How do you put together a 1,000-piece puzzle? Like many people, you might start by building out the puzzle’s frame to put the foundation in place. Then, you might pull together small sections, associating images seen on pieces with like colors or textures, or seemingly fitting shapes. As you do, you’re often referencing the big picture and hunting or pecking for that connection piece, building a patchy framework as you bring pieces together, aiming to reconstruct the model image.
In your team, the Data Analyst lays down the hundreds or thousands of pieces, painted with data that make up your project puzzle. As a key player of your project team, the data analyst follows a pattern to consistently and efficiently organize the pieces to help teams visualize the “data puzzle”. In its role, the Data Analyst serves as the “connector” between departments, associating meaningful images together, guiding teams and collectively building out the model image. Through the Data Analyst’s work, teams have greater visibility of project dependencies and better understanding of their role as they see their 1000-piece puzzle taking shape.
While there’s sometimes the perception the work of the Data Analyst is “boxed” within the project scope, this role often provides key pieces of information across workstreams by analyzing, cleaning, and visualizing the data needed to make timely 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 your team key player’s needs and translate those insights to teammates. Here’s how.
The Data Analyst’s Motto: Quality, Consistency, Efficiency
A Data Analyst’s job is to manipulate the relevant pieces of the puzzle and provide a clear and accurate data picture. Each piece must fit and connect to the next one. Data quality is central to the work performed by Data Analysts. Quality drives consistency in interpreting data sets. During the journey of the project, the Data Analyst works through large amount of data that will directionally paint the immediate next steps – or piece of the puzzle to assemble. But because data can rot quickly, the Data Analyst must also work with efficiency. It is crucial to bring an accurate and timely picture to the teams while giving them enough time to implement the findings. This level of delivery plays a dramatic role in the success of a project.
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 critical 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 perform. 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 paint an accurate picture – and the story along – 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 “Drawing The Picture” 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 have a better understanding about their work.
Visualization for Peers
When speaking to teammates, Data Analysts will need to go in greater details about their work as their 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 the room, this granularity in addressing nuances, errors found, or how the data’s been collected, cleaned, and profiled is of limited interest.
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 orient the work by the team and provide a different perspective. Many times, peers are working with the same data set and by sharing a defect you just cleaned or an error you just found, you could reduce or even eliminate extra or redundant work.
Visualization for Decision-Makers
Data visualizations for decision-makers differ from the nuanced reports for peers. These visualizations are often simpler and usually focus on a higher-level picture. Their purpose is to provide a data-centric and decision-making process to leaders and executives.
This higher level of visualization and the underlying interpretation require the Data Analyst to see the “bigger picture(s)” within the facts and figures. This exercise might require additional analysis, logic, and strategic thinking. The desired outcome is to paint a clear and unequivocal message to the project leadership team. The visualization provides the elements that make the most sense from a leader’s perspective to understand and take a position when addressing a specific solution or issue.
For any projet management leaders, your ability to make critical decisions or achieve timely progress towards specific milestones resides in your data analysts’ talent to anticipate your needs. By pulling data together and compiling data-driven insights, they provide you with that piece of the puzzle you were looking for to make that picture whole.