The process of predictive analytics has come far in the past decade. No longer is this process the sole responsibility of data scientists or IT staff. Today’s self-serve predictive analytics and forecasting tools are designed to support business users and data analysts alike.
Search Analytics or search-based analytics marks the advent of a new era of business intelligence, in that it allows business users to ask a question using natural language and that question is translated by the system to produce results.
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.
Smart Data Visualization can radically improve your business intelligence, data discovery and analytics. It can streamline the work process of business users, improve the accuracy of planning and forecasting and ensure better, more timely, more accurate business decisions.
This article summarizes our recent article series on the definition, meaning and use of the various algorithms and analytical methods and techniques used in predictive analytics for business users, and in augmented data preparation and augmented data discovery tools.
As self-serve business intelligence and data analytics has evolved, the concept of Social BI and business intelligence collaboration has become more prominent. Today’s trends in social networking and collaboration drive Social BI within the organization and, as this concept becomes more mainstream, business users and organizations reap the benefits of these collaborative efforts and of shared data analytics and data popularity.
There is a new business role on the horizon and, at first glance, it may seem very much like a role that was introduced a few short years ago. This new enterprise role is known as an ‘Analytics Translator’ and, while there is some confusion regarding the distinction between this role and the newly minted Citizen Data Scientist or Citizen Analyst, there are some subtle but important differences. In a previous article (What is an Analytics Translator and Why is the Role Important to Your Organization?), we discussed the definition of an Analytics Translator. Here, we will discuss the role of Citizen Data Scientist and Analytics Translator and how they differ. To understand these roles, let’s look first at the somewhat more familiar role of Citizen Data Scientist (AKA Citizen Analyst).
Today, enterprises recognize the critical value of advanced analytics within the organization and they are implementing data democratization initiatives. As these initiatives evolve, new roles emerge in the organization. The newest of these analysis-related roles is the ‘analytics translator‘. As the enterprise considers the relevance of this new role within the business, it is important to understand the responsibilities of an Analytics Translator, and how this role might help the organization to achieve its goals.
The concept of Clickless Analytics is one that will be happily embraced by business users and by the business enterprise. The reason is simple! Clickless Analytics allows users to find and analyze information without specialized skills, by using natural language.
Extract, Transform and Load (ETL) refers to a process of connecting to data sources, integrating data from various data sources, improving data quality, aggregating it and then storing it in staging data source or data marts or data warehouses for consumption of various business applications including BI, Analytics and Reporting. It offers high quality data, which otherwise resides in poorly structured heterogeneous, complicated data sources.
