Mapping Team Effectiveness Through Data Visualization

Companies are taking creative measures to counter ‘meeting fatigue.’ From cutting meetings to a magic length (at Google, this is 50 minutes) to stand-up meetings (yes, standing vs. sitting), leaders are trying everything to improve efficiency and effectiveness of meetings.

Yet, how often do we still leave meetings dissatisfied with the outcome?  It was too long…didn’t result in a decision…was monopolized by one or two players…left attendees with more questions than answers.

Leaders know that good meetings are a product of good leadership. While there isn’t a one-size-fits-all formula for effective meetings, objective attention to the flow of your meetings is important for team development.

Mapping Opportunities With Data

We recently approached a client leadership team meeting as observers. Combining data-orientation with an eye to group dynamics, we plotted discussion milestones, determined topic frequency, and tracked specific players involved in the discussions that led to decision-making.  We then mapped the trajectory of the 2-hour discussion, broken into 10 minute increments.

The result is a data visual (see below), which we reviewed with the participants to better understand the conversation flow and decision making process. In the software, this content is linked. This enabled the participants to understand which conversations drove key milestones and which participants were involved in those decisions.

To see the fully interactive visualization, click here

Discussion_Overview

This model of provides a simple, but effective way to review what occurred during the meeting, pinpoint where the conversation may have moved in an unproductive direction, and identify opportunities to improve meetings. In the chart above, for example, the first 30 minutes were spent jumping from topic to topic. Only after 45 minutes did they start to discuss the connections between the topic areas.

Our meeting datafication pilot highlighted some important takeaways for the client’s leadership team. Here are 5 highlights:

1. Open with a check-in – Get it out on the table. Where is everyone mentally? In the course of their day? Are they ‘bought in’ to the topic at hand? Sharing an agenda pre-meeting with opportunity for attendees to provide feedback helps ensure everyone is satisfied with the game plan before they enter the room.

2. “Parking Lot” ideas They are good ideas, but they aren’t helpful for this conversation. Make note of any ideas to be rainchecked. They certainly shouldn’t be lost, but they also don’t need to derail this meeting’s conversation if they aren’t relevant to the agenda or decisions that need to be made.

3. Stay out of the weeds This is easier said than done. But when we reviewed our pilot meeting map with our clients, they were struck by the amount of time spent hashing out details.  A take-away after reviewing their discussion data was that they got stuck in the weeds when trying to come to consensus.  They settled on accepting 80% consensus and moving on rather than drowning in minutiae.

4. Hear from everyone If a certain participant’s point of view isn’t imperative to the discussion, they shouldn’t have been on the meeting invite. If you haven’t tapped all of the voices in the room, there could be critical data that isn’t being considered in decision making.  Be deliberate about inclusion.

5. Ensure actions have owners If participants walk away without specific actions and clear accountability, seemingly productive meetings will have little impact.  Regardless of whether there are tangible actions that need to take place or simply giving deeper individual thought to certain ideas, team members should leave meetings assured of their next steps.

Visualizing the discussion process prompts teams to reflect on their meeting effectiveness AND group dynamics, which will improve team effectiveness overall.

Stuart Farrand

Analyst at Christopher Newport University
Stuart Farrand is an experienced analyst with an extensive background in diagnostic methods. His work focuses on the intersection of risk, emergence, and organizational culture. He specializes in using data insights that enhance how organizations identify, approach, and learn from complex issues and hard to detect risks. Stuart is a former member of the gothamCulture team and currently works in higher education.
Stuart Farrand

Organizational Development: Balancing Analytics and Intuition

analytics and intuition

For years, businesses have relied on experience, trial and error, and heuristics to make decisions.  But, change is in the air. Data analytics has added a new dimension to the decision-making process, giving business leaders access to new, previously unavailable insights.

Unfortunately, there may be a split in the business world regarding its use.  While some are skeptical about the move toward analytics, others believe it should be the primary tool for business.  In both cases, they tend to miss the bigger picture.

Analytics was never intended to replace intuition, but to supplement it instead.

A Beautiful Combination

The beauty is in the combination. Analytics provides context to the insights that (most) business leaders already possess.

