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Big Data: Problem Data Skews Predictions
Despite increasingly sophisticated algorithms, long-term and even short-term forecasting still often come up short and often miss the mark by wide margins. Why is that? Cleaning data and identifying which data is most important are key. Pew Research commented following…
Digital Twins: Data Models for Non-destructively Simulating Objects and Systems
As both software and hardware technologies advance, so does our ability to model and simulate the world around us digitally. Digital twins pair a computer simulation of a physical object with an actual object or system. Computer simulation is nondestructive…
Big Data: Successes Shine Bright, but the Technology is Littered with Failures
In 2011, McKinsey predicted that “Big data will become a key basis of competition, underpinning new waves of productivity growth, innovation, and consumer surplus—as long as the right policies and enablers are in place.” It wasn’t just McKinsey that saw…
Business Intelligence: Big Data Analysis Needs to be Complemented by Thick Data
Big Data’s premise is that once huge amounts of data are collected, it’s possible to notice trends and patterns, and those patterns in turn lead to conclusions and predictions about future trends. Big Data’s focus is algorithmic and sterile. The…
Cloud Computing: Cloud Vendors Simplify and Accelerate Big Data Adoption
Hadoop was the break-out product that enabled many businesses to get involved with analyzing Big Data. Unfortunately, using Hadoop is not an out-of-the-box experience where you’re ready to go after installing the software. Despite the complexity, many companies still continue…