Drowning in Imperfect Data
In a recent blog post Your Friend the Algorithm, Paul Barsch explains:
“With exponential trends of data growth and computational power colliding…companies are using technology…and sophisticated mathematical procedures to analyze data and make better decisions.”
Data frequently contains numerous variations caused by different conventions, lack of standards, omissions, and other inconsistencies.
Traditional approaches to data matching have heavily relied on data standardization to prepare records for matching. This preparation creates a consistent format that allows for more direct comparisons on parsed attributes with standardized values.
However, the problem with the traditional approach is that “the world is literally drowning in data” explains Barsch. “There’s too much data, and not enough analysis.”
Advancements in data matching technology using sophisticated mathematical algorithms are providing the capability to make better data-driven business decisions – without the prerequisite correction of data’s inherent imperfections.
According to Gartner Research, the volume of enterprise data doubles every 18 months. There is also a rapidly growing need for real-time analysis of these burgeoning data volumes in order for companies to remain competitive in a constantly evolving marketplace.
“Algorithms help tackle complicated challenges,” explains Barsch. “As data volumes and decision options increase, algorithms and the systems that run them take on added importance.”
The need to make imperfect data perfectly usable is becoming more important than ever.
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