It is often said that data is the new oil. While this analogy has its drawbacks, data, like oil, is not always immediately usable. Before it can be used, it must be cleaned, validated, enriched, and otherwise prepared for use.
In truth, many organizations have struggled with data transformation in the past. Due to the complexity and volume of data pipelines, manual collection, cleaning, contextualization, and real-time statistical analysis of data were considered almost impossible. However, a new group of technologies has emerged to automate data and DataOps workflows.
Robotic data automation (RDA), capable of automating data pipelines from disparate data sources, is one such solution. Similar to robotic process automation (RPA) and intelligent automation, RDA combines low-code bots and artificial intelligence to automate processes for collecting, cleaning, validating, extracting, metadata enrichment, processing, and data integration. The main difference is that RDA is specifically designed to automate data-related processes.
Instead of replacing ETL and ELT systems, RDA complements them by improving data access and sharing in distributed environments. By increasing the availability and quality of data for all forms of business applications, when combined with RPA and intelligent automation, RDA can serve as a powerful hyper-automation tool. Additionally, for organizations looking to use real-time data for predictive analytics, RDA could be a game changer.
RDA is often associated with AIOps, a combination of big data and machine learning to automate IT operations processes. Cloudfabrix, the most well-known vendor in the field, recently launched its Data Automation Fabric (RDAF), a new platform that integrates observability, AIops, and automation. According to the Cloudfabrix website, with RDAF, “you can automate core AIOps activities such as data metadata discovery, data quality analysis, data enrichment, data integration, incident resolution, and more.”
According to a 2022 study by Ascend.io, Data Automation Cloud, 3.5% of over 500 data analytics leaders surveyed are currently using data automation technologies. However, 85% of respondents indicated that their team is likely to implement data automation technologies in the next 12 months. So the question remains, will data science teams use RDA or the various other data pipeline automation tools on the market? Let us know what you think in the comments section below.