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Better governance helps eliminate mistakes in marketing data quality

Rachel Wheeler Archive

As businesses seek to acquire more information about their customers and the economic climate around them, they become increasingly reliant on data collection. They're in luck - given all the modern technology options available to consumers today, there are now more channels than ever for companies to collect data.

Thanks to the proliferation of social media sites, mobile apps and websites in today's digital climate, people are sharing information with their brands all the time, even when they don't mean to. Every time they get in touch for any reason - whether to make a purchase or request customer service - they're funneling data into organizations' systems.

Unfortunately, there are concerns about data quality that these organizations need to be aware of. It's especially dangerous when companies are collecting data from disparate channels - for example, some from Facebook and some from Twitter - and they need to bridge the gaps and bring all of that information together. In so doing, they need to eliminate problems such as duplicate entries and human errors resulting in bad data.

This is where data governance becomes vital. According to Smart Data Collective, it's underrated in today's business world. Timo Elliott, an employee of SAP BusinessObjects, told the news source in a recent interview that people often underestimate the need for governance.

"You have to think about how bad data can and will get into your system," Elliott explained. "Whatever the underlying technologies, the principal causes of poor data quality are going to be the same. The temptation is that lessons learned over the years are thrown out during the dash to big data. These projects are often driven by a need for better analytics, and if there's one thing that's going to expose bad data, it's a BI system."

Elliott elaborated that issues of data governance are especially important these days, as companies find themselves increasingly interested in real-time analytics. They want to get information into the hands of professionals who need it, as quickly as possible, but if they're working with inaccurate data, their efforts will all be for nothing.

There's no silver bullet to ensuring data quality, but companies need to intensify their efforts at every stop on the path to real-time analytics. They need to purify information as it's collected, migrated into databases and deployed in the customer environment. This is a multi-step process that will require continuous attention.

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