Tag Archives: gadget

Advanced Analytics, Part V

This post is related to the details of visualization of information for executives and operational managers on the mobile front-end. What is descriptive, what is predictive, what is prescriptive, how it looks like, and why. The scope of this post is a cap of the information pyramid. Even if I start about smth detailed I still remain at the very top, at the level of most important information without details on the underlying data. Previous posts contains introduction (Part I) and pathway (Part II) of the information to the user, especially executives.

Perception pipeline

The user’s perception pipeline is: RECOGNITION –> QUALIFICATION –> QUANTIFICATION –> OPTIMIZATION. During recognition the user just grasps the entire thing, starts to take it as a whole, in the ideal we should deliver personal experience, hence information will be valuable but probably delivered slightly different from the previous context. More on personal experience  in next chapter below. So as soon as user grasps/recognizes she is capable to classify or qualify by commonality. User operates with categories and scores within those categories. The scores are qualitative and very friendly for understanding, such as poor, so-so, good, great. Then user is ready to reduce subjectivity and turn to the numeric measurements/scoring. It’s quantification, converting good & great into numbers (absolute and relative). As soon as user all set with numeric measurements, she is capable to improve/optimize the biz or process or whatever the subject is.

Default screen

What should be rendered on the default screen? I bet it is combination of the descriptive, predictive and prescriptive, with large portion of space dedicated to descriptive. Why descriptive is so important? Because until we build AI the trust and confidence to those computer generated suggestions is not at the level. That’s why we have to show ‘AS IS’ picture, to deliver how everything works and what happens without any decorations or translations. If we deliver such snapshot of the business/process/unit/etc. the issue of trust between human and machine might be resolved. We used to believe that machines are pretty good at tracking tons of measurements, so let them track it and visualize it.

There must be an attempt from the machines to try to advice the human user. It’s could be done in the form of the personalized sentence, on the same screen, along with descriptive analytics. So putting some TODOs are absolutely OK. While believing that user will trust them and follow them is naive. The user will definitely dig into the details why such prescription is proposed. It’s normal that user is curious on root-cause chain. Hence be ready to provide almost the same information with additional details on the reasons/roots, trends/predictions, classifications & patterns recognition within KPI control charts, and additional details on prescriptions. If we visualize [on top of the inverted pyramid] with text message and stack of vital signs, then we have to prepare additional screen to answer that list of mentioned details. We will still remain on top of the pyramid.

default_screen

 

Next screen

If we got ‘AS IS’ then there must be ‘TO BE’, at least for the symmetry:) User starts on default screen (recognition and qualification) and continues to the next screen (qualification and quantification). Next screen should have more details. What kind of information would be logically relevant for the user who got default screen and looks for more? Or it’s better to say – looks for ‘why’? May be it’s time to list them as bullets for more clarity:

  • dynamic pattern recognition (with highlight on the corresponding chart or charts) what is going on; it could be one from seven performance signals, it should be three essential signals
  • highlight the area of the significant event [dynamic pattern/signal] to the other charts to juxtapose what is going on there, to foster thinking on potential relations; it’s still human who thinks, while machine assists
  • parameters & decorations for the same control charts, such as min/max/avg values, identifications of the quarters or months or sprints or weeks or so
  • normal range (also applicable to the default screen) or even ranges, because they could be different for different quarters or years
  • trend line, using most applicable method for approximation/prediction of future values; e.g. release forecast
  • its parts should be clickable for digging from relative values/charts into the absolute values/charts for even more detailed analysis; from qualitative to quantitative
  • your ideas here

signal

Recognition of signals as dynamic patterns is identification of the roots/reasons for smth. Predictions and prescriptions could be driven by those signals. Prescriptions could be generic, but it’s better to make personalized prescriptions. Explanations could be designed for the personal needs/perception/experience.

