5 Common Problems Preventing Your University from Making Smart Campus Decisions

Most colleges recognize the need to have detailed and timely data available for decision making. However, for most, there is an ongoing struggle to achieve this. There are as many reasons for this as there are colleges, each with their own culture, but some stand out: Partly a culture clash between de-centralized decision making and centralized data analysis, partly a lack of expertise in driving tech strategies by campus decision makers, partly a lack of managers knowing how to treat data content as an asset.

Yet there is agreement that campus execs don’t have the data they need in a timely manner to drive their institutions mission forward. Even the Bill & Melinda Gates Foundation is funding research into understanding this phenomenon.

So what does a college president do? Where do you start bridging this great divide?

A good approach is to avoid the temptation to initiate a full culture change all at once, or to launch a multi-year project, or to buy yet another expensive software license from a vendor. These will often collapse short of the goal, meanwhile putting off needed short term changes.

Better to dig into some of the key barriers that hold back progress at your end. By tackling problems in small doses, working on clarifying and fixing them one by one as a group you can build trust with the process, the people, and ultimately the data and technology such that the decision workflow begins to just incorporate data naturally.

Here are five scenarios that make the case:

Your data is not ‘decision ready’

    Colleges collect lots of data, and more all the time. They have libraries of reports – (some of which are actually used!). But there are problems: maybe there’ve been some differences in numbers that are hard to explain, concerns about bad data input or lack of quality checks, difficulty mapping between systems, resources too busy (with what?) to address any of this in the short term.

    Any such concern goes directly to trust. If I’m a decision maker and I don’t fully trust the data I’m looking for any of these reasons, why would I use it in my decision making?
    Start here. Trusted data is used – untrusted data is a large cost of time and money with little or no return. Assemble a team and go deep on a couple of key reports until you come to an agreement on definitions, sources, and corrected values and get the fix done and deployed. Then go for the next report, iterative and systematic until trust is restored.

Your data is not ‘decision supporting’

    You are making a decision and need some information to help. If you’re lucky there’s a report you can run in a list that extracts that answer into Excel. What if you need to get more detail about the sub categories or regions, or both? Or to drill down into the details so that your decision is more finely tuned – and effective?

    Do you have to go through hoops to get such answers? Do you have to get someone, who is no doubt busy, to do something for you? If it’s difficult to use data to support decisions, managers will continue to make decisions the old fashioned way, by ‘gut feel’, backed up by a couple of static spreadsheets. This does not imply that you need to buy software. First, try to get one report into a pivot table that’s easy to access with updated data and begin to change the thinking about possibilities.

Your decision process not yet ‘data informed’

    Perhaps the most common issue – people and process as opposed to data or technology. Even when data is available to help inform decisions about adjusting programs or curriculum, upgrading student services, or taking other steps to improve success, there’s inertia or some other barrier keeping that data from playing the role it should. Change is not easy, and the decision makers may not be trained in strategic use of data. You may need to step back and look at your decision process. ‘How did we miss that?” Perhaps by not looking more closely at the data you had when you made some key decision, or by continuing a long-standing top-down decision process. This has to be a practiced and deliberate shift in your exec meetings.

    Data is democratic – collaborative decision making is too. If you can’t actually make decisions that are dictated by the data in front of you, why bother gathering it?

Silos are a major roadblock

    Colleges are known for de-centralized management – much like other organizations that are led by practitioners. Recruitment, Enrollment, Registrations, Academic Affairs, IR etc. often work with only their specific data, as the student moves through these silos. The students, of course, don’t see it that way. They view the college (more accurately) as a continuum. Having a central database that reflects the needs of these groups in one place will help force cross departmental analysis and create improvements. But inertia and ‘the way we do things here’ can be major impediments. This lack of buy-in is perhaps the single biggest reason why colleges struggle with moving to data driven decision making.

    True collaboration requires that decision makers focus on the truth as it is, not as they may wish to see it.

We need more software!

    Well, probably not. Are you using what you have to its potential? If you bought something would it fit right into your master plan seamlessly? Often there is no real roadmap that connects technology strategy with institution goals – and software purchases are made to address individual issues with little regard to an enterprise plan. Software purchases often come at great expense of time and scarce funds, take longer than expected to deploy, don’t deliver quite as expected, and delay work on anything else, such as the crucial iterative changes referred to above.

    I’m not arguing against good software purchases, if they are part of a strategy and there is a clear plan for how they will help. But you should make sure you don’t put technology first, instead put people first. Use tools you have and dig into the data you have until you understand it, believe it can be useful, and start to trust it in your decision process. With this experience you’ll be in much better shape to understand your own true requirements and payoffs so you can then make informed software purchases.

