Data Analytics is one of the big buzz words in the educational world these days. It gives us a unique insight into how we can tweak the way that we approach education to promote efficiency and effectiveness in the learning world. Adjusting the thought process of an organization to utilize data analytics takes time to adopt, and can have some pitfalls that are easily avoided. Check out the list below of five ways to help your Data Analytics program thrive in your educational organization!

  1. Prioritize Data

    Using analytics in your organization can lead to great growth and innovation! Switching thinking to make analytics a priority, instead of being viewed as a cost center, will help move the organization as a whole forward. After all, analytics has the ability to assist students on their path to graduation by providing valuable teaching and research opportunities.

  2. Create Centralized Data

    This is one of the instances where the phrase, “Sharing is caring,” becomes very useful. You can increase overall data integrity by having one central point of data across the entire institution. One benefit of this is that you won’t have to worry about being bogged down by other operational functions, which is a pitfall that a lot of other organizations fall into. This also allows the ability to define specific standards for how data is collected, shared, and stored.

  3. Become A Communication Hub

    Whatever your role is as an analyst, it’s important to work on communicating with those outside of your department to help understand their needs as well as for them to buy-in to the process. Analysts are uniquely able to serve as a translator into the world of analytics to help determine how to tackle problems using data. This should assist with some of the resistance to data sharing that occurs in the higher education realm.

  4. Focus on Skill Growth

    Skills gaps can hinder any organization if they’re in data infancy stages, which is why many resort to hiring outside of the organization in the beginning. In order to make the transition to in-house capabilities, it’s useful to encourage some core skill sets for data analytics. Some of those are: cloud computing, data science, machine learning, statistics, design, and operations.

  5. Practice Patience

    Successful analytics programs can take time! You may lack the correct data or skills in the beginning, or it may seem that the results you hoped for aren’t happening. Either way, patience and experimentation are the key here! Over time as your familiarity grows, you’ll be able to identify problems clearly, conduct analyses, make necessary adjustments, receive feedback, and repeat as needed. Over time, the information gleaned will help your organization bloom!

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