When the Affective Computing Company built its Thrive platform, we were admittedly focused on developing a system that collected the best data possible. It was our obsession to combine the best processes, Ecological Momentary Assessment, Micro-Experiences and Interval Learning Methodologies developed at places such as the Learning Research and Development Center at the University of Pittsburgh and Cognitive Labs Carnegie-Mellon.

 

Decades of cheap web-based survey tools had created a low standard of information collection and we felt that we could improve that for our clients. After all, the more honest the data you are working with the more chance you have of understanding and responding to any situation. This is simply a matter of “Garbage in – Garbage out”.

 

It’s not that all Web Surveys are unreliable. Done correctly, they can produce dependable, even superior, results. But done poorly, as they usually are, they can dramatically distort results, leading management into bad decisions and even derailing careers.

 

For the first years of our existence, we focused on the “Front-End” of our platform, providing the standard .PDF’s and .CSV downloads as the options for looking at the results. Eventually, we developed an “In-Line” reporting frame that is a great way to see results along the way as opposed to a summary report.

 

This was required by the shift from Change Management Projects to the current ongoing Digital Transformation mode that leading companies have embraced. There is no end state so knowing the current state became a critical part of the ongoing effort.

 

When we were comfortable that our collection or “Induction” was a top tier product we looked for ways to take our feedback beyond reports and into visualizations that were designed to look and feel like they were customized to the customer’s concerns.

 

Removing the “data munging” from the process meant we had to have a system that could be easily modified to the customer desired vision while providing a consistent process that could be repeated and scaled.

 

We found a great answer to our problem in the ELK stack provided by Elastic, the components of ElastiSearch, LogStash and Kibana were the ideal solution to add to our induction process. With the ability to have near real-time updates to their information clients can now see clearly what areas are going well, what areas are struggling and even gaps to see what information is missing that could increase the quality of the feedback.

 

The role of Feedback has moved up the value chain in organizations over the last decade, changing from an interaction between managers, reports and peers in the moment that may or may not have been captured to becoming a more formal process where real changes take place in the moment once a situation is better understood.

 

Feedback has qualities such as specificity, context, and relevance that can be turned into new business metrics. These new metrics are designed to prevent repetitive mistakes, misperceptions and identify gaps in the knowledge required to complete the task at hand.

 

One of our clients, 21DayStory.com has created a modeling system that leads stakeholders through a process where their feedback becomes a representation of the Value created vs. the Complexity of the options. This happens over the course of the week but only requires a time commitment of 2 minutes a day.

 

Compare this to the series of meetings needed to gain the same knowledge in conventional manners and you see that hours of time are returned to the stakeholders’ schedules with an increase in the certainty of the decision.

 

The importance of making the feedback “come to life” was stated decades ago by Edmund Tufte, one of the leading pioneers of information visualization. There are many quotes but here are a few of my favorites; 

 

“The commonality between science and art is in trying to see profoundly – to develop strategies of seeing and showing.”

 

“The leading edge in evidence presentation is in science; the leading edge in beauty is in high art.”

 

“Beautiful Evidence is about the theory and practice of analytical design.”

 

With our Thrive System connected to the ELK stack, we can take our collected data and provide a myriad of ways to display and understand what it means to our clients, allowing them to be proactive in the way they manage and adjust to the changing factors in their environment based on low latency and continuous conversations managed by The Thrive System.

 

Below are a few early examples of client data that we are allowed to share, there are many ways to view, and these are just a few.

 

An Aspire360.io Dashboard that shows the outcome of a week-long conversation about entrepreneurial activities by the CEO’s of the companies. Without going too deeply into the actual contents what you can see in one screen shown engagement, preferences and ranked answers along with several attributes that are key to their process.

 

The image shown below is a Mock-up that we are currently building to display the results of one of our “Affective Measures”.

 

Affective Measures provide a new type of business intelligence that can be thought of as dynamic assessments that are contextualized and can be connected in the moment to a goal seeking effort, Sales teams, Project Managers, CEO’s, Chronic Care Patients all improve their chances with higher confidence.

 

Knowing what actions support or degrade confidence is a critical factor in all of those areas and many more, and it is built into the Thrive System so this information can be gathered easily and economically, with low time commitments.

In addition to the Confidence Model, your feedback can be used to show other factors that can influence your outcomes, such as Belief, Engagement and Trust within your group and their current context.

In one sense Thrive’s Affective Measures built through real-time feedback are the nest step for users of static trait measures Myers-Briggs or Predictive Indicator.

 

If you would like to learn more, have a chat or a demo, please contact me at jstafura@GoThrive.io.

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