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Optimizing Education Resource Strategies for Academic Success

Education Resource Strategies
Education Resource Strategies

Effective education resource strategies are crucial for universities aiming to optimize their operations, enhance student success, and achieve their academic missions. At Datatelligent, we specialize in providing innovative solutions that help universities manage their resources more effectively, ensuring that every dollar spent and every hour invested contribute to the institution’s goals. In this blog, we will explore the importance of robust education resource strategies and how Datatelligent can support universities in implementing these strategies to achieve excellence.

Understanding Education Resource Strategies

Education resource strategies encompass the planning, allocation, and management of an institution’s financial, human, and physical resources. These strategies ensure that resources are used efficiently and effectively to support academic programs, student services, research initiatives, and operational functions. Key components of education resource strategies include:

     

      1. Financial Management: Efficient budgeting, forecasting, and financial planning are essential for maintaining fiscal health. Universities must allocate funds strategically to support priority areas, manage costs, and ensure long-term sustainability.

      1. Human Resource Management: Attracting, retaining, and developing talented faculty and staff are critical for delivering high-quality education. Effective human resource strategies include recruitment, professional development, performance management, and succession planning.

      1. Infrastructure Management: Universities must maintain and optimize their physical infrastructure, including classrooms, laboratories, libraries, and student housing. This involves regular maintenance, upgrading facilities, and ensuring that spaces meet the needs of the academic community.

      1. Technology Integration: Leveraging technology to enhance teaching, learning, and administration is a key aspect of modern education resource strategies. This includes investing in digital learning platforms, data management systems, and cybersecurity measures.

    The Role of Data in Education Resource Strategies

    Data plays a central role in developing and implementing effective education resource strategies. By analyzing data on financial performance, human resources, facility usage, and technology adoption, universities can make informed decisions that optimize resource allocation and utilization. Key benefits of data-driven resource strategies include:

       

        1. Informed Decision-Making: Data-driven insights enable university leaders to make evidence-based decisions that align with institutional goals. For example, financial data can inform budget allocation, while human resource data can guide recruitment and retention efforts.

        1. Improved Efficiency: Analyzing data on resource usage helps identify inefficiencies and areas for improvement. For instance, analyzing classroom occupancy rates can inform decisions on space utilization, reducing wasted resources.

        1. Enhanced Accountability: Data transparency promotes accountability by providing clear metrics for performance evaluation. Stakeholders can track progress, assess the impact of resource investments, and ensure that resources are used effectively.

        1. Strategic Planning: Data supports long-term strategic planning by providing insights into trends, opportunities, and challenges. Universities can develop proactive strategies to address emerging needs and capitalize on future opportunities.

      How Datatelligent Supports Education Resource Strategies

      At Datatelligent, we offer comprehensive solutions that empower universities to develop and implement effective education resource strategies. Our expertise in data management and analytics enables institutions to harness the power of their data for optimal resource planning and utilization. Here’s how we can help:

         

          1. Data Integration and Centralization: We assist universities in integrating data from various sources into a centralized system. This unified approach ensures that all relevant data is easily accessible, facilitating comprehensive analysis and informed decision-making.

          1. Advanced Analytics and Reporting: Our advanced analytics tools provide customizable dashboards and reports that deliver actionable insights. University leaders can monitor key performance indicators, track resource usage, and identify opportunities for optimization.

          1. Financial Planning and Analysis: We offer solutions for budgeting, forecasting, and financial analysis that support strategic financial management. Our tools enable universities to allocate funds effectively, manage costs, and ensure financial sustainability.

          1. Human Resource Analytics: Our human resource analytics solutions help universities attract, retain, and develop top talent. By analyzing data on recruitment, performance, and professional development, institutions can optimize their human resource strategies.

          1. Facility and Infrastructure Management: We provide tools for monitoring and managing physical infrastructure, ensuring that facilities meet the needs of the academic community. Our solutions support maintenance planning, space utilization analysis, and capital project management.

          1. Technology Integration and Support: We assist universities in leveraging technology to enhance education and administration. Our solutions include digital learning platforms, data management systems, and cybersecurity measures that support effective technology integration.

        Real-World Impact of Effective Education Resource Strategies

        The impact of effective education resource strategies can be transformative for universities. Institutions that adopt data-driven approaches to resource management can achieve remarkable results, including:

           

            • Increased Operational Efficiency: Optimizing resource allocation and utilization reduces waste, lowers costs, and improves productivity, enabling universities to deliver high-quality education more efficiently.

            • Enhanced Student Success: By investing resources strategically in academic programs, student services, and support initiatives, universities can improve student retention, graduation rates, and overall success.

            • Strengthened Financial Health: Robust financial management ensures that universities maintain fiscal stability, manage risks, and achieve long-term sustainability.

            • Improved Campus Facilities: Effective infrastructure management ensures that facilities are well-maintained, meet the needs of the academic community, and support a conducive learning environment.

          Why Choose Datatelligent?

          Datatelligent is a trusted partner for universities seeking to optimize their education resource strategies. Our deep understanding of the higher education sector, combined with our advanced technology and commitment to customer success, makes us the ideal choice for institutions aiming to enhance their resource management capabilities. By partnering with Datatelligent, universities can leverage data-driven insights to achieve their academic and operational goals.

          In conclusion, effective education resource strategies are essential for universities striving for excellence. By adopting data-driven approaches to financial, human resource, infrastructure, and technology management, institutions can optimize resource allocation, enhance student success, and achieve their academic missions. Datatelligent’s comprehensive solutions empower universities to develop and implement these strategies, driving continuous improvement and academic excellence.

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          Blog

          How Education Tech Companies Enhance Student’s Success

          Higher Education Tech Companies
          Higher Education Tech Companies

          In today’s educational landscape, achieving students’ success is more crucial than ever. Education Tech Companies like Datatelligent, specialize in helping universities manage their data to improve student outcomes. By using advanced technology, we enable institutions to streamline operations, boost academic performance, and make informed decisions that lead to greater student success.

