Advancement in technology is presenting itself in higher computation power in smaller devices with the much faster speed every day. These improvements have enabled an extra ordinary era of communication and interconnection between devices that is referred to as IoT. Increase in communication of small devices along with exponential growth of machine learning, has drastically increased the implication of Big data and analytics to play a significantly more important role in our everyday lives
Place of Data Analytics in IoT
Data analytics and big data are playing a crucial role in enablement of IoT. Data analytics is working as the driving engine of IoT. As more devices are connecting every day, an enormous data is generated with millions of devices connected. As the number of devices connecting grows, the amount of data generated increases accordingly. At this exponential rate of data growth, the driving force of extracting intelligent insight from Big data is data analytics at scale. In short, Big data is what enables IoT and it does that through data analytics. Thus, data analytics and Big data are interrelated.
Organizations depending on their nature of business might use different aspects of IoT. Hence, it is important to choose the right data analytics platform. The best analytics platform should preferably contain three factors of future growth, right infrastructure size, and performance.
That being said, for an organization undergoing a test phase or performance learning stage, perhaps a single-tenant physical server, which is dedicated to a particular customer and a bare metal server is the ideal fit. While, Hybrid is the ideal approach for the growth of infrastructure and future. Hybrid deployments consisting of dedicated hosting, colocation, managed to host, and the cloud combines the ideal features from various environments into a single optimal environment. To handle IoT data, Managed Service Providers (MSPs) are also working on their platforms. To cover the complete IoT domain, MSP vendors are working on the tools, performance, and infrastructure side.
As we know, all IoT devices produce large amount of data. Thus, organizations should be ready both in infrastructure an in skills to handle such volume of data. Preparation can include analytics, statics preparation, metric calculation, and event correlation. The data is not stream data every time and the actions vary in a normal big data scenario. So, to manage the scale of IoT data, building an analytics solution must be done keeping these differences in mind.
The injunction of data analytics and IoT
IoT is changing our everyday lives in an exponential speed. From one or two decades ago till today, many ordinary devices have transformed to something far beyond what could have been imagined in the past. Smart phone, smart watches, smart cars, smart home, smart medical devices, smart traffic control systems and many more are just some of the examples. All such devices consistently produce huge amounts of data that requires massive infrastructure to analyze in real time. This is where IoT meets data analytics and Big data. Enterprises need the help of data analytics to take advantage of all that data available around them through all those small data producing devices. IoT with the help of data analytics is changing enterprises actions from reactive to proactive and from fixing issues to suggesting solutions before issues arise.
It is projected that by 2020 over 20 billion devices will be used across the planet. Thus, as the data analytics becomes more and more important to organizations concerns regarding security of the data increases accordingly. Organizations must consider further secure platforms against unauthorized data access.
Upon implementing IoT companies should be aware of the amours amount of data that they will receive. Hence, combining this data with companies existing data would require a continue expansion of enterprises storage capabilities. Nowdays, companies are moving towards Platform as a Service (PaaS) model. Paas offers advanced scalability, compliance, architecture, and flexibility to store the valuable IoT data. The options for cloud storage include hybrid, public, and private models.
If organizations consist of data that is dependent on regulatory compliance requirements that demand high security or sensitive data, a private cloud model can be an ideal fit. In other cases, the organizations can opt for a hybrid or public model for IoT data storage.
The kinds of devices that comprise IoT and the data they produce vary in nature. This includes communication protocols, various kinds of data, and raw devices and these carry inherent data security risks.
This different universe of IoT is quite new to security professionals and thus the security risks may increase due to lack of experience. Any attack here could not only damage the data but also the devices themselves. So, the organizations must make a few changes to their security landscape.
The number of devices connecting to networks is increasing rapidly, for authentication purposes, every device must have a non-reputable identification.
A proper network segmentation and a multi-layered security system will would be a useful way to prevent attacks. An IoT system that is properly configured must follow fine-grained access control (FGAC) network policies to determine which IoT devices can connect.
The combination of network access and identity policies and software-defined networking (SDN) technologies can be utilized to generate dynamic network segmentation. The network segmentation based on SDN must be utilized for point-to-multipoint and point-to-point encryption depending on some PKI/SDN amalgamation.
Big Data and IoT are both still buzz words to many organizations. However, one important thing to keep in mind is that, the impact or cost effectiveness of implementing such technologies should be evaluated beforehand. Collection of an enormous amount of data solely, will not be an effective action. Rather, collection an effective analysis of this data may save company billions of dollars. IoT and Big data analytics work hand in hand to derive invaluable insight from piles and pile of information. Embarking into IoT without proper data analytics strategy would very much likely be no other that wasting companies’ resources.
IoT is becoming an integral part of technology growth in most industry sectors. For companies to sustain their competitive advantage in this era they will have to transform the way they store and use the data available to them and prepare for greater transformation in near future. Hence, enterprises should consider a well thought plan by detecting the needs, issues, risks and safety measures upon carrying out new initiatives in engaging IoT, Big data and data analytics.