Location intelligence derived from location data is powerful in helping organizations achieve their goals and address some of society’s biggest problems. City planning, emergency management, and supply chain management are just a few of the powerful use cases we’ve seen for location intelligence, among many others. Businesses and organizations using location intelligence solutions must be wary of using bad location data since there are a variety of risks associated with using bad data that can seriously impact the bottom line. So, what are the hidden costs of bad location data, and how can businesses avoid using bad data?

The major difference between raw location data and processed location data is signal quality. Raw location data is unprocessed and lacks privacy-conscious filtering, making this data unreliable. Raw location data includes a variety of fraudulent or inaccurate signals that should not be used in analysis and should be removed during processing. This is why various hidden costs are associated with using raw data, most notably the cost of wasted data, since 45% of all raw location signals are typically removed during processing.             

Plus, ingesting and storing raw data means double the storage costs for half the usable data. There are also the costs of data processing to prepare the data for analysis. Raw data takes approximately five times longer to prepare for analysis, adding to the costs for a data scientist and a team of analysts to process the data. All these hidden costs can greatly impact the bottom line, and many businesses or organizations simply do not have the resources to handle raw location data management. So what are the other hidden costs of bad location data?

Privacy-compliant data handling practices are essential for any organization working with location data. Without the proper personnel to ensure privacy-conscious location data management, organizations risk damage to priceless assets like their reputation or relationships with employees, consumers, investors, and more.

The lack of privacy-conscious data handling practices goes far beyond wasted dollars and can have detrimental long-term effects on a company or organization. One of the most significant risks of poor data handling is the potential exposure of sensitive location information. Privacy-sensitive locations are places where connecting location data with other information about that location could reveal private or sensitive information about consumers. Using location data that may include data from privacy-sensitive locations can impact an organization’s reputation, fuel negative press cycles, and ultimately lead to lost revenue.

Given the complexity of cleansing and validating raw location data, it can be tempting to just work with the data as it exists. However, proper data processing is necessary because location data is not predictably wrong. For example, while 90% of location signals at one place may be accurate, in another place, less than 5% of signals may be accurate. Similarly, some devices will generate fewer, high-quality signals, while others will generate thousands of low-quality or suspicious signals. The volume of fraudulent activity also ebbs and flows. Raw location data is not predictable, so any analysis performed using raw data will be deeply flawed at best.

Furthermore, when using inaccurate data without proper context, analysis can become skewed, leading to poor data-driven business decisions. For example, let’s say a retailer is looking for ways to optimize its next advertising campaign by using location intelligence to understand its customers’ interests in the real world. After analyzing raw location data, the business owner sees that many customers seemingly visited a nearby stadium over the weekend, prompting the business owner to believe that many customers are sports fans. Creating a campaign based on these insights may not be the best idea because, as it turns out, half of those customers were really staying at a hotel across the street, and those at the stadium were there to attend a live music event. High-quality location data enriched with event metadata would have shown that these retail customers were business travelers and music lovers instead of sports enthusiasts, allowing the business owner to create the most relevant campaign possible. This, in turn, improves the return on investment, increasing the value of the ad campaign.

Working with bad location data means missing out on the added value of cleansing and filtering. For example, at Gravy Analytics, we process billions of pseudonymous mobile location signals every single day, and our data processing includes deduplication, cleansing of fraudulent signals, and flagging of potentially problematic signals. This is our location data forensics process, which filters and flags signals for added transparency and accuracy in analysis. This helps researchers and analysts trust the data they’re using while allowing them to decide which signals are the best fit for their analysis and which aren’t.

For example, one of our forensic flags marks some signals as “likely driving.” For a business conducting foot traffic analysis, driving signals may not hold much value, but for an organization conducting research for emergency response management, driving signals can provide an additional layer of useful information. Working with high-quality data that has been filtered like this can improve research and analysis, allowing for increased accuracy and visibility and leading to more effective decisions.

The best thing an organization can do to avoid paying these hidden costs of using bad location data is to understand its capabilities and needs first. What does your organization have the bandwidth for, and where can a third party help? Then, it’s important to choose a trusted location data provider to help you meet your objectives.

A location data provider like Gravy Analytics has the resources to ensure high-quality data and high-quality location intelligence solutions, increasing the accuracy of your analysis. By working with a reputable, third-party data provider, you can ensure that your organization avoids the hidden risks of working with low-quality data, ensuring that data is ingested, processed, and handled in a privacy-conscious way, ultimately helping you get the most out of location intelligence.

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