Using Data In A IoT Connected World
We live in a world powered by data and technology. Businesses, governments, organizations and scientists strive to find innovative ways to use, collect and store more data. Enough quality data allows for more informed decisions. Harnessing the power of data can inform marketing strategy to life and death situations, such as emergency disaster preparation. As fields such as AI and IoT emerge, technology continues to find more efficient ways to combine them.
Before continuing, it’s worth refreshing on what data science is. Data science refers to an interdisciplinary field between mathematics, statistics and computer science. Data and engineering teams are common in many businesses today. A key aim of data science is to determine how to collect large amounts of data and extract insights from it. With the IoT market expanding rapidly and producing so much data, there is a need to analyze it. Making data science key.
The ‘Internet of Things’ (IoT) is a broad concept. Essentially, any device that collects information (data) and can network via Internet connectivity (like WiFi or cellular data). IoT connected devices cover everything from sensors, security, wearables, intelligent appliances and more. Some examples of IoT devices are; smart watches, smart speakers, smart TVs, Nest thermostats, security cameras and more.
Why Combine Data Science & IoT?
Why should data science and IoT work together? The main benefit of combining data science and the Internet of Things is it allows more efficient and accurate data analysis. With technology’s ability to transfer data at higher speeds over the Internet, specialist expertise is necessary to manage data volumes. As IoT devices produce high volumes of data, data science helps process and extract insights into meaningful formats.
Most businesses are familiar with data. But as customers connect via more devices and IoT becomes ingrained in daily life, processing and quality data analysis can be challenging. To draw valuable insights from data requires invesmtent in technology and expertise. With the real-time nature of data, this can help businesses stay on top of current trends. When it comes to the ‘big data’ that IoT can produce, data science experience is likely a necessity.
Can They Work Together?
Can it be done? Yes. Should they work together? Definitely. The Internet of Things can collect data across multiple devices. IoT data at a macro level can be useful, however data science allows for deeper extraction of insights at a micro level. Whilst IoT devices collect data, data science provides algorithms for presenting and making sense of the data.
As an example, think of a smart TV manufacturer. At a macro level, sales data can inform the company where the most smart TVs are sold. This can help marketing and sales teams focus efforts. At a micro level, deeper data analysis from a data scientist or team may tell the manufacturer which apps are the most used on each TV, or the average power consumption of a particular TV model. This can help product teams improve design, UX and energy efficiency of future smart TVs, providing an improved product for better sales in the future.
There certainly is challenges in getting quality data from the amount of different data collection points we have access to today. Again, we’re seeing technology step up. Artificial Intelligence (AI) is at the cutting edge of processing large data sets. It can assist with collecting, storing and analyzing big data seen from IoT connected devices. Check the full details of one such platform which uses AI for analyzing big data sets.
Applications Of Data Science & IoT
Expanding on the smart TV example above, the application of data science with IoT (often called IoT analytics) is becoming an asset in industries such as retail, healthcare and manufacturing, to name just a few. The retail industry uses IoT and data science to controll stock and predicting demand. The impact of data science and IoT on healthcare has been literally lifechanging. Users of smart watches now have access to data covering sleep, steps, running distances / speeds and more, to help reach health goals.
IoT and Data Science Challenges
As IoT and data science work together, it’s worth noting that there are also challenges. Security, data storage, scaling and of course – costs. The Internet of Things allows for faster collection of some personal data, opening up concerns around consumer privacy. With the vast amount of data collected, organizations must secure this critical data.
Regarding scaling, solutions for vast amounts of data do have capacity limits. When working with data collection > storage > analysis at scale from a field as broad as the IoT, make sure there are scalable solutions in place. This may mean investing in IaaS (Infrastructure as a Service), Platforms as a Service (PaaS) or a level of enterprise connectivity that is capable of handling large data loads. All of these are investments, but plan accordingly when combining IoT with data science.
In a data-driven world, there’s no question that data science will be an essential field for many industries moving into the future. The ability of data science to gather and analyze data will be further improved with other technology such as AI. Using IoT and data science together, industries can collect data at faster speeds and produce more accurate insights. Consequently, this allows for better informed and data-driven decisions.
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