For many colleges, using data as part of the decision process is a culture change but well worth the effort. Improve your chances of success by learning what’s working well and building from there to address what’s not – step by step. The improvements in small areas will generate the needed incentive and enthusiasm to continue.

Thanks for reading. Please share this with others who might find it of interest. At Smart Data Decisions, we work closely with colleges and universities to address issues like this and help them develop a strong system of sharing data and making informed campus decisions. Our unique approach lets these institutions tackle big challenges in small, manageable steps, which leads to high success rates in achieving change. How can we help your institution this semester? Be sure to follow us on LinkedIn for this and other tips and commentary on helping higher education make better use of their data to enable smart data decisions!

Another Semester Spent Waiting for Buy-In on Your Dashboard Vision?

Everyone agrees that business intelligence (BI) in Higher Education is a big draw. We’ve been talking with a lot of institutional research (IR) departments over the last year and everyone sees the benefit of centralized data, automated reporting, distributed dashboards, better forecasts of student outcomes and finances, improved collaboration, and trusted data behind the decisions! (What’s not to like?)

Yet, everyone also feels the pain of still doing things the old way and missing the boat on this crucial need. You’ve been talking about this for a while and you’re not moving forward – and it’s frustrating. How can you get this train moving?

For all of the support we hear about the problems and solutions when we talk to IR professionals, we also hear of a lack of progress to ‘get off the dime’. And before long, there goes another semester without progress. Why is this?

Do any of our observations sound familiar?

  • The problem is nebulous, and therefore hard to ‘sell’ to whoever has the budget. IR generally does not have freedom to purchase.
  • The problem is scattered, and while IR is in the best position to pull it all together, they generally do not have enough authority to drive decisions – and must get a lot of buy-in from people above them or in other departments. They struggle with this.
  • IT is driving the train since everyone believes that this is a technology project (it’s not!, IT should certainly be in the middle, but this is a business project – period!)
  • Colleges think this is a big project, and they can’t commit spare time or money for a big project, so they continue to struggle along – with ever more requests and ever fewer resources.
  • Colleges start by looking for software rather than by prototyping with tools they already have, this puts any benefits off to the distant future and omits the people/process work that’s needed.
  • Colleges don’t typically have experience running a big project as an institution, and don’t know how to ask for or use outside help.
  • Colleges don’t really know what they want exactly out of a BI investment, or what a KPI is, or how they’ll use them when they get them in their fancy dashboards.

But how to get started?

If IR could just get the provost to see the vision that IR sees – a “college control center” where all the data is available, trusted, up to date and useful and that truly measures success and highlights steps to be taken to improve – then there would be no stopping it.

We see that success in BI projects is driven by a few key factors that are not technology and usually do not get on the list of requirements that IT puts together. They include good collaboration, clear business benefits and goals, deep attention to data quality, transparency, automation, and my favorite – change management. These are hard to describe, define and measure and so colleges often don’t take steps to address these, and projects either go nowhere or go down the wrong roads.

Our Suggestion

Pick small, manageable problems that need solving in the short term and contribute to success in the long term, and have IR put a team together to solve them. (SDD) has found that a good outcome from a low-hanging fruit project has dual benefit:

  • A problem gets solved, or at least improved.
  • And—perhaps more importantly—a team has a win. Next Problem Please? Instant incentive to move forward.

SDD delivers many focused workshops on executing key components of a “college control center” BI solution—any of which will help get you closer to your ideal vision. All other parts of your BI strategy essentially depend on these building blocks. We offer these as options for you to consider as a way to quickly and inexpensively get momentum and progress at one shot. Check it out.

We’re all in this together. Let us know how you’re coping and how you plan to avoid another semester of waiting.

Focus on How You Make Decisions, not Dashboards

Or maybe we could have said “Ask not what your business intelligence can do for you, ask what you can do for your business intelligence.”

Why do we start here? To get you thinking about how you’re trying to solve problems with your business intelligence approach. Right now, you might be actively looking for a new BI platform to solve your data visibility problems. That’s good. There are many good platforms to choose from out there in the market. We can suggest a few if you want guidance (but we’re not here to sell you software).

One word of caution if we may: the problem is not always the technology. Let us explain.

