contain a lot of white space, or a company logo, or a series of text and numbers that describe an order or a fee. This is the unstructured or semi-structured big data that RPA is asked to interpret and automate the processing.

Companies cannot simply take RPA software out of the closet and let it work with unstructured big data formats such as PDF documents. This is where IT comes in with technical leadership.

Follow these steps to implement RPA

To successfully implement RPA, there is a three-step tooling architecture that IT must first consider: ETL, RPA and AI.

  1. ETL: In front of an RPA process that uses big data, it is recommended to use an ETL (Extract, Transform, and Load) tool that can integrate with the incoming streams of raw and unstructured data that you receive from all your suppliers . This tool is designed to extract the data that is relevant to your business process, convert it into a usable format that your systems can use, and then load the data into your systems and into an RPA process.

  2. RPA: At the moment, the RPA process can take over because clean, high-quality data is now entering the RPA software, which makes the task of the RPA software to automate a company for something like invoices simple.

  3. AI: While the RPA software processes invoices, the business rules are invoked that experienced employees have encoded in the artificial intelligence engine (AI). For example, if the business rules embedded in an RPA see an invoice from Pearson Manufacturing with a “net 10-day” note and the normal net conditions for Pearson are net 30, the RPA process can identify this invoice as an exception that requires an person assesses and approves it.

Important tips to remember when implementing an RPA process

RPA software cannot do RPA alone. RPA automates business processes, but ETL automates the cleaning and transfer of data; you need both to fully automate a business process that depends on quality data. The third part of the puzzle is an AI engine that is included in the RPA and that contains the business rule set that you want the RPA software to apply to the items and operations being processed.

Tool integration is paramount. In the big data environment, RPA works best in combination with an ETL tool that can deliver clean data. Within the RPA software there should be a table with business rules that guide the decision making of the business processes of the RPA software.

It is imperative for end users and IT to understand that the implementation of an RPA process is not an isolated operation – it requires a range of other large data processing software to be integrated with the RPA. These tools must be compatible with each other, and they must work together seamlessly.

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