Expanding the Internet of Things: Big Data for Government and Communities
When we talk about IoT, we always end up talking big data. When we talk big data, we can't help but bring up IoT. Although it's now a lot cheaper to manufacture sensors and connect them to almost entirely pervasive networks, our ability to capture, combine and analyze massive amounts of data has increased in step with innovations in sensors. The inextricable link between IoT and big data means that, for many organizations -- state and local governments especially -- IoT is more feasible than many expect.
Most of the time when people present IoT solutions for governments, they present big picture solutions that, if they are not in fact, at least sound expensive. State and local governments are still by and large very much constrained by budgets and resources and large investments into technology that reeks of the "new" and "unproven at this level" seems scary: until they realize they likely have half the equation in place already.
The fact is that a lot of the sensors and data people talk about when they talk IoT for governments and communities are already installed and in use, whether they be automated parking meters, timed traffic lights, networked utility meters or garbage collection metrics. What's required to turn these disparate data collection systems into IoT isn't necessarily heaps and heaps of money and resources, but rather strategic investments, big-picture thinking and leadership to find and create opportunities to use all this already available data to provide value to communities.
By starting small and thinking of specific problems you want to solve, you can take stock of the data you already have on hand and use big data analytics to turn your existing systems into IoT solutions for your communities.
Big data can provide communities with on-the-fly capacity planning, insight into inefficiencies, and a better understanding of the ways people work and live in their community to deliver them better services. For example, in L.A., where upward of 40 to 50 percent of downtown traffic is created by people driving around looking for a place to park, the city was able to take already available data (from parking meters that were already "smart and connected" to provide credit card and other payment options) and combine it with location data from a mobile app to help residents and visitors navigate to the closest available parking spot, dramatically reducing the number of vehicles on the road weaving from block to block to find parking.
So what does this big data approach to IoT take? We've come to believe that it involves a system that has these three features:
- Data-friendly: It should be easy to add data to the system -- if departments, utilities and other partners can't easily add their data to your pool, you likely won't get enough of the right kinds of information you need to produce good insights and correlations.
- Scalable: Trying to predict the future (and buying enough hardware, storage and tools for it) is hard -- that's why most successful IoT programs start with small experiments and projects then scale up as demands on the system increase.
- Accessible: Both technical and non-technical people should be able to easily access, digest and manipulate the data -- bottlenecks on either end can mean slow delivery, or worse, death of the project.
There are a number of technologies that meet these requirements, and many can be combined to provide the full set of capabilities (for example, we've had success using tools such as Hadoop and Platfora in combination to process and then manipulate state and local government data). Using real-world data and combining it to provide insights is the heart of IoT, and governments and communities with the data piece taken care of are excellently positioned to use big data to get initiatives through to the finish line.
The future of IoT isn't only multicolored lights or a thermostat you control from your phone. We're seeing that the future of IoT will include the combination of public systems and data sets that already exist with big data solutions to improve the lives of citizens and their communities.