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IoT From Connected to Getting Smart | @ThingsExpo #BigData #IoT #M2M

The end point for many IoT initiatives is to create ‘smart’ entities – smart cities, smart cars, smart schools, etc.

Internet of Things: Getting from Connected to Smart

I wanted to gather all of my Internet of Things (IOT) blogs into a single blog (that I could later use with my University of San Francisco (USF) Big Data “MBA” course). However as I started to pull these blogs together, I realized that my IOT discussion lacked a vision; it lacked an end point towards which an organization could drive their IOT envisioning, proof of value, app dev, data engineering and data science efforts. And I think that the IOT end point is really quite simple…

Creating “Smart” Entities
I believe that the end point for many IoT initiatives is to create “smart” entities – smart cities, smart hospitals, smart cars, smart universities, smart schools, smart blogs, smart lawnmowers, etc.

Creating a “smart” entity is an outcome of optimizing the decisions and business initiatives that support an entity’s business and operational objectives

Yes, I think creating “smart” is as simple as that. But before I go any further, let me define some terms and a process that I am going to use throughout this blog (and likely in the forthcoming IOT and “smart” blog series):

Maybe the easiest way to understand the “smart” concept is with an example. For example, creating a “smart” city starts by first understanding the city’s business and operational objectives, which could include citizen quality of life, proactive business development, promoting tourism, top-quality schools and community safety. Next we need to identify the city’s 9 to 12 month business initiatives. One of the city’s business initiatives could be “improving traffic flow.” The decisions (or clusters of decisions) necessary to support the “improve traffic flow” business initiative could include (see Figure 1):

  • 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? Which potholes to fix first? When to repave a road? Materials and equipment needed to repave? 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? Hours and days of operations? Number of construction worker parking spots 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? Impact of location and size of parks on nearby traffic? Impact of hours of operation on rush hour traffic?
  • Schools decisions: Location and size of new schools? Hours of operations? Location of stoplights and stop signs?

Note: decisions tend to cluster around common objectives or “themes.” We call these “clusters of decisions” around a common objective use cases.

Getting Smart Exercise
Let’s see how this process of identifying the business initiatives, and ultimately the decisions, necessary to support a “smart” entity might work. I asked my University of San Francisco students to work in small groups to identify the university’s key business initiatives and to start brainstorming the decisions that the university would need to make to support those key business initiatives.

The results were very impressive. The students came up with a load of different business initiatives (and associated clusters of decisions) that a “smart” university would need to cover (see Figure 1).

Figure 1: Sample of Business Initiatives and Supporting Decisions

We consolidated the key business initiatives across the different teams and came up with the list in Figure 2.

Figure 2: Creating a “Smart” University of San Francisco

By applying advanced analytics (yielding predictive and prescriptive insights) to the growing wealth of internal and external data sources, organizations can make better decisions and enhance the success of the organization’s key business initiatives. Ultimately, this is what helps the university become smarter.

BTW, Don’t Forget the Human Component of IoT
One last point about IoT, it’s really more than just about “things.” To make the transition to smart, the discussion really needs to focus on the human component. Creating analytic or behavioral profiles on the humans involved in the operational and performance decisions – operators, mechanics, technicians, engineers – is critical if we want to optimize the decisions in support of a smart operational objectives. The IOT execution approach needs to include:

  • An engagement model and methodologies (Vision Workshop to Proof of Value to Operationalization) to capture and aggregate sensor and device data in order to build behavioral (analytic) profiles at the level of the individual devices and machines
  • Capabilities to leverage human data (e.g., notes, comments, email conversations, text messages, education, certifications, social media) to build behavioral profiles at the level of the human stakeholders. No two operators or technicians or mechanics are exactly the same; each has different tendencies, behaviors, levels of experience, areas of expertise, interests, etc. Consequently, we need to create behavioral profiles at the level of the individual humans in order to deliver highly relevant, actionable operational and performance recommendations.
  • Finally, we need capabilities to integrate external factors (e.g., weather, traffic, accidents, seasonality, holidays, economic situations, special events, product recalls, state budgets) in order to optimize operational and performance decisions that comprise our smart entity.

Most of the decisions that we are trying to optimize to create a smart operating entity involve humans. We’re not trying to create Skynet[1]. We’re trying to make the humans that are involved in the operations more effective with respect to the decisions that they need to make in support of their business initiatives.

Summary
The concept of “smart” should be utilized as an over-arching framework for organizations that are trying to envision the ultimate end point of their IOT journey. Integrating “Smart” into operational objectives can be a straight-forward and easy-to-grasp concept for any organization that embraces the Internet of Things. I’m excited to have the opportunity to help our clients integrate these concepts into their business models and strategies.

IoT Blogs
Here is a listing of the blogs that I have written on IoT, all in one place! Hope you enjoy them (as I’m sure that there will be more to come over the next several months).

1. Skynet is from the Terminator movies. Skynet is a highly advanced, artificial intelligence system that saw humanity as a threat to its existence and triggered a nuclear holocaust and an army of Terminators against humanity. Not a good situation.

The post Internet of Things: Getting From Connected To Smart appeared first on InFocus.

More Stories By William Schmarzo

Bill Schmarzo, author of “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science”, is responsible for setting strategy and defining the Big Data service offerings for Dell EMC’s Big Data Practice. As a CTO within Dell EMC’s 2,000+ person consulting organization, he works with organizations to identify where and how to start their big data journeys. He’s written white papers, is an avid blogger and is a frequent speaker on the use of Big Data and data science to power an organization’s key business initiatives. He is a University of San Francisco School of Management (SOM) Executive Fellow where he teaches the “Big Data MBA” course. Bill also just completed a research paper on “Determining The Economic Value of Data”. Onalytica recently ranked Bill as #4 Big Data Influencer worldwide. Bill has over three decades of experience in data warehousing, BI and analytics. Bill authored the Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements. Bill serves on the City of San Jose’s Technology Innovation Board, and on the faculties of The Data Warehouse Institute and Strata. Previously, Bill was vice president of Analytics at Yahoo where he was responsible for the development of Yahoo’s Advertiser and Website analytics products, including the delivery of “actionable insights” through a holistic user experience. Before that, Bill oversaw the Analytic Applications business unit at Business Objects, including the development, marketing and sales of their industry-defining analytic applications. Bill holds a Masters Business Administration from University of Iowa and a Bachelor of Science degree in Mathematics, Computer Science and Business Administration from Coe College.

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