Beyond the Numbers: A Data Analyst Journey | Anna Leach | TEDxPSU

Summary

The speaker reflects on their career journey, starting from a math degree and early roles in analytics to a more fulfilling position in higher education. Along the way, they highlight the importance of learning from others, overcoming biases, and embracing challenges. Their career evolution is marked by a shift from traditional data analysis to recognizing the power of storytelling with data, especially in education. The speaker emphasizes how data analysis can bridge gaps between educational practices and research, encouraging a deeper appreciation for the art and science of data, and its impact on relationships and decision-making.

Takeaways

• 😀 Life is a journey filled with twists and turns, and the path is often more complex than it first appears.
• 😀 Early jobs in analytics involved basic data tasks like filling spreadsheets, which wasn't the right fit despite the work environment being positive.
• 😀 In higher education, onboarding involved understanding business processes and how data is reported, but the real learning came from interacting with people.
• 😀 Working with data requires understanding both the system behind it and the quirks within the data itself, as errors are common.
• 😀 Bias in data analysis, like assumptions based on past reports or rushing through tasks to appear irreplaceable, can lead to mistakes and inefficiencies.
• 😀 Taking time to meet with colleagues and understand data from different perspectives is crucial to successful data analysis.
• 😀 As a data analyst, it’s important to take a step back, ask questions, and look for patterns before diving into a project.
• 😀 A big career shift to graduate school and part-time work led to new experiences, including presenting at conferences and embracing the power of storytelling in data.
• 😀 Data analysis can bridge the gap between research and practice, helping educators and researchers apply findings more effectively in real-world settings.
• 😀 Spending time with people, asking questions, and understanding data thoroughly are all essential to making informed, impactful decisions in data analysis.
• 😀 Data analysis is both an art and a science, accessible to individuals from diverse backgrounds and offering powerful insights that can enhance relationships and decision-making.

Q & A

What was the initial expectation the speaker had about their career path after completing their undergrad degree in math?

The speaker initially expected a clear and straightforward path, like most people do when embarking on new life phases, such as being a new parent or graduate. However, they realized that the journey was far more complex and full of twists and turns.

What were the speaker's first experiences in analytics after completing their degree?

The speaker's first jobs in analytics involved tasks like putting numbers into spreadsheets and performing basic formulas in Excel. Despite the friendly work environment, they felt that these tasks weren't the right fit for their skills.

How did the speaker transition into the field of higher education?

The speaker transitioned into higher education after several years of working in analytics. In this new role, they were involved in learning business processes, such as how students enroll in classes and how to report this data, though they found it wasn’t fully aligned with their mathematical background.

What major change in the speaker's career perspective occurred during a system conversion in higher education?

During the conversion from a legacy mainframe system to a PeopleSoft system, the speaker realized the importance of understanding the data more deeply and working with colleagues to grasp the full picture. This was when they truly understood the importance of data analysis and the nuances of working with it.

What was the speaker’s approach to understanding the data in their new system after the conversion?

The speaker spent significant time meeting with colleagues to learn how data worked in the old system and how it would work in the new system. They also spent time directly analyzing the data to understand its quirks and exceptions.

How does the speaker illustrate the concept of bias in data analysis with their son's behavior?

The speaker uses the example of their son’s condiment bias—his aversion to mayonnaise, despite never having tried it—to explain how biases form based on limited exposure or prior experiences. This concept parallels how analysts may make assumptions based on past reports without fully exploring new data.

What mistake did the speaker make early in their career, and what did they learn from it?

The speaker made the mistake of rushing through tasks to appear irreplaceable, which led to errors in data analysis, like using incorrect formulas. They learned that taking time to ask questions, seek clarity, and avoid rushing would lead to better results and more trust in their work.

How did the speaker’s perspective on data analysis evolve after several years of experience?

After several years in the field, the speaker learned to approach projects by taking a 30,000-foot view. They began asking more questions, analyzing patterns, and interacting more with people to understand the data better before diving into technical tasks.

What significant career change did the speaker make two years ago?

Two years ago, the speaker moved from Columbus, Ohio, to Tucson, Arizona, and decided to return to grad school. They shifted focus to a Master’s program in Learning Technologies while working part-time as an analyst.

What inspired the speaker to consider data analysis in the context of education?

While attending conferences and meeting educators and instructional designers, the speaker realized that there was a gap in how educational tools and teaching methods were assessed. They saw that data analysis could play a key role in helping educators make informed decisions based on data, rather than relying on intuition alone.