Earley, S. (2015). Analytics, machine learning, and the internet of things.. IT Professional, 17(1), 10-13

From Digital Culture & Society

Article review - Analytics, Machine Learning and the internet of things- By: Joshua Erdmann

A subject that has been increasingly studied by scholars within the overall topic of machine learning and artificial intelligence is the potential implications of technological devices producing more data than humans can process. Due to our massively connected world, combined with low-cost sensors and distributed intelligence, a popular question asked by scholars is whether or not businesses and current organizations will be able to adapt and evolve quickly enough to maintain their place in the competitive landscape. The key question here is, how will human beings make sense of and benefit from these new sources of information and intelligence that is now embedded in our environment. (Earley, 2015)

In the article written by Seth Earle, called Analytics, machine learning and the Internet of things, he provides a few arguments relating to this overarching issue of whether or not organizations can use these new technologies for their advantage. One major argument relating to this issue is the possibility of smart connected devices removing the need for humans to be included in the loop. What this means is that technology will be able to operate faster and more efficient then if a human was monitoring the function of the technology, therefore requiring the machine to think for it. This kind of technology could lead to devices making their own decisions such as self-adjusting, course correcting or repairing themselves as needed. (Earley, 2015)

Another major augment talked about in this article is the possibility that with progression in this technological field of artificial intelligence and machine learning that collections of devices will start to connect with each other to share data and ultimately become an ecosystem of data and devices sharing information between themselves. (Earley, 2015) What this means is that different devices will be able to be in constant communication with each other, similarly to an ecosystem where different technological devices will work together to achieve tasks. With that said, it is clear that there are multiple challenges organizations will have to work with to use these evolving technologies to their advantage. (Earley, 2015)

Some major challenges according to the article, that will need to be tackled by organizations looking to benefit from these emerging technologies would be the issue of understanding what role machine learning and predictive analytic models play within technological devices. What that means is that for organizations to use this information or the benefit organizations must understand what information is being collected and why as well as they need to be able to predict future ramifications through patterns found through the data. Another challenge organizations may have to do is rethink their current business models and current value chains. This is because with the increase in technology, there a new pathways to generate profit as well as ways to meet the needs of your consumer base. Machine learning and artificial intelligence will help organizations respond more competitively to market changes as well as will provide organizations with an increased potential to compete with their competitors. To illustrate these challenges the article uses a fitness bracelet as an example. Fitness bracelets are able access information and collect data about a users physical health and lifestyle. The data collected by these devices can offer insights about how a consumer uses their products as well as will offer them information as to consumer preferences. This type of information can be used when updating functions of the device to better serve the consumer and also make the device more personalized to the user. This information can then be collected in large data banks and if a lot of users are using these devices than organizations will be able better predict what larger populations needs and want. This data that is being collected includes demographic data as well as data on people’s lifestyles, which is useful for marketers, healthcare providers, insurance companies and government agencies. (Earley, 2015) With the use of machine leaning algorithms organizations will be able to use this data to make predictions based on re occurring patterns.

Looking at the article as a whole, probably the most significant contribution to the topic of machine learning and AI would be in the conclusion on the article. In the conclusion the author states that; “data has been called the new oil”, relating data to crude oil and claiming that data can be refined into high-value products through the use of these emerging technologies. This articles major strength is that it makes organizations think about the future and at the potential possibilities of where technology can take us. The article claims that organizations need to invest in building infrastructure now so that they are prepared in the coming years when supply chains and value creation will be dictated by machine learning technologies and AI.

One major weakness with this paper would be in the lack of information presented on how these emerging technologies may have a negative impact on organizations. Something that would have benefited organizations reading this paper would be to touch on the potential to abuse data collection technologies. When you take the human element out of something, you also take the emotion out of it, there for if computer systems are working autonomously without the input or monitoring done by a human, possible implications could arise. One example would be in insurance companies. Part of the goal of insurance companies is to provide help to people in a time of need, but if computer systems are constantly denying people based on pattern recognition, then the people suffering are the same people that need the help in the first place.

In conclusion, to answer the initial question of whether or not whether or not businesses and current organizations will be able to adapt and evolve quickly enough to maintain their place in the competitive landscape, I think it is quite clear that it is possible along as organizations take a proactive approach to this issue. In regards to the question as to how human beings will make sense of and benefit from these new sources of information and intelligence that is now embedded in our environment is uncertain but it is clear that there is a lot of opportunity but there is also a lot go negative implications the come along with the benefits.