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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.
Conclusion

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.

Categories
Blog Higher Education Student 360 Student Retention Students at Risk

Can Community Colleges Survive the Pandemic?

There is no shortage of adjectives to describe 2020 and 2021: catastrophic, heart-wrenching, impossible, irritating, devastating. Whether we are applying it to our own personal experiences or to the broader world around us, no one can dispute that the COVID pandemic has created an environment of change and disruption.

This has been felt profoundly throughout our educational system. Schools have had to reinvent how they operate, and nowhere has this been more disruptive than in the community colleges space. While COVID has created headaches and difficulties throughout the higher education landscape, community colleges have experienced the brunt of the COVID-related fallout.

Why?

Colleges and universities had already been experiencing issues related to the cost of higher education and the student loan crisis. Schools have seen costs explode while revenues decline. In addition to the significant investments schools have made to transition to online learning environments — from training, course development, and technology improvements — they have also had to invest in infrastructure improvements to address other COVID-related issues (more space for in-person education, air filtration systems, etc.).

All this is occurring while many current and prospective students are questioning the value of higher education. Many have opted to delay a return to school until after the pandemic. Some question the cost relative to their online learning experiences. Schools have lost additional revenue as many students have not returned to on-campus living, sporting and other events have been canceled, state and federal funding may not be available, and alumni have not contributed as generously as in the past.

This list does not even touch on the additional COVID fallout — anxiety, vaccine availability, variants, etc.

So why has this hit the community college sector harder?

Unlike other economic downturns where community colleges have seen their enrollment numbers climb, the COVID pandemic has produced the opposite result. Community colleges have seen enrollment and retention numbers take a significant hit compared to other higher education institutions, especially for first-year students, where they see a nearly 20% drop.

Students at community colleges have also reported greater barriers to enroll and stay in school:

  • Job uncertainty — Many students rely on income from full- or part-time jobs that either disappeared or were severely hampered during the pandemic.
  • Family demands — This was especially difficult during the stay-at-home order when students found it disruptive to take online classes while also balancing family/childcare responsibilities.
  • Technology hurdles — Many students reported limited or no access to the technology required to complete online learning. Reliable broadband technology, as well as access to a computer or tablet, was a significant barrier to the new normal.
  • Limited emotional connection — Community colleges have historically had a more difficult time creating a sense of camaraderie and engagement compared to other colleges and universities. The transition to online learning has only exacerbated this sense of disconnect.
Will they be able to recover? Yes.

The answer is data. Community colleges are sitting on a patchwork of information that can be harnessed to help them answer key questions and pivot to meet challenges more effectively. Whether it be diving into enrollment metrics to help them improve existing programs or identify new opportunities; working with faculty and students to proactively seek out students that are struggling and develop programs to improve retention; or better forecasting for budget needs, especially when revenue targets and funding sources are shifting — this can all be addressed

by collecting and mining the data within these colleges.

And that is what we at Datatelligent can help with. We have partnered with colleges and universities throughout the country to develop data solutions to help them use their data to navigate these difficult times.

To learn more about our work with community colleges, visit: datatelligent.ai

by Steve Wightkin, Chief Operating Officer, Datatelligent

Categories
Blog Data Maturity Other

What Does It Mean to Be Data-Driven?

Using data to drive decisions, being data-savvy, harnessing the power of your data…all of these buzz words get bandied about during meetings, at conferences, in the trades, and online, but what does it really mean? How do you even know if you are a data-driven organization?

At Datatelligent, and throughout my extensive career in data analytics, I see becoming a data-driven organization as an iterative process. Think of it as a continuum. While it might seem overwhelming to assess and implement the organizational changes required to be in the data-driven zone, it’s more realistic and manageable to break the evolution into steps.

Step 1: Data Aware

You first need to recognize that your organization has disconnected information — nearly every entity does. For those companies just beginning their journey, these pieces of data may be tracked using manual, non-standard reports. And decisions rarely are made using the available data.

Step 2: Data Proficient

Your organization is tracking and collecting data using standardized reporting tools or reporting platforms, but the information may not be shared collectively. The data may not be well utilized for decision-making or planning.

Step 3: Data Savvy

You’re getting there. Your company tracks and uses data to make some business decisions, but the information may be trapped in silos between groups/departments, and the methodology to collect and report data may be inconsistent.

Step 4: Data-Driven

You’ve arrived at the pinnacle! Your organization uses data-first thinking from the start. All people and processes are in sync for data collection, tracking, and utilization. And data analysis is embedded into your decision-making practices.

So How Do You Get There?

It’s essential to look at the big, broad picture for your organization first — try to establish a high-level plan that is connected to your institution’s mission. This will help ensure that the data you mine is connected to achieving the results that matter to your stakeholders. What data will help you reach your business goals? Do an honest assessment of where your organization is in its analytics maturity (as per the steps above). Do you need to make any changes/adjustments to get started?

Next, invest what you can and adopt an iterative approach. This is more cost-effective than tackling an enterprise-wide solution, and it allows you to focus on projects that bring immediate value and smaller wins. Snowball this and add on as you build confidence and support throughout the organization.

Lastly, it’s essential to establish a unified approach. Develop a unified data platform to be rolled out across all parts of the organization. Bring together stakeholders to set the tone of collaboration and resist the temptation to silo information. And empower staff to access the data and tools in order to make decision-making a data-driven exercise. Invest in consistency, connection, and training.

To learn more about how Datatelligent has helped companies large and small, as well as colleges and universities, become data-driven, visit datatelligent.ai.

By Larry Blackburn, Chief Solutions Officer, Datatelligent

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