Six step guide for high performing data teams

After working with hundreds of data teams, we believe there are five key factors that help can help you determine whether or not your data team is high performing and that you can do to help your data team transform into a high performing team.
Last updated
May 2, 2024

In order to be successful, data teams need to have a clear vision and direction. To ensure that you're providing the right support for your team, after working with hundreds of data teams, we believe there are five key factors that help can help you determine whether or not your data team is high performing and that you can do to help your data team transform into a high performing team.

As a manager or a leader of the data team, the most critical thing to keep in mind is that people are at the centre of any outcomes. Data teams that have with strong critical thinking skills, empathy for others' needs, and an understanding of how organizations operate can make a huge difference in an organization's ability to use data effectively.

They're also able to build relationships with people across your organization from other departments such as marketing and sales—which means that they can help you create more effective partnerships between those different parts of your business.

To ensure that you're providing the right support for your team, we’ve developed six key factors that can help you determine whether or not your data team is high performing and things that you can do to help your data team transform into a high performing team.

1. Set clear goals and make sure everyone understands them

In order to be successful, data teams need to have a clear vision and direction. Just like any high performing team, data teams need clear goals and vision on what they should prioritize and how they should focus their efforts. The “why” behind your team is just as important as the “how”. Understanding the reason for the data strategy and the goal the organization is trying to achieve you’re trying to achieve can keep the team focused when you hit roadblocks or start straying from the original path.

Make sure your goals are realistic—and that they can be measured. If you're working on a large project, try setting smaller milestones along the way so you can measure your success at each step. This will help keep your team motivated and engaged by offering frequent feedback on their progress toward hitting their overall goal.

2. Understand your organizations data needs and define a clear channel for feedback

When evaluating your organizations data needs, you should think about measuring a few components:

  1. What data sources are people trying to understand in depth
  2. What people are trying to use this data
  3. How do they want to use this data
  4. Are they technical? If so, are they hoping to use SQL to analyze this information?
  5. Are they less technical? If so, what are their preferred way of accessing this information

In addition to this, we recommend establishing a clear channel of feedback and questions, so people know where to access the data team. High performing data teams are extremely declined about the data they collect and the way they define that information in the data warehouse.

It is important to understand your data assets in order to move from a reactive to a proactive position. A data asset catalog should be created as a first step in standardizing the structure. High performance teams need to identify what assets are, how they're structured, where they're located, who owns them, and more.

With a wide range of systems to navigate, gathering data to analyze can be a complicated task for data analysts and other users in an organization. The use of data curation techniques helps make it easier to find and access data.

By doing this you can see where there are gaps in coverage and how different parts of your organization use the same asset in different ways. Standardizing data structures also allows for better integration between systems which makes it easier for people across departments or even across companies to understand each others’ data formats.

First, there are several ways that data can be standardized:

  • Collect data in common formats
  • Collect data based on pre-set standards
  • Transform data to a common format
  • Convert data to z-scores

One recommendation that we have during this step is to only collect and manage the data you need. This can be a difficult concept for companies that are used to gathering as many types of data as possible, but it's an important one because the more data you have, the less likely it is that you'll use it in any meaningful way. By using modern tools like Snowflake and Fivetran, the marginal cost of adding additional data to your warehouse is low. That said, the marginal cost of managing that same data is high.

Instead of trying to gather every bit of information out there about customers and colleagues, focus on collecting only what's necessary for making decisions and improving outcomes. This will make it much easier to share, document and use the data in the future.

In addition to ensuring that the right data is collected, high-performing data teams should also implement data contracts at the early stages to ensure data quality and schema consistency. An error, inconsistency, or other issue is commonly associated with raw data. These problems should be avoided or minimized by data collection measures. However, most of the time that isn't foolproof. To identify issues and correct them, data is usually profiled and cleansed.

3. Give people the tools they need, and training on how to use them

The best way to ensure your team is productive and efficient is by providing them with the tools they need to do their jobs. This means giving people access to the right data, as well as training on how to use it effectively. In addition, it means making sure the tools are well tested, and achieve a specified job before they are purchased. High performing teams can lean on either open source or closed source software, but understand the tradeoffs of each choice.

