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Robotic Data Automation: Automation of Data Prep
Yesterday we described the speed of adoption of RPA, Robotic Process Automation, the software automation of repetitive business processes. In this article, we turn from processes and look at data. Parallel to the RPA movement is one called Robotic Data Automation (RDA).
RDA automates data pipelines connecting different data sources. A principle motivation for the development of RDA is the need to ingest and keep up-to-date data contained in repositories like data warehouses, data lakehouses, and data platforms. RDA automates ETL/ELT processes to extract and move data from different sources, transform it, and finally ingest it into the new system. Similarly, RDA is also used as a front-end step for collecting and preparing data for use in AI operations.
RDA incorporates AI and low-code interactions to enable workflows that manage data cleansing, transformation, and contextualization. Like RPA, RDA automates repetitive tasks, and in this case, the tasks automated are those needed for ETL/ELT.
Vendors that are selling into the RDA market include SnowFlake, CloudFabrix, and Demio.
Valerie O’Connell, research director at Enterprise Management Associates, said that RDA may be hard to implement but brings many benefits. “The benefits and the gains are almost guaranteed, but equally almost without exception, it is going to be complex and difficult. The primary challenges include data accuracy or accessibility, conflicts within IT, fear or distrust of AI, and skills availability.”
