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

How to fail with data

Sometimes the most effective way of communicating the right way to do something is by highlighting the consequences of doing the opposite.  It’s how sitcoms work.  By creating humorous situations that highlight the consequences of breeching social norms, those same norms are reinforced.

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, harnessed his inner George Costanza to deliver an ironic, hilarious, and informative talk about strategies for failing with data.

What does this self-proclaimed ‘Tony Robbins of project failure’ suggest?

  1. Set unclear goalssetting unclear goals takes a lot of hard work and may require compromise. It’s way more democratic to let everyone set their own goals.  That way, everyone can have their own criteria for success, which guarantees that whatever you do almost everyone is going to think of you as a failure.
  2. Avoid Executive SupportGoing out and getting executive support is also a lot of work. It means going to busy executives, getting time of their calendar, and speaking to them in terms they understand.  It also means taking the time to listen and understand what is important to them.  Why not go it alone?  Sure, it’s unlikely that you will achieve very much, but it’ll be a whole lot of fun.
  3. Emphasize the Tech Make the project all about technology. And make sure to use as many acronyms as possible.  Larger outcomes don’t matter.  They are not your problem.  Focus on what you do best: processing the data and making sure it flows through your institution’s systems.
  4. Minimize Communication Why even bother to make people’s eyes glaze over when talking about technology when you can avoid talking to anyone at all? Instead of having a poor communication strategy, it’s better to have no communication strategy at all.  You’ll save the time and inconvenience of dealing with people questioning what you do, because they won’t know what you’re doing.
  5. Don’t Celebrate SuccessIf you have done everything to fail, but still succeed despite yourself, it’s very important not to celebrate. Why bother having a party when people are already getting paid?  Why take time out of the work day to reward people for doing their jobs?  Isn’t it smarter to just tell everyone to get back to work?  Seems like a far more efficient use of institutional resources.

Speaking from personal experience, Michael Berman insists that following these five strategies will virtually guarantee that you drive your data project into the ground. If failing isn’t your thing, and you’d rather succeed in your analytics projects, do the opposite of these five things and you should be just fine.