This article is the fifth and last in an editorial series that will provide direction for enterprise thought leaders on ways of leveraging in-memory computing to analyze data faster, improve the quality of business decisions, and use the insight to increase customer satisfaction and sales performance.
Using predictive analytics involves understanding and preparing the data, defining the predictive model, and following the predictive process. Predictive models can assume many shapes and sizes, depending on their complexity and the application for which they are designed. The first step is to understand what questions you are trying to answer for your organization.
Enterprise data assets are what feed the predictive analytic process, and any tool must facilitate easy integration with all the different types data sources required to answer critical business questions. Robust predictive analytics needs to access analytical and relational databases, OLAP cubes, flat files, and enterprise applications.
There is a vast array of predictive analytics tools, but not all are created equal. Software differs widely in terms of capability and usability — not all solutions can address all types of advanced analytics needs. There are different classes of analytics users — some need to build statistical models, others just need to use them.
This article is the third in an editorial series that will review how predictive analytics helps your organization predict with confidence what will happen next so that you can make smarter decisions and improve business outcomes.. It is important to adopt a predictive analytics solution that meets the specific needs of different users and skill sets from beginners, […]
The need for predictive analytics in the enterprise is clear, as it can provide smarter analysis for better decision making, increased market competitiveness, a direct path in taking advantage of market opportunity and threats, a way to reduce uncertainty and manage risk, an approach to proactively plan and act, discovery of meaningful patterns, and the means to anticipate and react to emerging trends.
As the primary facilitator of data science and big data, machine learning has garnered much interest by a broad range of industries as a way to increase value of enterprise data assets. In this article series we’ll examine the principles underlying machine learning based on the R statistical environment.