Blog Higher Education Industry

Top Data Challenges in Higher Education

Top Data Challenges in Higher Education


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.


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.

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.

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.


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 (
  • 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.
  1. Burns, Sean. “The Evolving Landscape of Data Privacy in Higher Education.” Educause, November 19, 2020.
  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.
  3. Gibson, David. “Big Data in Higher Education: Research Methods and Analytics Supporting the Learning Journey.” Springer Link, July 5, 2017.
  4. Florea, Diana and Florea, Silvia.  “Big Data and the Ethical Implications of Data Privacy in Higher Education Research.” MDPI, October 21, 2020.
  5. Times Higher Eduation. “Five actions for data-led transformation in 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.
Blog Higher Education Industry Student Retention

Understanding Trends in Undergraduate Degree Attainment



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.


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, 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.

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.

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.

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.

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.

  1. National Student Clearinghouse Research Center. “Undergraduate Degree Earners Report: Academic Year 2022-23.” April 11, 2024. “,by%2073%2C600%20(%2D2.8%25).
  2. Weissman, Sara. “Degrees Earned Fall Again, Certificates Rise.” Inside Higher Ed, April 11. 2024.
  3. Katharine Meyer “The case for college: Promising solutions to reverse college enrollment declines.” Brookings Institution, June 5, 2023.
AI Blog Higher Education Industry

Caution: AI Approaching Higher Education



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.

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.

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.

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.

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.

  1. Dastin, Jeffrey. “Insight – Amazon Scraps Secret AI Recruiting Tool that Showed Bias against Women.” Reuters, August 10, 2018.
  2. Angwin, Julia , Surya Mattu, and Lauren Kirchner. “Machine Bias.” Pro Publica, May 23, 2016.
  3. “What Are AI Hallucinations?” IBM.Com. February 1, 2024.
  4. Mihalcik, Carrie. “Google ChatGPT Rival Bard Flubs Fact About NASA’s Webb Space Telescope.” CNET, February 9, 2023.
  5. McMillan, Malcolm. “Bing ChatGPT Goes off the Deep End — And the Latest Examples Are Very Disturbing.” Tom’s Guide, February 17, 2023.
  6. Ahmed, Hafiz. “Challenges of AI and Data Privacy—And How to Solve Them.” @ISACA 32, (2021).
  7. Sassi, Steve. “AI Project Prep for Higher Education.” Datatelligent.Ai. March 26, 2024.
AI Blog Higher Education Industry

AI Project-Prep for Higher Education



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.
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. 

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.
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.

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?


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.


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.

Blog Higher Education Industry

Rappelling 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


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.

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.”


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.”
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.”

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.


1. National Student Clearinghouse Research Center. Current Term Enrollment Estimates: Fall 2023 Expanded Edition. National Student Clearinghouse. January 24, 2024. Accessed February 29, 2024. 

2. Drozdowski MJ, Earnest D. Looming Enrollment Cliff Poses Serious Threat to Colleges. Published January 27, 2023. Accessed February 29, 2024. 


Blog Higher Education Industry

Seven Steps to Building a Data-Driven Culture in Higher Education

Seven Steps to Building a Data-Driven Culture in Higher Education

What we are hearing

At Datatelligent, we spend hours a day listening to all our customers in Higher Education. In the listening, we hear a lot of recurring concerns and themes. Here are just a few things we hear: 

  • “Traditionally, we’ve had strong student retention, but lately, it’s trending downwards, and we want to know why.”
  • “We want to know who our students are and find out the kind of students who succeed in our programs, but we don’t know where to start.”
  • “We need to identify at-risk students better and faster so we can get them the resources they need before it’s too late.”
  • “We like to say we’re an institution that makes data-informed decisions, but in reality, we don’t look at the data because we don’t have an easy way to look at data.”
  • “We need to simplify data visualization. We need dashboards that tell our people, ‘Here’s what you need to know.'”
  • “We know we have the answers in our data, and we talk about unifying data so we can build the analytics we need to understand our students, but we have systems everywhere, and we don’t have the big-dollar budget to integrate them.”

When we hear this, we know we’re talking to customers on the journey to building a data-driven culture at their institutions. They are experiencing growing pains. Being good listeners, the team here at Datatelligent wants to minimize the pain and speed up the growth.

Building a data-driven culture

We hosted a recent webinar with Debbie Phelps at Cowley College, Executive Director of Institutional Effectiveness and proud “office of one.”  Debbie explained how she, with limited resources, built a data-driven culture where they truly make data-informed decisions.

Debbie started with a plan and made Datatelligent a partner in their journey. Our team and our solutions played their part, but Debbie was the driving force behind the journey to being data-driven. Here’s how they did it.

