For IoT, knowing who knows is a good thing to know.
“Who knows” has so many meanings Wikipedia needs disambiguation; https://en.wikipedia.org/wiki/Who_Knows.
This article has yet another meaning; asking questions about analytics for the Internet of Things (IoT).
Consumer IoT and Industrial IoT (IIoT) both benefit from a basic question; “Who knows?” How we get answers illustrates differences between consumer IoT and Industrial IoT.
A decision to purchase is one example.
Do we know who decides to buy?
In consumer IoT, there might be one purchase decision maker. Usually, sellers want to understand buyers. Using data mining and analytics, we might isolate and characterize a typical buyer. We may be able to determine whether some sales involve two or more decision makers.
With a small number of deciders, demographics are meaningful. Age, income, gender, geography; these are basic. We may find location, day of the week, or month coming in to play, as well as seasonality. These all map back to the one, or two decision makers and what influences them.
Consumer wearable IoT devices usually (roughly 90%) have accelerometers but less often use GPS. Does the consumer know how, when, or if a fitness device tracks their location? Probably not. Do they need to know? Probably not.
Does the data scientist or analyst need to know?
Depending on analytic goals, the answer varies.
Do consumers know who and what shapes purchase choices? Often, no. Big Data, some of it from IoT, can help answer questions.
Who knows consumers? The data scientist, maybe. Big data analytics offers rich insight for purchase recommendations, ad placement, making offers and consumer relationship management. This becomes powerful when many data types are blended; a classic big data gumbo of rich goodness.
IIoT can be quite different.
Both buyers and sellers are seeking insight. Industrial organizations have many ‘deciders’. Somewhere in most industrial organizations a purchasing department buys “things” and spare parts to repair “things.” Somewhere else a production organization uses “things” to create value. Somewhere an IT department tries to connect “things” and provide cyber security. Information governance will vary and data location varies (cloud, lake, warehouse…). Stakeholder use of information also varies, depending on corporate governance, culture, and the type of business.
Lone Star mapping of large firms shows there is wide diversity in how they make decisions and use information. Layered and siloed organizations are compartmentalized. Data in one place can be hard to understand elsewhere. Flat, empowered organizations are closer to their “things.” They often have more context and understanding about what IIoT data means.
Some examples of ‘who knows’ questions for IIoT
- Who knows what the service level agreement is with customers, vendors?
- Who knows the location of our “things”?
- Who knows the cause-effect relationships in our “things”?
- Who needs to know about a prediction?
- Who needs to know about an alarm?
- Who knows the business rules?
- Who knows if the business rules are actually followed?
Benchmarking shows dangerous uncertainty lurks in most analytics; https://www.lone-star.com/wp-content/uploads/2017/03/Modeling-Best-Practices-Benchmarking-Project-2017.pdf So, it’s best to ask these ‘who knows’ questions. No amount of machine learning can confidently guess what SLA contract terms say. If cause-effect relationships are known (like the SLA), it’s better to capture knowledge than create guesses with deep learning when we can avoid it.
Powerful consumer IoT applications may be based on a presumption that human preferences are fickle and best modeled with deep learning. Powerful IIoT applications are likely to come from asking the same question over and over; “who knows?”
So, for IoT success, and in particular for IIoT, we suggest three things:
- Ask “Who Knows?”
- Ask who needs to know.
- Work with Lone Star; our TruNavigator® and AnaltyicsOS™ solutions excel in complex industrial applications.
Or, you could just learn how to type the cool emoticon at the top of this article.
About Lone Star Analysis
Lone Star Analysis enables customers to make insightful decisions faster than their competitors. We are a predictive guide bridging the gap between data and action. Prescient insights support confident decisions for customers in Oil & Gas, Transportation & Logistics, Industrial Products & Services, Aerospace & Defense, and the Public Sector.
Lone Star delivers fast time to value supporting customers planning and on-going management needs. Utilizing our TruNavigator® software platform, Lone Star brings proven modeling tools and analysis that improve customers top line, by winning more business, and improve the bottom line, by quickly enabling operational efficiency, cost reduction, and performance improvement. Our trusted AnalyticsOSSM software solutions support our customers’ real-time predictive analytics needs when continuous operational performance optimization, cost minimization, safety improvement, and risk reduction are important.
Headquartered in Dallas, Texas, Lone Star is found on the web at http://www.Lone-Star.com
Recent Blog Posts
- Lone Star Analysis Wins D CEO Magazine’s Innovation Award for Artificial Intelligence and Machine Learning
- Lone Star Analysis Earns ISO Certification for AnalyticsOS® Cloud
- Lone Star Analysis Earns Recognition in Dallas Business Journal’s 2022 Middle Market 50 Awards
- Lone Star Analysis Earns Spot on Tech Titans’ 2022 Fast Tech List for Third Consecutive Year
- Lone Star Analysis Earns Recognition in Dallas Business Journal’s 2022 Best Places to Work Awards