I recently came across an excellent article in The New York Times from economist Adam Davidson (co-founder of NPR’s Planet Money). In it, he describes his experience working as a technical adviser on the film The Big Short and the unique group dynamics he saw while working on set (this is further expanded on in an interview with Russ Roberts on the podcast EconTalk).
I recently came across an article on Life Edited that discussed a study of how families utilize the space in their homes. The study tracked the movement of 32 families in the Los Angeles area over two days.
Each dot on the diagram below represents the location of one family member during a 10-minute interval. Not surprisingly, most families spend much of their evenings congregating in the kitchen and family room and very little attention is given to the porch or dining room. The article goes on to discuss how the space in homes could be optimized to accommodate the way families actually use it.
This brings up some interesting parallels to organizational culture. What if we used data visualization to map the movement of people within our workspaces? What insights would we find?
By applying the same principles laid out in the article, we would be able to determine which areas of our office are utilized more frequently. For instance, maybe the shared kitchen space is a significant congregation area throughout the day, but conference room #2 is only use for 30 minutes in the afternoon. At a very high level we can use this information to help design effective workspaces that facilitate communication and optimize the use of shared spaces.
Taking those insights further, we can explore how those workspaces are utilized. For instance, maybe the kitchen is not only a place to grab lunch but also where employees casually discuss business and some ideas they have for a new product line. In this sense, the kitchen serves both as a source of camaraderie and a facilitator of innovation. This example may be a stretch, but it’s fairly easy to see how different spaces support different aspects of the organization’s culture.
These insights would help us identify the key hubs where the “actual work” (i.e. the side conversations, backroom deals, and brainstorming sessions that keep organizations moving) happens.
From a leadership perspective, this information can help streamline and enhance the way organizations convey critical information.
By identifying the key congregation hubs and the type of discussions that are taking place, leaders now know where to place information (ex. update on a new safety policy), the content of the message (ie. use a humorous tone in the kitchen space), and the type of media to use (ex. quick graphic, display on a TV screen, a copy of the document, speaker announcement, etc.). This could help improve the way information is disseminated and reduce the likelihood that coworkers are oversaturated with information that does not resonate well.
“Casual Collisions”: Applications in the Business World
Companies from Pixar to Google have taken a similar approach to developing workspaces. Their approach (or philosophy really) is called “casual collisions” where office space is configured to optimize collaboration and facilitate employee interactions. As Steve Jobs once said “creativity comes from spontaneous meetings, from random discussions. You run into someone, you ask what they’re doing, you say ‘wow,’ and soon you’re cooking up all sorts of ideas.” Buildings, floors, hallways, and meeting spaces can all serve as a medium to foster creativity.
Google, while in the process of designing its new headquarters in 2013, felt that it was necessary to methodically plan out the configuration of the building to facilitate collaboration. To do this they conducted studies to determine how employees worked, what kind of spaces they preferred, and what groups/departments want to be close to each other. As a result, Google was able to configure the 1.1 million square foot building so that no employee would be more than a 2.5 minute walk from others they frequently collaborate with.
To push this concept further, an article published in the New York Times in 2013 provided a vision for the future, stipulating that through a combination of sensors, analytics, and technological improvements, offices could reconfigure each morning (by using sophisticated algorithms) to fill in structural gaps and place critical groups in closer proximity to address pressing tasks and challenges.
While that may seem like science fiction, there is evidence to suggest that more and more organizations are turning to analytics to figure out how to configure workspaces to ensure the right people are making connections.
It is likely that not every organization can conduct a study of this kind; factors such as costs, square footage, and geographic proximity of key departments can all limit the feasibility of this approach.
Still, data analytics can go a long ways to enhancing how we see our office spaces and can help leaders think more critically about how to improve organizational collaboration and communication to their team members through design.
