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There are fundamental differences between the processing approaches of traditional data and data streams arriving from the Internet of Things (IoT) devices or sensors. Static or traditional data analysis is a linear process, while IoT-generated data analysis is not. The technology and skills required to analyze IoT-generated data are totally different.
An important difference between traditional data and IoT-generated data is that the latter can be delivered in real time, which is critical for certain industries like banking, telecom and defense. Static data, on the other hand, does not provide real-time data, but still has a lot of utility. That said, IoT-generated data has been the center of attention for quite some time and there is a lot of buzz around it. That, however, does not mean that traditional data’s time has passed.
What Are Traditional Data and IoT-Generated Data?
Traditional or static data, simply put, is data that does not change. Let us understand this with an example. You are filling out a form where you are required to select your state of residence from a list. The list does not change because the number of states in the U.S. does not change (or, hasn’t since 1959, anyway). Now, this list of states is maintained somewhere in the system, and since the list does not change, it can be safely said that the data is not accessed or processed frequently.
IoT-generated data is the data generated by the sensors fitted into interconnected devices. In the IoT scheme of things, each device will have an IP address so that it is able to communicate with other devices having IP addresses. It can exchange data, for example. Now, these devices may be connected to a server which is collecting data constantly from these devices. For example, your smartphone may install an app which collects information on your health and sends it to a server that may be accessed by a hospital. So, you can imagine the amount of varied data flooding into the server every minute. The data is constantly and relentlessly changing. IoT-generated data, in a sense, is also dynamic data because it tends to change.
Given the totally different nature of the data, it is obvious that the approaches to store and process the data will be totally different. The paragraphs below discuss the main differences between traditional and IoT-generated data.
Differences Between Traditional Data Analytics and IoT-Generated Data Analytics
Since both types of data are different, the fundamental methods of storing and processing have to be different. The IoT-generated data has generated a lot of attention and praise, to the extent of some suggesting that traditional data has no place in the industry any longer. That is not true. The salient differences between the two types of analytics are discussed below.
Static Data is Not About Real-Time, While IoT-Generated Data Is
Let us understand this point with a couple of use cases. Let us assume that a bank wants to formulate a credit card protection policy and for that, it needs historical data on credit card fraud incidents. So the bank may want data such as time and date of the incidents, transaction details, ways the credit card details were accessed fraudulently, regions most prone to fraud and the transaction amount. This data is related to the past and does not change. It will not be accessed a lot — the bank will thoroughly analyze the data once, or maybe twice, and not more. So, you can see that static data does not provide any real-time intelligence.
IoT data, on the other hand, does provide real-time intelligence. Let us understand this with the example of a parking space management system. We know that in the IoT scheme of things, all devices must have an IP address, with the help of which, the device will exchange data or talk to other devices. Let us assume that the civic authority in a city that has seen an explosion in the number of private vehicles is having a difficult time allotting parking spaces to the cars. Given the shortage of parking spaces and the growth in the number of cars, optimal usage of the available parking space is extremely important. So, the cameras in a parking space can be fitted with sensors that can capture the details of a parked car and send the details to a server. So that parking slot will be shown as “occupied.” The moment the parking slot is vacant, the server will receive a notification and the next allotment can be done. Similarly, the number of vacant parking slots will also be known.
Storage Mechanism
The storage mechanism is driven by the unique requirements of both traditional data and IoT data. Traditional data is finite, and until now, many organizations have had their own data centers to store traditional data. However, traditional data centers are an expensive proposition as well. With the advent of the cloud, many companies are gradually opting for cloud storage. While traditional data may be a good fit for traditional data centers, IoT data is best accommodated in the cloud because the volume continues to increase, and it is an extremely expensive idea that the traditional data center storage should keep pace with IoT data. Cloud storage is flexible and the best option for storing IoT data.
Processing Mechanism
Traditional data can be processed with the help of standard querying languages such as SQL and analytics can be created with the help of standard programming languages. It does not take any new learning to perform traditional data analytics. The situation is a bit more challenging with IoT data, also referred to by many people as big data. Hadoop, to date, is the most popular framework for big data processing, but many are still tentative about it. Querying IoT data is not an easy task because the technology has not yet evolved and there is a lot of investment required to make the tools user friendly. The nature of IoT data is quite different from that of traditional data, and so the industry is still finding ways to get good analytics at lesser investments.
Conclusion
Their differences notwithstanding, traditional analytics can in some cases complement IoT analytics. In a sense, IoT data also becomes historic data after some time. The IoT onslaught notwithstanding, traditional data analytics is not going to go away anytime soon. IoT data and big data analytics is still being viewed tentatively and there is a lot of caution. It takes time for industries to adopt something that is new, complex and requires investments. Traditional data analytics is proven and established, on the other hand. Though it is an interesting situation, it seems that after a few years, IoT is going to gain a lot more credence and companies are going to shift away from traditional data analytics. For that to happen, IoT data analytics infrastructure needs to really mature and find acceptance. Change is — always — a slow and a complex process.
