High rainfall over the last week has led to flooding. Last night, there was a large number of burglaries. An escape of toxic gases this morning has led to emergency services requesting everyone to evacuate their premises. There are billions of barrels of crude oil that can be recovered over the next decade.
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Notice something missing with all of this? They may all be factually correct; they all discuss time – and yet they are all pretty useless to the reader due to one small thing missing – the “where?” aspect.
For example, I live in Reading in the UK – if the flooding is taking place in Australia, it may be sad, but I do not need to take any steps myself to avoid the floods. If the burglaries are close to my house, I may want to review my security measures. Likewise, if there is a cloud of toxic gas coming my way, I may want to head for the hills. An oil and gas company is not going to spend billions of dollars in digging holes in the hope of finding oil – they need to have a good idea of where to drill in the first place.
And the examples go on – retail stores figuring out where and when to build their next outlet; utility companies making sure that they do not dig through another company’s services; organisations with complex supply chains needing to ensure that the right goods get to the right place at the right time; public sector bodies needing to pull together trends in needs across broad swathes of citizens across different areas of the country.
The need for accurate and granular geo-specific data that can add distinct value to existing data sets has never been higher. As the internet of everything (IoE) becomes more of a reality, this will only become more of a pressing issue. The next major battle ground will be around the capability to overlay geo-specific data from fixed and mobile monitors and devices onto other data services, creating the views required by different groups of people in the organisation in order to add extra value.
I was discussing all of this with one of the major players in the geographic information systems (GIS) market, Esri. Esri has spent many years and a lot of money in building up its skills in understanding how geographic data and other data sets need to work contextually together. Through using a concept of layers, specific data can be applied as required to other data, whether this be internal data from the organisation’s own applications, data sets supplied by Esri and its partners, or external data sets from other sources.
The problem for vendors such as Esri though is the simplistic perception of location awareness that there is in the market. Vendors such as Esri and MapInfo along with content providers including the Ordnance Survey and Experian and others are perceived as purely mapping players – maybe as a Google Maps on steroids. This minimises the actual value that can be obtained from the vendors – and stops many organisations from digging deeper as to what can be provided.
For example, the end result of a geolocation analysis may not be a visual graph at all. Take the insurance industry, for example. You provide them with your postcode, they pull in a load of other data sets looking at crime in your area, likelihood of flooding, number of claims already made by neighbours, possibility of fraud, and out pops a number – say, £100 for a low risk insurance prospect, £5,000 for a high risk. Neither the insurance agent nor the customer has seen any map, yet everything was dependent on a full understanding of the geographical “fix” of the data point, and each layer of data only had that point in common. Sure, time would also have been needed to be taken into account – this makes it two fixed points, which could be analysed to reach an informed and more accurate decision.
The key for the GIS players now is to move far more to being a big data play that can be seen by prospects as a more key part of their needs. Esri seems to understand this – it has connectors and integrations into most other systems, such that other data sources can be easily used, but also so that other business intelligence front ends can be used if a customer so wishes.
So, what’s the future for GIS? In itself, probably just “more of the same”. As part of a big data/internet of everything approach, geographic data will be one of the major underpinnings for the majority of systems. When combined with time, place helps to provide a fixed point of context around which variable data can be better understood. It is likely that the GIS vendors will morph into being bundled with the big data analytic players – but as ones with requisite domain expertise in specific verticals.
As the saying goes, there is a time and place for everything: when it comes to a full big data analytics approach, for everything, there is a time and place. Or, as a colleague said, maybe the time for a better understanding of the importance of place is now.