Why is it that many large-scale change initiatives fall short of expectations? Some might say it’s because leaders weren’t communicating the effort effectively. Others might say employees were stuck in a “business as usual” attitude. I would argue that the failure of many change efforts can be attributed to three factors:
- The organization didn’t target the right individuals
- The organization didn’t incentivize the change to match the values of its employees
- The organization tried to make the change too substantial rather than incremental
In recent years, the term “hacking” has grown in popularity, especially “growth hacking” within the marketing field. Growth hacking involves using analytics to target specific consumer groups, test which messages are successful in driving viewership, and scale the most effective strategies.
This process can also be applied to implement organizational change, hence I’d like to term this alternative approach “orghacking.”
Orghacking offers a way to implement rapid, testable, repeatable, and scalable interventions that bypass conventional organizational limitations like hierarchy, stovepipes, and communications protocols. Each intervention caters to the values of key demographic groups and leverages the many social networks and relationships that exist among employees.
Changing Our Perspectives
Many large-scale change efforts see the world from a top-down perspective. Leadership has an idea, they develop a policy to capture the idea, and they rely on managers to implement the policy at the ground level. In this approach, information moves up through the hierarchical chain while decisions flow down.
The problem with this strategy is that it often fails to appreciate the complexities inherent within an organization. Employees often interpret and respond to situations differently. They may also interact and organize very differently across departments. As a result, organizations function more as a network of clusters, where employees congregate around certain individuals and processes and share ideas and values with those closest to them.
A top-down approach may easily glaze over these factors, leading to unintended consequences such as employees misinterpreting the policy or outright ignoring it. The disconnect between top-down strategies and the way organizations inherently operate makes it difficult to align the workforce to a new strategy and vision.
Orghacking, on the other hand, bypasses the standard top-down approach and instead moves from the focal point outward.
As the diagram above shows, orghacking involves a combination of process mapping and culture-based analytics to pinpoint what issues exist, where they occur, and who is involved. It then uses precise interventions to target hubs within the organization’s social networks, shape the intervention to tap into the influencers’ values to incentivize behaviors, allow the intervention to spread throughout the social network, measure its impact, and modify the approach.
In this way, orghacking flips conventional logic on its head by making interventions small in scope, targeted to the individual, and adaptable to new insights.
How does orghacking work?
Based on the diagram above, orghacking entails the following steps:
Step 1: Executing process mapping to understand challenges
One of the more succinct ways to identify bottlenecks is through process mapping. Process mapping allows us to see the flow of how products/deliverables are produced in an organization.
We can gauge how effective certain parts of the process are by obtaining feedback from focus groups, looking at financial data to assess returns, and examining process metrics to determine where delays occurred.
Through this approach, we can pinpoint specifically what challenges exist, what type of issue it is (people, process, tools related), and where it exists in the process.
Step 2: Leveraging analytics to discover insights about employees
Organizations are overflowing with data that can be used in orghacking. Everything from personality indicators to satisfaction surveys give us insights into the different types of people who work at an organization, how they think, and what they value.
Depending on the level of granularity in the data, we can even look at correlations among the responses to identify connections among different sets of values/attitudes and demographics. Examples would be if people who rate the organization low on trust also tend to rate the organization low on delegating authority. Or, whether males in purchasing tend to rate the organization low on trust also tend to value clearly defined processes.
The goal is to identify hidden insights about our employees and find connections. In the end, we can develop profiles for different types of people in our organization, each including a demographic indicator and one or more values/attitudes.
Step 3: Engaging in observation to understand how people organize
Emergence and self-organization are fundamental to how organizations operate. Understanding how people organize to get work done is a key component of orghacking.
Observations can be conducted in-person by seeing who talks to whom and/or through data driven methods such as counting the number of individuals that enter a given room or office. Observations should be validated with employees (even anecdotally) to verify their accuracy and determine the context of the discussion, like why people are congregating around a specific person. This helps us understand who are the key influencers in the organization that help move work forward.
Notionally, we assume that people congregate around others with similar values and perspectives, enabling influencers to spread ideas and permeate change.
