Using teams to perform work continues to increase in frequency. The more complex work of today requires skill sets that fewer individuals possess on their own. And processes like the concurrent engineering of new products necessarily involve knowledge in a wide variety of fields, such as Finance, IT, Production, Marketing and the like. A team possessing this wide variety of knowledge can be created by selecting individuals with different skill sets and charging them with achieving defined objectives.
But in order for teams to work well members not only need to possess the skills and knowledge required but must also be able and willing to share what they know. And they need to contribute their knowledge in a form that is intelligible by members from different specialties. For example, if individual pay is tied to relative contribution and there is a fixed budget this can put people into competition with each other… hardly the approach that would motivate them to work cooperatively.
And occupational differences may raise issues related to working together effectively. Assigning a Data Scientist to a Human Resource Department seems to be the ideal way to begin to use workforce analytics to improve processes. Staffing the organization requires sourcing talent and selecting the right people. Developing talent must be done in a manner that is both efficient and effective. Managing performance and rewards must result in employees viewing the process as fair, competitive and appropriate. Organizations are moving towards evidence-based management and workforce analytics can help HR practitioners improve all of these talent management processes, assuming they can utilize the knowledge produced by analytics in a way that provides value.
Charging a Data Scientist with the task of developing a selection model that will result in the right people being hired is a common occurrence today. Using multiple regression or some other statistical tool the Data Scientist will attempt to find the factors that correlate with successful hires, thereby providing the organization with the knowledge that can improve the quality of hiring decisions. And IBM and other consulting firms offer software that claims to be able to identify key talent that is in danger of leaving, which is potentially very valuable information. The provider of this service feeds relevant internal and external data into the software and makes predictions about the probability of someone leaving.
One of the challenges with selection models is that the Data Scientist is unlikely to know enough about what makes individuals likely to actually succeed on the job. In order to make sound predictions all of the factors that influence performance must be identified and then used to create the model. Ryan Leaf was selected ahead of Peyton Manning in the NFL draft. He was not successful, and Manning earned a spot in the Hall of Fame. Leaf exhibited greater athletic process in the Combine so on the surface the decision seemed correct. But Leaf’s personality lead to locker room conflict and negated the potential provided by athletic ability. Personality was not included in the model used for selection, so the decision was made based on incomplete data. Since personality is a very difficult thing to test for it is likely any model will be limited in assisting selection when personality is important.
When forming teams, it is often necessary to consider both cognitive and cultural diversity as well as technical diversity. Research informs us that more diversity in a team makes it more difficult to achieve consensus rapidly when decisions must be made. On the other hand, diversity will increase the variety of options considered, which may be more critical than achieving consensus quickly. How an individual defines issues and goes about exploring alternatives will influence that person’s opinions about how the team should function. An individual’s cultural orientation can also shape how the person values alternatives. As globalization has increased cultural diversity in workforces the issues associated with managing them have expanded.* In order for a culturally diverse team to be effective management must:
- Recognize cultural differences when they exist,
- Respect the right of others to hold different beliefs, and
- Reconcile the issues differences create.
There is a large volume of information about managing teams in the literature. They need to be a workable size. The members must cumulatively know what the team needs to know. They must be given clear objectives, as well as what resources are at their disposal. But individual diversity still can impede effectiveness if the members cannot find a way to work together effectively.
The earlier example given involving a Data Scientist being made available to an HR department illustrates yet another challenge… the members must be able to communicate with each other. And in order to communicate the parties must have overlapping knowledge. A PhD in Decision Science is not likely to have much information about human resource management principles. An HR practitioner may have limited statistical knowledge. The lack of a shared space can cause a disconnect when the two try to arrive at a mutually agreed to approach to what has to be done. When external data is used in analysis it must be relevant data. Someone must decide if data about what makes a Software Designer successful at Google should be used to predict outcomes in an entirely different type of organization. Using data from law firms that litigate may not be advisable if the organization does not litigate… the competencies that make someone successful in one place may have no application in another. Although the HR person might insist the Data Scientist read the job description for the role being recruited for this is unlikely to provide the understanding necessary to select relevant data.
There are jokes about the different perspectives of people in different occupations. Engineers will keep working to make the product the best it can be, no matter the cost or whether the customer cares about added capabilities. Accountants will cut funds off when the product achieves minimally acceptable performance. Much of this stereotyping is not warranted but there are effects created by how people are trained and socialized in their occupational fields. And when people from diverse backgrounds attempt to integrate their thinking it can present challenges.
All of these potential obstacles to team effectiveness can be dealt with. But failing to go beyond the simplistic requirement that collectively the team members have the necessary technical knowledge may result in teams that do not do what they must do because they cannot work together effectively.