There are five key factors that will determine where you do your analytics in the future. The first is speed. Do you need the information now, as in right now, or can it wait? The quicker you need answers, the less likely you are go to the cloud. Remember the 2017 eclipse? In California, it removed about six gigawatts of capacity from the grid by blocking the sun, according to CAISO, the operator responsible for 80% of the state’s power, or enough for more than four million homes, and then rapidly returned it within a few minutes. CAISO received power data from the grid’s generators every four seconds to prevent the fluctuations that can cause problems.
The second factor is reliability and safety. Oil companies and mining operations, for instance, are adopting cloud technologies for deep analytics. It makes sense because you can spin up thousands of servers at once to tackle massive computing problems, but the answer isn’t needed urgently. When it comes to ‘live’ operations, however, those remain local.
The third factor is bandwidth and bandwidth cost. If it’s a torrential amount of data being generated, and you don’t need all of it to make a sound analysis, then just send summary data. A ‘smart factory’ might track 50,000 sensors and generate several petabytes a day. Even a standard office building will generate 250 GB or more. Rather than try to analyse data in the cloud or control thermostats remotely, a lot of these jobs will be cheaper and more easily accomplished on a local level.
The fourth factor is the location of your challenge. Who needs the data? Is it the engineers at the plant or a whole slew of different parties, organisational departments or geographically dispersed stakeholders? If it’s a local problem, it can be stored and analysed locally. Local successes, of course, can then be replicated and shared across the enterprise.
The fifth and final factor is the complexity of your challenge. Are you examining a few data streams to solve an immediate problem such as optimising a conveyor belt in a factory or are you comparing thousands of lines across multiple facilities? Are you looking at a patient’s vital signs to determine a course of treatment, or are you developing a new therapeutic that requires studying millions of different proteins?
Depending on the nature of the analytics in question, many of these factors I’ve discussed will overlap; some may take a higher priority than others. In the end, as with many things data-related, when it comes to where all the world’s data and applications will leave, the answer isn’t so clear cut.
Michael Kanellos is an IoT analyst at OSIsoft