Analytic Process Automation
The Benefits of an
End-to-End APA Platform
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