          The Role of Education Tech Companies in Students Success

          Education tech companies like Datatelligent provide innovative solutions to improve students’ success. Let’s explore how we contribute to this goal.

          What is Student Success?

          Students success means helping students achieve their academic goals, personal growth, and career readiness. It involves giving students the resources and support they need to thrive in their studies and future careers.

          Why is Students Success Important?

          Focusing on students success benefits both students and universities. It leads to higher graduation rates, better job placements, and a positive reputation for the institution. Effective data management plays a crucial role in achieving these outcomes.

          How Datatelligent Supports Students Success

          Partnering with Datatelligent offers numerous advantages for universities aiming to enhance students success. Here’s how we help:

          Improved Data Accuracy

          Accurate data is essential for tracking student progress and identifying areas where support is needed. Our automated data management systems reduce human error, ensuring reliable data that universities can trust.

          Enhanced Decision-Making

          Our advanced analytics tools provide actionable insights. Universities can make informed decisions based on real-time data, whether it’s adjusting academic programs, improving student services, or identifying at-risk students.

          Increased Efficiency

          Universities handle numerous administrative tasks that can divert attention from student support. By automating routine tasks and streamlining processes, we help universities save time and resources. This allows staff to focus more on initiatives that directly impact students success.

          Targeted Support and Interventions

          Data-driven insights help universities identify at-risk students and tailor support programs to their needs. By understanding the data, universities can provide targeted interventions, ensuring that every student has the opportunity to succeed.

          Datatelligent: Your Partner in Achieving Students Success

          At Datatelligent, we are committed to helping universities harness the power of their data to promote students’ success. Our tailored solutions address the unique needs of each institution. Here’s what we offer:

          Data Integration and Management

          We integrate various data sources to create a unified, accessible data platform. This ensures all your data is in one place, easy to access and manage.

          Advanced Analytics

          Our predictive analytics tools provide actionable insights. By analyzing historical data and identifying trends, we help universities plan for the future and make data-driven decisions that enhance students’ success.

          Data Security

          Ensuring that all data is securely stored and compliant with regulatory standards is a top priority. We implement robust security measures to protect sensitive information and ensure compliance with regulations.

          Tailored Solutions

          Every university is unique, and so are their data needs. We work closely with each institution to understand their specific challenges and provide customized solutions. Whether it’s improving data accuracy, enhancing decision-making, or increasing efficiency, we have the expertise and technology to help.

          Success Stories: How Datatelligent Has Made a Difference

          University A

          University A struggled with managing student records and tracking academic performance. By partnering with Datatelligent, they integrated their data sources, automated data management processes, and gained valuable insights into student performance. This led to more targeted support for students and improved academic outcomes.

          University B

          University B faced challenges with providing targeted support to at-risk students. Our advanced analytics tools provided the data they needed to identify at-risk students and develop intervention programs. This resulted in higher retention rates and better student outcomes.

          University C

          University C needed to enhance their career readiness programs. By providing robust data sets and advanced analytics tools, we helped them track student progress and align their programs with job market demands. This improved job placement rates and student satisfaction.

          Conclusion

          As the education sector continues to evolve, partnering with education tech companies like Datatelligent can make a significant difference in achieving student succes. By improving data accuracy, enhancing decision-making, increasing efficiency, and promoting better student outcomes, Datatelligent is your ideal partner in fostering students success.

          Let us help you unlock the full potential of your university’s data to achieve greater performance and success. Contact Datatelligent today to learn more about how we can support your institution in promoting students success and navigating the complexities of data management and academic planning.

          Categories
          Blog Higher Education

          University Research Partners: A New Era of Data-Driven Decision Making 📈

          University Research Partners
          University Research Partners

          In today’s rapidly evolving higher education landscape, universities are increasingly seeking partnerships to leverage data for better insights and decision-making. These collaborations, known as University Research Partners, can take several forms, each with unique benefits for the institution. 🤝

          What is a University Research Partner?

          University Research Partners are organizations that collaborates with an educational institution on research initiatives. These partners can range from other academic institutions to industry leaders and data analytics companies.

              • External Focus: Some partners work with universities to conduct case studies within real-world industries. This offers students valuable hands-on experience and exposure to current industry practices 💼, while providing the partner with fresh perspectives and research findings.

              • Internal Focus: Other partners specialize in analyzing a university’s internal data. This includes student performance, enrollment trends, faculty productivity, and resource utilization. By uncovering patterns and insights within this data 🔍, these partners help universities optimize operations, improve student outcomes, and make informed strategic decisions.

            Datatelligent: Leading the Way in University Data Analytics 🚀

            Datatelligent has positioned itself as a premier research partner for universities seeking to harness the power of their internal data. We offer a comprehensive solution that leverages existing learning management systems (LMS) like Canvas, Moodle, or Blackboard, as well as other data sources within the university ecosystem.

            Our Approach:

                1. Data Integration: We seamlessly integrate with your LMS and other platforms to gather comprehensive data on student engagement, course performance, and faculty interactions.

                1. Data Warehousing: We utilize cutting-edge Snowflake technology to store and manage your data securely and efficiently. This scalable data warehouse provides a solid foundation for in-depth analysis and reporting.

                1. Data Visualization: We employ powerful visualization tools to transform raw data into actionable insights. Our custom solutions allow you to easily monitor key metrics, identify trends, and make data-driven decisions. 📊

                1. Machine Learning: We leverage advanced machine learning models to uncover hidden patterns and correlations within your data. This enables us to predict student outcomes, identify at-risk students, and personalize learning experiences.

              Benefits for Universities:

                  • Improved Student Success: Identify and address challenges early on, leading to higher retention rates and graduation rates.🎓

                  • Enhanced Faculty Effectiveness: Provide faculty with insights into student performance and engagement, enabling them to refine their teaching methods. 👩‍🏫

                  • Data-Driven Decision Making: Equip administrators with comprehensive data to inform strategic planning and resource allocation.

                  • Competitive Advantage: Stay ahead of the curve by leveraging data to drive innovation and continuously improve.

