The Internet of Things (IoT) is used to describe the collection of devices capable of accessing the Internet.
Specifically, the IoT is a network of objects capable of receiving and transmitting data.
What are IoT and Big Data?
Smarthomes, smartphones, and any other device attached to the word “smart” is part of the Internet of things. The term IoT was first used in 1999 by Kevin Ashton, working with Procter & Gamble. The idea of the IoT emerged from Ashton’s immersion in the supply chain and production industry and the usage of RFID in product identification, similar to how a QR code works today.
Big Data, on the other hand, was first conceptualized in 2005 by Roger Mougalas. Big Data is described as a process or a collection of procedures or methods to analyze data. However, Big Data is not only concerned with the methods to process or analyze data.
It involves massive and more complex sets of data, hence the name Big Data.
The amount of information processed every day, running through the IoT, is considered part of Big Data, and data scientists are interested in maximizing the use of these data sets.
How is IoT and Big Data Used today?
Google is a company that’s known to use Big Data gathered from phones. For example, during an earthquake, Google relies on the GPS and gyroscopes located on phones. Thanks to Big Data methods, they can develop warning systems for individuals in the vicinity of an earthquake.
To do this, Google would have to have information on how earthquakes travel (i.e. magnitude, intensity, frequency) and compares this to the patterns observed from phones. In a nutshell, phones provide data sets that contribute to Big Data.
A majority of the IoT network comprises smartphones, and the data sets gathered from these become more complicated as companies provide better hardware and software that can do a lot of things for smartphone users.
Big data is also used in other fields like medicine, cybersecurity, gaming and gambling, environmental projects, and government-related activities. In addition, it’s heavily used in predictive analysis and consumer behavior, two factors that drive economic growth and technological advancements.
What’s the Current Trend with Manufacturing Automation?
The current trend in manufacturing automation relies on teams of software developers who have to outline, identify, and conceptualize a process to maximize production capacity and increase efficiency while maintaining reduce costs for labor.
There are several tools available for software developers to help automate manufacturing. This includes JDE, C#, and other programming languages.
Current Developments on IoT and Production Automation
One of the best examples of production automation or manufacturing automation is in the automotive industry. There’s no need for manual labor for car trims since these can be achieved with software-guided laser cutting tools.
Industrial engineers collaborate with software developers in making these possible. Ford Motor and the BWM Group have made use of software-enabled tools to help them customize car parts. In addition, they use 3D printers to make car production more efficient.
On a smaller scale of business, the IoT enables individuals to involve themselves with production directly. For example, a mug printing business can set up a website where customers can pick preset designs (or have their design ready) and have a rig where their 3D printers receive the data and produce the desired product.
How Will IoT Impact Manufacturing Automation?
The IoT is already involved in manufacturing automation. Most production equipment can have code that’s designed to receive, analyze, and output new information. The development of faster wireless technologies allows for more units to be placed separately.
The lack of wires allows multiple configurations to be done. Although an industrial engineer’s job is to ensure production is maximized via the use of a carefully thought-out floor plan, Big Data and wireless technologies will eliminate the need for such roles.
The Importance of IoT, Big Data and Analytics in Marketing
Digital marketing relies today on a large volume of data. From a soft science, digital marketing has changed for salespeople and turned into a hard science demanding the measurement of data to remain viable in an increasingly competitive marketplace.
CEOs, CMOs and CFOs are working hand in hand today to find ways to monetize this large volume of data provided by analytics or social listening tools. Using data this way, they discover different trends or buying behaviors that a specific market owns.
Second party data is shared data considered cooperative data. Sharing this data with non competitors and a complementary vertical space.
Third party data is the refined data. Data management platforms aggregate first party data provided by multiple publishers. They package this data and then sell it to media outlets like DSPs and SSPs.
The gathered data needs to be analyzed. Analytics resembles the water cycle consisting of precipitation, condensation and evaporation. The cloud is the data that is already available, and by creating a target, a message and a medium, an advertising campaign can be generated, which rains in the targeted market.
The filtered data is collected into a pool of information where it gets analyzed and evaporates back into the cloud exactly like water that in its cycle.
In the cloud, data is refined to be used for healthy campaigns that have a greater impact and can make the target market happier.
The world of digital marketing, IoT, big data and analytics, is a pretty complex place, but the educated use of data and analytics, allows marketers to use information, to better understand the world around them.
Will IoT and Big Data Affect Employment Rates?
Technologies may replace laborers and workers, IoT and Big Data will still require human collaboration. We haven’t reached a state of technology where Big Data can produce its input and output.
Still, the size of data sets will eventually accumulate and become big and complex enough to provide patterns that can closely resemble the physical world (i.e. all iterations of an earthquake gathered from smartphones will eventually provide quicker and more accurate results for a large number of people).
