Internet of Things: “Connected” Does Not Equal “Smart”
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).
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).
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).
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).
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).
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!