There is little doubt that DevOps has become a term du jour in tech circles over the last couple of years. Many tech firms understand the concept as a culture transformation. And they likely understand that collaboration in order to speed up production cycle times can be a win-win for companies and consumers alike. But I’m still not convinced that we’ve fully explored what it takes to truly embed the principles of DevOps in sustainable ways that yield tangible results.
We live in an age of data. Big data. The ability to collect and use data to make business decisions has become table stakes for any organization looking to gain operational efficiencies, drive innovation, obtain market share, and manage targeted and timely development of human capital. Looking back even five years, a McKinsey Global Institute report communicated the value of big data.
“We estimate that a retailer using big data to the fullest has the potential to increase its operating margin by more than 60 percent. Harnessing big data in the public sector has enormous potential, too. If US healthcare were to use big data creatively and effectively to drive efficiency and quality, the sector could create more than $300 billion in value every year. Two-thirds of that would be in the form of reducing US healthcare expenditure by about 8 percent.” (MGI, 2011)
Over the course of these past five years, we have gained a lot of capability and capacity to help us manage all of this data. And yet, many organizations still feel as though they’re falling behind.
There I was, sitting in a conference room with my client, the Chief Human Resources Officer (CHRO) of a large, San Francisco-based company. I wasn’t quite sure what I was getting myself into. I had been onsite supporting an unrelated project when my client asked me to join her in a meeting with another consulting firm to review the results of the company’s recent employee engagement survey.
Guest article written by Levi Nieminen, Ph.D.
As part of the debate over whether to end traditional performance management and where to go from here, one fundamental question that needs to be addressed is whether a single HR- Talent Management system can achieve both evaluative and developmental objectives? In this brief article, I describe a few of the principles that OD professionals live by and the challenges they present for the designers and overseers of “performance management 2.0.”
“Blow up” performance management
Over the last several months, the list of high-profile companies who have “blown up” performance management (PM) as they (we) once knew it has grown to include GE, Microsoft, Adobe, Gap, Accenture, and Deloitte. These are just the most recent public denouncements of what is certainly a long standing and widely held discontent over PM and appraisal practices. Two years ago, CEB’s research indicated that upwards of 90% of companies were looking at major overhauls to their PM systems.
These days, it appears that the debate over PM is taking on both on new heights (see Bersin by Deloitte report) and adding new angles of aerial attack. As an Organizational Development (OD) professional looking in from a semi-outsider perspective, it occurs to me that the latest round of scrutiny has focused on the many ways in which traditional PM systems fail not only from an evaluative perspective (i.e., valid appraisal of people), which is old news, but also from a developmental perspective. That companies want to invest more in the development of their people makes good sense. Whether this responsibility can or should be housed within traditional HR departments and aligned in other ways with formal PM systems remains to be seen.
I am biased however, to think that PM 2.0 will fail on developmental objectives until the old principles of PM are replaced with a radical new set. Though a much longer list is certainly possible, here are 3 principles that most OD professionals I know live by, and which might provide useful guideposts for PM 2.0… if we are to take the new focus on development seriously.
Principle 1: You can lead a horse to water…
PM 2.0 needs to turn the concept of who owns employee development on its head. In the past, we have pointed to management, the HR-Talent Management-OD department, and most recently, team leaders (see Deloitte in HBR) as the owners of the development process. While we talk about the idea that managers need to “develop their people,” from the employee perspective, this makes development feel like something “they’re doing to us.” Once the whole activity takes on an odor of compliance, what follows more often than not are check-the-box actions and commitments. There is an art form to giving ownership to employees that will no doubt involve learning new and productive ways to lead the horses to water. And some leaders and some cultures will support these coaching behaviors more than others.
One organization that has been leading this charge is the Federal Bureau of Investigation. Rather than focusing their efforts on manager-supervisor engagement in the process, they have recently begun to shift toward fostering employee ownership. One practice involves training employees in how to seek, receive, and use feedback. Culturally, they recognize the need to attract and hire the right people for this strategy to be effective.
Principle 2: From big data to small data
Many of the emerging trends of PM 2.0 [so far] have focused on solving the old problems of how to evaluate people, for example, how to fix ratings. As a result, many of the proposed solutions focus on giving bigger and better data to management so that organizations can make smarter decisions about how to compensate and utilize its people. On the hand, this is really good progress!
On the other hand, this progress seems to do little to address the development objectives. While new data-driven solutions are certainly needed on this side too, what’s needed will likely look very different than the recent clamoring for big data. Instead, it’s much more likely to look like small data–informal, ongoing, un-documented, and owned by the individual.
