It is often said that data is the new oil. Though this analogy has its flaws, data, like oil, is not always immediately usable. Before it can be leveraged, it must be cleansed, validated, enriched and otherwise prepared for use. 

Truth be told, in the past many organizations have struggled with data transformation. Due to the complexity and volume of data pipelines, manually acquiring, cleansing, contextualizing and performing statistical analysis on real-time data was basically considered impossible. However, a new cohort of technologies have emerged to automate the data workflows and DataOps.

What is Robotic Data Automation (RDA)

Capable of automating data pipelines from disparate data sources, robotic data automation (RDA) is one such solution. Similar to robotic process automation (RPA) and intelligent automation, RDA combines low code bots with artificial intelligence to automate data collection, cleanup, validation, extraction, metadata enrichment, wrangling and integration processes. The main difference is that RDA is specially designed for automation of data-related processes. 

Rather than replacing ETL and ELT systems, RDA complements them by improving data access and sharing across distributed environments. As it increases the availability and quality of data to all forms of business applications, when combined with RPA and intelligent automation, RDA can serve as a potent instrument of hyperautomation. In addition, for organizations looking to leverage real-time data for predictive analytics, RDA can be a gamechanger. 

RDA is often associated with AIOps, the combination of big data and machine learning to automate IT operations processes. The most prominent vendors in this space, Cloudfabrix, recently launched its data automation fabric (RDAF) technology, a new platform that unifies observability, AIops, and automation. According to Cloudfabrix website, with RDAF “you can automate essential AIOps activities like data metadata discovery, data quality analysis, data enrichment, data integration, incident remediations and more.”

According to a 2022 study conducted by Ascend.io, the Data Automation Cloud, 3.5% of the 500+ data analytics leaders surveyed currently leverage data automation technologies. However, 85% of respondents indicated that their team will likely implement data automation technologies in the next 12 months. So the question remains, will data science teams embrace RDA or the various other data pipeline automation tools on the market? Let us know what you think in the comments section below.