                The Future of University Research Partnerships

                As the importance of data in higher education continues to grow, so too will the role of university research partners like Datatelligent. By partnering with us, universities can unlock the full potential of their data, drive meaningful change, and create a brighter future for their students and faculty. ✨

                Ready to learn more? Contact Datatelligent today to schedule a consultation and discover how we can help your university thrive in the data-driven age.

                Categories
                Blog Higher Education Industry Snowflake

                AIR Forum 2024: Insights and Takeaways

                AIR Forum 2024 Insights and Takeaways
                AIR Forum 2024 Insights and Takeaways

                AIR Forum 2024: Insights and Takeaways

                INTRODUCTION

                The AIR Forum 2024 was a resounding success, bringing together professionals from the field of higher education analytics. As we engaged with attendees, several key themes emerged, shedding light on the current landscape and future trends in data analytics for higher education.

                KEY TAKEAWAYS
                1. Carnegie Mellon’s Data Lake
                  1. The buzz around Carnegie Mellon University’s presentation was exciting! Attendees couldn’t stop discussing their innovative approach to data analytics. Specifically, they highlighted using Snowflake, a centralized data lake, to unify disparate data sources. This approach resonated strongly with our own Datatelligent Platform for Higher Education. Clearly, there’s a growing need for data-informed decision-making in educational institutions.
                2. AI Awareness vs. Implementation
                  1. While everyone acknowledges the potential of artificial intelligence (AI), practical implementation remains cautious. Attendees expressed a desire to leverage existing data and tools effectively rather than diving headlong into AI solutions. Our recent survey on data analytics in higher education confirmed this trend; awareness and interest in AI are high, but adoption remains gradual.
                3. Balancing Choices, Costs, and Flexibility
                  1. The data analytics landscape offers an array of solutions, but institutions grapple with trade-offs. Budget constraints drive the need for cost-effective options, while flexibility is crucial for accommodating future growth. Striking the right balance between affordability and scalability is a priority.
                CONCLUSION

                The AIR Forum 2024 underscored the importance of data-informed decision-making in higher education. As we navigate this dynamic field, let’s continue to explore innovative solutions, collaborate, and adapt to meet our institutions’ evolving needs.

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                Blog Higher Education Industry Snowflake

                Unified Cloud Data Platform in Just 90-Days

                Setting Up a Unified Data Platform in 90-Days
                Setting Up a Unified Data Platform in 90-Days

                Unified Cloud Data Platform in Just 90-Days

                INTRODUCTION

                In today’s data-informed landscape, educational institutions face a dual challenge. They must manage vast amounts of information while ensuring seamless access and security. The promise of a unified cloud data platform—a centralized hub for data storage, processing, and analytics—holds immense potential. But can it truly be deployed within a tight 90-day window?

                This article delves into the intricacies of integrating a unified cloud data platform specifically tailored for higher education. We’ll explore the critical components, address common roadblocks, and provide a roadmap to success. IT administrators, data scientists, and academic leaders must understand the nuances of this transformational journey to ensure a successful implementation.

                So, fasten your seatbelt as we embark on a 90-day adventure—a sprint toward data unification that promises efficiency, insights, and a competitive edge. Let’s explore how proper planning, strategic execution, and collaboration can make this ambitious goal a reality.

                ASSESS AND PLAN

                First, meet with the key academic, administrative, and IT stakeholders and rank-order their priorities, needs, and desired outcomes. For example, one goal may be to improve student retention by 15%. You would then determine the data and data sets required to track each student’s retention.

                SPONSORSHIP FROM THE LEADERSHIP AND FUNDING

                Once the priorities and desired outcomes have been determined (e.g., improved student retention, analysis of the admission funnel to improve enrollment, a better understanding of enrollment, prediction of when a faculty member or advisor should engage with an at-risk student, etc.), sponsorship will be a critical success factor in this initiative. Most data initiatives fail partly due to a lack of leadership support; enlisting operational leaders to champion the project will help smooth over any obstacles you may face during the project, including obtaining the necessary funds. After you secure funding for the 90-day project, consider requesting funding for the nine months remaining in the year to develop the analytic solutions that will deliver the desired outcomes. Obtaining funding for the full 12 months is ideal to avoid going back to the well for additional dollars.

                SET UP THE DATA PIPELINES AND THE CLOUD DATA PLATFORM
                Next, select the tools and technologies needed to integrate the data and build the data platform. For data pipeline tools, consider Azure Data Factory and Logic Apps, which work well with student information systems (SIS) platforms such as Banner, Jenzabar, Workday, Colleague, Slate, and PowerFAIDS. These tools are also compatible with learning management systems (LMS) like Canvas, Blackboard, and Moodle, and data stored in operational data stores (ODS), Excel, and SharePoint. These platforms accommodate diverse data sources, including dining hall swipe data, to determine if students are socially connected. Third-party sources such as National Student Clearinghouse, IPEDS, student surveys, faculty evaluations, Google Analytics, and social data for recruitment channel analysis can also be integrated.
                 
                The number of data sources is not limited, but keep in mind that the goal is to have the data lake live in 90 days, so you will want to limit the number of data sources in this initial 90-day period.
                 
                For the cloud data platform, consider Snowflake. It works great with Tableau and Power BI because you can get insights from data sets of all sizes. Additionally, Snowflake’s pay-as-you-go model means you only pay for the storage and compute that you use, making it very cost-effective compared to traditional data warehouses.
                 
                SET UP THE DATA LAKE IN THE CLOUD PLATFORM

                Once the tools are connected, set up the data lake in the cloud data platform. To meet your 90-day goal and desired outcomes, take only the necessary tables from the SIS, LMS, CRM, etc., to deliver the desired analytic outcomes. Once the data is in the data lake, you can now perform data transformations, creating the datasets that will drive your analytic solutions.