Step 4: Using all three to create custom-tailored interventions
Orghacking is different from other approaches in that it aims to change the most fundamental units within organizations. Ultimately this comes down to identifying the influencers and those closely connected to them, communicating in their language, and developing incentives based on their profile to drive the desired change in behavior. This can increase the likelihood that a message and intervention will stick.
Another difference is how interventions are implemented. Orghacking implements numerous bite-sized interventions that invoke small changes in someone’s behavior.
Each intervention is conducted using an A/B test approach, where there are intervention and control groups. This allows us to estimate the impact and effectiveness of any one approach. Since the change is small, it can be easier to assimilate, and follow-on interventions can be conducted in rapid succession. Interventions are also given time to work their way through the various social networks and will look different across groups.
For this reason, change occurs much more organically to the unique culture of a particular group or sub-group, allowing it to scale over time.
Finally, due to its small size and scope, the risks associated with any given intervention are fairly miniscule. The failure of any one intervention does not jeopardize the whole effort. In fact, failures give us ample opportunities to fine-tune our strategies.
Step 5: Gauging the impact of our interventions
It’s important to have a clear idea of the desired outcomes from an intervention. Outcomes should be measurable, even with something as simple as a yes/no metric. Outcomes help us determine whether an intervention was successful. The lessons learned from this step allow us to determine what went wrong and make adjustments to improve the approach in the future.
Step 6: Adapting strategies based on lessons learned
While some approaches succeed, others will fail. These opportunities enable us to modify our strategies to optimize the message and incentive.
Best practices within one intervention can be applied to others as well. Eventually, we can fine-tune our approach to a set of key strategies that work for a given group, or even across groups. Then, we can broaden the outreach of the interventions to other hubs and influencers. Over time, larger segments of the organization will start exhibiting the desired outcomes and effectively internalize the change.
Repeat Steps 4-6 until the desired end-state is achieved
Coming Full Circle
The effectiveness of change ultimately depends on how it is packaged. Orghacking uses micro targeting to fine-tune the package to better incentivize behaviors. By doing so, it gives us a highly adaptable and effective way to systematically internalize change within our organizations. In this way, it can be a preferable alternative to traditional top-down change strategies.
In my previous post, I discussed how self-organization (or emergent order) is the foundation for organizational success. In this post, I’d like to propose some ideas for working with emergent orders (rather than against them) to enhance the workplace.
First and foremost, the concept of emergence can be difficult to grasp.
Emergence is an impersonal process that involves the interplay between our actions and those of others. Individually, I can only influence a small portion of the whole, but collectively our actions have a profound impact on everyone involved. We often aren’t able to see the connections among our actions, the system as a whole, and how that system impacts other people.
In studying emergence, we often become fixated the parts that are closest to us, but neglect the bigger picture of how the parts are interrelated. Through emergence, we realize that everything is connected and the world becomes much larger than we previously thought.
Understanding the magnitude of the connections and how they are related is the most challenging (but also most rewarding) aspect of emergence.
The policies and procedures put in place by leaders in any organization do not fully define the underlying culture. They help guide the organization but don’t have as much impact on the day-to-day business of getting work done; that is the job of self-organization. Whether you recognize it or not, there are always undercurrents of communication and camaraderie running throughout any business environment. While it may not be visible on the surface, the behaviors of your colleagues are often the biggest drivers of your culture.
The relationship among emergence, culture, and policy is like a garden. If you provide the right type of nourishment and conditions, things naturally flourish. Sometimes you neglect to provide key nourishment and the plants wilt. Other times you may add too much and the plants suffer as well. The trick is finding the right balance to allow the garden to grow.
Similarly, policies can enable or inhibit our ability to self-organize. By clearly defining the conditions that enable self-organization to thrive, we can determine the right type of policies and procedures to channel our relationships in ways that strengthen our organizational culture. This will look different for each organization, but it becomes a powerful way for leadership to focus the existing underlying self-organization that is propelling the organization forward.
