Spatial Data Audits: Maximizing Value and Minimizing Risk Associated With Your Spatial Data Assets

By Kevin Pomfret , Cantor Arkema, P.C.
December 16, 2008

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Spatial data can be a valuable company asset and revenue source. However, they can also be a potential source of liability. The potential for liability is increased due to the number of unique legal and policy issues associated with spatial data - in areas such as data quality, data ownership, privacy and national security. A spatial data audit is a valuable tool to enable a company to maximize its revenue stream, as well as minimize any associated risk.

What is a Spatial Data Audit?
A spatial data audit is a method by which a company assesses its spatial data value and distribution chain. It involves a review of spatial data vendors, customers and internal policies and procedures. For example, a spatial data audit can be used to:

ensure that any spatial data obtained from a third party vendor have been collected in compliance with contractual obligations (for example, with respect to data quality and ownership) and in accordance with relevant laws and regulations
determine how spatial data are used internally (for example, what other sources of data are they used with or is the quality control sufficient for existing applications) and whether the data are stored in a secure manner (for example, which employees have access)
monitor customers’ compliance with licensing terms (such as royalty reporting and payments and protection of intellectual property rights) and applicable law, and verify that the spatial data are being used, marketed, licensed and distributed solely for the purposes for which they are intended

Once completed, a spatial data audit can help the company ensure that the spatial data being collected and processed are of sufficient quality for the purposes for which they are being marketed and used. It can also help to both maximize revenue, by verifying royalty payments, and reduce liability, by ensuring compliance with laws and regulations by all parties in the distribution chain. Finally, a spatial data audit can be used to develop and evaluate adequate internal processes and controls in anticipation of any third party investigations.

Data audits are not unique. In fact, they are common in industries such as financial services and health care. They are often used to develop internal information management policies, to monitor compliance with licensing terms and to ensure applicable laws and regulations are being followed. In fact, they are also becoming more common with spatial technology companies. For example, the Ordnance Survey (UK) recommends a data audit for positional data accuracy. Similarly, NAVTEQ has a Licensing Governance Group to monitor customers’ compliance with both financial and non-financial terms of its licensing agreements.

Important Factors to Consider
The type and extent of a spatial data audit can vary depending upon a number of factors. These factors include (i) the types and sources of the spatial data and whether the data originate from within the company or from third party suppliers; (ii) where the company fits into the spatial data value and distribution chain (i.e. collects, uses, aggregates, value-adds or distributes the spatial data); (iii) whether the eventual end user of the spatial data is a government entity, a business or a consumer; and (iv) the size and level of sophistication of the company being audited.

A spatial data audit can take many forms based upon the factors described above. It might, for example, consist of a rapid assessment of a company’s spatial data to determine if there is even an issue based upon type or use of the data. It may also include a review of vendors or customers to make sure that the terms of applicable license agreements are being followed. Alternatively, a company may determine it needs a full internal review of processes and procedures involving the collection, use, storage and disposal of spatial data.

What Does a Spatial Data Audit Look Like?
A spatial data audit may consist of one or more of the following steps:

a review of license agreements (company, spatial data vendors, resellers, OEMs, strategic partners and customers)
identification of applicable laws and regulations
written surveys targeted at selected employees, vendors and customers
interviews with selected employees, vendors and customers
review of marketing materials (both company and distributor)
review of formal (and informal) procedures associated with access, storage, use, disclosure and destruction of spatial data assets
workshops and seminars for employees
a written report and presentation to management and key company employees identifying potential ways to increase revenue and assessing the potential risks

Conclusion
As the amount and type of spatial data used in commercial applications increase, companies will need to adequately protect their data ownership and make sure that their data are being used for the intended purposes. They should also verify that they - and their customers and vendors - are in compliance with all applicable laws. As a result, companies that implement spatial data audits will gain a competitive advantage in the marketplace.

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Independent data auditing (#1) by Steven Ramage, 1Spatial
Date: December 16, 2008 09:22 AM

Hi Kevin,

I would add in 'independence' as a consideration when doing audits. If you are capturing or creating data with one set of tools then it is worthwhile having another set of tools (that work according to your data specifications or business rules) to validate those data and check conformance against your business requirements. Also independent organisations tend not to have a vested interest in the outcome of any quality assessment or data audit.

In terms of legal agreements, for example Service Level Agreements, it can also be possible to greatly reduce the level of data exchange between supplier and recipient, i.e. roundtrip engineering. This is where data is sent by a 3rd party, but it does not meet agreed or minimum SLA levels and has to be sent back to be reprocessed or 'cleaned' (again).

I know in some instances our customers just fix the data themselves to reduce the time it takes to make the data fit for purpose. If they have to send it back to the 3rd party supplier it can create an engineering chasm, i.e. where it takes too long between receiving useful data and being able to build it into their products or services. Sometimes there are just too many errors to capture them all and they just tackle the major errors that impact business continuity. Thanks for putting together a great article and highlighting these issues that impact the geospatial supply chain [data value and distribution chain].

Cheers
Steven

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