Month: August 2017
If your role in business demands that you stay abreast of changes in business analytics, you are probably familiar with the term Smart Data Discovery. You may also have read the recent Gartner report entitled, ‘Augmented Analytics Is the Future of Data and Analytics’, Published 27 July 2017, by Rita L. Sallam, Cindi Howson, and Carlie J. Idoine, highlighting the importance and benefits of augmented analytics, augmented data preparation and augmented data discovery.
This is a small note on small data. I hope it has a big impact. The common understanding of the world is that one should use predictive and prescriptive data on big data. A vast amount of data, classified and grouped, running analytics to predict what will be the next event that one or more elements of the group will take. Predictive analytics like this allows pushing of right products to e-commerce shoppers. I am sure you all have experienced this on the large e-commerce site and enjoyed it.
Gartner recently released a paper on Augmented Analytics which is described as “An approach that automates insights using machine learning, and natural-language generation.”
So as a gyani disseminating free gyan on the internet, I have chosen to write a few words on this.
This blog post of mine is an extremely biased and opinionated post. This is because any attempt to be fair and unbiased would not be thought provoking. We believe in clickless analytics and path to that includes learning and responding, predicting and suggesting, warning and solving. So, some of the points in this article may sound contrary to these but are not.
By providing sophisticated analytical features and algorithms in an easy-to-use self-serve environment, the enterprise enables business users to perform data preparation and test theories and hypotheses and prototype on their own. Rather than preparing data at the central meta-data layer, and restricting what business users can do and see, IT enabled (rather than IT controlled), self-serve data preparation allows users to compile and prepare data and use that data in analytics to test hypotheses, perform visualization and create and share reports, and create custom alerts and other information. Business users can control the data elements, the volume and the timing.