Covering Disruptive Technology Powering Business in The Digital Age

The convergence of IoT and Big Data 
The Internet of Things (IoT) and big data are converging to make business a lot smarter. The eruption of information is being powered by the explosive growth of connected devices. According to IDC, by 2018 there will be 22 billion of these – especially GPS trackers and phones. The latter have propelled the IoT to the highest point of the 2015 Gartner hype cycle: the peak of inflated expectations.

Enterprises and government agencies are using analytics to find new ways to leverage big data from these disparate information sources to enhance service, improve safety, increase efficiency and drive growth. Organisations typically have multiple goals for big data initiatives, such as enhancing the customer experience, streamlining existing processes, achieving more targeted marketing and reducing costs. Data analytics allow them to draw more insight from large and complex datasets, helping to predict future customer behaviour, trends and the likely outcomes of complex initiatives.

However while value can be found within each organisation’s dataset, the real value is located in the intersection between data sets. And for that to be released, three fundamental conditions have to be addressed: aggregation and sharing, analysis and protection.

The algorithm economy
It’s an era of extreme information and, while companies will have more (and more accessible) data, the data mountain itself presents a problem. To derive any value from it, organisations need to be able to draw conclusions from it in real-time. If a business can’t derive value from its collected data, then collecting data is just another cost.

Keeping on top of the data mountain is all about processing it in useful ways. That means asking the right questions, and that’s at the heart of what is now being called – by Gartner Group in particular – the algorithm economy.  Powerful and complex algorithms underpin the analytical tools that turn data into insights, help manage access to extreme information and drive innovation in rapid information processing.

Transportation systems, in particular are set to benefit from the convergence of big data and the IoT. Companies are increasingly using embedded sensing and tracking technologies to reduce business costs and improve safety. That’s generating a mass of data that algorithms can mine to show how to derive value that’s not yet being realised to help fleets of taxis, couriers, trucks and buses optimise route planning, improve service delivery and reduce fuel consumption. Simultaneously, government can make the data available to help consumers decide on their own best transportation choices.

Yet while other industry sectors such as aviation are already reaping the benefits of big data investments, mining data and distilling it into useful, publicly available conclusions remains a real challenge for government and the transport industry.

Turning mountains into vantage points: 
That is changing. Increasingly integrated technologies mean more devices talking to each other creating many mountains. The challenge is to bring these together. No matter how vast their individual proprietary data sets, companies working in isolation will not be able to get full value from the totality of information that is being captured. The road transport industry needs to come together as a whole and aggregate the information in way that can used by the entire industry, and not owned by any one player.

Stand-alone applications and infrastructure are evolving to become more integrated and communicative, and technologies are already available that will allow companies to aggregate staggering amounts of data from multiple sources, going well beyond the data held in their proprietary fleet tracking and other systems. If you can integrate feeds from CCTV, toll systems, Wellington’s intelligent motorway system, accident reports and other data sources you’d have an unprecedented collection of data waiting to be mined by public and private organisations to capture a more holistic view of real-world conditions that affect their operations.

Government organisations can mine this mountain of data to form a historical perspective to help design safer roads, such as identifying contributing factors to accident hot spots. But the real power is in using analytics to predict issues before they happen, such as being stuck in a traffic jam, and automating preventative action such as re-routing drivers or opening additional lanes.

Such action should help transport companies avoid traffic jams and crashes to save fuel, time and labour costs. Similarly, providing such information to commuters would allow them to opt to take public transport in response to traffic alerts.

The final requirement is protection. Privacy concerns about data security or data usage represent a liability, where disclosing confidential information could compromise the safety of employees, breach legal statutes or inadvertently relinquish an advantage to a competitor. This requires understanding the difference between collective anonymised data and an individual’s information and the appropriate use, access controls and data security required for each. But if the road transport industry can meet these requirements, today’s data mountain will provide a lofty vantage point to set future directions.

This article was originally published on www.itbrief.com and can be viewed in full

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