Beyond the RPA Hype

What you need to know about 360-degree business process automation

In the dynamic and often unpredictable global marketplace, organizations are facing urgent demand to digitize, while reconciling enormous legacy systems and a diminishing talent pool of developers.

Within this context, organizations seek agility, flexibility and speed to market so they can optimize business opportunities, remain competitive and, if needed, reinvent their organizations and their business processes to adapt to changing conditions.

This speed to market applies to new product launches and market opportunities, and it also applies to the business processes and data that support company operations. This is one reason why the ability to integrate diverse systems and data, and to create new forms of office automation such as robotic process automation (RPA), are vital. When RPA is combined with backend system integration and data management tools like ETL (extract, transform, load), companies position themselves for 360-degree integration of both data and business processes.

Why RPA isn’t an automation cure-all

RPA software partly or fully automates human activities that are manual, rule-based, and repetitive. RPA does this by replicating the actions of humans in rote tasks such as data entry, and in other highly predictable tasks that are amenable to machine automation.

Companies are intrigued with RPA’s potential to automate routine business processes and human labor. Robotic process automation market numbers reflect this. By 2022, the RPA market is projected to reach USD 2,467.0 million. RPA’s market is also projected to grow at a 30.14% compound annual growth rate (CAGR) between 2017 and 2022. Companies implementing RPA report that they are meeting or exceeding their goals for compliance, improvements in quality, accuracy, and productivity.

Nevertheless, the same industry research reveals that RPA in itself is not a universal fix for improving all business processes, nor can it accomplish all of the end-to-end data management and integration needs that exist in any company’s multi-system environment. Survey findings by UiPath found that 60% of organizations implementing RPA did not see time to market gains in RPA implementation, nearly 40% did not see cost reductions, and 40% did not see improvements in data management.


RPA versus ETL: What’s the difference?

The mixed survey results on RPA clearly indicate that more than just RPA is needed to speed time to market in business process automation and data management. If RPA can automate rote business processes that humans perform when they use systems, it must also be complemented by “backend” automation tools like ETL that are capable of picking up where RPA leaves off.

ETL performs data integration and automation between the actual systems themselves, where human interaction is not involved. It is only when both human to system and system to system data exchanges, processing and integration are all achieved that companies can begin to approach the 360-degree automation of business processes and data that they seek.

As a backend data automation complement to RPA, ETL does three important things:

  1. It extracts information from a data source;
  2. It transforms this data into formats that are needed by the systems the data is going to be transferred to; and
  3. It then loads the transformed data into the system(s) the data is targeted for.


In a way, ETL does some of the same things RPA does. Both RPA and ETL extract data from a source. Both RPA and ETL can also move data fields from a data source to a data target. However, if incoming data must first be transformed by a set of business rules that logically operate on the data before it can be ingested by another system, ETL is used because RPA can’t apply complicated process logic. ETL can also automatically update many disparate backend systems at once.

Complementary solutions for true integration in automation

The end result is that RPA excels in automating front end human work processes with systems and in eliminating human labor. RPA’S primary role is automating business processes, although it can perform simple data integration tasks, such as the move of a data element from one system to another.

ETL is also a process automation tool that is widely used by companies, but its focus is on moving and transforming data between systems. ETL plays a central role in corporate business processes, since a majority of these business processes must interface and integrate with numerous disparate systems that are both internal and external to the company.

While RPA and ETL are capable of operating independently of each other, they are more often both needed if a company wishes to achieve full 360-degree integration of its business processes and its data.

HULFT Integrate is a data integration ETL toolset ideally suited to work hand-in-hand with RPA for organizations wishing to achieve 360-degree integration of their business processes and systems. HULFT Integrate can also work independently as an automated data integrator.

Use Case: Combining RPA and HULFT Integrate for successful business and data process automation

Imagine this common scenario: You’ve just closed a deal with your customer that includes a 10 percent discount on all future orders for the next 12 months. The sales team enters the order into a legacy system, documenting the customer discount in a separate data field.

The order entry department within your company has implemented an RPA process that then scrapes the gross amount of the aforementioned sales order from the screen and sends that amount to a separate system – accounts payable. There’s a problem: the RPA process wasn’t set up to include the customer discount that is still residing in a separate data field, which means this critical piece of information wasn’t passed onto accounts payable.

As a result, your customer is billed the full gross amount of the order, and does not receive the discount that was promised. The customer now has to take time out of her schedule to call accounts payable, which knows nothing about the discount. Accounts payable directs her to customer service and their records are also incomplete.

Finally, the sales rep is brought in. By now, your entire customer experience has been compromised; you’ve failed to enhance customer satisfaction and reduce churn. Internally, your departments aren’t effectively collaborating across functions and levels.

This is a case where RPA has failed because it was automating a flawed process. In this case, it completely missed the discount data which caused a direct negative impact upon the customer.

An ETL tool, which has the ability to apply processing logic (e.g., “look at the discount field to see if it contains a value greater than zero; if yes, send discount amount”), could have prevented the flow of misinformation. An ETL tool, with its advanced logic and editing capability could have ensured proper data preparation and integration with downstream systems. In contrast, an RPA process will not calibrate variabilities – if something wrong occurs, it will move forward with no questions asked.

