Think Together... Mindmeld Tools
Pixar, Apple, Google and other industry-leading players spare no expense when it comes to resources for cross-discipline teams that collaborate on hard problems... but what about the rest of us?   For teams in "average" organizations:   brainstorm meetings and collaboration processes are very hit or miss. (But that's hopefully about to change).

World-class solutions

...don't happen unless you have cross-discipline thinking and problem solving among:

  • Marketers
  • Engineers
  • Executives
  • Lawyers
  • Artists
  • Writers
  • Investors
  • Product Managers
  • Etc...

To stay on top, companies like Google, Apple and Pixar, provide comprehensive support for internal collaborative processes, including mindmeld meetings that unite technical, creative and business thinkers.

But in "ordinary" organizations, like the companies you and I work for this doesn't happen. It's just assumed that somehow all the different types of minds and skillsets will figure out how to think together... which is crazy considering how important collaboration is.

Take-away: In most organizations... collaboration design is not on the radar.

Ironic, considering how much time we spend on meetings and team efforts!

Does team software help?

So how about group software: Slack, Trello, Chatter, Yammer, Jira, Skype, WebEx, Sharepoint, etc.? Unfortunately today's collaboration tools are not great solutions for information chaos and message overload. On a project I worked on recently, the love affair with Slack was over in 6 months.

Take-away: Current software products for collaboration and meeting process aren't adequate.

With this in mind, let's look at Pixar and other best-in-class players to see how they pull it off.

Team fabric brought to you by: Steve Jobs

Pixar has an extraordinary track record of movie industry success... due in large part to their very sophisticated collaboration culture that extends from the campus level to the central atrium and office structure that Steve Jobs thoughtfully designed. Many aspects of the new Apple "spaceship" campus in Cupertino show Jobs' stamp of collaboration design thinking.

Emergent collaboration

Steve Jobs designed the office structures and atrium at Pixar so it forces people with different skills and viewpoints to rub elbows and socialize in ad hoc encounters. The Pixar executives then developed a meeting culture in this space that mandates peer-to-peer exchanges between people with different skillsets and roles within the organization. Managers facilitate equal participation... everyone in the room contributes before the meeting can be adjourned

Safe enclaves

According to Ed Catmull, President of Pixar/Disney Animation:   A collaboration braintrust is defined as a group of top people who get together and solve problems with candid feedback in a safe environment.

"We remove the power structures from the room to make it safe for the leader of the project to listen better because there is no existential threat."

Explaining vs doing

When Pixar and Disney merged, Catmull says it took Pixar about 4 hours to explain their collaboration framework and 4 years to deploy it in the Disney context.

Collaboration design... not trivial stuff!

Take-away: Even with executive buy-in at the very top of the org, awesome mindmeld meetings are a difficult strategic undertaking.

So what about mindmeld meetings on a somewhat smaller and more focused scale?

Google facilitated sprints

The Google Ventures incubator holds 5 day workshops (documented in the book Sprint by Jake Knapp) to refine the product offerings of high-velocity start-ups, e.g., Slack, Medium, Nest, etc.

A GV sprint workshop is a series of structured discussions and brainstorms that iterate into a working prototype and well-conceived business model. These highly focused and choreographed facilitator mindmelds produce remarkable results but they require a rare set of moderation capabilities and so are not going to happen on the average day in the average org.

Magic mindmelds!

Pixar, Google, Apple and other best in class players have redefined meetings to be a spectrum of peer-to-peer, face-to-face encounters spread across a campus collaboration fabric that includes socially-enhanced building grounds, offices, common spaces, atriums and meeting protocols.

Take-away: Bringing structure and efficient process to meetings is a special and elusive abillity... get it right and you can author a book!

So what about mindmeld meetings for the rest of us... what's really possible? Can we somehow get better tools and meeting process in a system that's practical in the "real world."

The way forward...

The rest of this article looks at some success I've had using a unique mash-up of advanced collaboration software and facilitation methodologies that draw on a range of cognitive and social science disciplines. Here's the key success indicators:

  • Enable cost-effective cross-discipline collaboration and problem solving...

    for everyone!

  • Create ad hoc collaboration encounters...

    for remote and local users!

  • Help managers listen in a radically open way...

    remove power structures when necessary!

  • Provide non-intrusive shared thinking processes and patterns...

    software augmented facilitation!

  • Mitigate a wide range of fear-based group dynamics...

    some contributions should be anonymous!

  • Support diversity of views. Overcome differences in vocabulary and mental models that various silos have...

    tags and taxonomies can help!

  • Integrate content and workflow artifacts with collaboration process...

    seamlessly!

With the resources discussed below, the "faster, better, smarter" results of a Pixar mindmeld meeting or a Google incubator Sprint can be duplicated with a mix of collaboration software, facilitator methodologies and "group intelligence" process (examples below).

