Five strategies for succeeding with data in higher education

What important steps can you take to increase the success of your analytics project on campus?

At the 2017 Blackboard Analytics Symposium, A. Michael Berman, ‎VP for Technology & Innovation at CSU Channel Islands and Chief Innovation Officer for California State University, took a different approach to answering this question. Instead of asking about success, he asked about failure. What would project management look like if we set out to fail from the very beginning? As it turns out, the result looks pretty similar to a lot of well-meaning data projects we see today.


What important lessons can we learn from failure?

#1. Set clear goals

Setting clear goals is not easy, but it is an important first step to successfully completing any project. If you don’t know what you are setting out to do, you won’t know when you are done, and you won’t know if you succeeded. Setting clear goals is hard work, not only because it requires careful thinking, but also because it involves communication and consensus. Clear communication of well-defined goals creates alignment, but it also invites disagreement as different stakeholders want to achieve different things. Goal setting is a group exercise that involves bringing key stakeholders together to agree on a set of shared outcomes so you can all succeed together.

#2. Gain executive support

Garnering the support of executive champions is a crucial and often overlooked step. All too often, academic technology units are prevented from scaling otherwise innovative practices simply because no one in leadership knows about them. Support from leadership means access to resources. It means advocacy. It also means accountability.

#3. Think beyond the tech

IT projects are never about technology. They are always about solving specific problems for particular groups of people. For the most part, the people that are served by an analytics project have no interest in what “ETL” means, or what a “star schema” is. All they know is that they lack access to important information. What many IT professionals fail to appreciate is the fact that their language is foreign to a lot of people, and that using overly technical language often serves to compound the very problems they are trying to solve. Access to information without understanding is worse than no access at all.

#4. Maximize communication

Communication is important in two respects. It is important to the health of your analytics project because it ensures alignment and fosters momentum around the clearly defined goals that justified the project in the first place. But it is also important once the project is complete. The completion of an analytics project marks the beginning, not the end. If you wait until the project is complete before engaging your end users, you have an uphill battle ahead of you that is fraught with squandered opportunity. With a goal of ensuring widespread adoption once the analytics project is completed, it’s important to share information, raise awareness, and start training well in advance so that your users are ready and excited to dig in and start seeing results as soon as possible.

#5. Celebrate success

It’s easy to think of celebration as a waste of company resources. People come to work to do a job. They get paid for doing their job. What other reward do people need? But IT projects, and analytics projects in particular, are never ‘done.’ And they are never about IT or analytics. They are about people. Celebration needs to be built into a project in order to punctuate a change in state, and propel the project from implementation into adoption. In the absence of this kind of punctuation, projects never really feel complete, and a lack of closure inhibits the exact kind of excitement that is crucial to achieve widespread adoption.

Originally posted to

Also published on Medium.