This is a one-minute paper to promote thinking based on the content of the very first lecture in an Information Systems course. As I listen to the lecture, I look for points relevant to my field of interest, the Internet of Things. The introductory session surveys several different topics and I will focus on just one – information.
First, a distinction is made between data and information and what strikes me as particularly important is the fact that it is meaning which separates the two. I suppose it can be deduced also from the fact that the same data can generate different kinds of information, depending on how it is analyzed and to what purpose. Through data analysis one creates a specific meaning which is relevant to one’s context of investigation.
Continuing along this theme, the lecture surveys information characteristics (timeliness, frequency, accuracy, etc.). Those characteristics are viewed in the context of their impact on business. The discussion is brief and there is no dwelling on priority or comparative significance. Perhaps rightfully so since different businesses have different needs. Still, I believe just as meaning is key to our understanding of the essence of information, so do the appropriateness and rarity characteristics of information are key for deriving business value out of information.
IoT research has attempted to deal with appropriateness of information in several ways. Some research is aimed at providing models for generating reliable information from sensors data (Lewandowski & Thoben, 2017). While others attempt to provide a taxonomy for types of data analytics techniques used in across a wide range of IoT applications in different verticals (Siow, et al., 2018).
I think that IoT systems are sometimes viewed as data mining systems leveraging new connectivity technologies to haul data from edge to center. This view does not hold promise for generating surprising or rare information, not even for appropriate information, and frankly not even for any information at all. In other words, IoT platforms run the risk of creating efficient data pipeline with very minimal effectiveness as measured by information.
There is a mentality of collect the data first, worry about what to do with it later. Since we have the technologies to create vast data lakes we are tempted to do so. We also say: well, we may have this initial application in mind, but after we learn some more, surely, we will find other applications for the data we have already accumulated. Thus, we better start collecting now.
Nowhere is this approach more challenged than in consumer applications of data. When it comes to back-office decisions, sure you can gather telemetry data about your customers and figure out new ways to analyze it to improve operations and cut costs. But when it comes to value driven by information presented to the end user, we better have a better plan.
For example, using big data techniques, a service can offer “recommendations” for more of something: books, podcasts, things to buy, content to consume. It is obvious though this is not highly personalized. If big data is used to solve a personal problem, then the value is much more apparent.
Information appropriateness raises the issue of data privacy and trust. The greater the value provided to the user the more trust they have with the system and the more willing they may be to relinquish data in exchange for more value. But if users see that their data is applied in generic or business self-serving ways then trust could be broken. One attempt to deal with privacy in IoT is to contextualize the requirements – to differentiate the privacy concerns between groups and individuals in a way that benefits both the user and business (Zhou & Piramuthu, 2015).
In IoT, data efficiency considerations enhance edge computing trends. Instead of just collecting data and sending everything to the cloud, it is now possible to apply machine learning models at the edge for early information generation. This is more efficient and can better serve the user. Additionally, it can address trust issues by not requiring some of the data to be transferred over the Internet and to be centrally stored. Edge analytics is covered in Lalitha (2017).
These musings only scratch the surface, but I wanted to keep this post short. I find HBR’s Analytics 3.0 an interesting read.
Lalitha, B., 2017. Edge Analytics on Internet of Things: A Survey. i-Manager’s Journal on Computer Science, 5(4), pp. 36-40.
Lewandowski, M. & Thoben, K., 2017. Deriving Information from Sensor Data. 14th IFIP International Conference on Product Lifecycle Management (PLM), Volume 51, pp. 623-631.
Siow, E., Tiropanis, T. & Hall, W., 2018. Analytics for the Internet of Things: A Survey. ACM Computing Surveys, 51(4), pp. 1-36.
Zhou, W. & Piramuthu, S., 2015. Information Relevance Model of Customized Privacy for IoT. Journal of Business Ethics, 131(1), pp. 19-30.