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Why hyperautomation is vital to your IT strategy

Hyperautomation is unlocking the classic challenges faced by IT architects and data scientists

24 August 2021Andreas Wentz

Filling the gaps in automation

IT often develops a life of its own, and the legacy we have inherited is large and complex. We can all agree that the volume of data we process is growing exponentially, far beyond our human capabilities to leverage. Hyperautomation combines the strengths of different automation tools and other technologies - like Robotic Process Automation (RPA) and process mining, with AI. But too few organizations are taking advantage of the opportunities, and a couple of myths prevail.

How hyperautomation helps IT architects

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IT spreads in multiple directions. You’ll be familiar with the vision for this unfortunate architecture:

  • Before - ancient technology, countless interfaces, no overarching logic 
  • After - new platforms, software, and modernized applications, connected via well-defined and orchestrated interfaces

This leads to the first myth: Native automation is a cure-all. Let’s compare IT-centric thinking to landscape gardening. Our garden (infrastructure) is messy; we remove old plants (legacy), feed others (modernize), and plant new (contemporary software). But modernizing or replacing legacy systems can impact significantly on the daily core business. Plus, work and data are two separate ‘teams’:

  • The human workforce - supported by automation 
  • The digital workforce - software and bots 

A contributor to these teams should work in harmony with both. Native automation is typically strong for standalone work and machine-to-machine, but communication from human to machine or vice versa is weak. Check out this example and my thoughts on LinkedIn.

Hyperautomation is rapidly shifting from an option to a condition of survival, ranking outdated work processes as the no. 1 workforce issue. 

Digital Transformation and IT Automation Needs Drive Hyperautomation Opportunities, Gartner

Hyperautomation for data scientists

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Few organizations list data quality as a top KPI. While the ability to process data at scale is a familiar strategic objective, its quality is often sacrificed because of operational pressures and limited resources. Most data analysts and data scientists will tell you that quality is their no. 1 issue for leveraging big data and AI. Wouldn’t it be cool to free the data from its bonds in the systems-of-record, pump it into a data lake and apply AI to manufacture it to a high standard? In theory, yes, but let’s debunk the second myth: Artificial intelligence is the cure-all. Alas, our data scientists face similar challenges to our IT architects. After considerable work, we can visualize or report the desired insights to department heads and the c-suite. But the reality is that they are overloaded with work, overwhelmed by information - and not much happens. If this sounds naïve, I experience it frequently. 

Summing it up

The human workforce is under pressure, and our existing IT does not have the required maturity of automation. Also, many organizations can’t leverage their data because of its quality. Hyperautomation combines different automation and AI technologies, delivering scalable, end-to-end automation. Key take-aways: 

  • Businesses can achieve more responsive IT environments by moving from traditional automation to hyperautomation. For example, hyperautomation removes the manual elements of 1st line IT support
  • Hyperautomation can be integrated quickly and seamlessly across both human and digital workforces 
  • Hyperautomation technologies and Robotic Process Automation get work done. Data is automatically checked, prepared, and processed 
  • AI helps with automated process discovery and improves communication 

Integrating different technologies breaks up process and data silos and serves an overarching purpose - to be constantly available and fast for our customers. 

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About the author

Andreas Wentz

Trible Lead for Emerging Technologies , T-Systems

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