Every coach who has used a 360 with a client knows that there comes a moment-of-truth question when it’s time to ask the HR sponsor: “Who will own the data?” The old PM script that gives HR co-ownership of the data is one of the best ways to compromise the individual’s ownership of the process and certainly conflates the purpose of development with a new possibility that evaluation will sneak in. Even the best and most well-intended HR partners cannot be expected to un-see performance data they’ve seen and this can be a problem when it later comes time to weigh in on personnel decisions.
For PM 2.0 to truly prioritize development, organizations will need to add a healthy dose of small data that is owned by individuals and off-limits to corporate. This does not mean that the new systems will lack transparency, but that the modes for achieving transparency will need to be different. For example, the assessment data or feedback can be held and owned privately by the employee, so long as the process also encourages honest conversations about the key insights gained from that data. Those conversations are essential in order to gain the input and support of the boss, co-workers, and HR as the employee embarks on new development priorities and goals. As the next section describes, there is a certain “art form” in the coaching that is needed to guide a person through this process.
Principle 3: Feedback without coaching doesn’t work
Freeing managers from the burdens of ownership (Principle 1) does not let them off the hook. But it does allow for a shift in how they interact with the process and the skills they will need to build. In the big scheme of things, organizations might get more return-on-investment from PM 2.0 by wrestling a little less with the measurement of performance and a little more with teaching managers how to be good coaches for their people.
Recent research confirms that providing feedback without the adjacent support of a coach leaves a lot of the value in these exercises on the table, and in particular, whether the individual sees growth in him or herself as a leader over time. One reason is that the translation of the feedback into priorities and specific actions is rarely self-contained in the feedback. This takes work and requires not only a motivated individual who wants to change but also a supporting process that builds awareness and alignment with the key people around him or her.
In this respect, PM 2.0 will need to replace the old “compartmentalized” view of individual performance with a wider-lens that also shines a light on key elements of the team, organization, and strategy. The most value will be created when the development strategies for people accurately reflect the specific needs of the business strategy (read J. Boudreau’s, “Trouble with the Curve” for an interesting take on this). And it seems reasonable to expect even more demand on coaching and coaching skills as a more complex view of individual performance and the surrounding context is embraced.
Development and evaluation: A paradox?
Stanford business professor Charles Bonini described how it is not possible to create a model that is both accurate and useful. A model that is fully accurate is too complex to understand, and thus, we must compromise some accuracy in order to achieve some practical value. This is called “Bonini’s paradox.
As with the HR sponsor in the 360 moment-of-truth, the designers of PM 2.0 will need to decide what their ultimate priority is. If development is the priority, the new systems will need to be engineered with development principles in mind, and the solution will be as much about changing the culture as it is about improving the measurement. As my description of each principle has highlighted, these cultural shifts will most likely entail:
- The shift toward employee ownership of development and corresponding changes to how HR and managers support and bring accountability to the process,
- New norms that effectively balance privacy and transparency so that employees can own their feedback and data (e.g., 360 data) while also having the honest conversations needed to allow others to support their progress, and
- A shift in management style and skillset that moves away from “telling and directing” and moves closer to “asking and coaching.”
This article first appeared on Denison Consulting.
Levi Nieminen, Ph.D. is the Director of Research and a Senior Consultant with Denison Consulting. His work focuses on conducting applied research on organizational culture and leadership and translating that research into improved solutions for clients and shareable knowledge for the larger scientific community.
Out of the gate, I want you all to know that I’m not a tech expert. I’m happy that I’m able to navigate the typical business software and email on my laptop to get through my workweek.
That said, at the Seattle Interactive Conference a couple of months ago, I attended a presentation by Lucas Welch, Director of Communications at Chef, a Seattle-based tech firm that provides an IT automation platform to brands such as Target, Nordstrom and Facebook. Lucas’ presentation was on the topic of “DevOps.”
I enjoy attending conferences that are outside of my area of expertise namely because they help me to expand my thinking. With Seattle’s tech boom in full swing, the SIC event was something that offered the opportunity to understand more about tech subcultures and how tech companies are evolving in a rapidly changing business environment. What I didn’t expect was to learn about an entire tech movement whose success rides squarely on a topic I do happen to know a lot about: organizational culture.
If you do not manage culture, it manages you, and you may not even be aware of the extent to which this is happening. — Edgar Schein*
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.
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.
Latest posts by Stuart Farrand (see all)
- 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