Credentials

The approach to "virtual mindmeld meetings" described below is based on my work with senior facilitators and collaboration experts over the past 10+ years. This solution is an affordable, practical collaboration software platform that adapts to a very wide range of team problem solving exercises. Key building blocks are principles from meeting facilitation and group intelligence tools:

Building-blocks of a next-gen collaboration architecture

Here's the architecture of the collaboration platform I'm currently using. It integrates all the criteria outlined above, i.e., cost-effectively support high-quality mindmeld between different minds and skillsets... local and remote users... in ordinary orgs.. on a daily basis.

In this emerging solution, problem solving and knowledge sharing sessions are guided by group intelligence software. The text analytics server provides a hugely scalable indexed repository of heterogeneous document, web database and multimedia content (ElasicSearch codebase).

(See text at end of this page for more detailed description of this architecture.)

Capturing the facilitator process

Facilitators are experts with qualitative team process skills that overcome bad group dynamics and mental gridlock in meetings and conferences. Good facilitators are rare, specialized resources... not widely available in the average org but their practices and insights can be incorporated into software tools and process.

One of the key aspects of this solution is the ability to capture best practices from seasoned meeting facilitators and embed them in the collaboration server platform. Shown here are two mature and effective meeting facilitation techniques: Dynamic Facilitation and Dialog Mapping:

Dynamic Facilitation

Dialog Mapping

Group Intelligence Software

Along with techniques cherry-picked from meeting facilitation, more building blocks for the mindmeld collaboration system come from Group Intelligence, a category of team software that can move a cross-discipline braintrust through lightly structured brainstorm exercises with the goal of shared problem solving, co-creation or peer-to-peer decision making.

Group Intelligence software way more effective than Slack, Yammer, Jive and other social collaboration tools, but the currently available forms of this software are not fully commercialized or productized.

Take-away: Meeting facilitators are expensive to hire and time consuming to train. Group Intelligence software captures the tacit knowledge of a seasoned facilitator and makes it available in easy-to-use team software.

Converge /diverge a solution space

One of the key principles of Group Intelligence is the pattern of converging and diverging ideas created by the group.

Expanding the scope of discussion is known as "divergence" in facilitator-speak. Narrowing down the concepts for discussion is "convergence" or "reduction."

Cognitive patterns of collaboration

To navigate a shared reasoning experience, team members must jointly engage in a sequence of cognitive thinking patterns that are supported by neural structures managed by the prefrontal cortex (Damasio and Damasio, 1992; Holyoak and Kroger, 1995).

Diverge — move from having fewer concepts to having more concepts.

Converge — move from having many concepts to focusing on a few concepts deemed worthy of further attention.

Organize — move from less understanding to more understanding of the relationships among concepts.

Elaborate — move from having concepts expressed in less detail to having concepts expressed in more detail.

Abstract — move from having concepts expressed in more detail to having concepts expressed in less detail.

Evaluate — move from less understanding of the value of concepts for achieving a goal to more understanding of the value of concepts for achieving a goal.

Build Consensus — Move from having less agreement among stakeholders to having more.

Initial concept input

A mindmeld elicitation session starts with an input tool that gathers concepts from a local or distributed team of knowledge workers and domain experts. In this example, team inputs are "bucketed" in SWOT categories, but the categories are completely flexible and can be different for every exercise.

Multi-scale rating tool

After concepts are received from team members, subsequent exercises organize, rank, rate and prioritize the items.

Check-off exercise

The multi-check tool lets users prioritize items they have defined in a previous exercise.

Science of collaboration

Under the covers, team problem solving exercises are assisted with patterns drawn from cognitive science and Group Intelligence research. One model for shared cognition is based on thinklets, small units of useful, repeatable collaborative thought process. Each block in this diagram represents a thinklet.

(A Conceptual Foundation of the ThinkLet Concept for Collaboration Engineering; Briggs, et. al.)

Thinklet examples

This is a partial list of thinklet collaboration patterns. A group intelligence session comprises a set of thinklets organized into a generative flow with a tangible outcome. Thinklets are: "Named, packaged facilitation strategies that create predictable, repeatable patterns of collaboration."

(Collaboration Engineering: Foundations and Opportunities, Gert-Jan de Vreede, et. al.)

Group intelligence exercise

Shown is a guided cybersecurity attack and penetration exercise. The same process flow could be applied to marketing, product design, risk management, decision support, health care, etc.

Different thinklet shared thinking patterns can be used at each step in the collaboration process.

The Semantic Knowledge Exchange Platform

ElasticSearch delivers Information Retrieval (IR) and Natural Language Processing (NLP) capabilities that can organize, query and socialize content that is formally published, web crawled, user-generated or operationally created in structured, unstructured or semi-structured formats.