Training can be informal or formal, but it should always be ongoing. The easiest way for you to evaluate whether or not someone needs training is by observing their performance in real-time; if they can't make sense of the data you've provided them with, then they probably need some more help understanding it—not just a quick tutorial on how something works (which may not stick).

Training should also be tailored specifically toward each individual's expertise and background knowledge—no one knows everything! So while someone might understand how certain functions work without instructions, others might need more time working through tutorials before learning anything new. And when possible, try letting someone who has done the task before teach others: If a coworker who has worked at another company comes along on an office visit, why not ask them about what kinds of tools they had access too?

4. Provide mentorship and leadership, but let people work independently

Mentors and leaders should be taken from outside the team, as it's important that they not become too close to their mentees or followers. It's also a good idea for mentors and leaders to have good communication skills, so that they can effectively guide their mentees and followers through challenges.

5. Make sure everyone has access to the right data sources

Data quality is a top priority for high-performing teams, so it's important that your team has access to the data they need. You also want to make sure you are able to track and manage who has access to what data. This will help prevent security breaches and other issues that could arise from unmonitored usage of sensitive information.

As the demands on data teams increase, it’s critical that organizations ensure their teams are equipped with the right tools and processes to make sound decisions.

A critical component of a high-performing data team is having an effective approach for operationalizing insights from data. Seventy percent of respondents say that less than half of their decision makers use insights from data in their work most or all of the time, making it obvious that many organizations just haven't mastered how to operationalize data effectively.

To get started on building your own high-performing data team, consider these tips:

  • Establish clear KPIs and metrics for performance evaluation and reward systems aligned with those goals
  • Ensure everyone has access to proper training so they can be successful at leveraging new technologies or processes

In addition, data teams should think about putting all this data into one place and make it easily accessible to every member of your team. Ensure that all information about your company’s data is accurate, complete and up to date – making it easy for everyone in your organization to find out about the information they need at any given time. This will allow employees to focus on tasks that require knowledge from many different sources so they don’t have to spend time searching through different systems or asking colleagues for help when trying to find something specific.

We’ve found that a simple document or wiki page doesn’t cut it. A central location needs to be searchable, highly organized, and constantly updated with new information.

The best way we know how to do this is by using Secoda. Secoda is a platform to search, synthesize, store, and share your data knowledge in one collaborative and searchable platform.

6. Be clear about ROI expectations

It's important to be clear about what you expect from your data team, and to communicate those expectations clearly. As a data manager, it's your responsibility to explain the ROI expected from each project. This will help your team members make good decisions about which projects they focus on first. To do this:

  • Calculate the expected return on investment (ROI) for each project. The ROI can be calculated by determining how much time goes into executing a project, then calculating how much money is saved or generated by using the results of that project. For example, if it takes an analyst one month to gather data needed for a six-month marketing campaign and then use that data in their campaign strategy development process, they may save their company $3 million over six months by implementing those strategies earlier than if they had waited until two months before launch date before getting started with their research and analysis workflows. So there would be an estimated $1 million in savings per month ($3 million divided by 6 months). Multiply this number by four ($1M/mo x 4) = $4M total potential savings over 24 months!
  • Communicate these ROIs clearly with everyone involved in decision making processes so that everyone has an idea of what success looks like when working towards specific outcomes with limited resources (time).

With less than 10% of data professionals having more than 10 years of experience in the field, there is still room for data teams to become more efficient as the professional matures.

As a result of this gap in knowledge and skill level between what's needed today and what most data professionals have been trained on, organizations need to up skill their employees on new technologies and data analytics practices. But even when up skilling efforts are successful, it takes time for new skills to become deeply embedded in your team members' cognitive models—which translates into lost time while waiting for results from less-experienced employees versus hiring more seasoned ones who can hit the ground running with higher quality work faster.

During the past few years, data analytics has changed dramatically, providing companies with more insight into their operations and customers that can help them improve their performance.

Creating high-performing teams requires more than just collecting data; you should also make sure that the process of accessing, understanding and using it is clear to everyone in the organization.

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