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Internet of Things (IoT)'s Impact on Big Data and Data Science

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"The Internet will disappear. There will be so many IP addresses, so many devices, sensors, things that you are wearing, things that you are interacting with, that you won't even sense it. It will be part of your presence all the time. Imagine you walk into a room, and the room is dynamic. And with your permission and all of that, you are interacting with the things going on in the room." – said Eric Schmidt, Google chairman.

If you are thinking that Internet has changed your life, hold on- the Internet of Things will change it all over again. IoT is driving a new tech trend and data science plays a vital role in this. IoT here means machines embedded with iBeacons or sensors that collect and store data for analysis. The tidal wave of big data generated from the Internet of Things will drive increasing demand for data analytics. This article sheds some light on what IoT brings to a data scientist’s table and what it means to the data science industry.

internet of things data science

Just imagine when you buy grocery from your App - the App sends you recipes relevant to the ingredients you have added in your shopping cart , your coffee cup tells you how much milk and sugar needs to be added to your coffee or how hot or cold you want it, the alarm clock triggers an action to your car on when to start or a mobile app that reminds you to keep your wallet, id card, laptop and other important items for the day before you leave for office. Yes, all this would be possible in the next 5 to 10 years as IoT meets data science and advanced big data analytics.

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"Your apartment is an electronic orchestra and you are the conductor. With simple flicks of the wrist and spoken instructions, you can control temperature, humidity, ambient music and lighting. You are able to skim through the day's news on translucent screens while a freshly cleaned suit is retrieved from your automated closet. You head to the kitchen for breakfast and the translucent news display follows, as a projected hologram hovering just in front of you. You grab a mug of coffee and a fresh pastry, cooked to perfection in your humidity-controlled oven, and skim new emails on a holographic tablet projected in front of you. Your central computer system suggests a list of chores your housekeeping robots should tackle today, all of which you approve."

This extract from Eric Schmidt & Jared Cohen’s book “The New Digital Age” perfectly describes how your life will be in 2033.From being able to turn off the lights at home from 10 miles away to leaving it to the refrigerator to decide when bread and butter needs to be replenished- IoT and Data Science are tidal waves of the future, all set to take the big data world by storm.

2015 was the year of “IoT” hype but we can expect 2016 to be the year of “IoT” filled with technological advancements, innovations and excitement in the analytics space. By end of 2020 there will be tens of billions of devices connected to the Internet ranging from network sensors to industrial robots, generating a total expected value of $14 trillion. The large volumes of data generated from these IoT devices needs to be analysed to extract knowledge and valuable information hidden in it. This is where IoT intersects well with the data science process as the two emerging big data trends “IoT” and “Data Science” perfectly fit one another. The novel and innovative IoT demands, over the next 5 years will make the job role of a data scientist undergo various identity changes making it practically difficult for companies to do analytics.

Data Science for IoT -Similarities and Differences Unleashed

 

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Big Data IoT Data Science

We all know that Data Science is a multidisciplinary field that involves extracting knowledge and valuable insights from structured or unstructured data but is data science for IoT different or is it the same as the standard data science process - is the question in hand. The math concepts for data science include Bayesian Statistics, Matrix Algebra, Optimization techniques and implementation of supervised and unsupervised algorithms. All these are applicable to IoT datasets as well. Programming for IoT datasets involves using tools like Python and R - similar to other datasets, but typically for time series applications.

There is no standard methodology to solve data science for IoT big data problems. The major difference for data science with respect to IoT is, that it mainly emphasizes on cognitive computing, real-time processing, time series data analysis, geo spatial data analysis, deep learning, edge computing and in-memory processing. Data Science for IoT requires data scientists to be well-versed with various strategies for integrating hardware and sensor fusion (complex event processing).

A data scientist is likely to be fascinated by working on IoT problems as it is a cutting-edge specialization with lots of rich data, excitement and hype. As IoT data is collected through autonomous digital sensors, data scientists can tackle an IoT project with practical assumptions for capturing data at high levels, to ensure information quality. IoT sensors collect diverse data points related to user interaction, location and device operation. Most of these data values from sensors, are encoded logically but in rare cases these are custom made with data science in mind, so that a data scientist assumes a better starting point to proceed with analytics.

The overall signal quality of IoT big data puts a data scientist’s quantitative skills and creativity to test, as they might be required to jump out of their comfort zone to provide valuable insights through advanced signal processing methodologies. The temporal nature of signals from IoT sensors are more challenging to a data scientist’s job role working with IoT domain, as they have to interpret a relevant high-level enquiry into something that is measurable requiring them to have workable statistical vocabulary.

Similarly, anomaly detection and time series analysis are a vital part of data science in the IoT, unlike featurization which is enough to tackle other kinds of data science projects. To keep up with the speed of data generated by IoT connected devices and business requirements, data scientists have to automate their quantization strategies. Data scientists looking to work in the IoT domain should be able to learn and adapt to what works and what does not because the rules of data science for IoT are still on-board.

Challenges with Implementing Data Science for IoT

Building data products and running analytics applications with IoT is not a simple task as businesses have to consider the performance limits, data type and volume changes, the cost of skilled data scientists and dynamic modelling. Organizations engaging in IoT must carefully consider how to analyse the data they gather from the effects of IoT connected devices, as there are huge opportunities for cost saving and enhancements in various business functions like manufacturing, product development, service ,etc. However, gathering insights from IoT datasets does not come without its challenges-

Security

Security is a major concern with increasing number of sensors and other connected devices, as IoT devices expose off the limited privacy users are left with, in the digital era. Organizations need to take preventative measure to avoid any data leakage, that can attract undesirable attention from cyber criminals. Before putting up an effective data science strategy in place by reducing the chances of data storage in insecure locations or by providing access to external third parties.

Scale

Another major challenge surrounding IoT big data is its scale. IoT devices and sensors generate big data every second and organizations have to implement a better approach to logging this big data. If the scale of data is not taken care of with a better approach then it can saturate even the bulkiest big data solutions available today.

Increase in investment Cost

Cost definitely might not be major concern for businesses planning to implement IoT but it’s still critical for few of them. It is true that implementing IoT ponders revenue growth but not to be ignored is the fact that, it might increase investment costs based on the complexity and novelty of the data science solution being developed.

Lack of Data Science Skills

There will not be enough skilled data scientists graduating from the universities who can make sense of the IoT data, to help organizations make smarter decisions.

With the ability to collect so much big data on user activities and behaviour at micro-level - data science for IoT is paving way into the broad class of robotics. As manufacturers rush to ship novel innovative products like virtual reality headsets, drones, cameras, self-driven vehicles prickling with sensors, that collect data and share it with analytics applications -organizations need to develop better strategies on how to manage the flow of increasing big data for data science and analytics to derive valuable business insights.

As IoT triggers mass extinction, companies that have strong data science teams to discover more information by inference, are likely to survive in the IoT evolution or they should be ready to face the risk of being left behind. As the big data exploded by the Internet of Things grows at a compound annual growth rate of 66%, there are immense opportunities opening up for data science professionals. As organizations step up their data science strategies to tap the new data streams from IoT devices - there will be need for more people who can get hitched with the data from fitness Wearables, smart watch, smart TV’s, driverless automobiles and make sense of it.

So are you ready to get your piece of the overall data science pie for IoT? If not, then start honing your data science skills in Python and R now to have an edge over your peers - as 2016 will be the year of IoT and Data Science.

 

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