Location data quality

By Jessica Krokowski

Those in the GIS arena are often well versed in mapping, geocoding and spatial analysis.

What we forget sometimes, however, is that we are also data stewards.  Except instead of customer names and product sales, our data revolves around location.

As data stewards, we must all recognize the cost poor data quality.

According to recent studies, a full 63% of organizations had no idea what poor data quality may be costing them.  The latest Gartner study quantifies that cost at an average of $8.2 million a year per company, with 22% of respondents calculated their annual losses at $20 million or more.

For GIS professionals, poor data quality translates into poor analysis, poor customer service and poor decisions.  That’s why today’s leading Enterprise Location Intelligence solutions come with built-in data quality functionality – so you can manage, cleanse and publish geo-data, maps and analysis through a single source.

Address quality is particularly important, so organizations should look for tools that offer the ability to cleanse data, standardize addresses and validate that source addresses are correct before applying geocodes.

While some mapping and geocoding vendors partner with third-party name and address data quality vendors, it is more effective when these capabilities are integrated in one platform.  This way you can standardize and correct address information in a single pass using multiple parsing and matching algorithms— giving you the ability to potentially resolve and standardize addresses a stand-alone algorithm might have rejected.

Mapping tools should support postal processing (CASS-Certified^TM in the US) which provide for Delivery Point Validation.  Postal data by itself is not enough. There are millions of households, for example, that receive mail at a Post Office Box—so you need to combine information from postal sources with a street segment database, such as Tele Atlas.

A relatively new innovation in location data quality is GeoConfidence. This process overlays basic geocoding data with additional data such Zip+4 determinations – and creates a buffer known as a “confidence surface” around data points.  The importance of this is simple: even when address-quality is high, there are certain degrees of variance in longitude/latitude estimations.  GeoConfidence helps to pinpoint the extent to which variation may come into play by taking each location point and determining how reliable that geocode really is.

When it comes to location data quality, vendor expertise, including a track record of successful implementations across a variety of business cases, can prove invaluable both during your planning and implementation stages. Experienced vendors such as Pitney Bowes Business Insight can add value by suggesting the appropriate data augmentation databases and providing suggestions as to how other clients with similar issues have utilized data quality technologies.

Overall, solutions need to be simple to use and flexible enough to meet different business requirements. A single technology platform that matches up with your overall corporate objectives can help ensure that a consistent standard will be applied in every market.  Take a few moments to learn more about Enterprise Location Intelligence today.