With an expected 20.8 billion connected things to be in existence by 2020, these devices are producing data at an astonishing rate in the UK, writes Phil Codd, pictured, Managing Director at SQS.
The Internet of Things (IoT) is having an increasing impact upon our ever-evolving lives. However, what many take for granted is the fact that algorithms are at the heart of the devices generating this data. Algorithms are essential to the running of everyday products, from the brakes in your car to trades on the stock exchange, creating our economy’s secret weapon of success and mass destruction in equal measure if risks aren’t mitigated for.
Behind the invisible cogs lies hidden value. In the wise words of Peter Sondergaard, Senior VP of Gartner: “Data is inherently dumb – algorithms are where the real value lies. Algorithms define action”. Knowledge is power, and algorithmic data analytics unlocks that power, meaning businesses are able to maximise on data driven decision management. In turn, they can keep ahead within a competitive landscape, where an ill-informed decision could be costly to both reputation and profitability.
To ensure the worst doesn’t happen, businesses must have experts in place who can analyse and manipulate the right algorithms to produce the most beneficial actionable data, to unlock its future success. Once a high level of understanding is gained, businesses can capitalise even further by sharing algorithm assets through open sourcing these across the market place. Many company’s may be reluctant to share such assets, but sharing can be useful as this will co-dependently enable you to benefit from feedback and improve your original algorithmic assets as a whole.
An important area we must address is the fact that with the booming algorithmic economy that has been created around IoT, there also comes an increased risk of cyber attacks. Individuals with malicious attempts could tamper algorithms and essentially bring a business to its knees. Ensuring security is considered as a crucial factor when developing algorithms is imperative to preventing such situations. The complexities of today’s threats means it is no longer viable to simply add on a security layer at the end and rely on testing just before the project goes live – this is too little, too late.
Given the importance of security in today’s interconnected IT landscape, most software development lifecycle models require security checks to be present at all stages. This ensures security is baked-in from the beginning, but we also need to recognise that security is not a static attribute of quality and once software is released its security must be continuously reviewed to ensure that it is not affected by newly discovered vulnerabilities. By doing so businesses can unlock the true benefits of the increasingly algorithmic economy, whilst mitigating the risks.
