Comprehensive Location Intelligence Across the Globe

Location data is one of the most powerful tools businesses can use to gain insights into customer behavior and make informed strategic decisions about logistics, site selection, advertising, growth opportunities, and efficiency. But location data can only make a difference when it’s accurate and on point. 

Poor-quality location data can lead to missed opportunities, wasted resources, and incorrect insights. In this post, we’ll discuss the three essential criteria to consider when evaluating location data quality, so that you can confidently use location data to drive your business forward.

✓ Credibility: How credible is the data you use? Location data can fail to meet this criteria in a number of ways, from data collected without proper consumer consent to data marred by collection devices or processes with technical issues. The key here is to know a) if the source of the data is credible and b) if the processes used to collect it are trustworthy.

✓ Quality: Are you using high-quality data? It’s vital to make sure that the data you use has high confidence scores, is free of duplications, and is up to date and accurate. 

✓ Usability and applicability: Are you confident assessing what your data can (and cannot) tell you? Do you know where and how to leverage it to drive growth? The essential point here is to identify where location data can boost your business and what the best practices are for evaluating what it’s telling you. 

Let’s take an in-depth look at each of these criteria and see how they can help your business leverage trustworthy and accurate location data.

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The importance of credible data

Data credibility is all about trustworthy sources and methods. For example, if data is not anonymized or protected appropriately, it may be compromised, leading to data breaches or misuse. Given this, how can you be sure that your data vendors are engaging in best practices by protecting consumers’ privacy and avoiding biased or cherry-picked datasets?

One way is to verify your data vendor’s privacy practices by ensuring that they are transparent about their collection sources and methods. For the datasets in question, have consumers been asked for their consent to be tracked? Some mobile apps, for example, may collect location data in the background without explicitly informing users or receiving their consent, potentially exposing sensitive information about their daily routines and behaviors and raising legal concerns. 

Moreover, data privacy can become a heated public issue that puts pressure on vendors. As consumers become increasingly aware of the potential risks and ethical concerns associated with location data, they are more likely to scrutinize the practices of location data vendors and demand greater transparency and accountability. This scrutiny, in turn, can lead to media attention and regulatory action. 

For these reasons, businesses should check that their data vendors employ ethical and transparent standards and engage in best practices to ensure that they have a data partner they can rely on. 

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The value of high-quality data

Credibility is about assessing the trustworthiness of the data source and its methods; data quality is about the caliber of the datasets themselves. Are there errors, duplications, or inaccuracies that can lead to problems down the road, like showing a distorted picture of a city or neighborhood? 

One area where quality can be a big issue is international location data. For example, consider the case of a QSR that is looking to expand from the U.S. to Mexico and turns to geospatial intelligence to inform its decision-making. As the QSR looks for sources of data, some important questions to ask include: does the data come with metadata, confidence scores, or similar indicators? Does it contain inaccuracies and need to be enhanced? For instance, when dataplor recently sampled INEGI records for Cancún, we found that 91% contained some form of inaccuracy. Only by catching these errors ahead of time could the QSR avoid wasting time and resources. 

While it’s possible for companies like the QSR to fix this data themselves, it’s usually more cost-effective to buy accurate data upfront. One way that dataplor ensures the accuracy of its international data is by using trained human validators with local expertise to work in tandem with its AI processes. Validators anticipate and catch issues like cultural differences in address structure or language variations like wordplay in business names. This real-time, on-the-ground feedback mechanism enables dataplor to identify duplications and errors, enhance their data, and offer clean and actionable datasets for business to use and realize results out of the gate. 

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The power of actionable data

Once you’ve established that your data is credible and high-quality, the final step is determining what you can — and cannot — do with it. It’s essential to have processes in place, and/or to work with a data services provider, who can help you understand what the data is actually telling you, so that you can leverage it for maximum effect. 

For example, let’s consider a retail company that is looking to expand its business internationally. They have access to data from their existing stores, but is this data of the appropriate scale? Are there enough data points to draw meaningful conclusions about consumer behavior and preferences in a new market? Additionally, consider whether the data provides global coverage. A business looking to start a new location in the Palermo neighborhood of Buenos Aires needs to be sure the datasets it’s using accurately reflect the area. The business can’t just rely on guesses about international locations based on more complete data in markets like the US and Canada that are easier to cover. 

Furthermore, companies need to be honest about their goals for using data. What do they hope to achieve with the data? Do they need additional datasets to complete their insight puzzle? For instance, a retail company might have point-of-interest (POI) data, but they may require additional mobility and demographic data to make optimal decisions about site selection for new stores. By understanding what the data can and cannot tell them, and what additional data they might require, companies can make better-informed decisions that facilitate growth at scale.

Location data is one of the most valuable tools a business can have to shape decision-making about the future, map the competition, and find or enhance pathways to growth. But much of the location data available for purchase is of poor quality and can be more damaging than helpful. By evaluating the credibility, quality, and actionability of location data, companies hoping to use it to grow can set themselves up to do just that.