Harnessing Data Management in Higher Education

About the Episode: 

Jamie and Timothy are joined by Katie Tomlinson, Senior Director of Analytics and Business Intelligence for Education Dynamics. They discuss the importance of data management in higher education, covering best practices for data selection, collection methods, roles in data management, and the impact of AI on analytics. Katie emphasizes the need for accurate data, effective documentation, and the integration of AI tools to enhance decision-making processes within institutions.

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Key Takeaways

• Data Management Essentials: Organizing, storing, and ensuring data quality are fundamental for effective data analysis and informed decision-making.
• Defining Objectives for Effective Analysis: Establish clear goals and relevant data sources to maximize data’s potential impact on institutional outcomes.
• Preparing Data for Analysis: Data cleaning, transformation, and integration are crucial steps to achieve actionable insights.
• AI and Analytics: AI can streamline analysis, improve campaign efficiency, and democratize data usage, but requires appropriate guardrails for reliability.

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Episode Summary

What is Data Management, and Why Is It Crucial in Higher Education?

Katie Tomlinson breaks down data management as the foundational process of organizing, storing, and protecting data throughout its lifecycle. She explains that robust data management supports a wide range of institutional goals, from understanding student behaviors to predicting retention outcomes. As more institutions become data-driven, having a solid approach to data management enables them to harness insights from current and prospective students to achieve their objectives. Without clear data structures, institutions risk making flawed strategic decisions.

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How Can Institutions Pull Relevant Insights from Data?

Once data is organized, the next step is selecting and analyzing the right data to support specific institutional goals. Katie emphasizes defining objectives before diving into analysis, which allows for targeted data selection and processing. Data collection methods vary, including both manual techniques like RFI forms and automated tools such as web crawlers. She mentions tools like Supermetrics and Rivery, which simplify data integration for platforms like Google Analytics and LinkedIn. Katie also highlights the importance of data quality checks and thorough documentation to ensure reliability and transparency across teams.

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Who are the Key Players in Data-Driven Decision-Making?

Katie emphasizes the importance of identifying a data source “owner” for each dataset—someone responsible for overseeing data definitions, metrics, and documentation. She notes that successful data initiatives usually involve a small, cross-functional team where strategic decision-makers and technical doers collaborate closely. This minimizes confusion and empowers stakeholders with a clear understanding of the project’s objectives. Katie recommends balancing technical skills with institutional knowledge, advising campuses to invest in professionals with a computer science or data science background to avoid common pitfalls.

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How Can Data be Prepared for Analysis?

Katie describes the data pre-processing stage, also known as data wrangling, which ensures data cleanliness and readiness for analysis. This involves steps like data cleaning to remove duplicates or correct errors, data transformation for uniform formatting, and data integration to combine sources. She emphasizes that structuring data properly not only supports current analysis but also facilitates future projects, making it a worthwhile investment. Tools like Excel, SPSS, R, and Python, as well as visualization software like Tableau and Power BI, play key roles in this process depending on the data’s complexity and intended use.

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How Can Data-Driven Insights Enhance Marketing Strategies?

Clean, organized data can significantly boost the effectiveness of marketing efforts by clarifying key performance indicators (KPIs) and supporting campaign management. By integrating accurate data pipelines, institutions can better understand their target audiences, enabling segmentation and persona-based marketing. Katie suggests that this structured approach can also help institutions harness AI for campaign optimization, offering more personalized and efficient outreach to potential students.

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What Role Does AI Play in Higher Ed Data Management?

The conversation wraps up with a look at AI’s transformative potential in higher ed. AI and large language models (LLMs), such as ChatGPT, can automate data processing, helping institutions quickly spot trends and make faster, data-driven decisions. While Katie sees great potential in using AI to complement existing analytics, she stresses the need for robust guardrails to prevent misleading outputs. When AI is applied thoughtfully, it can reduce manual data work, allowing teams to focus more on strategic insights and actions.

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Connect With Our Co-Hosts:

Jamie Boggs

Timothy Davis

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About The Enrollify Podcast Network: The EduData Podcast is a part of the Enrollify Podcast Network. If you like this podcast, chances are you’ll like other Enrollify shows too!  

Some of our favorites include Generation AI and The Higher Ed Geek.

Enrollify is produced by Element451 — the next-generation AI student engagement platform helping institutions create meaningful and personalized interactions with students. Learn more at element451.com.

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