Why Spatial Data Governance Matters
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.
What’s the difference between data governance and data management?
Data governance is built around a framework of people, processes and technology that enables organisations to improve and maintain data quality and reliability on a continual basis throughout its lifecycle – from data gathering, storage, management and processing through to the disposal of data. Good data governance increases trust in data sharing, strengthens mechanisms to increase data availability and overcomes technical obstacles to the reuse of data.
Data governance enables organisations to monitor, manage and maintain data quality, integrity and reliability on a continual basis.
Data management on the other hand can be seen as the implementation of those policies and procedures in order to conform to data governance guidelines for data accuracy, quality, currency and compliance. Data management is often seen as the "umbrella term" for all the different disciplines that can be used to manage and improve the data, of which data governance is one of these. According to the data management association DAMA, data governance is at the centre of data management because it underpins all the other data management activities, as illustrated in the DAMA DMBOK® Wheel.
How to get spatial data governance right
Effective spatial data governance is best achieved through user-controlled, enterprise-wide automation. This ensures that data governance becomes a repeatable process, rather than an event. By combining the latest technology and expertise, an organisation can enrich and enhance its data (incorporating both spatial and non-spatial data), enabling confident and informed decisions.
How 1Spatial can help
1Spatial has helped over 1,000 organisations automate the data validation, auditing, cleansing, synchronising and maintenance of their spatial (and non-spatial) data across the entire data ecosystem, enabling better decisions and greater insights. Our rules-based approach to managing data quality means expert business knowledge can be turned into user-managed rules, thereby ensuring that the best judgements about maintaining data quality are applied objectively and consistently, while at the same time minimising user-based errors.
You can read more about our unique rules-based approach to data governance here.
On-Demand webinar: An introduction to spatial data governance and management
While some struggle to get it right, every company can succeed by reframing data governance from simply being a set of frameworks and policies to embedding it strategically into the way the organisation works every day. Find out how data governance and spatial data quality and management go hand in hand by watching our webinar which took place on 21st March 2023.Watch on-demand webinar