                RESULTS, VALUES, AND ACCEPTANCE

                As you approach the 90-day initiative’s conclusion, reconnecting with the stakeholders and leadership to share the results is crucial. Here’s a summary of the key achievements:

                • Successful Setup
                  • Data Pipelines: Establish robust data pipelines.
                  • Cloud Data Platform: Implemented a scalable cloud data platform.
                  • Data Lake: Created a centralized data lake.
                • Data Integration:
                  • Connected 2-3 key data sources,
                  • Configured data pipelines to automatically refresh the data lake regularly.
                • Centralized Repository:
                  • Developed a single data lake repository for the centralization and collection of data.
                • Preparation for Analytics:
                  • Prepared datasets with predefined key metrics that will automatically feed into analytics solutions in the next phase (e.g., admissions funnel, enrollment trends, at-risk students, student success, etc.) running in Tableau or Power BI.

                Sharing these accomplishments not only highlights the progress made but also sets the stage for the next phase of development. This will ensure continued momentum and support from leadership and stakeholders.

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                Blog Higher Education Industry

                Top Data Challenges in Higher Education

                Top Data Challenges in Higher Education
                Top Data Challenges in Higher Education

                Top Data Challenges in Higher Education

                INTRODUCTION

                I’ve been in my current position at Datatelligent for a little over a year as a business development representative and have talked to many different leaders in higher education. My job is to hear the data challenges that individuals might be facing and discover strategies and solutions for institutions to turn these challenges into strengths. I’ve sat in hours of meetings, webinars, and speeches. Also, I have listened to Directors of Institutional Research, Directors of Institutional Effectiveness, and CIOs spill their challenges to me and strategize what steps they need to take to put a plan in place to improve their data maturity. From my discoveries, I’ll provide the top three data challenges currently in Higher Education for 2024. Also, I want to give some tips on how to avoid these challenges based on what I’ve learned.

                DATA SILOS

                For one thing, if you’re currently involved in higher education or have been in the past, I’m sure you experienced data silos or have heard from fellow peers the headache it can cause daily. Here are some of the headaches:

                • Fragmented Decision-making: Data silos hinder collaboration and lead to fragmented decision-making within institutions.
                • Inefficient Reporting: Siloed data is crucial for generating accurate reports, but it is time-consuming and error-prone.
                • Personalization Obstacles: Integrated data is crucial for personalized student experiences, but silos prevent the practical tailoring of services.
                • Strategic Implications: Addressing data silos requires breaking down barriers, investing in infrastructure, and fostering a data-driven culture.

                In 2024, we want our day-to-day operation to go smoothly and get all the data we need at our fingertips. Data silos have been throwing a wrench in this for a long time and are a top three challenge, I hear.

                DATA SECURITY AND PRIVACY
                Equally as crucial as siloed data, data security and privacy are critical aspects of higher education. As institutions collect and manage vast amounts of student and organizational data, safeguarding this information becomes paramount. Cyber threats can put a lot of stress on schools in order to protect information from being stolen. Data security involves safeguarding institutional assets through access controls, encryption, firewalls, and regular audits. On the other hand, data privacy ensures confidentiality and compliance with regulations like GDPR and FERPA. Transparency and ethical handling are key—like sealing letters in envelopes and treating data responsibly. Together, data security and privacy create a well-guarded digital library, allowing students, faculty, and staff to learn and collaborate without fear.
                 
                TURNING DATA INTO ACTION

                Lastly, you collect all the data you need, but using it effectively is another challenge. Many schools fail to transform the data into meaningful actions that drive positive outcomes. It requires defining clear objectives, selecting the right analytic tools, and translating insights into actionable steps. Balancing ethical considerations and fostering a data-driven culture are essential for success. Turning data into action is mentioned when talking to many individuals in higher education who don’t know the correct steps to take.

                HOW CAN I AVOID THESE?

                As I’ve mentioned, I have listened to countless meetings and read about many possible solutions for these challenges. Some things I recommend for becoming a more data-informed institution are:

                • Consolidate Data: Bring together data from disparate tools and datasets into a central data warehouse with good reporting tools. We at Datatelligent can help with this and recommend using a cloud-based data warehouse called Snowflake. Snowflake has great elastic scalability, robust security features, and seamless integration with popular BI tools and data services. This allows institutions to gain a more holistic view of learning processes and support mechanisms. How universities can break down data silos and generate new insights | THE Campus Learn, Share, Connect (timeshighereducation.com)
                • Advanced Reporting Tools: Advanced reporting tools like Tableau and Power BI can be beneficial for summarizing, visualizing, and comparing data. These tools provide better insights than standard tabular reports. At Datatelligent, we can help with this by having our own Datatelligent Platform for Higher Education that can consolidate data and provide data visualization tools like Tableau and Power BI.
                • Privacy Offices: Establish privacy offices and full-time privacy positions within institutions. Develop and improve privacy and practices.
                • Compliance Management: Manage Compliance across campus operations by understanding and adhering to privacy legislation and regulations.
                • Data-Driven Thinking: Leverage data analytics by encouraging data-driven thinking. Areas where data analytics can be beneficial include personalized learning experiences, academic analytics, and critical care assessments. Towards Evidence-Based, Data-Driven Thinking in Higher Education | SpringerLink
                • Optimize Data Strategy: Focus on data governance, create data leadership, establish actionable data strategies, utilize a cloud data platform to centralize data, create an analytics team, and implement data visualization tools like Power BI and Tableau.
                CITATIONS:
                1. Burns, Sean. “The Evolving Landscape of Data Privacy in Higher Education.” Educause, November 19, 2020. https://www.educause.edu/ecar/research-publications/the-evolving-landscape-of-data-privacy-in-higher-education/introduction
                2. Komljenovic, Janja. “The future of value in digitalized higher education: why data privacy should not be our biggest concern.”  Springer Link, November 19, 2020. https://link.springer.com/article/10.1007/s10734-020-00639-7
                3. Gibson, David. “Big Data in Higher Education: Research Methods and Analytics Supporting the Learning Journey.” Springer Link, July 5, 2017. https://link.springer.com/article/10.1007/s10758-017-9331-2
                4. Florea, Diana and Florea, Silvia.  “Big Data and the Ethical Implications of Data Privacy in Higher Education Research.” MDPI, October 21, 2020. https://www.mdpi.com/2071-1050/12/20/8744
                5. Times Higher Eduation. “Five actions for data-led transformation in Higher Education.” https://www.timeshighereducation.com/hub/p/five-actions-data-led-transformation-higher-education
                6. Masterson, Douglas, PhD, Davis, Christopher M., PhD, and Carbonaro, Suzanne, MEd. “Transforming Data into Meaningful Information.” The Society for College and University Planning, Jan-Mar 2023. https://www.scup.org/resource/journal-transforming-data-into-meaningful-information/
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                Blog Higher Education Industry Student Retention