1. leadership commitment and vision
  • Leadership Buy-in: Without this, the plan to build a data-driven culture goes nowhere. University leaders, including administrators, deans, and department heads, must champion the importance of data-informed decision-making. Their commitment sets the tone for the entire institution.
  • Vision Statement: Develop a clear vision statement that emphasizes the value of data-driven practices. Communicate this vision consistently to your team.
2. Infrastructure and data systems
  • Data Governance: Establish robust data governance practices. Define roles, responsibilities, and processes for data management. Ensure data security, privacy, and compliance. This takes a lot of work, but our customers, like Cowley College, who do this, see bottom-line lasting benefits and improve the success of their students – the real reason behind what we do.
  • Integrated Systems: Invest in systems that allow seamless data integration. Siloed data inhibits effective decision-making. This is where the Datatelligent Platform for Higher Education really helps you build your data-driven culture.
3. Data Literacy Training
  • Training Programs: So many institutions make the mistake of taking a “build it and they will come” approach. Not Cowley College, and not Datatelligent customers. We always advise and help you design regular workshops and training sessions on data literacy. Your team should understand basic statistical concepts, data visualization, and interpretation.
  • Department-Specific Training: Don’t forget to tailor training to specific roles (e.g., admissions, student services, finance). Each department has unique data needs.
4. Transparency and Communication
  • Transparency: This is an essential part of your governance and security plan. Also, make it a part of the training. Be patient about data sources, methodologies, and limitations. Your team should know where the data comes from and how it’s processed.
  • Regular Updates: This ensures everyone is on the same page in a data-driven culture. Provide timely updates on institutional performance metrics. Dashboards and reports should be accessible to all team members.
  • Feedback Loop: Just as Datatelligent listens to customers, as a data steward of your institution, it’s important to listen to your “customers.” Encourage your team to provide feedback on data quality and usability. Act on their insights.
5. Data-driven decision-making processes
  • Define Key Metrics: Key performance indicators (KPIs) relevant to each department. For admissions, it might be enrollment rates; for student services, retention rates; for advisement, identifying the students at risk and designing academic plans that ensure student success.
  • Use Cases: Illustrate real-world scenarios where good data and data visualizations influenced decisions. Share success stories to inspire everyone.
  • Cross-Functional Collaboration: Encourage collaboration across departments. Data insights often emerge at the intersection of disciplines. If you get the chance to talk to your peers at Cowley College, this is something they do well.
6. Ethical Considerations
  • Privacy and Consent: Your team should understand the ethical implications of handling student data. Ensure compliance with privacy laws (e.g., FERPA). This has remained constant, and hyper-vigilance is needed as AI tools are rolled out to enhance analytics.
  • Bias Awareness: Train your team to recognize and mitigate biases in data analysis. Ethical use of predictive models is critical, especially now that we have entered the age of AI, which, not surprisingly, mimics the same biases as its human counterparts.
7. Continuous Improvement
  • Assessment: Regularly assess the effectiveness of data-informed practices. Are we moving the needle on student retention? Are we identifying students at risk sooner? Are decisions improving? Is your team using data effectively?
  • Celebrate Wins: We encourage all our customers to do, acknowledge, and celebrate instances where data-informed decisions lead to positive outcomes. Recognize every team member’s contributions.

We have learned from our customers at Datatelligent that building a data-informed culture is a long but rewarding journey. It requires collaboration, adaptability, and a shared commitment to student success – the real motivation behind what we do. By empowering your team with data literacy and fostering a culture of curiosity, colleges, and universities can thrive in an increasingly data-driven world that will soon have jet-fueled added to the engines once AI tools catch up with the rest of us data-driven thinkers.

Blog Human Services Industry

Data-Driven Innovation: A Dive into Datatelligent’s Impact on Tri-Town YMCA

Data-Driven Innovation: A Dive into Datatelligent's Impact on Tri-Town YMCA

In a recent interview on WGN Radio’s “Your Money Matters,” the spotlight shone on a groundbreaking partnership between Datatelligent and Tri-Town YMCA. The interview highlights the transformative power of data-driven innovation in community development.

Datatelligent, an active member of Innovation DuPage, has been at the forefront of leveraging data to drive positive change. The company’s collaboration with Tri-Town YMCA is a testament to the potential for innovation when technology meets community initiatives.


The interview delves into how Datatelligent’s expertise is being harnessed to enhance the efficiency and impact of Tri-Town YMCA’s programs. By employing data-driven insights, the YMCA aims to optimize resource allocation, improve program effectiveness, and ultimately better serve the community.

Tri-Town YMCA’s commitment to community development aligns seamlessly with Datatelligent’s mission to empower organizations through data. By integrating data analytics into their decision-making processes, the YMCA can:

  • better understand the community’s needs
  • identify areas for improvement
  • tailor their programs to make a more significant impact.


For an in-depth exploration of this exciting collaboration, listen to the full interview here. Gain insights into how data-driven innovation is shaping the future of community development. Learn how Datatelligent and Tri-Town YMCA are working together to create positive change.


Technology is playing a pivotal role in shaping our communities. Datatelligent’s collaboration with Tri-Town YMCA exemplifies the potential for data-driven innovation to drive positive social impact. This partnership is a beacon, showcasing how businesses and organizations can create a better, more informed future.

