Software As A Service (SAAS) is a way/means of delivering the functionality of software over the internet as a service. SAAS applications run on a SAAS provider’s servers. The provider manages access to the application, including security, availability, and performance. Important aspects such as security (partly), stability and scalability are addressed by the SAAS provider. The concept was fostered by two major developments: (a) Higher bandwidth, greater accessibility and falling rates of internet usage and (b) Open source software movement. Hence SAAS users save on the initial costs, efforts in upscaling and get the service they want at a nominal fee.
Today a number of SAAS have proliferated. Developers have integrated multiple functionalities and built-in flexibility tools such as coding environments to customize the multiple SAAS across a platform. Functionalities on these platforms are restricted only by imagination. These range from ERP software with functionality similar to that being offered by SAP, to GIS software offering functionality similar to products being offered by ESRI ArcGIS, Hexagon Geospatial ERDAS, PCI Geomatica, ENVI, etc. As an exponent of the latter class, I will dwell on it in this write-up.
GIS is a collection of people, procedures (rules defined by users), hardware, software and DATA to solve real world problems with geospatial connotations.
Why GIS? Managing resources has gone global. We have to often add the question or dimension “Where” to our considerations owing to a lot of factors such as ease of doing business, cost of labor, resource localization and much more. It is therefore logical that “Where” be seamlessly integrated into our ability to collate, analyze and disseminate data on the cloud. Other factors bringing GIS to the fore today are increased availability of low-cost data sets from an increased number of sources such as more number of satellites, UAVs and our desire to use the paradigm of convergence of evidence to analyze imagery data using non picto graphical data on a GIS platform; after all “a picture is worth a thousand words”. Cloud computing platforms would do well to bring Geospatial analytics as a SAAS with functionalities and intuitiveness offered by specialist software. This will allow for mature GIS analytics capability built into cloud computing platforms.
Why GIS as SAAS? There are a multitude of reasons why traditional GIS has moved onto SAAS platforms.
(b) Large volumes of data have resulted in decreasing cost in acquisition of data.
(c) Flexible GIS data gathering platforms such as UAVs offer the ability to users to generate data when and where they want (Reference is made to PWC report ‘Clarity From Above’ which brings out that that the UAV industry would be in the region of USD 127 Bn without factoring into it the raison d’etre of UAVs i.e. producing geo spatial data).
(d) Capability to analyse geospatial data is the bottleneck today. In the past data was scarce as was the ability to process it. Today due to a multitude of sources and the simple equation between demand and supply besides means of making data available across the world, data is reaching the masses. However, locked and expensive processing tools are now the bottleneck (in the past data availability was the bottleneck). The need is to make analysed data available to the end user in the desired format, when and where he wants it with the highest possible confidence levels at costs proportional to the functionality demanded.
(e) Emergent users in the field of geomatics include academia, start ups, NGOs, solution providers and a host of independent users. They bring to the table factor diversity, innovativeness and sharing of seemingly unrelated data sets. Operating on limited budgets, a number of them fostered and even developed the ‘Pay as you Use’ concept of software. These were users unwilling to lock in a large amount as initial capital, only to use a fraction of the capability bundled by the developer only to be told that the next version offered greater functionality.
(f) Development of programming languages such as Python. These languages offer greater flexibility, save time through their add on libraries and are free to use and program in.
Stakeholder analysis will dictate trends, developments and drivers of this movement.
(a) Users: A variety and more number of users will migrate onto these platforms thereby adding diversity. New entrants will include NGOs, Academia and startups – essentially cost-conscious users. This will place demands on the software capability for example, integration of geospatial with non-structured social media data. A new concept called geo-social data will emerge. Or consider Geo-medical data guiding health programs. It will also result in customized processing capability whilst still relying on standardized (OGC compliant) data sets. New entrants will also subject geospatial data to non-traditional treatment, which will result in new insights.
(b) Standalone Geospatial Software Developers: The dinosaur staring back from the mirror will advise them to evolve, devolve and decentralize. Increased modular structures with specialized processing capability will have to be developed which will be R & D driven besides having greater connect with emergent users (again academia, marginal users and collaborative platforms). Better APIs for integration using NLPs will be the norm. Software will need to become platform agnostic.
(c) SAAS Providers: They will need to provide more intuitive interface and products besides integrating functionality such as AI & ML through emergent programming languages. These platforms must facilitate big data analytics inclusive of geospatial data. Security of data and integration of technologies such as blockchain will move to centre stage.
Trends. Inclusive and insightful data will drive geospatial decisions. Academia, NGOs and the marginalised will have a greater say in decision making which will be modelled and hence likely to be more accurate. Different perspectives from new user segments will be akin to the concept of ‘Convergence of Evidence’ and hence data will be subject of greater scrutiny. Greater emphasis on security of data, privacy and network security will be seen. Black box type geospatial solution providers will develop increasingly modular and customised solutions in order to remain relevant.
Given these considerations, it would make business and indeed functional sense to developing intuitive geo spatial modules that replicate and replace existing geospatial tools to address needs of semi aware users and attract non traditional users through differentiators like seamless availability, cost arbitrage and integration of non structured and non geospatial data for value enhancement. This added functionality will enable SAAS providers of the cloud computing sector to move users from “Where?” to “Aware”.