Journey to the centre of decision-making
Journey to the centre of decision-making
In our previous post in this series, we talked about the “location opportunity” and how effective management of spatial data could support better decisions.
But, how do organisations put geospatial data at the heart of smarter decision-making?
Data sources, existing and new
For many, it begins with the realisation that a wealth of geospatial data already exists, untapped, within the organisation. Often, it is locked in departmental siloes. Sometimes it is locked in the heads of experienced employees.
Organisations have gathered a wealth of geospatial data over the years, spending large sums of money to derive the information needed for critical decisions. Too often, this was done in isolation and the data sits in disparate and decaying siloes, inconsistent with other data-sets, often duplicated but of uncertain quality.
When a new decision comes along, the organisation can’t find or can’t access the data it collected before.
There are many new sources, too: GPS data from trackers and IoT sensors, location data from social media and mobile phones.
Understanding the quality of data from these old and new sources is vital.
Understanding the data’s origins or provenance can be important in understanding its usefulness and credibility: data collected for a national survey may not be sufficiently accurate at a street level. As some organisations have found, publicly available and commonly used data may not really be fit for the purposes to which it is put.
Assessing, maintaining and improving the quality of geospatial data is vital to its effective use in decision-making. Not only can poor quality data result in wasted effort (and even destroy otherwise sound initiatives), it is also essential for safety reasons. As organisations integrate, and then share, information from transport systems, power networks and emergency services, accuracy and reliability become of paramount importance. Just a few metres’ error on a road layout can send vital emergency services many miles and precious minutes in the wrong direction. Misrepresenting the position of an electricity line can endanger the lives of workers and lead to power outages across a city.
An added dimension of complexity
The potential complexity of mismatched data is compounded for spatial data.
In non-spatial scenarios, a single “piece” of information is quite small (the few words of a Google search term, the digits of a date of birth). With spatial data, however, a single piece of information is much larger: every feature interacts with its neighbours; every building has location, footprint, form and function.
Where non-spatial data is typically a full record of a particular piece of information (for example, a date of birth), spatial data is more usually a simplified representation of a real-world feature. The extent of that simplification will affect the usefulness of the data for other purposes. For example, a low-resolution aerial photograph may be adequate for a land-use survey, but not for a land transfer record.
In addition, spatial data is not transient; a building has history, as does a shoreline. A feature’s function or form today is not what it was five, ten or a hundred years ago. Seeing change over time can provide valuable insight: historic flood patterns or coastal erosion perhaps, or the London Docklands’ evolution from port, through dereliction, to rebirth as an area of upmarket offices and apartments.
Geospatial data is more complex than many data types. It becomes “big” faster than other data; and users encounter size-related problems much sooner. Consequently, the management of spatial data presents greater challenges.
The value of geospatial data as a corporate asset and an aid to informed decision-making is clear.
But, even if the value is unquestionable, the cost of managing it sometimes seems unaffordable. Managing data from multiple, disparate sources is complex and sounds both time-consuming and expensive.
Cleansing data for a single, point decision is a major project. Maintaining it for ongoing interrogation can sound impossible.
For geospatial data to deliver on its promise, it must be made cost-effectively reliable.
Over the next few weeks, we’ll look at effective approaches to cost-effectively managing this complexity.
To learn more and read how customers have benefitted from effectively managing their spatial data, download our Little Book of Spatial Data Management, here.