10 Trends Shaping Big Data in Financial Services
The world is flush with news around the concept of big data – the influx of data from the Internet, mobile devices, social media and...
From the Floor at AHIP: It Can’t be Done Without 21st-Century Technology!
It would be hard to find someone to argue against the idea that healthcare has enormous room for improvement. While other industries became process-focused,...
How We Prevent and Predict Power Outages
Today, the power infrastructure has evolved into an automated and intelligent network of information that enables real-time balancing of electricity supply and demand. This...
Top 3 Reasons You Need a Chief Data Officer
In order for any business to realize the value of its data, the data should be activity managed across the entire organization. And that means...
5 Ways to Turn the Healthcare Consumerism Revolution Into a Competitive Advantage
With millions of previously uninsured healthcare consumers entering the marketplace, a dramatic shift from group to individual sales, and the impending launch of public...
The 7 Most Important Sections of a BI Business Case
Almost one-third of all business intelligence projects fail to “meet the objectives of the business,” according to Gartner Research Inc.
Andy Hayler echoes that sentiment by...
Patterns and Real-Time Response in Healthcare
We can expect significant changes ahead in healthcare as a surge of digitized data becomes available to healthcare providers and insurance payers alike. The...
5 Reasons to Integrate Analytics into Your CRM System
Are you managing customer experience from the outside in?
Customer experience may seem like a simple concept – how your customers perceive their interactions with your...
How to Give Sports Fans the Ultimate Mobile Experience
Professional sports teams, from the 49ers to The Golden State Warriors, are releasing mobile apps for sports fans. Basic apps include player stats, game schedules and promotional...
5 Sure-Fire Steps to Ensure Data Cleansing During ETL
Inappropriate, incorrect, duplicate, and missing data are prime examples of dirty data.
Dirty data contributes to inaccurate and unreliable results. If dirty data is used...