Taming the Data Chaos

HfS ranks integrated data as the most important input resource that integrated intelligent automation needs to be successfully implemented and scaled. But the data lake presents big challenges for companies using outdated OCR technologies and lacking integrated data strategies

Robotic Process Automation (RPA): Technology that uses software bots to automate simple, repetitive tasks previously requiring a large amount of manual, error prone work. Stand-alone RPA solutions are often a challenge to scale across an organisation.

Integrated Intelligent Automation Platform: Technology that takes an integrated approach towards AI, analytics andautomation, enabling end-to-end, straight-through processing across the enterprise

(Outdated) Optical character recognition (OCR): “Optical character recognition (OCR) refers to both the technology and process of reading and converting typed, printed or handwritten characters into machine-encoded text or something that the computer can manipulate.”

The number 1 challenge to the success of automation programmes for survey respondents is the inability toscale

Analyst Report Primer: Scaling Intelligent AutomationA glossary of themes, terms and stats from HFS’s 2019 survey and report

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Stand Alone RPA vs IntegratedIntelligent Automation Platforms

According to HFS, about 51 percent of leaders feel RPA is meeting automation needs for up to 60 percent of tasks. But there are significant opportunities for integrated automationto act as glue between multiple tasks automated with RPA,with other actions and decisions supported by AI andanalytics.

The Survey Respondents: • 317 senior enterprise leaders from Global 2000 organisations• 9 industries including the public sector• 3 major geographies: the Americas, Europe and APAC

The Analysts: HFS Research

The Report: “Solve the Automation Scale Challenge with Integrated Intelligent Automation Platforms”

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Straight-Through Processing Requires Process Discovery

Process Discovery: The use of software to gain deeperinsights into processes and maximise process optimisation.

HFS explains, “dependence on huge volumes of data to train inferencing models is a practical constraint in client scenarios where there are voluminous data sources but no integrated view, and the data quality is hard to validate and is therefore questionable.” Fractal science requires less data and quickly and accurately uncovers data patterns

HFS survey results underscore the importance of utilising process discovery tools to determine current state processes so that organizations can make smarter automation choices and layer in AI components that enable end-to-end process automation.

Fractal Science uses a premise of self-similarity to remove the need for absolute value matches when processing data. Artificial intelligence-based solutions that use fractal science principles require:• Far less data to recognise a pattern • Thinner infrastructure to deliver higher prediction accuracy • No dedicated resources – product does it all• Less work to implement and deploy

(Modern) Cognitive Machine Reading (CMR): Consisting of a range of integrated AI capabilities, CMR provides a centralised intelligent document processing platform for reading,comprehending and extracting handwritten documents, as well as semantic extractions from source documents with mixed data types.

73 percent of enterprises surveyed are using extraction tools on the diverse types of unstructured data, yet the data ingestion technology layer is not integrated across different sources.

Process discovery was second on the list of priority technologies important to survey respondents in achieving their automation goals.

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