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How to Make Your Data Team a Priority This Year

Data teams exist to produce effective results. They’re tasked with solving complex problems, helping the organization run more smoothly, and preventing significant missteps. But a company can’t have a winning data team if the group and its work aren’t a priority.

Teams can falter when communication about existing problems is poor and the right tools aren’t within reach. Employees will spend more time putting out fires than helping the business manage and leverage its information proactively. Weak internal support and unrealistic expectations are also problematic for data teams and can block their success. This article discusses ways businesses can prioritize data teams and give them what they need to thrive.

Provide Up-to-Date and Powerful Tools

Data teams don’t like being the last to know about issues that have gone unchecked for weeks, months, or longer. Employees in other departments might notice an issue but fail to report it. Sometimes it’s because others don’t recognize a concern as a legitimate problem that needs fixing. Other reasons include a lack of know-how, insufficient reporting systems, and sheer inertia.

However, not alerting data teams about potential issues can turn less complex data or processing errors into full-blown outages. Giving data teams modern and sophisticated tools, such as data observability solutions, helps teams see what’s happening throughout the data pipeline. They’ll know when servers are failing, which data records are missing or mismatched, and where processes are slowing down. Most importantly the data team will be the ‘first to know and the first to fix’ and this helps improve the quality and reliability of data throughout the organization.

Instead of completely relying on human communication and oversight, data teams will have the power of AI on their side. They won’t waste time backtracking on issues that grew bigger over time. Problems can be nipped in the bud once teams receive alerts from an observability application. The AI that runs these solutions will also fix the simplest of errors, increasing data teams’ efficiency.

Clearly Define Projects

If teams are given data models to build or problems to solve, clear and accurate instructions are a must. Say marketing needs a predictive model for which consumers are most likely to buy a specific product. Yet what the marketing director really wants is something that’ll predict campaign effectiveness within separate consumer segments.

It does the data team a disservice to define the project the first way. The group will waste its time building a model that forecasts who’s going to buy the product. Marketing won’t get what it needs, and campaign efficacy won’t increase. Executives won’t see the financial impacts they were hoping for, and the blame game will start.

To help others in the company, data teams need the right parameters to begin with. If a group works on too many projects that go nowhere, they’ll likely lose motivation. Lower morale and engagement lead to less productivity and lower commitment to solving problems. Involving data teams in the process of determining a project’s scope helps ensure problem definitions are clear from the start.

Structure Teams According to Strategies

Expecting data teams to juggle numerous projects and deliver them all within short time frames is unrealistic. However, groups may work under these conditions when there aren’t enough resources. Teams may be too small, or their members may lack required expertise. The number of departments or individuals the data teams assist also factors into the equation.

It might be obvious that you can’t expect a small data team of three or four to serve an entire enterprise. But some business leaders try to pull this off because they haven’t thought through their data strategy and team structure. A Harvard Business School article recommends considering three factors when creating data teams.

Leaders should think about size according to project and data volume, as well as how many people need the team’s support. Centralization is another key factor. Some companies do better with one team, while others require multiple groups to work with separate departments. Guiding all of this should be an overall data strategy that spells out how the business intends to use information. Which decisions require data and which ones do not?

Build and Invest in Data Teams’ Knowledge

Because of the more technical nature of data teams’ responsibilities, business leaders might assume groups have all the knowledge they need already. Business managers could also become intimidated by data teams’ tech abilities. But when it comes to learning on the job, data teams aren’t any different from their less technical counterparts.

Research shows that 85% of big data projects fail outright, and 87% of data science projects don’t make it to production. Logging what went well and what didn’t during and after a project is something teams can do to improve. However, retraining and support from others in the organization are also necessary.

Data teams, like other departments, often have a mix of skill sets and knowledge. Someone could be more adept in database designs and structures. Another team member may be a whiz at analysis, while someone else can design systems in their sleep.

These skill sets may be complementary, but it doesn’t mean that they’re always up to date or comprehensive. Project failures can reveal the areas where data teams may need additional training. Like their counterparts throughout the company, data teams need to be a part of any learning culture.

Conclusion

The work data teams do is often essential to an organization’s success. If businesses don’t prioritize these groups’ perspectives and needs, the resulting inefficiencies will speak for themselves.

Providing the right tools, accurate project scopes, supportive structures, and learning opportunities will help data teams feel seen and heard. Recognizing the importance of these groups’ contributions and moving their visibility to the forefront should not be up for debate.

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