When trying to channel emergent orders, there are a couple ground rules to remember:
1. People respond to incentives: Rules and incentives guide our behavior. We use incentives to help make decisions and plan for the future.
2. Institutions matter: Values, structures, and processes that have stood the test of time probably serve some purpose. Although institutions may need to change, their impact on the workplace cannot be ignored. We also cannot expect institutions to change overnight.
3. Work is social: At the end of the day, most change efforts aim to improve the way we work together. It’s important to focus on how work is actually being accomplished: How do departments communicate? Where are the breakdowns? And who are the influencers? We cannot neglect the social aspects of work.
How Self-Organization Can Be Used to Your Advantage
The ground rules above provide a context for understanding workplace dynamics. Many change efforts fail because we neglect to appreciate the role incentives, institutions, and social networks play in our everyday lives.
For example, focusing solely on incentives (greater productivity) at the expense of collaboration can make people feel isolated and hurt the organization overall. In addition, trying to force two groups to work together without understanding their underlying (and often different) values can cause headaches and animosity.
If we have a firm grasp on the ground rules, leveraging self-organization becomes substantially easier. People naturally organize to get work done, this may look different in different parts of the organization or when focusing on different challenges.
There isn’t necessarily a one-size-fits all strategy, but there are some general guidelines that we can employ to use self-organization to our advantage:
1. Establish clear expectations: Establish clear expectations which people can use to guide their actions and steer their interactions. Expectations should be applied consistently across the organization.
2. Keep communication open: Since work is social, it is critical to ensure people continue to communicate. When bottlenecks happen, don’t hesitate to roll up your sleeves and help forge new partnerships.
3. Leverage focal points: Who/where are the hubs were people/information congregate? What is happening in the hubs? Who are the influencers? These can serve as great opportunities to spread information and implement change efforts.
4. Reward problem solving: People like to be recognized for their accomplishments. Solving complex problems involves many people cooperating across different parts of the organization. It’s important to recognize their contributions both individually and collectively as a team. It’s also critical to encourage these individuals to share best practices with others and cross-pollinate ideas (culture is contagious).
5. Think through unintended consequences: Every action has the potential to create outcomes we couldn’t have anticipated. Before beginning a change effort, it’s important to be cautious and weigh the costs and benefits of different options. Think back to the ground rules. There needs to be an “exit strategy” when unintended consequences happen.
6. Be open to new directions: Emergent orders can take on a new shapes as the organization changes. Policies and guidelines should be general enough to accommodate these changes. When unintended consequences happen, we should be flexible and modify our guidance as needed. We should never pigeonhole ourselves to move in a single direction.
Although we often don’t notice it, emergence plays a vital role in our organizations every day. Emergence is the natural outcome of many people working together to achieve common goals. It is an important (and under-appreciated) contributor to the success of every organization, but leveraging it presents challenges in that we can’t fully understand how all the moving pieces fit together.
Sometimes we aren’t aware of how our policies and processes impact our ability to self-organize; when we act, we could be hurting our organization in the long run. By being cognizant of how incentives, institutions, and social networks shape our culture, we can take proactive steps to ensure policies enable (rather than inhibit) self-organization
Although it’s been out for a couple years, I recently reread Nassim Nicholas Taleb’s books The Black Swan and Antifragile. When they came in out the midst of the recession, they quickly caught the attention of readers looking for answers as to why we didn’t see the financial crisis coming and how can we protect ourselves in the future.
When I picked them up again recently, I realized that Taleb wasn’t focused specifically on finance. Rather, the applicability of his risk mitigation paradigm across disciplines and markets. In this light, he offers some excellent insights that are especially useful for shaping and enhancing organizational cultures.
What is Fragility?
Taleb defines fragility as systems that are negatively impacted by shocks, disruption, and disorder. At the opposite end of the spectrum, he invents the term antifragility (mainly because there is not word in the lexicon that captures this concept) to describe systems that grow and flourish when exposed to shocks, disruption, and disorder. Sitting in between these extremes is robustness, where a system remains neutral and neither gain nor decline from random events.
