Traditional analytic tools, such as Tableu and Looker, cannot adapt to the fast iteration nature of modern consumer - oriented business as because it requires professional training to be use.
Augmented analytics can help where traditional analytics fail. It improves businesses through self-service analytics, automation and collaboration.
Self-service means that it turns the entire data warehouse into a useful tool for everyone. Instead of asking professional analysts for help, you can write your own query or generate a fancy report without knowing SQL or any technical scripting language.
Automation means that the system uses AI or machine learning to remove users from repetitive tasks, making the whole process more thorough and efficient.
Collaboration means that communication takes place within the analytical tool itself, making it easy to follow everyone’s thoughts.
Data quality should always be the number one priority for any company dealing with data analytics. Good data quality practice includes two parts – the maintenance of a data dictionary and constantly monitoring of data.
Businesses are best off owning their data , which means they need to own the definition of the data, and preferably, even own it in their own data warehouse. They can then purchase reporting tools, query automation software, data collaboration platform etc. to gain insights from data.
Building good data analytical capabilities is not just about hiring engineers or data scientists and using visualization tools. You also need a third-party service to make your data easy to consume and make everyone a professional analyst on their own.
Working remotely increases the importance of augmented analytics, in particular its self-service and collaboration features, in any company's data practice.