Internet of Things: “Connected” Does Not Equal “Smart”
"Connected" and "smart" are not synonyms, and bridging the gap takes a lot of upfront work; but with work invested in identifying, understanding and supporting the key decisions, the more productive the data science will be.
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!
Bio: William (Bill) Schmarzo, the "Dean of Big Data," is the CTO of EIM Service Line at EMC. An avid blogger, Bill speaks frequently on the use and application of big data and advanced analytics to drive an organization’s key business initiatives.
Original. Reposted with permission.
- Simplifying the Internet of Things Conversation
- Deep Learning for Internet of Things Using H2O
- Whether Evolution or Revolution, The Internet of Things is Here to Stay