The concepts of fragility, robustness, and antifragility ultimately come down to risk.
Fragility comes about when we assume too much risk in a particular area. This hinders our ability to adapt when risks become actualized. As an example, Taleb points to the financial sector during the financial crisis where firms had invested significant portions of their portfolio in high risk areas. Since they had not changed their practices from previous crises, they were susceptible to the same issue areas.
All it took was one big shock and these organizations crumbled. Hence; “fragile.”
Conversely, antifragility occurs when we diversify our risks and use failures (which are small because risk is dispersed) as opportunities to learn and improve the system. Taleb uses the airline industry as an example of antifragility.
When accidents occur, the airlines conduct a thorough after-action review to determine the root cause of the failure. They then take that information and use it to update their systems and practices in their existing and future fleets. Although tragic, the accident serves to make every subsequent flight safer and improve the airline industry as a whole.
At the outset, the distinction may seem fairly simple: fragility occurs when we have concentrated risks, antifragility occurs when risks are dispersed. But Taleb points out another critical aspect of the equation:
We have no way of knowing (1) the real level of risk we have (Taleb is skeptical of models, especially since we rarely consider the assumptions and limits they are based on), and (2) when risks will come to fruition (Taleb believes in the inevitability of large, unpredictable “black swan” events).
We are largely working in the dark and must act as if we will be exposed to risk at any moment. Therefore, the ultimate goal of antifragility is to determine how to live, act, and thrive in a world we do not fully understand.
Building An Antifragile Culture
So far, we have outlined the central ideas within the fragility/antifragility framework. Here’s a quick recap of what we’ve covered:
- Risks are inevitable and we have no idea when they will happen
- Fragility = bad for growth, results from concentrated risks and lack of feedback loops, crumbles under risk
- Antifragility = good for growth, results from dispersed risks and active use of feedback loops, flourishes from risk
How can this concept foster resilient organizational cultures?
It is not a huge leap to think that organizations and their cultures can also be fragile or antifragile. Fragile cultures are those that are unable to adapt to changing environments and unforeseen risks.
Fragile cultures are characterized by:
- Highly centralized organizational structure
- Dominance of one or two departments in the decision making process (all departments become exposed to the risks inherent to the dominant groups)
- Attitudes of risk avoidance and insulation from change
- Unsupportive of “tinkering” with new ideas on a small scale
- Lack of (or disinterest in) feedback loops to integrate lessons learned
Antifragile cultures are those that are well versed in change and use dispersed risks as opportunities to learn more about how their organization functions under pressure and implement improvements.
Antifragile cultures are characterized by:
- Moderately decentralized (“lean” or “flat”) organizational structure
- All departments have say in decision making process (departments are represented in key decisions and given autonomy internally)
- Embraces risk as opportunities for learning, disperses risks across the organization so no one risk can have a significant impact
- High support of “tinkering” as way to test and improve the system
- Significant interest (and use of) formal and informal feedback loops to integrate lessons learned
To illustrate the differences in these types of cultures, we can point to two real world examples.
Most large firms lean more toward the fragile side of the spectrum. Many are characterized by rigid processes, interdepartmental conflict, risk avoidance, disinterest in new ideas, poor communication, and the consolidation of risk into one or two significant projects.
Startups, on the other hand, lean more toward the antifragile side of the spectrum. Many are characterized by agile processes, manageable conflict, risk acceptance embracing new ideas, frequent communication within and across departments, and decentralized risks across a number of projects.
This is not to say that startups are more praiseworthy than large firms. At some point, most firms will mature and transition into formalized organizations. The challenge is making this transition without jeopardizing the firm’s ability to thrive under change.
How Your Organization Can Thrive
Here are some recommendations to foster antifragile practices within your growing organization:
- Keep Decision Making Local: People closest to a problem are often the most equipped to solve it. In addition, this encourages experimentation with new ideas and strategies.
