In the book Moneyball: The Art of Winning an Unfair Game Michael Lewis tells how in 2002, Oakland A’s manager Billy Beane used nontraditional statistics to turn the small-market franchise into a team that could compete with big-market franchises. The story holds lessons for IT management about the importance of understanding objectively the strengths, weaknesses and behavior of individual players in order to build a successful team.
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Beane dispensed with the traditional—and subjective—baseball wisdom that scouts relied on to draft players and created an objective method for scouting based on statistics that weren’t valued by his competitors. By taking this new approach Beane revolutionized the way baseball is managed and played, changing fundamentally the concepts behind building a winning team. Theo Epstein, general manager of the Boston Red Sox used a similar approach to win World Series championships in 2004 and 2007.
Like Beane, Steve Randle, Vice President of IT Operations for XO Communications wanted to find an objective way to understand his organization and how to make it more successful.
Like many IT executives, Randle used metrics such as uptime, server statistics and project completions to illustrate his team’s achievements. While these metrics paint a useful picture, Randle realized that there was a more fundamental reason his organization was successful—because of employees’ knowledge and relationships—and he wanted to document it.
Beane found that on-base percentage (how often a batter reaches base for any reason other than fielding errors, a fielder’s choice, a fielder’s obstruction, or catcher’s interference) and slugging percentage (a measure of the power of a hitter calculated as total bases divided by number of at bats) were better statistics to evaluate players than the traditional tools used by teams at the time.
Randle saw that individuals’ knowledge and their ability to collaborate and share information were fundamental factors in determining whether his IT organization would achieve its goals. However, traditional means of evaluating personnel, such as yearly reviews or peer reviews, focus on the individual, not his or her relationship with teammates. Randle wanted to document his staff’s interpersonal and interdepartmental relationships.
In 2007, Randle learned about Social Network Analysis (SNA), and he saw a tool that could produce the desired objective measurement. Through the use of SNA, Randle explored a new perspective: That people and information are primary IT assets and should be valued at a premium. An individual’s ability to solve problems, connect with resources, anticipate issues, and communicate details is crucial to organizational efficiency and effectiveness.
How Social Network Analysis Works
Social Network Analysis is a statistical method used to analyze organizational structures. SNA provides a visual and mathematical analysis of human relationships that can be used to evaluate communication patterns and find holes in an organization’s communication network. The tool uses people’s personal connections to compute an organizational communication network and to map information flow between people and departments.
“When I saw the SNA output I realized I had found a new way to quantify the relationships within and across my teams. It let me see those who consistently communicate well with others,” says Randle.
The first step in Randle’s Social Network Analysis was to survey everyone in his organization. He asked only four questions:
- Who do you go to for advice or information when assessing a difficult problem or discussing ideas?
- Who do you depend on to get your job done?
- Who do you communicate with most frequently? and
- Who is your most valued contact within XO IT Operations?
Each person was allowed to name up to three people from within the IT Operations department in response to each question. The answers to the survey were used to generate a set of network diagrams called sociograms similar in form to a router network diagram. In a sociogram, the network nodes represent people, and the connections between nodes indicate communication between individuals.
One sociogram showed nodes that were sized according to the number of ties connecting an individual. “It was immediately apparent which people were central to my organization and who had many ties to others on my team,” says Randle. “This showed me those who were strong communicators and sharers.”
In social network analysis, what Randle saw intuitively is called centrality-a measurement used to define the person’s relative importance to an organization based on his location in the network. Centrality takes different forms. Degree centrality counts the number of ties a node has to others and is used to measure a person’s overall activity in the network. Betweeness centrality shows the degree to which a person connects different groups, thus controlling information flow across the network. Closeness centrality is based on the number of hops it takes for a node to traverse the entire network, and it indicates a person’s access to resources.
These centrality measures enabled Randle to view his organization from varying perspectives and identify important traits of both people and departments.
Evaluating the Team
The SNA revealed both positive traits and potential problems within Randle’s organization. He found that there were individuals in all four of his IT Operations departments that demonstrated high degrees of all three centrality types. Randle was pleased to find that his Enterprise Network department was highly connected to all teams within IT Operations at both a management and individual contributor level. This high connectivity of the Enterprise Network department was especially important to Randle because it’s a function that all his other teams rely on.
