Solving the Dirty Data Problem: Clean-Data Practices for Tech Innovators [Ebook]

Data is today’s gold for businesses, representing huge potential value. But there’s a catch: the data must be uncovered, clean, and structured.

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3 Steps to Get Clean, Structured Data You Trust

It’s their least favorite part of the job, yet it consumes most of their time. Data wrangling – or gathering and preparing dirty data so it can be used in critical business applications – is the biggest problem in data science today, according to a Kaggle survey. So how can you get clean, structured data you can trust?

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The Leading Causes of Dirty Data

The world is producing data at exponential rates. By 2025, the global datasphere will include 10 times the amount of data generated last year alone, according to IDC’s Data Age 2025. All of this data generation compounds an already common problem: “dirty data.”

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3 Kinds of Data that Do More Harm than Good

Data is today’s gold, representing huge potential value for businesses. By analyzing lots of data, you can discover patterns that can help you introduce efficiency, make smarter decisions, innovate products, and reduce operational liabilities like cost and risk. By using hidden data, you can transform your business or disrupt an industry by using available data to solve a painful problem.

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