- Encourage Frequent and Open Communications: One of the major causes of distress within organizations is the inability to communicate information across departments. Open communication sets a precedent that new ideas are welcome and establishes a feedback loop to incorporate lessons learned and best practices.
- Encourage Risk Taking on a Small Scale: Many organizations focus on “avoiding” risks, but this may unnecessarily weaken the organization in the long run. Avoiding risk prevents us from learning from our failures, risks accumulate and overtime may become systemic. Dispersed risks enable organizations to try new ideas without putting the entire organization in jeopardy.
- Celebrate Failure: Every failure is a learning opportunity for everyone. Failures enable us to identify the root causes of the issue, correct the issue, and improve the overall system. As long as failures are small and dispersed, they serve to benefit the organization as a whole.
- Hedge Against the Future: It’s difficult to accurately predict what the market will look like 5-10 years down the road. Organizations should be cautious of ventures which could be a liability if the market takes a sudden turn.
Risks aren’t confined to the financial world, and are inherent in all aspects of our organizations, including culture. The way we approach risk heavily impacts whether we succeed or fail in the ever-evolving marketplace.
Fragility and antifragility are two ways of understanding and addressing organizational risks. By using antifragile practices to leverage small risks as opportunities,we can improve the way we manage our organizations and enhance our ability to thrive during periods of rapid change.
How does your organization manage risk through culture development?
The world is a fascinating place.
Ever wonder how we are able to accomplish so much without one person directing all the moving pieces? iPhones, Wikipedia, cars, the shoes you wear, last night’s dinner; all these things were made possible through the efforts of thousands of people each pursuing their own ends, and in doing so they cooperated to make our lives better.
Economists call this emergent order, more commonly known as self-organization.
Although difficult to wrap our heads around, emergent orders are actually very common. Markets, law, and language are all examples of unplanned systems that evolve naturally through our interactions. The beauty of emergent orders is that they are able to thrive because they constantly change and adapt to new circumstances.
Organizations, on the other hand, are commonly thought of as “islands of planning in a sea of emergence.” That thought is more or less correct; organizations involve layers of managers directing employees to address different challenges. But underlying that structure are rich environments of employees and teams connecting and cooperating to achieve great feats.
Self-organization is the life force that enables “work” to happen.
Because it’s difficult to pinpoint, it often goes unrecognized. Therein lies a significant organizational culture opportunity.
Below are several examples to help illustrate the relevance of and importance self-organization:
Organizational Culture and Values
Why do different departments use drastically different terminologies for the same thing? Why are some organizations more cohesive than others?
The answer is simple, although the mechanics behind it are pretty complex: Coworkers have a lot more influence on each other than we give them credit. Our attitudes and actions are as much a product of ourselves as they are the people around us. As we intermingle, our values cross-pollinate and shape the organization’s overall culture. And in doing so, the culture can take on a life of its own.
When one person has difficulty shaking habits that have emerged over the years, changing the culture becomes challenging. But within this challenge lies a hidden blessing: with enough positive enforcement, new habits can form and shape the culture over time.
Here’s a bold claim: the majority of work gets done outside the traditional chain of command.
In other words, people, driven by their desire to do their jobs successfully, self-organize to achieve their mutual goals. If you look at social networks within organizations, you’ll find that it may look very different from the org chart. The hubs are not necessarily managers, but rather employees with the broadest social connections who are able to bridge the gap between departments.
Given the ability and motivation, employees will diligently seek out solutions to their problems by building partnerships and sharing ideas. This is really a textbook case for emergence.
Rules, Policies, and Regulations
Sure, a lot of rules and regulations are cumbersome. Most people grudgingly accept them, and in a lot of cases they can be somewhat arbitrary. But, behind each rule there’s a history, an event, or chain of events that brought it into being.
For example, recently gothamCulture has engaged in a long-term effort to create a culture of safety for one of our clients. Most organizations have safety policies, resulting from years of trial and error, and a process of learning from their successes and failures.