But Randle also noticed that as in many other organizations, there were silos within his IT Operations organization. He found groups that were jointly responsible for key IT-wide initiatives that could increase their communication to benefit these projects. And there were cross-functional groups that could perform more efficiently by better connections with the teams they supported.
Randle remembers a project to upgrade a key customer care system that did not go smoothly. There were communication disconnects between the project managers responsible for the upgrade. Key dependencies on other teams were not uncovered until late in the project. One result: Server administration teams were not brought into the loop until the 11th hour, which led to configurations being done on the fly without the desired level of planning. When Randle looked at the SNA results he immediately saw that the project management team responsible for the upgrade operated in a silo, and that silo correlated to the lack of communication that had jeopardized the upgrade.
Stronger Management in the Front Office
Beane used his insight to ensure the A’s minor league management and player development personnel focused on plate discipline and long, patient at bats in order to build up-and-coming players’ skills. Similarly, Randle used Social Network Analysis to identify effective practices among his management team. Examining his managers, senior managers, and directors provided a clue into how the department’s silos had emerged. Randle found there were strong ties between three of the four first-tier managers, but one department was clearly more isolated than others. At the second and third management tier there were only a couple of connections between departmental managers.
“The management only analysis let me visually show my direct reports tangible evidence that there where some communication gaps. This quantifiable data was helpful to foster discussion with my management team,” Randle says. The SNA revealed which two of his direct reports were the best interdepartmental communicators. Randle can leverage these two leaders to strengthen communication across all of his departments by involving them in cross-functional projects.
By increasing openness and collaboration at the management level and bridging communication gaps, Randle hopes his management team will lead by example and that this will have a trickle-down effect that increases communication throughout the entire organization.
New Stars in the Clubhouse
Randle also had a few surprises when individual contributors where analyzed. “This highlighted individuals I knew were central to the organization, but also revealed some that I would not have guessed would be as critical to my organization as they are,” says Randle.
Beane discovered a key statistical ratio called “on base plus slugging percentage” (OPS), which is the sum of on-base percentage and slugging percentage. This OPS measure shows a player’s ability to have a high batting average, draw walks, and hit for extra bases. If a player has a high OPS, they are likely to see a higher number of pitches per plate appearance, which means they can help to wear out the pitcher-an important offensive strategy.
SNA enabled Randle to better identify his key players and subject matter experts. He found several employees who ranked highly in all areas of centrality. For example, the head of Randle’s antivirus team has high centrality, which enables this leader to quickly assemble and disseminate information when a virus threat emerges. By quickly pulling in members from all of the IT Operations teams, threats have been neutralized and resolved rapidly, protecting valuable company data and information.
The data also allows Randle to see the potential impact of employee departures. When Randle found out that a senior manager on his team would be leaving the company, he referenced the SNA and identified an individual who had no connections within the department except to the departing manager. Randle was able to pair this person with other team members through informal meetings and mentoring, facilitating new relationships and ensuring the individual did not continue in isolation when his manager left.
The Playoff Push
Randle accomplished his main goal–to foster discussion with his management team about communication and collaboration using objective data produced by the Social Network Analysis. Now, says Randle, he will be able to use the results from the SNA, to build better communication within his organization, increase collaboration, and thus build a more effective team.
The next steps for Randle are to use the analysis for succession planning and help him determine who should be involved with critical cross-functional initiatives.
In regard to succession planning, by studying the SNA results Randle can see relationships that would be lost if key individuals left the company. He plans to use this information to build connections proactively between teams to mitigate the risks of a departing key team member.
Many of Randle’s critical initiatives and projects involve multiple IT Operations departments. The SNA will help him to engage the right people on these project teams. He says that teams with members who show high centrality will be able to more effectively access needed resources across the organization, and will more readily share information with other groups who need to be involved with a project in a timely manner. With better planning, Randle expects better results.
Brad Johnson is a software developer. He worked for Steve Randle as a senior manager of data center operations at XO Communications from 1998 to 2005. He is currently a developer with HRsmart, a provider of human resources management software.