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.

Blog Food Assistance Human Services

How Food Banks Benefit from Partnerships

How Food Banks Benefit from Partnerships

How might your organization benefit?

As a food bank, food pantry, or food council, your mission likely involves reducing food insecurity or hunger and increasing access to healthy food. You probably ramped up during the pandemic and are still waiting for the need to decrease. You have likely already squeezed out every bit of efficiency in your operations with today’s tools.

But what if you could add a tool that, within three short months, can maximize the data collected in your various systems and communicate it effectively to stakeholders? Have you considered a partnership with Datatelligent and Tableau?

Datatelligent is a data analytics company that partners with nonprofit organizations to organize existing data. Conversely, Tableau is a leading data visualization tool that can transform complex data into easily understandable and actionable insights. Food banks, food pantries, food councils, and their human service partners have worked with us in many ways.

Data analysis and visualization

Organizations often input information about donors, volunteers, food types, needs, geography, expenses, and more into various software systems. Datatelligent pulls data from disparate systems and layers on Tableau’s data visualization tools. We transform this data into dashboards where various staff leaders can answer questions and make data-driven decisions through self-service.

Streamlined operations

Datatelligent can combine data sets that previously lived in data silos or even simple spreadsheets. Using data analytics, Food Banks and Pantries may identify bottlenecks or inefficiencies in their processes. With info from across their service area, Food Councils can visualize the trends in needs, resources, and gaps in the overall food assistance ecosystem. With the help of Datatelligent and Tableau, they can implement changes to streamline their operations, improve efficiency, and ultimately serve more people in need.

Improved fundraising and communications

How do you prove you are making a difference? Data analytics can help you identify donation trends, understand your donor base, and target communications. More importantly, effective use of data can help justify sustained or increased funding—especially when it illustrates how the services provided meet the need or highlights precisely who is most in need. Datatelligent and Tableau equip organizations with strategies that are more likely to resonate with donors and result in increased funding.

Contact us to learn how your Food Bank, Food Pantry, or Food Council can work with us. Tell the story of your food ecosystem and your organization’s impact. Consider partnering with Datatelligent and Tableau to take your organization to the next level.

Blog Faith Member/Volunteer Engagement Outputs & Outcome

Religious Organizations Embrace Technology and Data Post Pandemic

Religious Organizations Embrace Technology and Data Post Pandemic

Faith-based organizations, like many other institutions, are increasingly relying on technology to accomplish their goals. From online worship services to digital giving platforms, thriving congregations and ministries have been quick to adopt new technologies to meet the needs of their communities.

The pandemic spurred changes in attendance from online and outside service attendance in the spring of 2020 to a hybrid of in-person and online attendance in 2022. The Pew Research Center documented the changes through five surveys between July 2020 through November 2022. Virtual viewing of services online or on TV increased, in-person attendance declined, and overall participation remained nearly steady with a slight decline since the pandemic.[1]

While attendance may have been tracked by religious organizations before, deeper analysis can help leaders understand which programs are most popular—whether in-person or virtual–and for whom. Virtual attendance creates opportunities and challenges to meet a new definition of the sense of “community” religious institutions seek to create. Heidi Campbell, professor of communication at Texas A&M University, stated, “Digital technology in the church and in ministry is here to stay. And our future is definitely hybrid.”[2]

One of the most significant changes that religious organizations have experienced in recent years is the integration of data into their operations. The Pew Research confirms what congregations may observe anecdotally in the demographic differences of attendees by participation type. By using data analysis tools, congregations, and ministries are gaining insights into their members’ needs and preferences, which allows them to better tailor their programs and services.

Faith-based organizations are also turning to social media and technology to improve their outreach efforts. By engaging with their communities on social media, organizations can spread their message to a wider audience and engage with individuals who may not be able to attend in-person events or who joined the virtual religious community. They can use social media analytics to understand which posts are most effective and how to better engage with their followers. Others are using virtual reality technology to give members a virtual tour of their facilities or to provide a more immersive worship experience.

It’s important to note that while technology can certainly be a valuable tool for faith-based organizations, it is not a replacement for the personal connections and relationships that are at the heart of congregations and ministries. However, by embracing new technologies and integrating data into their operations, religious-based organizations can better understand and meet the needs of their communities.

[1] Pew Research Center, March 2023, “How the Pandemic has Affected Attendance at U.S. Religious Services,” by Justin Nortey and Michael Rotolo, and accessible at
[2] Council on Foreign Relations (CFR), “Religion and Technology Webinar” held March 23, 2023, and accessible at In this webinar, Heidi A. Campbell, professor of communication at Texas A&M University, and Paul Brandeis Raushenbush, president and CEO of Interfaith Alliance, discuss the meeting of religion and digital culture, and its effect on religious communities. Carla Anne Robbins, senior fellow at CFR, moderates.

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