4 Reasons Why Data Quality Trumps Data Quantity

4 Reasons Why Data Quality Trumps Data Quantity

4 Reasons Why Data Quality Trumps Data Quantity
Data is a central part of smart business decisions today, so it can be tempting to think it’s important to collect everything and anything when it comes to data. But gathering huge amounts of information isn’t always the right strategy when mining for insights that truly matter.

The key to actionable business intelligence — the kind of data insights that provide real value in making decisions about how to run your company — is having the right kind of data, not having massive volumes of data.

In this era when tracking and collecting data is more common than ever before, here are four reasons why it’s arguably better to focus on data quality over data quantity. 

One-third of data pros spend up to 90 percent of their time cleaning raw data for analytics. This is a huge problem for data specialists, who are hired for their technical skills, not to serve as so-called data janitors.

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Keeping massive amount of surplus data bogs down data workers and widens the “time-to-insights” window significantly, which has a direct negative impact on business performance.

Rather than investing precious time on cleaning up data, businesses need to reign in data collection, taking the time to analyze what components are actually needed to form insights, and then adjusting systems accordingly. Data intelligence is predicated on efficiency and agility.

Wasting time cleaning up a sloppy data collection process only hinders the data insights team, hurting their ability to do their jobs properly.

Granted, data preparation on any level requires the appropriate amount of time and energy. But by simply hoarding mountains of unneeded data and then having to wade through it all, time spent on data preparation multiples significantly.

IT infrastructure and operation costs are already a huge chunk of enterprise spending, representing 60 percent to 70 percent of a typical enterprise IT budget. And collecting an endless stream of meaningless data will only cause this price tag to rise. Moreover, in 2014 it was estimated that companies spend “$50 billion a year on too much data.”

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The reason for the high price of too much data is simple: It costs money for the infrastructure of data storage, maintenance of data, data migration and more.

The greater the volume, the greater the cost.

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