The other day a co-worker shared the ?Management Tip of the Day? from the Harvard Business Review. These brief emails always get me thinking. The topic this day was ?3 Characteristics of a Real Team?. These tips highlight three characteristics that together maximize the potential of a team. These very much apply to teams in the data community. I thought I would reflect on what they mean in ?data speak?.
A meaningful and common purpose. This is more than an outside mandate from the top of the organization. To be successful, the team must develop and own its purpose.
I have spent the past 20 years working in data teams and with other data teams. These teams have often been disjointed and non-cohesive. Sure, the team was built because data was the focus of the individuals? day-to-day work. The first four characters of data analysts, data modelers, database administrators, data architects, and data warehouse architects job titles seem to indicate the group they need to call home.
The real data team should encompass all of these roles. What makes it real is the last five words of this characteristic ?develop and own its purpose?. Rather than bringing these individuals together and have them form sub-teams that focus only on their responsibilities, the real approach is to bring the data workers together and work as a single team with common goals and objectives. A common purpose does indeed build on the strength of the skills, backgrounds, talents and diversity of a team. As we know, there is strength in numbers and this common purpose becomes even stronger with the larger team.
Adaptable skills. Diverse capabilities are important. Effective teams rarely have all the skills they need at the outset. They develop them as they learn what their challenge requires.
Adaptivity is not a strong point for most technical people including data folk. The techie personality thrives on growing within their area of expertise and becoming the subject matter expert in his or her domain. Stepping outside of that box and adapting is indeed difficult for most of us.
A real data team takes an inventory of the skills and talents of the team members. It?s not a bad thing to have that heads-down DBA who knows the ins and outs of DB2 or Oracle. The same goes for the data modeler who is savvy in business speak and is able to translate it into data speak. These individuals bring great strength to the team. As real data team members, they must respect and rely on each others knowledge and expertise.
There are times when data professionals need to adapt to be more effective. This is the fact and each of us has to work on cultivating our adaptivity in work situations. It will only help us and our team.
Mutual accountability. You can’t force trust and commitment. Agreeing on the team’s goals is the first moment at which team members forge their accountability to one another.
The data community is a community where accountability is a foundation for our work. The integrity, security, responsiveness and reliability of the data we manage require that we be accountable. The topic at hand is mutual accountability. Being accountable to a peer can be difficult at time. Most of us work in an environment where we are accountable to a manager or supervisor.
The real data team stretches the accountability envelope to include this accountability across the team. This translates into working as a team that clearly defines their goals, objectives, deliverables, processes, service levels, and responsibilities. A clearly defined work environment helps work flow through the team with minimal missteps and misunderstandings.
Mutual accountability falls into the same category as adaptive skills. They are both soft skills that take a little extra effort for data professionals to embrace. These two characteristics drive the dynamics of a team. The better we become with both relates directly to our performance as a better team.
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