Why “connected” does not always mean “smart” with IoT
Why “connected” does not always mean “smart” with IoT

Why “connected” does not always mean “smart” with IoT

James Norman, UK Public Sector CIO at EMC, argues that that the potential of IoT can get lost in the endless discussions about the technologies which underpin it. So asks, how do we actually realise the business value of the Internet of Things?

First, let’s start with some basic definitions:

  • Internet of Things (IoT) is the maturation of the Internet in which everyday objects or devices (things) have network connectivity, allowing these objects or devices to send and receive data about their operations.
  • Internet of Everything (IoE) is a broader term that refers to devices and consumer wearable products connected to the Internet and outfitted with expanded digital features.  Internet of Everything connects devices AND humans.

If you add millions of different “connected” devices and massive amounts of data, you get lots of confusion, unless you first determine what you are trying to do with that wealth of data.

“Connected” doesn’t mean “Smart”

Being able to capture, store and manage the data created by connected devices and humans creates amazing possibilities.  I, like many people, wear at least one fitness band, which tracks my steps, workout time, sleep, heart rate, and more.

But that data by itself isn’t useful unless I apply some analytics to understand what it’s telling me about my health. I want to see trends, outliers, patterns and any other imbalances to help extract the right insights and recommendations to make better decisions about my diet, exercise, sleep, and overall wellness.

Let’s extend this example to the “connected” city, comprised of a wide range of devices (traffic lights, parking meters, weather instruments, etc.) and video cameras (traffic, pedestrian and bike traffic flow) generating data about city operations.

A citizen could combine these sensor and video-generated data with other data sources, such as social media, city reports and local events to create a rich perspective on the city’s activities, problems and overall economic and social vitality.

However, having a “connected” city does not mean that you have a “smart” city.  So how do we get smart?

Also read: Red tape threatens to dumb down smart cities

How do we get smart with IoT?

Getting smart starts by understanding the city’s key business initiative or business objective. For example, let’s identify and understand the decisions that city management (our key business stakeholder in this example) needs to make to support the business initiative of “improving traffic flow.”

The city’s management would need to look into everything from traffic flow, road repairs, maintenance, construction permits, events management, and how it would affect local parks and schools.

Each grouping of decisions equates into a use case, or the “how” we will accomplish the “what” of the business initiative.

What data should I consider?

Once you know the decisions, the next step is to brainstorm the questions stakeholders need to answer in support of key decisions.

This process will help to identify variables and metrics that might be better predictors of the decisions we are trying to make. While most organisations have a good handle on the “descriptive” (what happened?) questions, the business stakeholders struggle with the “predictive” (what is likely to happen?) and the “prescriptive” (what should I do?) questions.

Brainstorming predictive and prescriptive questions typically uncovers numerous new data sources that are worthy of consideration.  And this is a key point: ALL data sources are worthy of consideration. Do not filter the data sources at this point in the process.

Next, we assess the business value and implementation feasibility of each of the brainstormed data sources.  This is where we determine the business value and the implementation feasibility (over the next nine to 12 months) of each of the data sources vis-à-vis the use cases.

What analytics should I use?

The final step is testing different analytic models that might yield the optimal decisions.  Data enrichment techniques such as RFM (Recency of activities, Frequency of activities, Monetary value of activities) will be employed to transform base metrics into potentially actionable metrics.

It’s not unusual to test ten to 20 different analytic models using the wealth of base and transformed metrics to isolate the ones that yield the best results and goodness of.

For example, we might test the below analytic algorithms:

  • Association Analytics to identify events that tend to happen in combination or identifying the association between one event that might lead to another event
  • Time Decomposition to identify events that are driving traffic jams
  • Behavioural Analytics to identify and quantify the impact in changes in driver and traffic behaviours
  • Sentiment Analysis to analyse social media data to uncover areas of constituent dissatisfaction and under-performance
  • Cluster Analysis to identify groups of drivers and/or events that impact traffic flow


Transitioning from “connected” to “smart” takes a lot of upfront work, but the more work that is invested in identifying, understanding and supporting the key decisions necessary to support the targeted business initiative, the more productive the data science will be.

In the end, all of this connected IoT data is only valuable if we are using it to make better decisions.

James Norman is UK Public Sector CIO at EMC

Also read: Making sense of IoT with Big Data analytics