The Ledger

Predictive Analytics vs. Machine Learning

Although machine learning and predictive analytics are both centered on efficient data processing, the two are very different. Machine learning is a method of computational learning underlying most artificial intelligence applications and is considered a modern-day extension of predictive analytics. Predictive analytics can be defined as the procedure of condensing huge volumes of data into information that humans can understand and use. While businesses must understand the differences between machine learning and predictive analytics, it’s just as important to know how they are related.

Read More at The Digitalist by SAP >

Moving Beyond Traditional Budgeting

Management accountants are always looking for a way to make their budgeting process easier. An operational budget has significant advantages over the traditional budget because it is driven by an operational income statement. An operational budget is a great demonstration of the analytics operational inadequacies of traditional accounting and it gives companies and enduring competitive edge. The limitations of the traditional budgeting process have been recognized for a long time, and many management accountants see the need for a change.

Read More at Strategic Finance Magazine >

Improve Performance with Opportunity Costs

“Clearly defining and quantifying the benefits that are given up due to suboptimal performance can be a strong incentive for making necessary improvements.”

Opportunity costing is key to connecting monetary and operational metrics and optimizing the conversion process. Opportunity cost is defined as a benefit that could have been received, but was given up to take another course of action. How does this apply to manufacturing cost performance?

Read More at Automation World >

The Key Elements of a Big Data Warehouse

A Big Data warehouse is an architecture for data management and organization that utilizes both traditional data warehouse architectures and modern Big Data technologies, with the goal of driving analytics and business intelligence across the organization. The goal of the Big Data warehouse is a lot like the traditional goals of the enterprise data warehouse: delivering intelligence and analytics to decision-makers to drive business efficiency and effectiveness. There are six key elements that play a role in a Big Data warehouse architecture.

Read More at The Digitalist by SAP >