Additionally, the backend data integration that ETL performs can ensure that initial order terms such as discount and notes about a customer are passed to follow-on systems such as CRM, customer service, marketing, manufacturing, and sales.

To improve the process with a goal of 360-degree data and process integration, you decide to add ETL as a backend data integration tool for business processes so situations like failing to give customers promised discounts will not occur again.

The end result: RPA automates an upfront business process for order entry, but HULFT Integrate takes over the data integration process from there, ensuring that data is properly edited and prepared before being passed to other downstream systems. HULFT also passes this cleanly edited data to other systems throughout departments within a company that require the information. The next time your customer calls in with a discount question about an order, the person at the receiving end of the call will have the information needed to quickly resolve questions and concerns.

A closer look at HULFT Integrate

HULFT Integrate is a powerful Enterprise Application Integration and ETL tool that provides seamless connectivity and integration to many disparate systems and databases. HULFT Integrate contains a library of over 60 APIs that are point-and-click enabled from a graphical user interface.

The functionality built into HULFT Integrate’s user interface enables programmers and system integrators to quickly drag and drop data conversion and processing logic for script development, debugging, logging, triggering and self-documentation. This capability eliminates hours of hand coding and facilitates secure, high speed data integration that is compatible with major data destinations as it extracts, transforms, and loads data.

HULFT Integrate does the “heavy lifting” in system and data integration, and is a complementary “backend” solution that works cooperatively with RPA technologies that front-end initial business input into systems. Because HULFT Integrate goes beyond the extract, transform and load functionality of ETL, and has additional EAI abilities to cross-link and integrate applications, it provides the kind of robust systems and data integration that are needed for most RPA processes.

Side-by-side comparisons: Traditional RPA and HULFT Integrate

Traditional RPA
HULFT Integrate


Automates repetitive business processes such as data entries.
Automates data management.


Users tend to be in the line of business or operations.
Users tend to be in either line of business, operations, or IT.

Data Transformation

Able to move data fields from one data file or system to another.
Able to apply transformational logic to incoming data fields to ensure compatibility with data field formats in other files and systems.


Used on the frontend of business processes to load data into systems that would ordinarily be keyed in by business users.
Used on the backend of systems to interface with and update numerous systems and files, many of which users never touch.

Conclusion: Closing the risk gap in your RPA investment

Risk-fraught situations like losing a valued customer over a discount misunderstanding can occur when the customer touch points throughout your organization do not have the information they need to successfully interact with customers, or when an order is miscalculated because of an initial programming oversight. No company wants to assume these risks to its revenue base or its reputation.

To avoid this situation, 360-degree visibility of customer interactions and transactions are needed by the customer-facing functions in the company. Data integration must be sufficiently robust so that every system touched by a business process has accurate and current information.

Integration of this magnitude goes well beyond first-level data. It involves backend systems integration from data integration tools that, by design, are created to automate the processing, preparation and passing of data between many disparate systems used by many disparate user departments – this is beyond RPA capabilities.

HULFT Integrate fills this need. It can operate as standalone data integration that prepares and passes data to your backend systems, or in tandem with front end user-defined RPA processing.

Over 10,000 companies in 43 countries rely on HULFT to:

  • Renew and modernize legacy systems without replacing years of organizational investments or disrupting the flow of business.
  • Address gaps in IT and development resources. The power, flexibility, and ease-of-use of HULFT Integrate means companies can do more with less.
  • Automate, orchestrate, and accelerate their data, no matter where it resides. HULFT enables organizations to be nimble and agile, even while living with legacy systems.

Glossary of Terms

  • 360-degree automation
    • A machine-based means of assuring secure connections, information transfers and interactions between all corporate systems and business processes.
  • Backend systems
    • Non-user facing, “behind-the-scenes” systems that engage in the “downstream” processing of data needed in enterprise computing.
  • Business process automation
    • The act of assigning repetitive, highly predictable operations and functions performed by humans to machines.
  • Data extraction
    • The retrieval of data from various sources.
  • Data transformation
    • The process of converting data from one format or structure into another.
  • Data loading
    • Copying and loading data/data sets from a source file or database to a destination/target file or database.
  • Data management
    • The validation, storage, securing, governance and processing of data to ensure data accessibility, reliability, and timeliness for users.
  • EAI
    • Enterprise application integration. A middleware integration framework that enables the integration of systems and applications across an enterprise.
  • ETL
    • Extract, transform, load. A three-part data integration process in which 1) data is extracted from a source file or database; 2) the data is then transformed by using codified business rules that instruct how the data is to be prepared and formatted for a target system or systems; and 3) the transformed data is loaded into the target system(s).
  • RPA
    • Robotic process automation. A software-driven process that partly or fully automates human activities that are manual, rule-based, and repetitive into machine actions that replicate these human activities so the machine can take on rote tasks such as data entry, etc., without a need for humans to do them.
  • Source file
    • A file from which data is initially extracted. (This can also be a database.)
  • Target file
    • A “destination” file or database in which data is loaded once the data is retrieved from a data source and transformed into a compatible format.


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