Big Data

Internet of Things: “Connected” Does Not Equal “Smart”

Bill Schmarzo By Bill Schmarzo CTO, Dell EMC Services (aka “Dean of Big Data”) March 28, 2016

The hype is huge around the Internet of Things (IoT) and the Internet of Everything (IoE), but the potential often gets lost in endless discussions about the technologies that underpin the IoT and IoE (sensors, beacons, telematics, fitness trackers, mobile apps, global positioning devices, etc.). Millions of different “connected” devices + massive amounts of data = lots of confusion, unless you first determine what you are trying to do with that wealth of data.

So how do we actually realize business value? How do we get to the “Make me more money” conversation (from the business perspective), or the “Make Life More Easy” conversation (from the ME perspective)?

“Connected” Does NOT Necessarily 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 a 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 (Facebook, Instagram, Yelp) + citizen comments (emails, phone calls) + city reports (police blotters, fire reports, emergency services, construction permits, work orders, building hours, etc.) + local events (concerts, sporting events, farmers markets, parades, festivals, etc.) 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?

How Do We Get Smart?

Getting smart starts by understanding the city’s key business initiative or business objective (i.e., “what” we want to accomplish). 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.” 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?

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 organizations 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 (see Figure 1).

Figure 1: Descriptive, Predictive and Prescriptive Questions

Figure 1: Descriptive, Predictive and Prescriptive 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 1) the business value and 2) the implementation feasibility (over the next 9 to 12 months) of each of the data sources vis-à-vis the use cases (see Figure 2).

Figure 2: Data Assessment Matrices

Figure 2: Data Assessment Matrices

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 10 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 fit (see figure 3).

Figure 3: Testing Different Analytic Models

Figure 3: Testing Different Analytic Models

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
  • Behavioral Analytics to identify and quantify the impact in changes in driver and traffic behaviors
  • Sentiment Analysis to analyze 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

How Do We Realize Business Value?

So how do we realize business value from this Internet of Things? Let’s build on the “smart” city example. Each “smart city” groups of decisions has business (“make me more money”) and citizen (“make life more easy”) ramifications. Each set of decisions, or use case, can be summarized to highlight the benefits and execution issues for each of the key stakeholders (see Figure 4).

Figure 4: Use Case Value Determination

Figure 4: Use Case Value Determination

With each of these use cases now fully fleshed out, we are in a position to prioritize which use cases (or groups of decisions) we should undertake first based upon business value and implementation/execution feasibility (see Figure 5).

Figure 5: “Smart” City Priority Matrix

Figure 5: “Smart” City Priority Matrix

Summary

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, whether it’s the connected Bill Schmarzo or the connected city, all of this connected data is only valuable if we are using it to make better decisions. Making better decisions…now that’s how we become smarter!

Bill Schmarzo

About Bill Schmarzo


CTO, Dell EMC Services (aka “Dean of Big Data”)

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 the strategy and defining the Big Data service offerings and capabilities for Dell EMC Services Big Data Practice. As the CTO for the Big Data Practice, he is responsible for working with organizations to help them identify where and how to start their big data journeys. He’s written several white papers, is an avid blogger and is a frequent speaker on the use of Big Data and data science to power the 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 was ranked as #15 Big Data Influencer by Onalytica.

Bill has over three decades of experience in data warehousing, BI and analytics. Bill authored Dell EMC’s Vision Workshop methodology that links an organization’s strategic business initiatives with their supporting data and analytic requirements, and co-authored with Ralph Kimball a series of articles on analytic applications. Bill has served on The Data Warehouse Institute’s faculty as the head of the analytic applications curriculum.

Previously, Bill was the 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 degree in Business Administration from the University of Iowa and a Bachelor of Science degree in Mathematics, Computer Science and Business Administration from Coe College.

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