It is believed that data is now considering as the new spark of this era. However, the world produces an average of 2.5 million bytes of data per day – that’s more than we are supposed to handle. On the other hand, exploiting this vast amount of data can reveal exciting information that can change the field. Globally, the I-o-T has also risen by a noticeable ratio and it is estimated that 1.6 trillion dollars will be spent by 2022.

On the other side of the coin, data is not small at all – revenue is expected to reach 265 billion dollars by 2022. Though, the data remains important to both. However, the basics of these two technologies are different because they are two completely different concepts. It would be better if we comprehend it deeply because our goal is to discover that it can be used together in different areas to gain a competitive edge. Data processing happens when data is collected and translated into useful information.

I-o-T and Data Science are Two Exclusively Different Concepts

Internet-of-Things (I-o-T)

Imagine a home where your home appliance is connected to the Internet. This is where I-o-T expands the connection from digital devices to physical Internet-connected devices we use every day. All remain online, allowing seamless connectivity to collect and share real-time measurement data and remotely monitor and manage.

When connecting to the I-o-T platform, data from all of these devices are integrated and analyzed. All the same, when ready, it can reveal user information such as models, potential problems, and even suggestions for improving their performance. For example, an I-o-T platform can continuously assess traffic conditions and advise a driver to change lanes or adopt driving methods that improve safety depending on the weather.

Data Science

The term, data science is used in most articles and publications, as it is generally understood. If your data is consistent, you analyze it and create dashboards and reports to better understand how your business is doing. Then they jump into the future and start producing predictive analysis. All and above, with predictive analysis, you predict possible future events and creatively predict consumer behavior.

Differences in Data Flow Management and Processing

All the same, data science contains a bulk of information. However, it does not allow this type of simultaneous data processing to process important information or models for real-time decision making. The analysis is performed later and there are delays or irregularities in receiving and processing data. The data collected is processed immediately in real-time to increase performance, correct errors, or diagnose the problem early. In I-o-T, the data flow is constantly monitored in real-time for accurate data-based decisions and analyzes.

Because the data collected is analyzed only in a certain range, data science solutions are mainly used in areas such as dimensions planning, forecast maintenance, and so on. Although I-o-T is a matter of time, temporary situations, such as traffic management, are considered to be used to simultaneously collect and process data to maximize and address their performance. According to the study, it is clear that data science certifications and I-o-T analytics play a major role in human resource management.

I-o-T Applications for Data Science

If I-o-T is the backbone of the infrastructure, I-o-T analytics is the key to creating significant insights based on the vast amount of data from everyday sensors and devices. Here are examples of I-o-T applications in data science:

  • Retail: Since 72.5% of international retailers want to invest in the Internet – of – Things to improve their business plans, it is not surprising that I-of-T is revolutionizing the retail industry. Through inventory management and product demand forecasting, retailers can place orders and increase demand, ensure customer satisfaction, and improve the user experience.
  • Health: I-o-T-related things, like smartwatches and fitness bands, are just a few examples of how I-o-T can improve our health. By tracking your sleep, pace, mileage, the devices allow us to detect our behavior.
  • Smart – towns: By analyzing sensors, cameras, and data, common cities generally appear to be safer, greener, and more efficient communities.
  • Factories and warehouses: Thanks to I-o-T analysis in the field of asset control and maintenance forecasting, factories, among other things, are moving towards optimization. Companies can anticipate engine failure, reduce maintenance costs, and monitor their key assets to prevent theft and optimize maintenance routes.

Carrying I-o-T and Big Data Together

However, this technology requires new infrastructure, including software and hardware applications, and an operating system; organizations need to manage the flow that will move and explore it in real-time as it develops. Here’s the data science; large analytical devices can process large amounts of data generated from I-o-T devices, creating a consistent flow of information. However, for their analysis, the I-o-T provides data that data science analytics can process to obtain the necessary information.

All the same, the I-o-T manages the data on a completely different scale, so the analytical solution must respond to its rapid processing and accepting, and then to its rapid and accurate retrieval. There is a multitude of solutions available that allow you to analyze large data sets in near real-time and necessarily form a complete database for a small server that manages up to 100 TB of such small hardware – which is necessary.

Though, the next-generation analytical database uses GPU technology and allows for even greater hardware reductions; 5 TB on a laptop or large car base. It helps I-o-T organizations connect many of the databases that are evolving, helping them adapt to changing flows and get real-time responses, solve the size challenge, and reduce performance.

Conclusion

The development of data processing processes into information is an important part of the success of data science and I-o-T. All the same, it has been observed that many professionals notified giving them your data and they will give you the new one. With more connected devices, companies have more options to use these devices to collect relevant and useful data that can improve their business processes.