Why processing power and forensic flags matter in revealing usable data
Are you aware of the distinction between raw and processed location data? A critical hurdle for users of location data lies in sifting through data noise. In fact, an average of 45% of raw location signals must be discarded during processing due to flawed data. Without privacy-conscious filtering, deduplication, and cleaning, raw location data is often unreliable.
There are also the economic implications of managing raw versus processed data. Storing unprocessed data not only doubles storage expenses but also yields merely half usable data. Preparing raw data for analysis is a time-consuming process, taking roughly five times longer than working with processed data. This increases costs significantly, requiring additional resources from data scientists and analysts. Such hidden expenses can profoundly affect an organization's bottom line, with many lacking the necessary resources to manage raw location data effectively.
At Unacast, our years of experience in processing location data have made us intimately familiar with the various quality issues present in location signals. We've honed our skills in employing heuristic and machine learning techniques to identify and rectify problematic data, as well as to augment valid data with new insights. As the leading provider of enterprise location intelligence, Unacast is deeply committed to the utmost standards of data processing and quality.
Data Collection
Our sophisticated algorithms allow us to confidently process location signals from a variety of data sources without sacrificing the quality of data crucial for our clients. This independence from relying solely on a single SDK or data source grants us the versatility to work with only the most reliable data providers. Moreover, it empowers us to discontinue using data providers if their location data deteriorates, and to onboard new ones as needed.
By continuously monitoring the quality, scale, and density of the location data supplied by our partners, we ensure access to reliable data at all times. Our confidence in this methodology is so strong that we have opted to exclusively license data from other entities in the ecosystem instead of collecting location data through our own SDK. This strategy allows us to concentrate on our core competency: developing sophisticated analytical products.
Data Processing
Location data processing starts by removing flawed data and merging data for the same device across multiple apps and sources. Every day, we manage billions of pseudonymous location signals, processing an almost equal mix of U.S. and international data. We then consolidate duplicate data, preserving all its attributes.
By merging device data from multiple sources, we unlock the ability to spot trends and irregularities that elude other data providers. For example, some devices exhibit normal behavior most of the time, but intermittently “burst” a high number of locations.
This blending of data sources is key to identifying such patterns. In the example provided, it suggests that an app on a device is likely giving out flawed impression signals when connected to a power source, while it transmits legitimate movement data on battery power. Moreover, analyzing device data across several sources aids in identifying instances where legitimate data is misrepresented through time-shifting, leading to conflicting location signals. Such anomalies can make a device appear to move at implausible speeds or in erratic patterns.
Forensic Flags
Our algorithms tag each processed location signal with forensic identifiers, or "forensic flags." These flags are attached to each location signal record, playing a crucial role in our diverse product processes by helping to determine the relevance and use of each signal.
We share these forensic flags and their definitions with our data customers, enabling them to integrate this intelligence into their own workflows.
Why not eliminate all flagged mobile location signals? It's because forensic flags serve a dual purpose. Beyond signaling potentially flawed or suspicious data, they offer diagnostic insights and details about the signal's origin and accuracy. This dual functionality empowers our customers to sift through the data, selecting segments that align with their specific needs. For instance, while signals with lower accuracy might not be ideal for pinpointing customer visits to a specific Starbucks, they are valuable for broader neighborhood analyses or identifying overall trends. Businesses aiming to refine their data quality can leverage these lower-quality signals to enhance their detection algorithms, ensuring more precise outcomes.
At Unacast, we understand the critical importance of data processing when it comes to utilizing location data effectively and ethically. We have seen firsthand how raw location data can be unreliable and how much of it is discarded due to quality issues. With our expertise in identifying and rectifying problematic data, as well as augmenting valid data with new insights, we are committed to providing the highest standards of data processing. Our forensic flags not only enhance our location data but also play a crucial role in determining the relevance and usability of each location signal. So why settle for unreliable and unusable location data when you can benefit from our advanced processing power and forensic flags?
Book a meeting now to learn more about our methodology.
