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How is Analytic Process Automation Different from Point Solutions?

Analytic Process Automation (APA) might be a new term for you, despite its wide success. It might even sound like many of the other automation tools available today. But there are major differences between Analytic Process Automation platforms and other analytics, data science, and process automation point solutions, such as Business Intelligence (BI) and visualization, Robotic Process Automation (RPA), Business Process Automation (BPA), Extract, Transform, and Load (ETL) tools, and Data Science and Machine Learning (DSML) tools.

For starters, many of the tools today require advanced knowledge, expert skillsets, or weeks and months of education to use. Further, they can take months to implement and realize transformational impact. Early results are also not easily scalable, repeatable, or automated. Others are niche point solutions that address only discrete data analytic and process tasks and can’t cover the entire data-driven business process from data inputs to data outcomes.

See how Analytic Process Automation Compares to Point Solutions

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Analytic Process Automation removes the barriers to data analysis by converging the capabilities of multiple tools into one platform for providing true end-to-end, self-service analytics across data access and prep, analytics and data science, and process automation to accelerate insights and actions.


RPA automates repetitive tasks via bots, while APA can take inputs from bots, automate a complete data-driven business process, and then publish analytic outcomes directly to bots, RPA, and BPA systems.


These tools are IT-centric or end-user tools focused on source-to-target mapping and transformation of data into data warehouses and data lakes that can take months to implement and often require knowledge of SQL.


Perform advanced analytics using available datasets, but require expert skill sets and domain knowledge, leading to data analytics queues.


Typically standalone options available and accessible only by data scientists, limiting the upskilling of a workforce and creating data analytics queues.


Tend to present data in a visual output format and focus on historical information that looks backwards (descriptive analytics) instead of ahead (predictive and prescriptive analytics).


Incorporate machine learning, AutoML and AI, but require specialized training such as R and Python coding.


Organizations that invest in integrated automation platforms that span analytics, data science, AI, and process automation will extend the reach of their transformation initiatives and build a sustainable competitive advantage.

— John Santaferraro, Research Director, Analytics, Business Intelligence, and Data Management, EMA