data analysts project management

Data Analysts: A Core Piece of the Puzzle in Project Management

By: Ana Rotaru


How do you put together a 1,000-piece puzzle? If you’re like many people, you might start by building out the puzzle’s frame to put the foundation in place. Then, you’ll start to pull together small sections based on clear images seen on the pieces — like colors, like textures, that sort of thing. As you do, you’re often referencing the big picture and hunting or pecking for that connection piece to bring big pieces together and start to see the picture form in front of you.

The role of a Data Analyst is a lot like those connecting pieces. This key player on the team offers the picture, painted with data, needed to help teams start to see what the final result will look like. Likewise, the Data Analyst serves as the connecting piece between departments, bringing two separate images together into one same image or common goal. Through the Data Analyst’s work, teams can know with more certainty that their project is coming together as expected and pinpoint where their role fits in.

data analysts in project management

While there’s potential for it to sound like the Data Analyst is just one person within the project, that person’s work contributes to many different areas of the puzzle by analyzing, cleaning, and visualizing the data needed to make intelligent decisions. Oftentimes, a message that comes through your data can provide a key answer to the entire team. Skilled data analysts correlate those data points to each key player’s needs and translate those insights to teammates. Here’s how.

Quality, Consistency, and Efficiency in Data

Quality is key when it comes to a Data Analysts’ work. That requires this key player on a team to infuse excellence, consistency, and timeliness when interpreting data sets. 

A Data Analyst’ job is to tell a clear and accurate story through data so that the data can guide the project on a short and/or long-term perspective. This key player must work not only with accuracy but also with a certain level of efficiency. Because data can rot quickly, it is crucial to bring the correct story to teams while giving them enough time to implement the findings. It’s this level of delivery that can play a dramatic role in the success of a project.

Accurate Interpretation

Data modeling entails creating and designing various infrastructures to align with the scope of the project — but these infrastructures are only half the battle. Data Analysts must also interpret the findings from that data to answer big questions, such as:

  • What is the team doing and what should they be doing?
  • What are the answers needed to keep the guardrails up for this project’s scope?
  • What key takeaways is the team seeing or not seeing?

This level of interpretation extends beyond the rows and columns of data points. For example, I had 16 million rows of data on one project. I had to find data for a specific country through those rows and columns. That required me to split the data points into various pieces, which Excel could not do. Instead, I leveraged more complex tools, such as Python and SQL to draw the story out of the data.

Interpreting the data before presenting it to the leadership requires a Data Analyst to identify different patterns and trends. In order to tell a clear story out of those patterns and trends, a Data Analyst must understand the full scope of the project, including the solution the team is looking for.

Know Your Audience When Telling a Story With Data Visualization

The impact of a Data Analyst’s work all comes to a head when data visualization is used to tell a strong story. Data visualization is the use of charts, graphics, and tables to help others make sense of the many data points. Many people don’t know the root of this story or where it starts. 

In order to be most impactful, Data Analysts know that visualizations must be clear, simple, and created for a specific audience. This often means creating one result for peers and another result for decision-makers. 

  • For example, consider needing to filter around the number of licenses for one year for a specific country. We have different filtering requirements. We need to develop one result for the leadership, but when we speak to our teammates, we can break it down into various categories so they can understand more about their work.

Visualization for Peers

When speaking to teammates, Data Analysts will often go in much more detail about their work than when speaking to leadership. That’s because peers need to fully understand the methodology and infrastructure used to interpret the data. This understanding could help them in their roles or could help paint a clearer picture of how the data fits into their department. Outside of the room, no one else really cares about the nuances, errors found, or how the data’s been collected, cleaned, and profiled.

It’s important to expand on key points and solutions when speaking to your team or other Data Analysts. Identifying the pathways to the solution found will help impact the work from a different perspective. These peers could be working with the same data. Sharing a defect you just cleaned or an error you found could eliminate extra work on their side, saving the entire team time.

Visualization for Decision-Makers

Data visualizations for decision-makers are very different than the nuanced reports for peers. These visualizations are simple, clear, and to the point. In taking this more straightforward approach, decision-makers can quickly see the path ahead to make more data-centric decisions. 

This higher level of interpretation requires the Data Analyst to see the big story within the facts and figures. Bringing a clear message to the team requires more analysis, logic, and strategic thinking around what message would make the most sense from a leader’s perspective when needing to understand a specific solution or issue at hand. 

When pulling data together and conveying useful insights to leadership, Data Analysts must think like a leader. In doing so, they can share a good story from the data that will impact the project decisions and help shine a light on progress toward a specific Key Performance Indicator (KPI).

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