The word Data and Data Science have taken the business world by storm. Nowadays, improving business productivity and performance greatly depends on collection and analyzing data. Businesses have been processing data for ages but the introduction of Internet of Things (IoT) has been a game changer. Data collected through IoT is analyzed using different techniques as compared to that collected traditionally. Furthermore, Data Scientists require more sophisticated skills for analyzing IoT data.
IoT has definitely caused a lot of hype across the business industries. It is also a rapidly advancing field so keeping up with the latest trends is a must for many businesses. The roots of IoT are spreading far and wide, so it is quite easy to come across the term. But is not just another complex technology that businesses are using to outsmart each other. If utilized the right way, it can produce an unparalleled intelligence for a business. And as usual, this depends on how businesses process the excessive inflow of data from IoT software and hardware.
A business has multiple sources of generating data but IoT changes the value of the data. In comparison to traditionally collected data, one generated through IoT is in real time. This is immensely useful for industries that can flourish given a constant stream of freshly generated data. Another simple way to differentiate between traditional and IoT-generated data is to say that the latter is dynamic. Traditional data does not change. For example, if a device is used to select a city from a specific country, the list of the cities is going to remain the same. On the other hand, if data is being rendered from a device that belongs to an IoT network, it’s going to have an IP address that delivers the information to other devices with IP addresses. This information may be collected by a server that is accessible to different companies. So let’s say one of these devices is your smartphone and is collecting information on your online shopping habits. So if the server is accessed by an online store, they will have your information.
Now that you understand how IoT is being used to collect data, it is obvious that there is an excessive amount of data being collected at any given time. It also makes the data complex. Furthermore, the way IoT-generated data is stored and processed also varies.
Traditional Data Science has been supporting businesses on static data and it will be wrong to undermine its importance. However, the business world has become highly competitive and it is only going to intensify in those terms. For this purpose, newer and smarter technologies are needed. Therefore, businesses are now finding it necessary to invest in IoT Data Science.
In traditional Data Science, the analytics are static and restricted in use. The information that is received may not be updated so the results achieved after processing may not be smart or usable. On the other hand, since IoT data is being received in real-time, the analytics complement the latest market patterns. This allows making these analytics more actionable and intelligent as compared to traditional ones.
However, it is not so easy to process such complex information. There are many sensor sources within an IoT network. It becomes important to differentiate between the multiple sensor points and external components that may be responsible for adding to the data points. Also, as more technology layers are added or integrated with IoT, it becomes more difficult to structure and process the multitudes of incoming data. So yes, Data Scientists do need to up their skill in order to comprehend IoT-generated data.
As the popularity of IoT increases, a surge of data lies in the future. It is bound to the change the way we have viewed Data Science for some time now. The boom in data is not only going to require better infrastructure but smarter Data Scientists. We will need an infrastructure that can reliably process a constant stream of complex data. And it will be a serious waste of time and money if we can’t use this surge of data to make more and better sense out of it. Therefore, this explosion of data should be an opportunity for data scientists.
By taking advantage of Data Science for IoT, we are not only helping the business industries in terms of better performance and profit. In fact, we can create a better world. With smarter analytics, we can resolve multiple problems faced globally.
Data Science for IoT can help overcome some global challenges. It can help generate more accurate decisions. This means smarter solutions for the consumers around the world. IoT also creates the concern of privacy for people but if you add security technologies like Blockchain to IoT, then you can realize many benefits. With such combined technologies, it will become easier to protect patents and eliminate piracy. IoT Data Science also allow integrating artificial intelligence. This means that processing of data will become easier as devices will be able to self-learn about identifying patterns. The opportunities that can be exploited using IoT Data Science are aplenty.
The above discussion shows a picture of a very bright future. But it is not as easy to realize to make these opportunities happen. There are multiple challenges that arise from IoT Data Science. For example, how to make machines communicate important data if they belong to different manufacturers. The integrity of the data is a question too. Who will own this data and who will be or not be allowed to access the data? The frequency of data sharing needs to be managed as data nature varies. Then there is the ever-present challenge of customer and device security.
In conclusion, IoT Data Science are bound to change the future but not without hurdles.