Rise of the Machines: Why IoT Will Power Big Data Growth

To find out which companies and industries are best positioned to benefit from big data, we may need to turn to science fiction. Take the autonomous factories of I, Robot, the internal scanner of Fantastic Voyage or the driverless cars of Total Recall. Just don't mention The Terminator.

Although the fact that we can tell machines what to do is nothing new, especially for companies such as Siemens, Proteus and Rio Tinto, the game-changing aspect is that now the machines can talk back. The Internet of Things (IoT) relies on the connected nature of "things" to users, organizations and other "things" themselves. As machines are becoming smarter, how is this collectively making organizations smarter? Can we make better decisions based on what these visions from the future are telling us?

3 Components for IoT and big data success

Creating a successful big data capability to process and understand sensor data requires an organization to possess some key characteristics.

Innovative culture: a drive to invest in new technologies and strategies; an organizational commitment to inventing new ways of performing tasks.
Data flow: a source of information, often collected across years of operation, which can be analyzed to gather insights.
Implementation method: a process that can accept analytical output and maximize the impact of a big data project.

Industries operating in the IoT space often involve manufacturing, logistics or any process that uses machines to provide information, internal or external, to a system. Companies focused on IoT generally possess the right balance of an innovative culture, an established data flow and an implementation method. Specifically, these companies exhibit the following traits.

Innovative culture

Business leaders who have pushed for the adoption of new technologies are the same individuals who can drive the adoption of big data analytics.
Leaders who can recognize a clear return on investment from the application of data may be willing to make the strategic decisions necessary to stay ahead of the pack.
Investments in the capture of historical data and operational reporting have already proved their value, and the advent of big data will only increase it.
Engineers are equipped with the analytical and mathematical skills to implement analytics and contribute to the evolution of data-driven decision making.
Process-oriented and quantitative personalities welcome suggestions to measurably improve current operations (and create more green on their status reports).
Projects and analyses may already be underway on small scales that can be expanded to produce wide-ranging returns.

Data flow

Investments in sensors, as well as traditional process management, have facilitated the existence of a large amount of machine-created data.
Operations (maintenance, alarms, etc.) that use sensors for traditional reporting create data that may be very useful but often is under-used.
Semi-structured log files of maintenance or dispatch systems may not currently be used, but they can be monetized with text analytics.
An expanding number of external sources (e.g. manufacturer information, market data, weather readings) can be integrated.
Mixing data from multiple sources is tricky, but if you have metronomic sensor data it helps.

Implementation method

Machines are ideally suited to process quantitative information, as well as offering the potential for feedback to the analytical process.
Machines don't lie, so when you perform A/B testing to examine hypotheses and prove theories, your results are conclusive and can identify the return on investment in big data initiatives.
Continuous feedback loops help in tweaking testing.
Industrial systems and networks can be accurately mapped as a platform for inducing change.
Integrating analytics into complex systems can be a challenge due to the complexity of the relationships between actors. In an industrial system of machines, these relationships can be quantified and mapped.
Well-defined operations may also aid the creation and scoping of big data projects.

What's in store for those focused on IoT?

On top of the advantages that IoT-focused organizations benefit from, the market pressure for using data will only increase. Early adopters and first movers will be able to operate more effectively to gain a competitive advantage. Price-constrained industries such as utilities, mining, and oil and gas must keep costs at a minimum; user-facing industries such as healthcare and consumer packaged goods need to provide top end-user experience; and supply chain and logistics operations across a range of industries need to operate at the highest levels of efficiency.

Those organizations connected to IoT are uniquely positioned to take advantage of big data and maximize its potential. The rise of the machines can be harnessed to allow us mortals to make better decisions and create a revolution in the way we analyze information.