Data quality is an integral part of any organisation, and yet is often a forgotten discipline. It is clear that the manual maintenance of large-scale datasets is time consuming, expensive and often unrealistic, so what can we do to combat the problem? This column will address the latest spatial data quality issues, why they matter, the industry’s reaction and look at different opinions and solutions.
Ask someone about problems with their spatial data quality and the answer is often: “what problems?”The stigma attached to having less than perfect data quality often results in individuals or organisations sweeping the issue under the carpet; an understandable, if undesirable, reaction as reputations need to be protected and budgets may not cater for manual correction of data. A further point to consider is that there are often no mechanisms in place to measure or detect poor spatial data quality; it can be the hidden dagger under the cloak. How can the data controller or owner act on problems that they don’t know the existence of, or simply cannot see visually? Despite its controversial reputation, the forgotten or neglected (wilfully or otherwise) discipline of spatial data quality has recently emerged as one of the most important considerations for individuals and organisations alike. Spatial data quality has been thrust into the limelight not only in the world of GIS but across a variety of industries.
Here at 1Spatial we have over 35 years of experience in building and managing large spatial datasets, but have begun to observe a change in organisations’ requirements. We are increasingly responding to requests to integrate traditional and spatial data within the typical GIS and CAD environments. A variety of new commercial areas with this need are also emerging, including retail, financial services and telecommunications to name but a few. Organisations are starting to sit up and take notice as spatial data quality solutions become available, with the promise of improving efficiencies, gaining return on investment and, crucially, ensuring that third party users, stakeholders and citizens have access to accurate, fit for purpose data. What has brought about this change?
Some would argue that with access to geographic information becoming ever more global, it has never been more important to consider how the world perceives your data and, in turn, your organisation. Reputations can be made or broken by the experience of a third party utilising your data. Who wants to use a map that takes you to the wrong location, or an online service that gives you incorrect information? Next we should consider the importance of open standards and data integration; the aggregation of local and regional datasets in Europe as a key INSPIRE activity spring to mind immediately here.
Consider the many problems of integrating levels of datasets, even down to language semantics, and how inferior data quality could lead to the magnification of problems inherent within the datasets. Improving data quality and the use of open standards will here work hand in hand to make data integration a success, and will be discussed in greater depth in future columns. Finally, on a more intrinsic level organisations base critical decisions on their spatial data every day.
The realisation that their data quality may not be perfect has led to consternation, and raised some pertinent questions: how does poor data quality affect our business, what costs do incorrect decisions based on flawed data incur, how does this affect our satisfaction of regulatory requirements and is there a way to monitor overall spatial data quality as part of our business processes?
As an industry we need to look forward and make changes. Having data quality measured and corrected should not be seen as derogatory (for both individuals and organisations alike). Department or enterprise-wide spatial data quality management is a difficult task for those without expertise; taking external assistance and working towards improvement should be applauded not discouraged.
A greater focus on the use of open standards, and in establishing the management and maintenance of spatial data quality as an essential part of best practice will drive forward the issue across all industries. In real world scenarios spatial data quality is making an impact.
From your GPS system taking you to the right location, to a fast decision on your house extension application, from obtaining customer satisfaction to reaping corporate return on investment; for consumers, citizens, providers, mapping agencies and businesses, the issue of spatial data quality is significant to everyone. With the continuing progression towards an information-driven society, it looks set to become an evermore pervasive and important factor in all our lives.
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