“Data-Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media.
Marketers today have many data sources available to them, giving them the option to tailor data sets based on their specific needs. Whether it’s for audience segmentation or business operations, it can be tempting to buy the biggest data set available. However, it’s important that marketers think twice before diving in, especially when it comes to location data.
Large location data sets often include raw data that has not been normalized or verified, leading to potential integrity issues.
As such, it’s essential that marketers look for two key components before selecting a data set: data normalization and quality assurance.
Improving accuracy and ensuring usability
Data regularly fluctuates due to a variety of factors, such as new suppliers or compliance regulations. Plus, on its own, it can be redundant or erroneous.
For example, while one location may have 95% accuracy, another location may have less than 5% accuracy. Similarly, the types of signals emitted by devices can vary in number and quality, further skewing the accuracy of the data.
In fact, up to 45% of raw data can be unusable. But companies still need a place to store it, which typically requires purchasing costly data storage.
And beyond the sheer size of raw location data sets, raw location data needs processing to determine what’s usable. This too can be costly, not to mention time-consuming. In short, raw location data that hasn’t been analyzed through a quality assurance lens is unpredictable.
So what can marketers do? First, data normalization can ensure data sets are consistent and prevent results from being skewed. It provides a level of stability to a data set, allowing marketers to develop more accurate analyses and create a clearer picture of changing trends over an extended period. From there, a quality assurance process can weed out any data that’s not viable, leaving marketers with more meaningful, actionable data.
Prerequisites to data success
Data normalization and quality assurance are key to ensuring that a data set can be trusted. Without that level of confidence in the data, marketers cannot be sure that the data they’ve received is accurate, potentially invalidating any analysis they develop.
Data has the power to unlock so many unique insights and trends, but if it hasn’t undergone the necessary procedures, that potential has been wasted.
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