As business organizations fight for competitive advantage, funding for projects and large expenditures can fall by the wayside. In today’s competitive business market, every senior executive looks at risk, value and calculations like return on investment (ROI) and total cost of ownership (TCO) before approving a budget.
Data literacy is a very popular idea these days. As business users adopt and embrace data and advanced analytics, features like predictive analytics for business users make it easier for a user with average skills to leverage data to make decisions and share information and, in so doing, to become more literate about data analytics.
When a business commits to data democratization and to improving data literacy, it must add advanced analytics tools that will support these initiatives. The education of business users is crucial if these projects are to be successful, but no business has the time or the money to schedule intensive training.
A business that does not optimize its resources is doomed to fail. In this rapidly changing business environment and market, every organization must make the best of precious human resources. No one has enough funding to hire additional resources to get the job done and, when there are extra funds, those funds are quickly earmarked for new products, marketing and other crucial activities.
So you want to transform your business users and encourage learning for Citizen Data Scientists to enable data literacy across your enterprise? If your business is like most, your average business user doesn’t know (or need to know) the details of sophisticated algorithms and analytical techniques. But, you DO want to encourage data literacy and provide your business users with the tools they need to perform analytics.
ElegantJ BI is pleased to announce that it will participate in The Vibrant Gujarat Global Summit 2019, where it will engage with partners and clients and demonstrate its Smarten product and innovative approach to advanced analytics. The Summit will take place at the Mahatma Mandir Exhibition cum Convention Centre, Gandhinagar, Gujarat, India January 18 through January 22, 2019.
One of the most common excuses used to avoid data democratization and self-serve augmented data discovery is that the organization cannot guarantee data integrity and that, if business users have access to dated, incorrect or incomplete data, the resulting decisions will not be better but rather worse than the decisions made today. Further, this theory says that the distribution of this inaccurate data will have a systemic effect on truth and promote disastrous business decisions. There are several mistaken assumptions in this theory, and several ways an organization can guarantee success in a data democratization initiative.
This article will focus on the Naïve Bayes Classification method of analysis.
In this article, we discuss the analytical method known as frequent pattern mining, previously known as ‘association’.
In this article, we will discuss the KNN Classification method of analysis.
