The geospatial analytics market is presently experiencing considerable and steady growth; in fact, the market is expected to grow in value to USD 96.3 billion by 2025*. Demand for geospatial data, and for the systems to manage and maintain such data, continues to grow.
Today, geospatial data is everywhere – from utility smart meters to insurance company in-vehicle trackers. Even in supposedly traditional organisations like city councils, every department relies on location data: traffic flows, public facilities, the location of field staff and of client-citizens. Data centres are stuffed with disparate sets of spatial data.
Increased complexity, increased opportunity
With increased volume, and increased complexity, comes opportunity. Combining data from multiple sources multiplies the insight available: preventative maintenance can be planned more effectively; customer service can become more usefully targeted and emergency services can be more accurately deployed.
However, an abundance of different and disparate data sets, nestled in siloes across the organisation presents challenges too. The data was collected over different periods, at different frequencies, to different levels of accuracy and for different purposes. It is stored in different formats and at different levels of quality and completeness. Integrating that data to support a valid, single point decision is hard.
Managing and maintaining it for regular interrogation is even harder. The potential time and cost can appear unmanageable.
Continual process
Years of experience in data management has proven that it must be a continual process to be effective. Data is constantly changing (through data edits or bulk import of new data), as is the world it represents. Tasks like data cleansing cannot be effective as a one-off project.
So, can data management really be made easier and smarter for better decisions and greater returns?