Worldwide digital transformation technology investments will total more than $7.4 trillion in 2023.

(Source: The IDC's Customer Insights and Analysis Group )

Geospatial data continues to be in high demand to support these transformation goals. Private and public sector organisations are using geospatial data to help them plan ahead to meet future demand - whether this is for housing, retail, investment modelling, Net Zero ambitions, schooling, rail infrastructure or defence objectives. And as geospatial data becomes more affordable and more available, the need for quality, 'trusted' geospatial data has never been greater.

Quality data is a critical enabler for digital transformation

Trusted data delivers confidence in decisions, enabling organisations to accurately analyse trends, draw logical conclusions and produce powerful forecasts. "Trusted data" means the data is of a high quality, accurate, up to date, accessible, interoperable and in a standardised format to be usable.

Why is it difficult to achieve a state of 'trusted data'?

Without the right data, none of the analysis, predictions and modelling can be properly performed. What is of greater concern however is that poor data is much more commonplace than most organisations are led to believe. In our 30 years of undertaking data quality assessments, we usually find there are significant discrepancies between the organisation's expectations about the quality of its data and the reality.

The geospatial data quality conundrum

"Quality" is notoriously difficult to achieve with geospatial data because it is often held in different formats and systems, of differing levels of quality and completeness, and requires constant updating due to its transient nature. Geospatial data is also hard to manage using traditional tools because it doesn't conform to the 'normal' rules of data (think aerial maps, satellite images or other data that simply consists of lines and dots). As a result, organisations may spend millions in investing in new technology during a digital transformation process, only to find that their data is not up to scratch, resulting in time and effort wasted in having to undertake expensive manual fixes.

The need for spatial data governance

Getting data governance right means the quality will be managed consistently and continually without the need for regular (usually manual) checks and fixes. In an ideal, spatially-governed data world, organisations will always derive value from their data through analytics, digital, and other transformative opportunities because the data is - and remains - reliable.

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1Spatial plc published this content on 13 March 2023 and is solely responsible for the information contained therein. Distributed by Public, unedited and unaltered, on 12 March 2023 00:15:06 UTC.