Two recent books help illustrate this point: The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies, by Erik Brynjolfsson and Andrew McAffee, and Average Is Over: Powering America Beyond the Great Stagnation, by Tyler Cowen. Both provide a glimpse of the many ways technology will change the workplace over the course of this century.

Both books use the example of freestyle chess to demonstrate the potential of partnering human intuition with data processing.

For those who are not familiar, freestyle is a type of chess match where humans partner with computers and compete against each other.  Not surprisingly, there are many instances where computers have defeated a chess master. And yet, humans cooperating with computers have easily defeated both humans and computers acting alone.

The reason behind this is what’s interesting. Computers use probabilities to determine an optimal move.  Humans, on the other hand, rely on their experiences and intuition to identify opportunities and implement different strategies.  The computer provides the raw processing power, while the human provides a superior understanding of the game’s mechanics.

The freestyle chess example is a simple illustration, but it demonstrates something more complex for modern organizations seeking new direction: the potential of depolarizing analytics and intuition.

The Analytics Potential

Leveraging analytics, we can aggregate, process, and understand more information than was conceivable even 20 years ago. Analytics Training is a great way to be able to be able to reach your businesses highest potential as it will allow you to have the greatest understanding of how to read your results and invest that knowledge back into the business.

However, it’s important to note that analytics is based on models. In using it as a tool, we need to understand the basic assumptions and limitations of using a model, and use our intuition and experience to fill in the gaps and enhance the analysis process.

Organizational development is a field full of data. In many cases, there is too much information available for one individual to thoroughly digest, analyze, and interpret.  As in freestyle chess, analytics uncovers the trends, but the human in the mix determines which trends are most relevant. That’s how organizations come to understand which trends are worth further investigation.

By utilizing a broader set of tools through that balance, organizations can improve their ability to process information and understand the world within their walls.  It all comes down to balance. Rather than separating the qualitative from quantitative process from one another, organizational development should be informed by both data and intuition in order to drive the desired outcomes.

How Decentralizing Data Informs a Successful Organizational Culture

data decentralization

With the rapid adoption of technology into today’s organizational culture, data collection and analysis is becoming a common component of the way we do business. However, this new reliance on data brings a new host of challenges, including how to combine and share this information across the organization in order to get as much value out of it as possible.

Every department has unique data needs, and while it’s important to analyze the data for individual departments, managers and leadership need to aggregate departmental data to gauge the overall health of the organization.

There can be huge differences in how departments structure and format their data. So, while combining multiple data sources has significant advantages, the disjointed nature of departmental data formats has led some organizations to rely on a less than ideal “top down” or “one size fits all” approach to analysis.  In some cases this approach can work, but it often forces the organization to follow an architecture that reduces the data’s usefulness within each department.

Attempting to simplify the process by generalization may support the manager’s agenda, but the end results are not nearly as specific or as meaningful as they could be.

So, how can organizations ensure data and its analysis serves the greatest number of departments and people as possible, while still benefiting the business as a whole?  Below are several ideas to consider:

Determine which questions need to be answered.

First and foremost, data has to help us answer questions.  It is essential to clearly articulate which questions are more critical to managers and business units, and to ensure that data can be captured to support those.

Establish similar terminologies.

Despite the likelihood that each department has its own unique culture and terminology, organizations should strive to use a similar language across the board.  This will help promote cross-communication and maintain a certain degree of data integrity for managers.  Where this is not possible, organizations should develop a “translation table,” (i.e. a “Rosetta Stone” for the business) which can serve as the key to understanding the different terminologies.

Agree to a common set of core variables.

Each department has its own set of questions that will require a unique set of data, but managers need to address questions that span departments. To ensure they can, organizations should agree on a core set of variables that each department will collect.  This can be something as simple as time, location, identification number, costs, and labor hours, etc.  The key is to agree on how these variables will be defined and documented. Yes – that could require a bit of time and finesse. While this may be the most taxing part of the process, it is the most critical in the data decentralization process.

In an ideal world, individual departments and leadership would easily find a compromise for their various data needs.  On the one hand, each business silo need data that is specifically relevant to their operations. On the other hand, managers need data sets that address broader questions and issues. If the overriding concern is real usefulness in the data, the time it takes to create systems that allow for both will be well worth effort.

How is your organization decentralizing data to inform your culture?