Without question, data is one of the most valuable assets for an organization. Not only does data access help make more intelligent decisions, but having these data points at your disposal also improves business processes, understanding of current performance, and forecast business trends.
Seeing the current and future economic value data brings to an organization might have you wondering why data doesn’t appear on the balance sheet. I’ve thought the same.
The importance of data to a company’s bottom line shows just how crucial it is to know who operates with data. For most firms and on most projects, the Data Analyst is the key puzzle piece to making all of these important activities possible.
Data Analysts are more than number crunchers. These team members also play a vital role in today’s technology-driven world organization by gathering and interpreting the data, reading the data, and then telling a story based on the data.
On the surface, that definition might seem simple and straightforward. Don’t let that fool you. The nuances behind what a data analyst does daily can be hugely impactful for an organization’s bottom line.
The Critical Role of the Data Analyst
A good Data Analyst embodies a combination of analytical, technical, and numerical skills in order to parse through big data sets. But their role is so much more than that. Data Analysts are also leaders on the team.
Typically, the Data Analyst job description looks a little something like this:
- Experience with object-oriented programming (Python, R, Java)
- Expertise using Excel – familiarity with pivot tables and regular expressions is required
- Experience using SQL and translating queries for business and technology leadership
- Ability to quickly learn new analytical methods and connect the dots
- Ability to clearly communicate technical Excel formulas to business and technology leadership
- Proactive note-taker and critical thinker who ask questions and has a strong drive to learn
- Understanding of database architecture, insertion and deletion anomalies
While all of the skills listed here are critical to the role, the reality is that a quality data analyst must also possess more than expertise in the tools themselves. Skilled Data Analysts must also know how to think critically when using these tools.
Because team leaders and decision-makers rely so heavily on what Data Analysts uncover from the data, people in this role must have the judgment to figure out what data to collect and how to communicate the findings effectively.
Data Analysts often meet with leaders from various departments, from marketing or data ops to workstream leads. It’s critical that when the Data Analyst gets in the room with these various key players, they know how to communicate and tell each person the same story but with a different vocabulary. For example, IT speaks differently than someone in Business Operations. The Data Analyst must speak these various departmental languages throughout the company to connect the dots between the teams and give the same clear picture to each key player.
There is a four-step approach to how this process unfolds — identifying, collecting, interpreting, and then visualizing the data.
Identifying the Data
Before the data analysis journey begins, it’s crucial to have a well-defined roadmap in place so the Data Analyst knows what they’re working to identify on a project. This step is challenging and requires cross-departmental collaboration to bring to life.
Data Analysts must use the stakeholders to guide the project while turning their needs into a specific technical approach. A Data Analyst will meet with business leaders, IT teams, and more to decide the right data points to load into the interface.
After the meetings, the Data Analyst identifies how each object is linked to different methods or functionalities, the important data fields or numbers, and the role of every single data point. Nothing should be sitting in these data sets just for the sake of being there. Everything must make sense for a database or interface, requiring the Data Analyst to understand how each database is built.
Making the Identifying Step a Success: Data Analysts must be skilled interviewers to be able to think through so many roles. One of the most crucial questions for a Data Analyst to ask from the outset is this — what is the logic behind the interface or database?
Data Analysts must think not only as a stakeholder in the project but also as a programmer. Visually describing how a program or interface works offers a bigger picture to the Data Analyst that will help them understand how to start and prioritize the next steps. Identifying how each object is linked to different methods, functionalities, data fields, or numbers helps them to know how data points are linked to a working method and how those working methods link to the project goals.
Collecting and Cleaning the Data
Once a Data Analyst has identified the data and the project goals, it’s time to gather the data around the project’s scope, interfaces, teams, and more. This step requires close collaboration with several key players on the team so that the Data Analyst knows what they’re sourcing as they extract the data from the interface.
Collecting and cleaning the data is a time-intensive step. To put quality assurance protocols in place, the Data Analyst must ensure that the correct data is in the right place. That’s especially important when the project is large. The Data Analyst must align the data collection process with the business goals.
Once collected, the Data Analyst must then process and clean the data. Data is big. Most of the time, it’s not organized. There’s a technical aspect to knowing how to clean it and, ultimately, how to model the data.
Making the Collecting and Cleaning Step a Success: The most challenging part of this step is not the collection itself. The hardest part of this step is cleaning the data set to bring each data point together and tell a strong story. Because data comes in various forms, Data Analysts work hard to convert data from various databases and interfaces and then pull it all together to tell a strong story. Some interfaces use the same data but in different forms. Others include data that is inaccurate or inapplicable. Data Analysts work hard in this step to ensure that only the right data is being used so that the story found in the data is accurate and clear.
Profiling and Interpreting the Data
With the right data in hand, it’s time to leverage the tools to profile and interpret what’s inside these big data sets.
Profiling and analyzing data with Excel, Python, SQL, Tableau, and more requires technical skills and some coding knowledge. Data Analysts cannot do their job well without having the right tools in place for profiling and interpreting the data. This requires predictive knowledge so that the Data Analyst knows what to look for.
In other words, think of this step as an equation. Simple math requires you to infuse logic, processes, and analysis before you can summarize and extract insights. Data analysts must approach data sets like an equation, ensuring that each element works together consistently to tell an accurate story.
Making the Profiling and Interpreting Step a Success: Data Analysts are often the first ones to uncover the message behind the data. Once uncovered, they can identify what’s relevant to share with the team and what needs to be given to each department.
Data and Results Visualization
As the saying goes, a picture is worth a thousand words. Perhaps there’s no other place that that statement is more true than with data visualization.
Data visualization allows Data Analysts to disseminate the correct message to the appropriate parties using visual elements, such as dashboards, graphs, or other imagery. This step makes the next action steps clear to teams with or without rich data knowledge. Democratizing the data in this way allows teams to fix current business problems, enhance ongoing business processes, or even create new strategies.
Making the Data Visualization Step a Success: Data visualization comes in various shapes and sizes. The goal of this step is to choose the right visualization technique to connect the dots and summarize the results before presenting them to the team.
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