When Data Goes Bad: How To Improve Data Quality?
From human error consequences to overcoming data silos, you face a number of issues as you strive to improve data quality. The secret to success is developing a robust strategy.
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From human error consequences to overcoming data silos, you face a number of issues as you strive to improve data quality. The secret to success is developing a robust strategy.
Learn from misleading data visualization examples to minimize the risk of bad data presentation that may lead to poor business decisions.
Big Data is gaining traction among various industries. According to IBM, people produce 2,500 trillion bytes of data daily. 50 billion IoT and other connected devices gather, analyze, and share it (as CISCO states). Big Data unlocks an excellent opportunity
In the modern world, data is king. It tells us everything we need to know about, well, just about everything. Data finds its usage in all spheres, from governmental processes to business, multinational enterprises, and more. But how does one
Most businesses deal with a gigantic amount of data on a daily basis. The question is how to make the most of it. It turned out the biggest issues associated with Big Data are not really analytical ones. In many
IT departments crave freedom from the monotonous work of non-stop report generation. Not to mention that many employees appreciate the idea of self-sufficiency when addressing their information needs. Besides, data continues to grow dramatically and businesses have to make important