Smart data discovery is not dependent on a few geniuses sitting in a science lab somewhere! With appropriate data discovery tools your team members can leverage their knowledge of their role and domain, and the needs of their division, team or business unit to find the data and insight that is most meaningful to them.
Imagine you are trying to search for something in a store, but there is no one there to help you and all of the products are mixed up. You have to walk up and down every aisle, trying to find what you want and if you want to compare two things you will have to remember where you found that other product or drag it with you and then try to remember where to put it when you are ready to put it back. Not fun!
As advanced analytics and self-serve, augmented analytical tools make their way into the average enterprise, the average organization struggles to quantify the effects and, moreover, to understand and leverage the changes within the business.
Self-Serve advanced analytics and data discovery software is an important competitive tool in today’s rapidly changing environment. Data resides in a lot of places within the organization and access to that data in an intuitive, integrated environment is important.
Augmented analytics, augmented data discovery, advanced data discovery…all of these terms can seem intimidating but they don’t have to be. In fact, it might be less intimidating to think of advanced analytics software as Smart Data Discovery. When you use that term, you acknowledge the most important factor in this process, namely that the software itself is ‘smart’ and that using advanced analytics tools is not a process that is limited to data analysts or data scientists.
Augmented Analytics and augmented data discovery is a form of advanced data discovery that automates data insight using machine learning and natural language generation. It automates data preparation and makes it easy for business users to enjoy data sharing.
Your enterprise and business users do not have the time or the inclination to sift through complex data or wade through instructions to learn how to use complicated business intelligence or advanced analytics tools. They don’t want to document requirements and wait for IT or analysts to produce a report (only to find out that the data is incomplete or that the data was delivered too late to make a confident decision).