                Understanding Trends in Undergraduate Degree Attainment

                Understanding Trends in Undergraduate Degree Attainment
                Understanding Trends in Undergraduate Degree Attainment

                UNDERSTANDING TRENDS IN UNDERGRADUATE DEGREE ATTAINMENT

                INTRODUCTION

                The pursuit of higher education is a critical milestone for countless individuals around the world. Whether it’s an associate degree, a bachelor’s degree, or a specialized certificate, earning an undergraduate credential opens doors to career opportunities, personal growth, and societal impact. In this blog post, we delve into the latest findings from the National Student Clearinghouse Research Center’s report on undergraduate degree earners for the academic year 2022-23.

                OVERALL DECLINE IN DEGREE EARNERS

                The report reveals a concerning trend: the number of undergraduate degree earners has declined for the second consecutive year. In the 2022-23 academic year, there was a 2.8% decrease, resulting in 99,200 fewer graduates compared to the previous year. This decline raises questions about the factors contributing to this downturn.

                FIRST-TIME COMPLETERS
                First-time completers, who represent 73.3% of all graduates, experienced a decline of 73,600 individuals. These are students who successfully complete their degree requirements for the first time. The 2.8% decrease in this group reflects broader challenges in higher education. As institutions adapt to changing demographics, economic shifts, and technological advancements, understanding the needs of first-time completers becomes crucial.
                 
                CERTIFICATES: A SURPRISING RISE

                While overall degree attainment declined, there’s a silver lining: the number of students earning certificates reached a ten-year high. Certificates, often shorter and more focused than traditional degrees, provide specialized skills and knowledge. The report attributes this increase to a 6.2% rise in first-time award earners. Whether in fields like healthcare, information technology, or skilled trades, certificates offer a pathway to employment and career advancement.

                ASSOCIATE AND BACHELOR’S DEGREES
                Despite the surge in certificates, associate and bachelor’s degrees remain foundational. These degrees continue to be valued by employers and serve as stepping stones for further education. However, institutions must address challenges such as affordability, access, and student support to reverse the decline in degree earners.
                STRATEGIES TO REVERSE THE DECLINE

                To combat this trend, educational leaders and policymakers can consider the following strategies:

                • Strengthening Student Support and Flexibility:
                  • Support systems: Enhance academic advising, tutoring, and mental health services, and establish mentorship programs to support students throughout their educational and career journeys.
                  • Flexible learning options: Expand online and hybrid courses, and offer more classes during evenings and weekends to accommodate non-traditional students and those with additional responsibilities.
                • Improving Educational Pathways and College Readiness:
                  • Short-term and stackable credentials: Develop certificate programs aligned with industry needs and offer credentials that can be built upon towards a degree.
                  • College readiness initiatives: Collaborate with high schools to ensure students are prepared for college and offer bridge programs to ease the transition to higher education.
                • Enhancing Financial Accessibility:
                  • Increase scholarship and grant awareness: Promote the availability of scholarships and grants to help reduce financial barriers for prospective students.
                • Adopting Data-Informed Strategies and Promoting Lifelong Learning:
                  • Data-driven approaches: Use analytics to identify at-risk students early and tailor programs to meet diverse needs.
                  • Lifelong learning culture: Encourage continuous education for adult learners and partner with businesses to support education benefits and career advancement.
                CONCLUSION

                The decline in undergraduate degree earners is a multifaceted issue that requires a collaborative and strategic response. By enhancing financial aid, strengthening support systems, and promoting flexible learning options, we can create a more inclusive and supportive educational environment. Additionally, by fostering a culture of lifelong learning and utilizing data-driven approaches, we can ensure that higher education remains relevant and accessible to all. As we work towards these goals, we can reverse the current trend and pave the way for a brighter future in higher education.

                CITATIONS:
                1. National Student Clearinghouse Research Center. “Undergraduate Degree Earners Report: Academic Year 2022-23.” April 11, 2024. “https://nscresearchcenter.org/undergraduate-degree-earners/#:~:text=The%20number%20of%20undergraduate%20degree,by%2073%2C600%20(%2D2.8%25).
                2. Weissman, Sara. “Degrees Earned Fall Again, Certificates Rise.” Inside Higher Ed, April 11. 2024.  https://www.insidehighered.com/news/students/academics/2024/04/11/degrees-earned-fall-again-certificates-rise
                3. Katharine Meyer “The case for college: Promising solutions to reverse college enrollment declines.” Brookings Institution, June 5, 2023. https://www.brookings.edu/articles/the-case-for-college-promising-solutions-to-reverse-college-enrollment-declines/
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                AI Blog Higher Education Industry

                Caution: AI Approaching Higher Education

                Caution AI Approaching Higher Education
                Caution AI Approaching Higher Education

                CAUTION: AI APPROACHING HIGHER EDUCATION

                INTRODUCTION

                Interest in Artificial Intelligence (AI) is growing across all industries, spurred by daily advancements that showcase its potential to enhance efficiency and predict trends. Higher education institutions, faced with declining enrollments in part due to shifting demographics, are especially interested in using AI to improve their operations around student recruitment and retention. But before colleges and universities start using AI, it is crucial to consider the responsible incorporation of AI, ensuring its use enhances existing processes while mitigating potential pitfalls.

                ethical considerations

                Using historical data by AI introduces the risk of perpetuating existing biases, a challenge highlighted by Amazon’s reevaluation of an AI recruitment tool biased against female candidates¹. Similarly, the application of AI in risk assessments within the legal system² has faced scrutiny for racial biases. These examples underline the urgent need for comprehensive AI governance frameworks, discussed during the March 2024 Data Analytics Alliance for Higher Education meeting, that prioritize ethical data use and rigorous oversight to combat bias.

                AI “Hallucinations” and Misinformation
                The phenomenon of AI “hallucinations”³ — baseless but authoritative assertions made by AI systems — has raised significant concerns regarding the use of tools like ChatGPT. Examples such as Google’s Bard AI misrepresenting facts about the James Webb Space Telescope⁴ and Microsoft’s Bing chatbot displaying unpredictable behavior and professing “love” for a New York Times columnist⁵ highlight the risk of misinformation. These incidents reinforce the importance of strong training data curation to mitigate the spread of misinformation in educational settings.
                 
                PRIVACY AND SECURITY

                The deployment of AI in analyzing large datasets accentuates privacy and security concerns, particularly around the potential for de-anonymization. AI’s ability to infer sensitive personal information from non-sensitive data⁶ introduces new data protection challenges. Therefore, adopting AI technology requires robust privacy safeguards, including secure platform designs and ethical data handling practices.

                DATA IS THE KEY
                A foundational principle for effective AI utilization is the term “Garbage in, garbage out,” emphasizing the critical role of data quality. Higher education institutions often rely on data from student information systems (SIS) and learning management systems (LMS) to train AI models. However, these sources frequently contain incomplete or inaccurate data, potentially leading to unreliable AI outputs.

                To navigate these challenges and lay the groundwork for effective AI implementations, the use of a Unified Data Platform (UDP) is vital. A UDP consolidates and harmonizes data from diverse systems, ensuring AI models are trained on high-quality, comprehensive datasets. Key characteristics of an effective UDP include:
                 
                • Centralized Data: Aggregates data from various institutional systems and external sources, providing a complete data ecosystem for accurate AI analysis.
                • Scalability: Offers a scalable infrastructure to accommodate increasing data volumes and complex AI use cases.
                • Robust Security Measures: Incorporates advanced security features to protect sensitive data, ensuring privacy and compliance with data protection laws.
                • AI-Ready Infrastructure: Facilitates the deployment of AI by ensuring the platform and tools are primed for AI applications, supporting advanced analytics, and making data AI-ready.
                HOW TO GET STARTED

                In response to growing inquiries from our higher education customers interested in AI, Datatelligent recommends that customers consider its Datatelligent Platform for Higher Education, which leverages a UDP to develop standard analytic solutions that most colleges and universities need. Karl Oder, one of the Chief Architects of the platform, talked about what we are doing with the platform. “We’re busy creating several AI prototypes with our partner, Snowflake, using the AI-Ready tools they provide.”

                In addition to getting your data “AI-ready” by establishing a UDP, schools should also spend time prepping for the AI Project⁷, starting with selecting the right use case. For higher education institutions, Datatelligent has developed prototypes on our platform that can accelerate this process, including:

                • Admissions and enrollment – predictive factors that will influence admissions and student enrollment projections
                • Student success and retention – identifying student success characteristics and predicting students at risk of leaving.
                • Graduation and program success – predictive factors driving graduation rates and overall program success.
                CONCLUSION

                Integrating AI in higher education calls for a balanced, thoughtful approach that acknowledges AI’s transformative potential alongside its challenges. By addressing issues of bias, misinformation, privacy, and ethical governance through strategic planning, institutions can harness AI to enhance educational outcomes and operational efficiency. Central to this endeavor is establishing a Unified Data Platform, ensuring data integrity, and laying a solid foundation for the responsible use of AI technologies.

                RESOURCES:
                CITATIONS:
                1. Dastin, Jeffrey. “Insight – Amazon Scraps Secret AI Recruiting Tool that Showed Bias against Women.” Reuters, August 10, 2018. https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G/.
                2. Angwin, Julia , Surya Mattu, and Lauren Kirchner. “Machine Bias.” Pro Publica, May 23, 2016. https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing.
                3. “What Are AI Hallucinations?” IBM.Com. February 1, 2024. https://www.ibm.com/topics/ai-hallucinations.
                4. Mihalcik, Carrie. “Google ChatGPT Rival Bard Flubs Fact About NASA’s Webb Space Telescope.” CNET, February 9, 2023. https://www.cnet.com/science/space/googles-chatgpt-rival-bard-called-out-for-nasa-webb-space-telescope-error/.
                5. McMillan, Malcolm. “Bing ChatGPT Goes off the Deep End — And the Latest Examples Are Very Disturbing.” Tom’s Guide, February 17, 2023. https://www.tomsguide.com/opinion/bing-chatgpt-goes-off-the-deep-end-and-the-latest-examples-are-very-disturbing.
                6. Ahmed, Hafiz. “Challenges of AI and Data Privacy—And How to Solve Them.” @ISACA 32, (2021). https://www.isaca.org/resources/news-and-trends/newsletters/atisaca/2021/volume-32/challenges-of-ai-and-data-privacy-and-how-to-solve-them.
                7. Sassi, Steve. “AI Project Prep for Higher Education.” Datatelligent.Ai. March 26, 2024. https://datatelligent.ai/ai-project-prep-for-higher-education/.
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                AI Project-Prep for Higher Education

                AI Project-Prep for Higher Education
                AI Project-Prep for Higher Education

                AI PROJECT-PREP FOR HIGHER EDUCATION

                INTRODUCTION

                After almost a year of weighing the pros and cons of AI at your institution, creating an action plan, and cross-collaborating with your peers from other institutions, your team has finally decided that AI is the future for recruiting, retaining, and ensuring your students’ success. Congratulations! Before answering one of the dozen daily emails in your inbox from AI software and services vendors, you should first take some cautious pre-project steps if you are serious about the success of your future AI project. Here are six AI project prep steps you should take before setting up any meeting with an AI software or service provider:

                1 – choose and define your use cases

                You probably already have the high-level Future Action Roadmap¹ if you’ve come this far, but you can’t, in project terms, “boil the ocean” with a “big bang” AI project covering everything on the map. No Higher Education institution has that kind of time or money. It’s time to pick one to three high-profile use cases where the need is most urgent. Perhaps it’s all about predicting student behavior and outcomes so you can sooner identify students at risk. Or maybe your school would like to personalize student learning and support services to help increase retention. It is very likely your marketing team has been in touch with you about how AI will help them segment and target prospective students for recruitment and deliver personalized and engaging marketing campaigns that can increase awareness, interest, and conversion rates for enrollment.

                In March 2024, Scott Sorenson, Executive Director, Data Privacy & Analytics from the University of Alabama at Birmingham, shared how they built a pilot using Salesforce’s AI-powered Tableau Pulse at the Data Analytics Alliance for Higher Education. They focused on use cases for the Marketing and Advancement Departments. Right away, they involved the participating departments, and the team at Tableau helped them build the business case for approval. The Results: Success. The Advancement team liked it and will include it on their IT roadmap, and the Marketing team loved it and wanted it yesterday. Some lessons learned from the UAB pilot:

                • Get the interested teams involved early and define the roles each will play.
                • Develop air-tight use cases founded on strong business reasons.
                • Even if business reasons are solid, leave plenty of time for executive iterations and approval.
                • Developing AI and Data Governance will take twice as long as you think it will.
                2 – DON’T DELAY YOUR AI GOVERNANCE AND SECURITY
                Lessons learned on the UAB AI pilot segue perfectly into perhaps the most crucial pre-project activity: AI Governance and Security. This should be a strong focus at the beginning of your AI journey, as it is foundational for your institution’s success. Some things to consider:
                 
                • Ethical Considerations: All policy decisions should align with ethical principles and the DNA of who you are as an institution. Ensure transparency, fairness, and equity. Institutional leaders (Chancellor/President, Chief Academic Officer, Chief Information Officer) are pivotal in driving ethical AI practices.
                • Senior Management: Define roles, responsibilities, and accountability related to AI governance and ensure that senior management oversees AI initiatives.
                • Risk Assessment and Iteration: Regularly assess risks associated with AI implementation and adjust policies accordingly.
                • Data Security and Privacy: Data handling and privacy protection will help keep your student and staff data safe. Mistakes made with the mishandling of private data can have serious consequences, so It’s imperative to put guidelines and best practices in place for collecting, storing, and processing data used in AI systems.
                • Transparency and Accountability: Not surprisingly, AI has the same biases as its human counterparts. Make AI algorithms and decision-making processes transparent with regular reviews, carefully define responsibilities, and hold accountable AI system performance and outcomes. 
                3 – IT’S ABOUT THE DATA

                The legacy systems used by your staff for the past two decades need to be assessed to determine if they are truly ready for AI. Do you have a Unified Data Platform to collect, store, process, analyze, and share your data with data visualization tools? How reliable, relevant, complete, and diverse is your data? Work may need to be done with Data systems before choosing the AI solution. The Data sources you will need will depend heavily on the use case. Here are just some Data systems commonly used for AI:

                • Student Information Systems (SIS) – Holding admissions, enrollment, grades, and financial aid information is often critical for Student Success analysis.
                • Learning Management Systems (LMS) – Platforms like Canvas, Blackboard, or Moodle will facilitate online learning, course management, and distribution of educational content.
                • Human Resource Systems – These systems handle employee data, payroll, benefits administration, and recruitment processes.
                • Vendor-based Systems – Specialized software for recruitment, student success, assessment, space management, and more.
                • External Data Sources – Registers, databases with scientific information, and other external systems that can support enrollment, recruitment, marketing, and student success decisions.
                4 – CHOOSE YOUR AI SOLUTION
                These steps are in this order for a reason. Not until the first three steps have been started and the first draft has been completed can you even begin to make an informed decision about AI technology that will bring your use cases to life. The questions to ask:
                 
                • What type of AI solution best suits your problem or opportunity? The areas with the biggest impact on securing your institution’s future success are typically Student Success, Enrollment, and Retention. The market is catching up quickly with AI offerings to support these initiatives.
                • Do you want to build your own solution from scratch or use an existing solution from a vendor or a partner? The old rule of buy to compete, build to differentiate still applies to AI Projects. Buying off-the-shelf (OTS) AI solutions should be where you start. It is still the lowest-cost entry to AI. Building your own should be for ambitious projects where no other OTS solution exists for an identifiable, mission-critical, market-differentiating AI use case.
                5 – DEFINE THE KEY PERFORMANCE INDICATORS (KPI)

                At the start of the UAB AI pilot project, after the use cases, governance, data, selected solution, and approvals were in order, Sorenson met with his Marketing and Advancement teams to define what metrics they wanted to see. From there, he determined what data was needed for the metrics. He then asked them what success looked like to them. UAB implemented a pilot, but it’s no different from deploying the actual AI Solution. In fact, it’s more critical.

                Defining KPIs will determine how you build the solution. Some common metrics used in higher education include the following:

                • Number of student minutes on a website – Does it lead to a greater conversion percentage to enrollment?
                • Year over Year (YoY) percentage of resources used by at-risk students – Does it correlate to YoY percentage of Student retention?

                Setting these KPIs will guide improvements toward success and ensure the Leadership Team that your investment in AI is seeing the hoped-for impact on enrollment, retention, and student success. What are the key performance indicators (KPIs) you will use to track its progress and results?

                6 – AND FINALLY, PLAN YOUR IMPLEMENTATION

                Before you even select a vendor to implement, how will you deploy your AI solution in your institution? You’ve determined what data from which systems are needed, but now it’s time to consider how you will integrate and unify your data into a usable format for AI Analytic consumption.

                Keep in mind, just because the letters A and I are in front of your project, it is still an IT project, and the best practices for this haven’t changed much in the past couple of decades. Bring all the lessons learned at your institution, your institution’s developed best practices, and industry PMO Best Practices² to this project, as you would any project.

                RESOURCES:
                CITATIONS:

                1. Jenay Robert and Nicole Muscanell, 2023 Horizon Action Plan: Generative AI (Boulder, CO: EDUCAUSE, 2023) 2023 EDUCAUSE Horizon Action Plan: Generative AI

                2. Abudi, G. (2011). Developing a project management best practice. Paper presented at PMI® Global Congress 2011—North America, Dallas, TX. Newtown Square, PA: Project Management Institute. https://www.pmi.org/learning/library/project-management-best-practice-organization-6167

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                Rappelling the Enrollment Cliff

                Rappelling the Enrollment Cliff
                Rappelling the Enrollment Cliff

                RAPPELLING THE ENROLLMENT CLIFF

                What IS THE ENROLLMENT CLIFF

                Higher education enrollment in the United States has been declining since 2010, a trend aggravated by the pandemic, resulting in a staggering 15% drop and the loss of 3 million students nationwide over a little more than a decade.1 Educators expected college students to come back once the pandemic lifted. Unfortunately, this has not happened due to a variety of reasons including students questioning the high cost and overall value of college to pending demographic shifts referred to as the Enrollment Cliff. 

                 A Cliff? Yes, a decline in birthrates during the 2008 Great Recession equates to an estimated 15% drop (roughly 576,000 students) of 18-year-olds eligible to enroll in college starting in the Fall of 2025.  As an article in Best Colleges put it, “The enrollment cliff poses a Darwinian threat to higher education, allowing only the wealthiest and market savviest to survive.” 2

                ADDRESSING THE SHORTFALL

                How can schools address this shortfall in available prospective students? In their analysis, Best Colleges identified characteristics of schools that are successfully navigating the Enrollment Cliff: 

                • Possess a deep understanding of their student body.
                • Excel in fostering student success.
                • Demonstrate adeptness in identifying and attracting students who are best suited for their programs.
                • Remain attuned to emerging trends and popular programs among their students. 2

                Those who know their students best will have the best data about their students. It’s only common sense.

                aSSESS MARKET SAVVINESS
                In a recent webinar with Datatelligent, Cowley College shared how they are grabbing the rope and rappelling gear in preparation for the cliff: they built a data-driven culture and started making data-informed decisions about their enrollment, retention, and student success. 
                 
                “We were already seeing a lot of these challenges in enrollment and retention a few years ago, students questioning the value of Higher Education, poor management of our internal resources, and staffing challenges,” said Stefani Jones, Director of Student Enrollment and Success at Cowley College. “We asked ourselves, ‘how do we do what we need to do with what we have?'” 
                 
                Seeing these trends, Cowley knew they needed to understand their students, and what types of students enrolled and thrived at Cowley. They also needed insight into the effectiveness of their marketing and recruitment strategies and activities. Like many institutions, the data about their students was scattered across different systems and compiled into spreadsheets and inadequate reports. They lacked the data insights they needed to make meaningful decisions to overcome enrollment challenges.  
                 
                “It was difficult to tell what was working,” said Jones. “Whether it was marketing strategies or recruitment efforts, we couldn’t see if any of it related to an increase in student applications. We were doing everything manual and requesting reports we then had to compile.”

                BECOME DATA DRIVEN

                The team at Cowley, partnering with Datatelligent, built a platform that unifies their Data and provides Analytic Solutions. Using the Enrollment and Admissions Trends Solution, Jones states Cowley can see and act on the following:

                • Track marketing and recruitment efforts and tie to enrollment trends. “We can see when we get an uptick in applications and tie it back to activities in the past two-week period to identify if our efforts are working.” 
                • Identify which undergraduate programs are trending. “We can now identify programs of study that are a hotter trend this year or in the upcoming semester. This allows us to work with Academics and help them to grow and move resources to the programs where we see student interest. “
                • Insight into performance of high school partners. “We can finally see which high school partners are doing well and converting into enrolled students and identify which high school partners we need to get into a little more and provide additional services.”
                KEEP STUDENTS YOU HAVE
                Once marketers and recruiters have successfully attracted and enrolled students, it is critical that schools do everything they can to retain and help their students succeed. This is a key component to becoming a Data-Driven Culture. Leveraging the Student at Risk Solution, Jones explains how Cowley College has improved the student experience and increased retention by making real-time, data-informed decisions:
                 
                • Identify Students at Risk – based on a set of risk factors tailored to the trends and circumstances of your students, programs, and region or state. “We didn’t have in place the risk factors that advisors could act on and reach out to students proactively and see how they can help. Now we do. This helps in our retention efforts.” 
                • Real-time information about student and program performance – This allows you to quickly identify opportunities to improve the student experience. “At the end of the semester I would collect all the information advisors and Department chairs wanted to provide me about students and programs, and I would capture it all on spreadsheets. Everything was extremely manual.”
                • Provide targeted, proactive intervention – “Prior to bringing our data and analytics to one platform, advisors would have to go to multiple tools to get the information on their students. Now it’s all in one place and very useful to the advisors and us.”
                Conclusion

                In the face of the Enrollment Cliff and the changing landscape of higher education, institutions must adopt a data-driven approach to navigate these challenges effectively. Cowley College’s proactive stance demonstrates the importance of understanding students, tracking trends, and making real-time, data-informed decisions. By unifying data and leveraging analytics, institutions can attract, retain, and foster student success amidst ongoing uncertainty. Embracing this mindset will be crucial for institutions to emerge as leaders in higher education’s evolving landscape.

                REFERENCES

                1. National Student Clearinghouse Research Center. Current Term Enrollment Estimates: Fall 2023 Expanded Edition. National Student Clearinghouse. https://nscresearchcenter.org/current-term-enrollment-estimates/. January 24, 2024. Accessed February 29, 2024. 

                2. Drozdowski MJ, Earnest D. Looming Enrollment Cliff Poses Serious Threat to Colleges. BestColleges.com. Published January 27, 2023. Accessed February 29, 2024. https://www.bestcolleges.com/blog/looming-enrollment-cliff-poses-serious-threat-to-colleges/ 

                 

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