ElegantJ BI, an innovative vendor in Business Intelligence and Augmented Analytics, is pleased to announce that ElegantJ BI is named as Representative Vendors in the Gartner ‘Market Guide for Traditional Enterprise Reporting Platforms‘ report published September 28, 2017.
Predictive analysis is very important to your organization. If you can dependably predict and forecast results, you can stay ahead of the competition, address changing customer needs, capitalize on opportunities and solve problems before they negatively impact your enterprise.
Every enterprise is talking about Business Intelligence and Advanced Analytics. Every enterprise has considered the benefits of implementing self-serve analytics across the organization and involving business users in the process. Panel discussions at technology conferences and hallway conversations among executives and IT staff encourage the trend and create anxiety within the organization that has not yet embraced advanced analytics at the grassroots level.
Oh, the confusion of advanced analytical terminology. Read a report, attend a conference and your head is swirling with terms like ‘assisted predictive modeling’, plug n’ play predictive analysis, smart visualization, augmented data discovery and augmented data preparation. Add to that, the sophisticated concepts of auto-suggest, auto-recommend, time series forecasting, causation and prediction and classification techniques and you may feel that you need a degree in data science to do your job.
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.
What did we do about planning and forecasting in the past? Well, a lot of what we did was based on guesswork. We might look at historical data and plan for growth that was unrealistic because it did not reflect the competitive market growth or the advent of online competition.
To be successful in business, every organization must find a way to accurately forecast and predict the future of its market, and its internal operations, and better understand the buying behavior of its customers and prospects. With the democratization of business intelligence and the advent of self-serve business intelligence tools, organizations hope to encourage and create Citizen Data Scientists and enable the average business user to leverage sophisticated predictive algorithms and BI tools without the expertise and skill of a trained data scientist. This approach allows users who are not statisticians, data scientists, programmers or analysts to leverage self-service tools to confidently make business decisions, share data, and reports and present data in a way that allows the organization to identify trends, patterns, opportunities and challenges.
It seems that everyone today is talking about Citizen Data Scientists. For some organizations the concept is perhaps frightening. The term may conjure up images of novices trying to do the work of someone with years of training and failing miserably in the process. For others, the image may be of an organization run amuck with business users leveraging sophisticated advanced analytics tools to create and share data that may be misinterpreted or distributed improperly.
If you are living in India, and since you are reading this and it is very likely you are, the possibility you said: so what? You have worked in multinationals operating in India, Indian multinationals, Nationalised corporations or corporations which are the property of the nation and never, never have you found Analytics हर हाथ में.