 

Personal experience

We consume information in various contexts. If it is release of the project or product then the context is different from the start of the zero sprint. If it’s merger & acquisition then expected information is different from the quarterly review. It all depends on the user (from CEO to CxOs to VPs to middle management to team management and leadership), on the activity, on the device (Moto 360 or iPhone or iPad or car or TV or laptop). It matters where the user is physically, location does matter. Empathy does matter. But how to reach it?

We could build users interests from social networks and from the interaction with other services. Interests are relatively static in time. It is possible to figure out intentions. Intentions are dynamic and useful only when they are hot. Business intentions are observable from business comms. We could sense the intensity of communication between the user and CFO and classify it as a context related to the budgeting or budget review. If we use sensors on corporate mail system (or mail clients), combine with GPS or Wi-Fi location sensors/services, or with manual check-in somewhere, we could figure out that the user indeed intensified comms with CFO and worked together face-to-face. Having such dynamic context we are capable to deliver the information in that context.

The concept of personal experience (or personal UX) is similar to the braid (type of hairstyle). Each graph of data helps to filter relevant data. Together those graphs allows to locate the real-time context. Having such personal context we could build and deliver most valuable information to the user. More details how to handle interest graph, intention graph, mobile graph, social graph and which sensors could bring the modern new data available in my older posts. So far I propose to personalize the text message for default screen and next screen, because it’s easier than vital signs, and it’s fittable into wrist sized gadgets like Moto 360.

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Wearable Technology. Part II

This story is a logical continuation of the previously published Wearable Technology.

Calories and Workouts

Here I will show how two different wearable gadgets complement each other for Quantified Self.  For the beginning we need two devices, one is wearable on yourself, second is wearable by your bike.

First device is called BodyMedia, world’s most precise calories meter. It measures 5,000 data snapshots per minute from galvanic skin response, heat flux, skin temperature and 3-axis accelerometer. You can read more about BodyMedia’s sensors online. BodyMedia uses extensive machine learning to classify your activity as cycling, then measuring calories burned according to the cycling Big Data set used during learning. Check out this paper: Machine Learning and Sensor Fusion for Estimating Continuous Energy Expenditure for excellent description how AI works.

Second device is called Garmin Edge 500, simple and convenient bike computer. It has GPS, barometric altimeter, thermometer, motion detection and more features for workouts. You can read more about Garmin Edge 500 spec online. My gadgets are pictured herein.

04_gadgets

On the Route

The route was proposed by Mykola Hlibovych, a distinguished bike addict. So I put my gadgets on and measured it all. Below is info about the route. Summary info such as distance, time, speed, pace, temperature, elevation is provided by Garmin. it tries to guess about the calories too, but it is really poor at that. You should know there is no “silver bullet” and understand what to use for what. Garmin is one of the best GPS trackers, hence don’t try to measure calories with it.

Juxtaposition of elevation vs. speed and temperature vs. elevation is interesting for comparison. Both charts are provided by distance (rather than time). 2D route on the map is pretty standard thing. Garmin uses Bing Maps.

02_map_elev_speed_temp_dist

Burning Calories

Let’s look at BodyMedia and redraw Garmin charts of speed, elevation and temperature along the time (instead of distance) and stack them together for comparison/analysis. All three charts are aligned along the horizontal time line. Upper chart is real-time calories burn, measured also in METS. The vertical axis reflects Calories per Minute. Several times I burned at the rate of 11 cal/min with was really hot. The big downtime between 1PM and 2:30PM was a lunch.

An interesting fact is observable on Temperature chart – the Garmin was warm itself and was cooling down to the ambient temperature. After that it starter to record the temperature correctly. Another moment is a small spike in speed during downtime window. It was Zhenia Novytskyy trying my bike to compare with his.

01_calories_elev_speed_temp_time

Thorough Analysis

For detailed analysis of the performance on the route there is animated playback. It is published on Garmin Cloud. You just need to have Flash Player. Click this link if WordPress does not render the embedded route player from Garmin Cloud. There is iframe instruction below. You may experience some ads from them I think (because the service is free) …

The Mud

Wearable technology works in different conditions:)

03_mad

 

 

 

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