1. 5 Obstacles Big Data has to overcome in an IoT context

     The internet of things is coming. We know that. Advancements in Low Powered Wide Area Networking (LPWan) and IPv6 technology ensure that everything from household objects to industrial technology will be connected using a WiFi connection. This knowledge provides us with a rather unique problem – what are we going to do with the sheer amount of data that these devices provide? Researchers regularly cite figures like 30 or 40 billion connected devices by 2020, so how will we process all of it? These are obstacles that Big Data analytics firms have to deal with in the coming years.


    1. Data Security


    The upcoming explosion of data is a scary prospect, and keeping this data private and secure is an even bigger worry. Consumers may be okay with companies using the data to target advertisements directly towards them – smart thermostat detecting that your house is frequently cold? Amazon adverts for blankets pop up on your smart TV – but what about when a malicious individual gets hold of it? In a consumer setting this could mean someone finds out when your smart home locks are usually turned off, but it has even larger implications for enterprise. Large retailers, banking institutions, grocery stores- all would be the recipients of harsh cybercrime invasions in an attempt to get at the big data that IoT provides.


         2. Number of tools and technologies


    Every day a new open-source project is announced in the Big Data space, promising companies the most cutting edge data analytic software available. And this is evolving quickly. The access technologies surrounding IoT are moving towards standardisation, but the proprietary software which is used to analyse big data is not. What this means for enterprises is that if they swap their product they may have to completely overturn their entire infrastructure which deals with the aggregation and processing of IoT-fueled big data. This is a cost which companies will avoid until a solution is provided.


          3. Overwhelming amount of data


    Companies often brag about the size of their datasets housed in massive warehouses across the globe which are bursting full of consumer and business data. This can just as easily be a problem because with that much data, only extremely specialized analysts will be able to derive some form of value from it. Beyond this the companies will have to spend time and money cleaning and processing this data before they get anywhere near anything resembling pragmatic usability.


        4.  Experimentation but little widespread adoption (until now?)


    At least in Europe Big Data is often referenced in the same sentence as ‘experimentation’. There’s advances in software and hardware weekly, but large industry sectors still rely on their tried and true systems which they’ve had in place for years. The hesitation to jump on these new technologies likely stems from the potential risk if it all goes horribly wrong, but with IoT forcing Big Data in to the limelight will they have much choice if the new systems are the only reasonable options?


        5. Addressing data quality


    Even if the IoT allows us to be constantly inundated with data concerning everything in our lives, do we really want that? To use a recent consumer example, we can look at the Wynd air purifier. This collects data on the dust content, smoke and pollution in the air around your desk. An interesting idea in theory, but how can we call data of this sort relevant? If we lived in a dangerous place this might be helpful, but the idea is that these gadgets are to be used on your office desk. This isn’t an example of big data until we have thousands of these gadgets providing a constant stream of data to ourselves and big corporations. What will they do with it? What will we do with it? That is still to be seen.

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