Knowledge Exchanges are advanced discussion platforms that help users share knowledge and solve problems in topic areas they are passionate about.

Put these two together and you have...

The Semantic Knowledge Exchange is a sophisticated social discussion and problem solving platform fully integrated with leading-edge machine-learning based computational text analytics. With this approach, knowledge workers, customer communities, healthcare workers and other virtual teams can collaborate to solve problems in the virtual Knowledge Exchange while working in the context of large, complex bodies of semantically organized content.

Key capabilities of the KE + ElasticSearch Platform

  • Faceted search for large heterogeneous content bodies
  • High performance log processing and analytics dashboards
  • Behavior, sentiment and reputation analysis

Underlying algorithms and services:

  • Machine Learning based Information Retrieval ( IR/ML)
  • Natural Language Processing (NLP)
  • Document Understanding
  • Graph-based Reasoning
  • Bayesian reasoning
  • Support Vector Machines (SVM)
  • Belief networks
  • Document Classifiers
  • Business Intelligence data mining

When combining big data technologies with knowledge exchanges, we are working in an exciting new realm where content information retrieval ( IR), text analytics (TA) and machine learning (ML) are used to pre-digest vast amounts of structured and unstructured data which can be continually fed into collaborative knowledge workflows in a semantically accessible and familiar form..

The ES + KE platform integrates advanced IR / TA / ML capabilities with existing business platforms, including collaboration suites, Business Intelligence software, CRM, SFA, legal systems and content management ( CMS) applications. Making mountains of content understandable

Unstructured Data Analysis

Capabilities include semantic analysis of unstructured or “semi-structured” text content such as web pages, documents, social media, research papers, reports, medical records, work logs and forms, RDF triplestores, and any free-form text:

  • Sentiment analysis (evaluating the sentiment of the author of a document)
  • Named Entity Recognition (parsing out significant references to real world objects)
  • Document classification (different ways of cauterizing and classifying documents)
  • Relevance recognition (determining how relevant a document is to a given topic)
  • Paragraph Gisting (extracting the core meaning of a paragraph)
  • Ontological search (recognizing similarities from context)
  • Semantic filtering (recognizing what a reference is about from context)
  • Auto-generation of tags to add to the search space of user-generated content
  • Auto generation of links between documents
  • Associative retrieval of documents

Structured Data Analysis

Support for semantic analysis of structured data such as that found in relational, B.I. or flat databases is supported with following capabilities:

  • Faceted search (allowing repetitive searches to filter the results of prior searches)
  • Linked Data Analysis (across structured data sets)
  • Data Mining on Big Data collections (pattern matching and selection)
  • Predictive Analytics (locating and identifying trends in structured data)
  • Data Record Classification (naïve bayes, k-nearest neighbor)
  • Knowledge Workflows Driven by Machine Intelligent Information Retrieval

In a traditional information retrieval application .. documents are indexed with TF/IDF and then query against them to get a search results listing…

Wikipedia on TF/IDF search technology

The above solution can support a very wide range of TF/IDF applications with customized ElasticSearch IR / TA platform.. and can also do the opposite… index a large set of queries ( i.e., rules, business logic, metadata structures, exploratory categorization routines) and then throw incoming documents and structured content against the indexed queries..

This so called “reverse indexing” approach makes it possible to quickly parse and process a large number of heterogeneous documents, papers, research notes, transaction records, annotations, social media content, and unstructured / semi structured text records looking for categories, topics, tags, and fuzzy emergent patterns.

More importantly the engine helps capture and share the intelligence for these reusable queries between knowledge workers.

In IR/ TA terms.. the underlying mechanism is called percolation.

ElasticSearch reverse indexing document percolation

With the python API to ElasticSearch we can write software that extends the IR/TA capabilities into the most advanced reaches of probabilistic machine learning. By integrating ES and KE we can create new forms of social knowledge sharing applications in finance, insurance, intelligence, marketing, publishing, e-commerce and healthcare.

With the big-data KE , we can target any mountain of data that needs to be searched and organized and then used in a collaborative knowledge workflow.. “semantic knowledge exchange” to the rescue :-)


Final take-away: In the fields of cyber-security, healthcare, finance, marketing and risk management, the solution shown above has achieved good results because it integrates key practices of leading meeting facilitators with the software tools of Group Intelligence... enabling "Pixar-quality" mindmelds in average orgs on a regular basis.


Please visit the sister site....

A similar info page where "strong tie" social network theory and virtual knowledge exchange is applied at a larger scale to online communities: community.mindmeldtools.com ... organic reach with influence here we come!!

Thanks to Bob Briggs, Karen Chenette, David Tobey, David Boje and the many other PhD researchers, collaboration software experts and shared thinking advocates that I have communed with over the past 20 years.

Copyright © Steve King 2016