What is Big Data
The term ‘Big Data’ is simply a shortcut to mean ‘a lot of data’ in the technology vernacular. To quantify ‘a lot’ we need to step into the realm of measurements like exabytes; a number not easily fully appreciated. The best comparison we’ve seen is that of a measurement with which you’re likely more familiar – the gigabyte. If one gigabyte was the size of Earth, an Exabyte would be tantamount to the size of sun.
Simply, the value of big data is the ability to capture a lot of information, do so continuously and from a multitude of sources, and draw correlations and inferences from possible relationships and the subsequent interpretation. A prime example of big data in use is the use by social media organisations. They’re able to make recommendations and adjust your personal feed by analysing hundreds, if not thousands, of data points from your profile and behaviours. Then through their various algorithms, take inputs from the collective of other profiles on their platform, they can enhance – the moral efficacy and actual social value of this aside – your user experience.
Modern storage capabilities have grown exponentially in the past few decades, with densities adhering to something approximating a parallel to Moore’s Law. So over the past twenty or so years, we’ve been acquiring and storing data at an unprecedented rate. It is, however, only more recently that the computing power to process these oceans of data has been available to any organisation, let alone entities outside scientific research or national security. With the advent of new tooling in the form of Artificial Intelligence (AI), more specifically, the branch of AI called Machine Learning (ML), underpinned by advances in computational power, the type of data being created can now be put to practical use.
This now-possible usefulness is further bolstered by the fact there are now professionals available with the requisite skill sets to make use of this data to create insights – relatively rare even 5 years ago. Moreover, there’s been a dramatically increased accessibility and availability of the type of computational power necessary to processes made possible through cloud technologies. This means no longer having to use dedicated infrastructure on-premises like research-orientated data centres that were at the forefront of early adoption in the use of big data; a larger user base, means more rapid evolution in a field.
Big data is assisting in deciphering the billions of real-time data points collected by Internet of Things sensors. Big data analytics solutions organise unstructured data collected by IoT devices into consumable insights that teach businesses on how to optimise their processes. Big data and analytics solutions are advantageous for shepherding the volume of data flowing in from IoT devices. Developers specialising in the Internet of Things are developing platforms, software, and applications that corporations and organisations can use to manage their IoT devices and the data generated by them.
Ostensibly, the Internet of things is a series of tributaries leading into the ocean of big data. While not the only source of data, the tens of billions of already connected sensors and devices, are a large and growing percentage. Big data and the Internet of Things have an interdependent relationship. Big data is helping to improve decision-making by analysing the data generated from connected devices, and IoT is generating the very data that big data needs. The more the internet of things grows, the more demand there will be for big data capabilities, and the more valuable the data analysis, the more IoT devices will be deployed.
IoT and Big Data Working Together
As a result of this interplay between the two disciplines, there are myriad of benefits. To begin, there are two groups of stakeholders: enterprises that can benefit from the information supplied leading to increased efficiencies, and the end-users that subsequently have improved services. Organisations aiming to incorporate IoT into their networks are seeking increased revenue, increased production, increased efficiencies, and cost reduction. The advancement of big data technologies benefits IoT-rich companies, as they strive to strategise how we accumulate, view, and utilise data.
We’ve already seen countless examples of IoT and big data working together for collective benefit; even while these interrelated disciplines are still arguably in their infancy. For example, scientific research by The Climate Corporation has seen their IoT and data analysis capture and interpret data in the context of previous historical data. This is traceable to dramatically improved farming efficiencies in and of itself by influencing the time for planting and harvesting etcetera. As big data further marries up these insights with still other data sources such as those from AgEagle Aerial Systems, the IoT data from these drones provides even more granular information down to the specific field, on a specific farm, allowing for optimal use of herbicides and pesticides while each crop is growing in a climate-optimal window each year. The financial and environmental benefits are dramatic.
The intelligent use of IoT and big data together by so-called, ‘smart cities’ can have a pronounced impact on the quality of life for their citizens. By using CCTV cameras to monitor for movement patterns identified as potentially criminal, appropriate actions can be taken. Through IoT enabled traffic systems, big data analysis leads to movement through the city being faster, smoother, and causing less pollution. In Valencia, Spain – a poster child for the movement – IoT and big data helps organisations, public utilities, and the city government make decisions based on areas as diverse as shipping and logistics, public transportation timetables, energy use, and even automation of the systems that help prevent storm damage.
IoT devices combined with Big data are being utilised inside healthcare to anticipate epidemics, treat disease, enhance quality of life, and minimise avoidable deaths. In a clinical setting, devices like ECG, CAT, MRI, etcetera, are being combined with big data and used in evidence-based care. This has proven capable of delivering needed outcomes for each patient while improving safety and efficacy of therapeutic interventions. They might together assist the sector in addressing issues connected to healthcare quality variability through initiatives like gene therapy – powered by deep learning and IoT enabled monitoring – and in avoiding the escalation of healthcare spending. Additionally, consumer grade products are being used to educate users about healthy lifestyle choices that promote well-being and the consumer’s active involvement in their own care.
IoT Security and Big Data
We’ll be exploring the security and privacy implications of this intermarrige between IoT and big data in coming weeks, but suffice to say, there are enormous challenges presented by IoT.
CyAmast addresses the problem of securing the essentially chaotic Volume, Velocity, and Variety (3Vs of big data) of the data that quintessentially defines ‘big data’, as it pertains to IoT. The traditional measures for security aren’t up to the task as we’ve previously discussed, so our solution takes a different approach. Improved security and data privacy is certainly an important facet CyAmast addresses. Worth noting in the context of this article, is also the additional benefit of ‘cleaner data’. By identifying and actioning anomalous data, compromised or malfunctioning IoT devices can be taken offline or segregated until addressed. Obviously this provides a safer environment, but it also means that the ‘data in’ for analysis by big data systems will be of higher quality. Through reducing the ‘noise’ of anomalous data that’s being fed into the machinery of your organisation’s big data, CyAmast reduces risks while also ensuring the purity of the data being analysed.
CyAmast means safer, more secure, more private, and healthier networks and data.
