Smart Data Visualisation is a smart choice for your organization. Not every user can anticipate the best way to display and analyze data. Not every user can gain enough insight into the data in advance to understand and recommend how best to visualize data to reveal problems, opportunities or results.
Have you heard of Social BI? If you haven’t, you should really look into it. Your business users are used to social networking and the sharing of data and information in their personal life and at work and business intelligence applications should not be an exception to this data sharing.
Why do we need a graph? Me personally, have never needed a chart to check my sales data. I have used graphs to impress my seniors, who I am not sure if they get impressed with weak numbers presented in innovative graphs.
As the numbers get larger and inconsistent, a graphical display will help. I do not want to dilute your vision by giving an example, but the temptation is too high. – World population and comparison of per capita GDP and the number of people who are HIV positive on a 5.5-inch cell phone screen. Geo-mapping is the likely and the only way you see it at the moment. I agree that there is no other way. And I hope you can visualise some hypothetical data on a cell phone and sense how it will look with filters and options.
Smart Data Visualization! This concept seems alien to some people. Is it data that can read your mind and automatically display itself in a way that will help you understand? Is it a method you use to see data in a clear way; a technique you learn in a class? The answer is yes…and no.
ElegantJ BI is pleased to announce an exciting partnership with Herald Logic Pvt. Ltd. Herald Logic specializes in technology-enabled distribution management solutions, delivered via its unique managed service cloud-hosted model. This partnership will benefit customers and stakeholders of both organizations and provide world-class integrated solutions. The ElegantJ BI business intelligence solution is powered by unique Managed Memory Computing and the Smarten approach to advanced data analytics. ElegantJ BI products have been listed in multiple Gartner reports.
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.