This particular organization simply didn’t sit down and lay out an ideal set of safety policies. In many cases they had had to figure things out the hard way; piece by piece. In all cases, accidents, as unfortunate as they are, force organizations to reevaluate the way they protect employees from harm. Rules are constantly evolving, and policies change, bit by bit, to ensure certain standards are maintained.
Sometimes policies are well-intentioned missteps, but the process behind it involves the nuanced interplay among people’s values, attitudes, and actions over time.
Why It Matters
So why does self-organization matter? How is emergence relevant to you or your organization?
Work is inherently social. It is a rich ecosystem that is constantly moving toward some end. We cannot effectively understand organizations, let alone start to change them, without appreciating the role emergence and self-organization played in how getting the organization to its present state. By doing so, we reveal the many different avenues to implement change effectively.
Below are some great resources on emergent orders:
I, Pencil – Leonard Read
Valve Corporation and Spontaneous Orders – Yanis Varoufakis
Emergence – Jane Adams
The Use of Knowledge in Society – Friedrich Hayek
Where Good Ideas Come From – Steven Johnson
Leadership and the New Science – Margaret Wheatley
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
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.
- The Hollywood Model and Adapting to the Future of Work - December 22, 2016
- Using Data Visualization to Optimize Our Workspaces - April 23, 2015
- Redefining Business As Usual: An Introduction To Orghacking - February 3, 2015
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.
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?
In my previous post, I talked about the status of Big Data. In this post, I’d like to discuss some of the ethical issues we’re facing in the data world.
We are at an interesting crossroads between data and culture. Today, we have the ability to collect and analyze large amounts of data (much of it from social media) but our increased use of data is changing how we view concepts such as privacy and confidentiality. In light of the NSA Surveillance debate and a recent thought experiment from Facebook, people are beginning to question the boundaries between acceptable and not acceptable use of personal data.
This is more of a question of ethics than anything else, but it will likely become an integral aspect of how companies engage consumers and define their brand and image. In this new environment, some of the challenges businesses and consumers face include:
- The nature of private vs. public information: What does privacy actually consist of when we are all connected through social networks?
- The confidentiality vs. profitability of information: Is data kept “in-house” or is it sold for use by other businesses?
- The use of analytics to serve the customer vs. “manipulate” the customer: Are analytics used to better understand customer preferences or to subtly sway the customer and restrict their choices?
It doesn’t take long to realize that there is a fine line that separates these areas. In many cases, the distinction is blurred and hashing out the details will require a broader social conversation that weighs the costs (in terms of privacy) with the benefits (in terms of improved customer service) of our growing reliance on analytics. On the one hand, there is uncertainty surrounding how our data and information is used, while on the other hand, we gain the ability to more precisely fulfill consumer needs and even improve more fundamental factors such as the safety and reliability of the products we purchase.
These are key questions organizations need to ask, especially with regard to how they define their mission to customers. They also have implications for how an organization is perceived publicly and the type of culture they embrace internally (see Chris Cancialosi’s article on establishing a Leadership Brand). When launching a new data-driven initiative (whether Big Data or conventional), there are several questions to consider:
- Could it cause significant distress to a customer by revealing potentially embarrassing or unwanted information?
- Are you trying to understand a customer or strongly steer their behavior? Does it sound eerily similar to something out of 1984 or Brave New World?
- How secure is the data from potential hackers? What are the associated risks?
- What is the problem this initiative will solve? What is the method to acquire the data, and how is it better than other options?
These are complicated questions that won’t be answered any time soon and there are a number of different perspectives on what needs to happen (see here, here, and here for some examples). At the end of the day, the key question is one of tolerance: how much “privacy” are consumers willing to give up for the benefits data provides (see my post for an example of this trade-off)? This will be different for each person, so it is important for consumers and organizations to become educated in understanding how data is used, weigh the costs and the benefits, and make informed choices.