Making sense of IoT with Big Data analytics

    Making sense of IoT with Big Data analytics

    The Internet of Things is ingrained in today’s technology world, and is already becoming big news across a variety of industries. IoT sensors can be found in devices from smartphones and black boxes to cars and ships – all of which highlights the need for data analytics.

    It’s fair to say that while IoT is a relatively new phenomenon, there’s already a lot of opportunity. However, that’s not to assume this sphere of tech is without its challenges, because there are loads to choose from.

    The most challenging of all has to be the fact that IoT is creating masses of data that has nowhere to go. Indeed, according to a report from Cisco, the Internet of Things will create 400 zegabytes of data per year by 2018.

    As a result, organisations need to decide how they’re going to manage and make sense of the large volume of data they collect from IoT. There is, you could say, a big challenge with Big Data.

    What’s Big Data?

    Big Data, quite simply, refers to large sets of data which are far too complex for everyday data handling applications. It has become relevant in the last few years as technology has become more advanced.

    Due to these advancements, we’re at a point where organisations on a daily basis are collecting vast amounts of data – from business transactions, social media and, indeed, the Internet of Things. This opens up a number of problems they have to face and overcome in order to keep operations running smoothly. These include challenges with analysis, curation, storage, data sharing and privacy.

    But with all complications, there are always opportunities. If businesses manage Big Data correctly and accurately, they’re able to make operations easier for themselves, make better decisions, spot trends and reduce costs and risk. The question is, how do they do this?

    Data science is a good starting point

    While the Internet of Things is still in the early stages of its evolution, now is the perfect time for businesses to start thinking how they’re going to cope with such tech and the data it brings.

    Mike Weston, CEO of data science consultancy Profusion, believes that the key thing for businesses at this stage is based around planning. He suggests: “Outline exactly what your goals are, and make it clear what you want to achieve. From there, putting in procedures and infrastructure that can collect information, clean it and make it accessible becomes a lot easier.”

    Planning ahead may sound easy, but collecting data is pointless unless you have the tools to make sense of it. But data science, Weston says, can help. He tells Internet of Business: “Unlike normal data analytics, data science can go well beyond a cursory examination of information to allow the real-time analysis of disparate data sets to reveal profound and well-hidden insights.

    “With enough information, future behaviour or actions can be predicted with a startling degree of accuracy. By collecting information from smart devices and marrying it with online behaviour, demographic information, economic news and other sets of information, a complete picture on an individual or a set of consumers can be created instantly. Then an ultra-personalised marketing campaign could be created.”

    Ian Murphy, a principal analyst at Creative Intellect Consulting, sees a hardware-based solution here. He says: “The answer is solar powered, Linux-driven micro servers that are capable of managing large amounts of data in-memory and refining the data before it is transmitted.

    “In the IBM Zurich research lab, scientists are already testing such micro servers that are no larger than a current DIMM module for a server, and when they go to production, they expect to reduce them potentially to the size of a compact flash card.

    “This would enable the micro servers to be embedded into a wide range of IoT devices in the industrial, safety and information gathering worlds where they could carry out point of acquisition work through the use of their compute power.”

    Sometimes you just have to make compromises

    Roman Blinds is a manufacturer of blinds based in Yorkshire. In recent times, it’s been experimenting with apps that work with automated blinds to help maintain a preselected temperature or light level.

    Although such apps would provide the business with mass market opportunity, it’s been struggling with the amount of data that comes from the sensors it’s been working with.

    To deal with this situation in the short-term, the firm has had to limit the sensors in order to prevent the blinds from adjusting. As well as this, it’s been putting the data into a simple database, which it says is “not the most efficient way of doing things”. Nevertheless, it’s not given up and aims to get its blinds to market soon.

    Collecting so much data can bring about privacy concerns

    Clearly, by combining different sets of data in a process of analysis, businesses are able to identify potential leads. However, when you consider just how much data can be involved, there are obviously going to be privacy concerns. Most of this date is based on customer and client information, after all.

    This is why transparency is extremely important. Weston says: “People need to understand and approve of what you intend to do with their information. Also, and it should go without saying, a business needs to offer something in return for collecting and using personal information, whether it’s an improved product, better customer service or more relevant marketing information.”