How Data Powers IoT and its Workforce

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How Data Powers IoT and its Workforce

Let's take a high-level look at how data, AI, and connected devices are creating opportunities for efficiencies in modern workforces.

· IoT Zone ·
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The World Economic Forum defines the ‘fourth industrial revolution’ as a digital revolution evolving since the mid-20th century. IoT technology is supplementing this transformation by creating digital interactions for everything, from a water bottle to cars, home, and even cows.

Cisco predicts that by 2020, a whopping 30 billion connected devices will be operational.

With IoT transcending into people’s day to day lives, with smart homes, smart cars, and smart products, the shift is also contributing to a scenario of labor displacement of low-skilled jobs in many industry sectors.

Although it is still contested whether technologies are depleting or creating jobs.

With AI entering the workforce, automation robots are replacing tedious processes and tasks such as stocking and other manual labor work. For instance, by the end of 2017, Quartz recorded that Amazon had 75,000 more robots and 24,000 less retail employees.

A Case of Data, IoT, and Workforce Opportunities

When we talk about IoT and the growing connected world, Product Data forms the core of it. If you look at how the technology works, data lies at the foundation of all IoT applications. This exists in the form of information about products. Installing an IoT platform to support and store these digital interactions, can help build further IoT applications on the web and mobile.

There is a significant amount of manual data work that underlines these applications and web interactions. Converting physical to the digital is not quite possible unless someone manually creates and activates these digital interactions for entities in the IoT realm. Entities such as books, bottles, clothes, houses, cars, etc., which cannot connect to the internet themselves, but need to be digitally represented.

An IoT platform is capable of storing this product data without any limitations to the number of attributes associated with its digital counterpart. This product data can be added in the form of product descriptions, images, videos, manufacturer and seller details, other attributes like height, weight, volume, and color, etc.

Enriching and optimizing this data further leads to significant use cases and innovations related to the Internet of Things.

This is the data that eventually goes into packaging and labels of consumer products, sometimes in forms of QR codes, barcodes, and other digital labels. In the case of CPGs, these play an even bigger part for advocating consumer transparency and better-informed consumer decisions. Being able to read and send data to the web or mobile application users, is key for such initiatives. This product data is further used by logistics, tracking and even marketing companies to refine operations and for advertisements.

As per KPMG’s 2016 Global CEO Outlook, 84% of CEOs claimed that data quality is a huge concern that plays a significant role in the ability to make sound business decisions. As a result, it becomes even more critical to ensure that accurate and complete product information of the particular product’s digital instance has been processed on the IoT platform.

Not only this, IoT devices, in turn, generate massive amounts of real-time data, which needs to be analyzed, structured, leveraged and strategized into building more sustainable business mechanisms. This further creates opportunities for researchers, analysts and project managers to work with IoT enabled data analysis and leverage high-skilled job opportunities.

Data lies at the heart of IoT, while an IoT Platform gives life to its applications.

IoT is enabling data processes to build better quality data from its very inception. To leverage the tide of IoT driven data, manual data entry practices must be improved along with technological advancement.

Observing best practices while entering product data on the IoT platform is the core on which companies innovate new use cases for IoT. Strong quality assurance and compliance can make sure the building block of IoT applications are error-free. Recording and maintaining accurate and complete product data in a standardized form is what truly drives IoT and its workforce.

This data is essentially the driving force at both ends of the business. For end consumers as well as companies deploying these technologies.
ai, iot, iot data, use cases

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