This article provides a brief explanation of the KMeans Clustering algorithm.
This article provides a brief explanation of the KMeans Clustering algorithm.
This article provides a brief explanation of the definition and uses of the Descriptive Statistics algorithms.
This article provides a brief explanation of the Holt-Winters Forecasting model and its application in the business environment.
Self-Serve Data Preparation is a critical component of augmented analytics. If these terms seem foreign to you, just know that they represent the future of business analysis. As organizations adopt self-serve business analysis, the business user with average technology skills must be able to leverage tools that are sophisticated, yet easy to use.
Terms like Advanced Data Discovery and Augmented Analytics can seem mysterious and daunting for the average organization. Managers, executives and IT staff may believe that business users cannot and will not adopt advanced analytics tools because these tools can only be used by data scientists, programmers or business analysts.
Smart Data Visualization is a crucial component of augmented data discovery. This critical feature enables sophisticated analysis with guided visualization tools that auto-recommend displays and data views based on data type, volume, dimensions, patterns and nature of data.
Hospitals and healthcare systems are turning to predictive analytics tools to plan and forecast and understand what, when and how to support patients.
If your organization is planning to implement advanced analytics tools or to democratize the use of data discovery tools, your IT staff and senior management are probably concerned about losing control of data access and about data security. Data governance is a real concern and it should not be minimized but there is no reason to change course and decide against data democracy just to accommodate data governance.
Self-Serve advanced analytics and data discovery software is an important competitive tool in today’s rapidly changing environment. Data resides in a lot of places within the organization and access to that data in an intuitive, integrated environment is important.
In this article, we will focus on the identification and exploration of data patterns and the trends that data reveals. The business can use this information for forecasting and planning, and to test theories and strategies. Let’s look at the various methods of trend and pattern analysis in more detail so we can better understand the various techniques.