In almost every industry, Excel spreadsheets are the standard for viewing and working with data due to the ease of use and visual nature of Excel. SQL databases offer more efficient and powerful ways to analyze large amounts of data, but Excel is hard to beat when it comes to visualizations, especially for self-service analytics.
A multitude of factors influence any organization's decision on whether or not to move their data and computing to the cloud, including efficiency, collaboration, security, and of course, cost. Amazon Web Services (AWS) just announced the launch of a new Total Cost of Ownership (TCO) calculator aimed at helping customers make the decision to move to the cloud.
In a previous post, we discussed getting started with predictive analytics and recommended executing a proof of concept to prove the general benefits of predictive analytics to your organization. But how do you make sure that you're providing specific, actionable predictive analytics insights that deliver real value to your organization?
Scott Burnell is a technical consultant who specializes in business analytics and reporting.
In a previous post, we discussed how beginning with a proof of concept can be the best way for an organization to get started with predictive analytics. However, if you’ve never worked with predictive analytics before, it may be challenging to design and execute your proof of concept.
Predictive analytics is emerging as a key methodology for organizations that want to leverage their data into a competitive edge that will help them predict customer behavior, understand trends and make more informed business decisions.
Zoe Cler is an experienced 110 Consultant and a frequent user of PowerPivot. She shares her ten favorite reasons to use Excel PowerPivot below: