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Blog Higher Education Industry Snowflake

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

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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AI Blog Higher Education Industry Snowflake

The Importance of AI-Powered Analytics in Higher Education

The Importance of AI-Powered Analytics in Higher Education

The future of AI is now

At Datatelligent, we look to the future for ways to help our customers solve decades-old Higher Education problems. We hear a lot of questions lately about AI and what it means for Institutional Research. Questions like, “How can Generative AI and Large Language Models help our analytics? Will adding AI extract the predictive insights we need to help students and help us with retention, recruitment, and funding?”

Well, it’s funny you should ask. On November 15, 2023, we are hosting a webinar on these very topics with our most AI-innovative partner, Snowflake. Elevate Education: AI Solutions for Higher Education.

Snowflake is moving fast, at Chicago-blizzard pace, embracing all that’s AI and announcing earlier this month, during Snowday, a host of new AI tools.  We will in turn innovate with our Higher Education customers and implement these tools into the Datatelligent Unified Data Platform.

The Definition of AI in Higher Education

AI-powered analytics is the use of artificial intelligence to analyze large datasets to identify patterns, trends, and insights. Here are some of the areas will innovate with AI-powered analytics with our higher education customers:

  • Student success: AI-powered analytics can be used to identify students who are at risk of dropping out or failing a course. This information can then be used to provide targeted interventions, such as tutoring or academic advising.
  • Student Recruitment and Enrollment: considered one of the holy grails of analytics, identify the best mix of students who will benefit and are succeed from the specialties offered by the institution. Closely related, AI can help identify so you can focus recruitment on the students that will help your institution win and retain their grant funding.
  • Enrollment Trends: Identifying the trends early that will impact future enrollment. Linking to all sorts of internal and external data sources, AI-powered insights helps plan for student recruitment in fast-changing demographics.
  • Faculty Planning: Recruitment doesn’t stop with students. AI can help with faculty planning, identifying the educational specialties that are in demand now and in the future. Recruitment efforts and education certifications can be planned years in advance.
  • Personalized learning: Personalized learning experiences can be created for students using insights from AI-powered data. This can be done by adapting course materials, providing individualized feedback, and recommending additional resources.
  • Administrative efficiency: Why not have that AI-bot be the helpful assistant it wants to be, automating scheduling, grading, and admissions processing? This can free up time for faculty and staff to focus on more strategic initiatives.
The Challenges of AI

Of course, AI is not the magic pill to make all our analytic and Institutional Research headaches go away. At Datatelligent, we help mitigate the challenges AI-powered analytics brings to higher education:

  • Data quality: AI-powered analytics immediately bring up Data Quality.  Institutions need to ensure the data is accurate, complete, and consistent, or your “insights” will be none of these.
  • Bias: AI algorithms can be biased, which can lead to unfair or inaccurate results. Data Analysts need to be aware of the potential for bias and make sure a human takes steps to mitigate it.
  • Ethics: The use of AI in higher education raises a number of ethical concerns, such as the potential for surveillance and discrimination. Institutions need to develop clear ethical guidelines for the use of AI in higher education. At Datatelligent, we have a well-developed AI Governance and Ethics framework.

Overall, AI-powered analytics has the potential to revolutionize higher education. As with any revolution there are always challenges, which is why it’s best to align with an ally before overthrowing any king. We’ll be talking about the AI Revolution on November 15. We hope to see you at the Datatelligent and Snowflake AI Solutions for Higher Education webinar.

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Blog Faith Member/Volunteer Engagement Outputs & Outcome Snowflake Tableau

Empowering Faith-Based Organizations with Data: How Tableau and Snowflake can Increase Data Maturity

Empowering Faith-Based Organizations with Data: How Tableau and Snowflake can Increase Data Maturity

Faith-based organizations play a significant role in communities all around the world. These organizations have unique challenges when it comes to data management and analysis, as they often rely on volunteers and need more resources. However, modern tools such as Tableau and Snowflake can help faith-based organizations increase their data maturity and make more informed decisions.

Tableau is a data visualization tool that allows organizations to create interactive and engaging data visualizations. On the other hand, Snowflake is a cloud-based data warehouse that provides a secure and scalable platform for storing and analyzing data. Faith-based organizations can use these tools together to gain insights into their operations and make data-driven decisions.

Here are a few ways that faith-based organizations can use Tableau and Snowflake to increase their data maturity:

  1. Track and analyze donations: One of the most critical aspects of any faith-based organization is donations. With Tableau and Snowflake, organizations can track and analyze donations over time to understand trends and patterns. They can create dashboards that show how much money they have received, where it came from, and how it was used. This can help them make more informed decisions about fundraising and budgeting.
  2. Monitor participation: Faith-based organizations rely on attendance and engagement to gauge the effectiveness of their programs. With Tableau and Snowflake, organizations can track attendance and engagement metrics over time. They can create dashboards that show how many people attended each event, how engaged they were, and how long they stayed. This can help them identify which programs are most effective and where they need to make improvements.
  3. Monitor volunteer engagement: Volunteers are a critical part of many faith-based organizations, and it can be challenging to track their recruitment, training, assignments, and recognition. Tableau and Snowflake can help organizations evaluate volunteer performance by tracking hours worked, tasks completed, and opportunities for volunteer training or recognition. Data-driven volunteer management helps direct attention to the volunteers who might need additional support.
  4. Analyze program effectiveness: Faith-based organizations run a variety of programs, from education and outreach to charity and support services. With Tableau and Snowflake, organizations can analyze the effectiveness of these programs by tracking metrics such as program attendance, participant feedback, and outcomes for service recipients. This can help organizations identify which programs are most effective and where to improve.
  5. Monitor and improve operations: Like any organization, faith-based organizations have operational challenges that can be difficult to manage. With Tableau and Snowflake, organizations can track operational metrics such as budget, staff time, and inventory. They can create dashboards that show how these metrics change over time, identify areas of concern, and make data-driven decisions to improve operations.

Tableau and Snowflake are powerful tools that can help faith-based organizations increase their data maturity and make more informed decisions. Faith-based organizations can gain insights into their operations and make data-driven decisions by tracking donations, attendance, volunteer performance, program effectiveness, and operational metrics. With the right tools and strategies, faith-based organizations can use data to improve their programs and services and positively impact their communities.

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Impact of Generative and Predictive AI
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The Impact of Generative and Predictive AI on Higher Education: Revolutionizing Administration, and Student Success and Student and Faculty Satisfaction

This article highlights the profound influence of generative and predictive AI on higher education. It discusses how these technologies are streamlining administrative tasks, enhancing student success through early interventions, and improving overall satisfaction for both students and faculty. Institutions are leveraging AI for better decision-making, personalized learning experiences, and optimized course offerings, all of which contribute to a more efficient and effective educational environment.

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