From enabling intelligent automation to guiding strategy decision making to shaping new commercial products, the modern enterprise runs on data. 

However, while organizations are capitalizing on enterprise data like never before, they’re also generating higher volumes than ever before, more than any human alone can manage. In fact, it is often said that data scientists spend roughly 80% of their time wrangling and cleansing data. This means that, when left to do things manually, data scientists are only able to devote 20% of their time to actually analyzing data.

Furthermore, talent shortages, budgetary pressures and the rising prominence of data in the business means that data scientists are now tasked with accomplishing more than ever. In order to ensure they’re able to focus on high value, strategic priorities, organizations are now embracing data automation to eliminate tiresome manual processes. 

Data automation is an umbrella term for software that automates the data management process from data collection/extraction to data processing to data visualization. Organizations rely on data automation to ensure the massive amount of data they produce every day is properly processed, transformed into usable data assets and routed to the proper data analytics platform without human intervention. 

Not only does automating data workflows increase operational efficiency and reduce overhead costs, it also improves accuracy. As business process automation tools such as robotics process automation (RPA) rely on steady streams of highly accurate data to function, automated data processes are a foundational aspect of larger BPA and hyperautomation initiatives. It also enables data observability. 

What is Extract, Transform and Load (ETL)?

One of the most common forms of data automation is Extract, Transform and Load (ETL), a data integration process that combines data from multiple data sources into a single, consistent data store that is loaded into a data warehouse or other target system. Not only does ETL extract data from multiple sources, it can also cleanse, validate and consolidate data so that it's ready to be analyzed.

To elaborate: 

• Extract. First, data is aggregated from one or more sources, validated, then held in temporary storage, where the next two steps are performed.
• Transform. Data is processed and structured to ensure its format is consistent with its intended use case. The goal is to make all data fit within a uniform schema. According to Informatica, typical transformations include aggregators, data masking, expression, joiner, filter, lookup, rank, router, union, XML, Normalizer, H2R, R2H and web service. 
• Load. Last but not least, the transformed data is migrated into a permanent target system. This could be a database, data warehouse, data store, data hub, data lake or an analytics platform.

For example, intelligent document processing (IDP) tools use ETL, amongst other technology,  to extract, transform and transport data from physical documents to a database. In addition, ETL is frequently used to migrate data from on-premise data storage to the cloud. 

What is data analytics automation?

Data analytics automation refers to use of computer systems and machine learning to automate data analysis processes such as:

• data discovery
• data preparation
• data replication
• data warehouse maintenance
• data visualization
• data maintenance
• data collection

While some data analytics automation tools are simple, script-based solutions that automate one or two processes, there are larger, more comprehensive solutions available.