There’s a clever acronym for the convergence of social, mobile, analytics, and cloud (SMAC). With SMAC there’s a great deal of opportunity for improved efficiency of services for government and enterprises that engage a large customer base through websites, social media and mobile applications. Using these many touch points yields a large volume of data that can be analyzed for greater insight and improved operations.
Many are calling this the fifth wave of computing that comes after mainframes, mini-computers, personal computers, and the Internet. SMAC is aptly named as it is a disruptive change that can definitely cause a wake-up and some pain, particularly for smaller players. The primary issue is in the convergence, and the need for a good handle on all four of these technologies. This isn’t to say that smaller players don’t have access to all four, but it’s larger consulting and services firms that tend to command confidence.
You may consider that geospatial is outside of the realm of these technologies, but that’s not the case as location is integral to the analysis. Social media and mobile locations are key ingredients to understanding customer behaviors or the needs of citizens for government services. This spatial analysis is increasingly taking place on the cloud with tools that are often outside of the control of consultants.
To be sure, the people with geospatial skills can be adept at processing this big data, but there’s a need to get closer to the data in order to make this convergence work for you. Customers won’t be looking for maps or reports, they’ll be looking for speed of analysis that feeds a workflow in other systems in order to deliver an answer or service to customers more quickly. SMAC fits into a trend for greater automation and automated actions with quick response.
The coming of SMAC is being built upon increasing computing capacity that is only getting bigger and faster. The cloud offers infinite computing for simultaneous processing of large volumes of data for much quicker returns on such compute-intensive operations as complex spatial analysis and the visualization of that analysis. Combine that speed of analysis with the capacity to store the large volumes of social and mobile data, and you have the potential for more closed systems that belong in the hands of those that can integrate and create the insight that more customers are after.
If you use Google Maps on an Android phone, you may have noticed real-time routing to guide you around heavy traffic. The idea that you’re handheld can route you around congestion in real-time with a free service is absolutely mind boggling in light of the high cost that these services have commanded in the past. With this real-time example, that is built upon Google’s command of the speed of all other Android users around you, you start to get the idea of the control of data that’s needed in order to deliver this kind of useful service.
The increasing digitization of business processes is giving rise to new skills to architect the flow of information. The winners here will be those that can leverage all the inputs for improved understanding and better business efficiency. The map still has an important role to play here, but instead of a snapshot or even a real-time map, increasingly it’s about the predictive map.
Maybe you noticed the recent news that Google Earth Engine is being used to create malaria maps that predict where the disease is most likely to spread. This is the kind of insight that is certainly possible with all the data and our increasing understanding of our world. It doesn’t seem that tricky to combine insight into wetlands, rain and past infections to pinpoint this kind of insight for infectious disease. To provide value to those that provide a service it’s a matter of architecting similar systems that learn much more complex alignments than the optimum breeding grounds for disease.
The future for geospatial consultants and practitioners certainly lies in this new evolution of IT. Those that will thrive will stay ahead of this need for faster decisions and will acquire the tools and skills to be more predictive rather than mired in slow and siloed operations.