Internet of Things: Getting From Connected To Smart

This blog is a continuation of my blog the “Internet of Things:  Connected Does Not Equal Smart.”  I got feedback that I didn’t push far enough into the realm of “smart” in that blog; that I needed to give more guidance and examples to help organizations to transition from “connected” to “smart.”

Fortunately, I’ve had a few customer engagements and a recent lecture at the University of San Francisco to try to refine the approach. 

What is “Smart”?

The first thing we need to do is to define what we mean by “smart.”

“Smart” is the sum of the decisions (optimized) in support of an entity’s business or operational objectives

It may be easiest to explain “smart” with a couple of examples, so let’s continue with the “smart” city example that I covered in my previous blog:

Getting smart starts by understanding the city’s key business initiatives or business objectives (i.e., “what” we want to accomplish). For example, let’s identify and understand the decisions that city management needs to make to support the business initiative of “Improving traffic flow.” This could include:

• Traffic flow decisions: New roads? New lanes? New turn lanes? New bike lanes? Pedestrian crossings? Railroad crossings? Bus stops?
• Road repair and maintenance decisions: Fixing potholes? Repaving surfaces? Materials and equipment needed? When to fix potholes and repave streets?
• Construction permits decisions: Types of permits needed? Impact on traffic flow? Length of time to complete the work? Number of employees to consider?
• Events management decisions: Traffic (cars and pedestrians) attending proposed event? Impact on normal traffic flow? Date, time, location and duration of events?
• Parks decisions: Location of parks? Size of parks? Hours of operation? Park equipment maintenance?
• Schools decisions: Location and size of new schools? Hours of operations? Location of stoplights and stop signs?

So our “Smart City” initiative would seek to optimize the decisions necessary to support the business and community objectives of that city (see Figure 1).

Figure 1:  Becoming a "Smart City"

So I put the process to the test by using my USF students as my guinea pigs (yet again).  I asked them to work in small groups and to identify the decisions that the University of San Francisco would need to make in support of becoming a “Smart University” (see Figure 2).  

Figure 2:  Becoming a "Smart University" Assignment

Not surprisingly, the results were very impressive.  The students came up with a load of different decision areas (see Figure 3).

Figure 3:  Sample of Some of the Students' Ideas

We grouped all of their ideas into common themes and came up with the list in Figure 4.

Figure 4:  Becoming a "Smart" University

Getting “Smart” Summary

I am very excited about the potential to use the concept of “Smart” as an over-arching framework for organizations that are looking to envision the ultimate end point of their big data journey.  

The “Smart” framework not only provides a structure around which to identify and prioritize an organization’s big data opportunities, but it provides a compelling vision of what might be possible when the entirety of the organization can rally behind a common big data vision.  Time to get “Smart”!