We work with many companies to help them get more out of their current business intelligence tools and technologies. For example, we’re helping many universities and colleges in higher education learn better ways to visualize, collaborate and respond to issues around their assessment, enrollment, graduation rates and so on. When we start working with a university, we guide them through an auditing process called STEP (Student Engagement Profile). This lets us learn what’s working and what’s not—and helps us make informed suggestions to the university on how to correct some low-hanging fruit problems that can make big differences quickly. We’ve learned a lot from speaking to all of these institutions in the last year. The common theme is that the problems you’re trying to solve won’t always be solved by piling on more technology!

Many of you are doing well with your current technology. You have clean trusted data centrally in place, excellent interaction between IR and end users, automated and self-serve reporting/analysis, and some approach to modelling, alerting and planning. In fact, most of the schools we spoke to are doing these things very well, or well enough.

The real problems are what you’re doing with the technology. Don’t focus on making nice dashboards (that’s the easy part). Focus on making the RIGHT dashboards, and sharing them, and getting buy-in and trust of the numbers, and—most importantly—recognizing trends and approaching problems and taking responsive action as a group to correct or prevent these problems. That’s that hard part—and it’s not about technology. It’s about people and processes.

Ask yourself how well you’re able to achieve these indicators of success:

  • Good collaboration, change and priority processes
  • Good data governance
  • Broad use of data
  • Good feedback loops
  • Established data dictionaries
  • Good communication and balanced authority between departments

Solving these problems is not as easy as buying more logins to Really getting your entire business intelligence approach to work well for you means learning how to collaborate, share, decide and respond as a group. These are the challenges we’re seeing in the higher education space alone and why we’re able to play an important role in helping guide universities—step by step—through a clear process to correct these issues.

So, we challenge you to ask whether you’re doing all you can as a team to get the most out of your existing BI technologies. With careful step-by-step guidance, you can address these problems in a matter of months and be a stronger, more responsive organization—all without needing more technology. offers step-by-step workshops that guide organizations through establishing and improving processes, workflow, use of tools and technologies, collaboration and decision-making to drive toward best practice implementation of business intelligence. For more information about these workshops please visit and contact SDD to learn how they can contribute to your team.

SDD Speaks Out about Helping Universities Battle Enrollment beyond Dashboards

“Ask anyone in higher education you’ll hear about their intense focus on falling enrollment and retention. As these trends expand, so too does the complexity and urgency on maintaining an accurate picture of what’s going on under the surface—why students don’t enroll and why they drop out at certain points in the program”, said Bob Scott of “We spent a considerable amount of time with professionals in higher education learning how they address these issues. I can tell you that it’s not achieved with just dashboards. It requires a considerable amount of data management, reporting, distribution, collaboration and communication across departments to fully-recognize what’s happening and execute plans to take corrective action. This holistic approach was the catalyst behind our higher education guidance.”

“It’s not just big schools that are facing this complexity,” said Scott. “We’re finding the same challenges from smaller, 2-year schools that have big school problems but small school budgets. Schools like the University of Science and Arts of Oklahoma and Southern University at Shreveport can easily describe challenges that align perfectly with larger schools that we’re working with in the US. The complexity and urgency is not unique to large, high-profile institutions.”

“Despite having more technology at their disposal, institutional Researchers that we speak to on a regular basis cite ongoing frustration with wanting tools that don’t overload users with flashy eye-candy graphics or endless feature/functionality configuration options, but instead allow users to focus on their real-world needs”, said Bob Scott. “It’s one thing to be able to create dashboards that show enrollment trends. It’s entirely another to create a system that plugs into the ecosystem and engages team members to realize what’s happening under these trends and prompt them to take preventive action before it’s too late. What seems like an obvious lesson for any technology company—put users first, not technology first—has become something of a motto for us in recent years. As a result, we’re morphing into much more than a ‘dashboard’ company—thus the added automation and monitoring layers to our offering.”

At the upcoming North East Association for Institutional Research (NEAIR) conference in Philadelphia, the team will discuss how this expanded business intelligence approach is effectively assisting institutional research departments. The gathering of IR professionals will take place Nov 8-11 at the Hyatt Regency at Penn’s Landing, Philadelphia. Joining Bob Scott on the agenda are scheduled speakers Kati Haycock, President of the Education Trust, Rev. Peter M. Donohue, Villanova, Dr. Neil D. Theobald, Temple and Dr. Karen A. Stout, Montgomery County Community College. For more information about SDD’s Higher Education business intelligence workshops, visit

Do You Know Why Your Enrollment Will Drop This Semester?

If you’re reading beyond our headline above, you’re probably among thousands of higher education professionals increasingly concerned over falling enrollment and urgent retention issues. It’s probably the key subject of your weekly meetings…and your monthly meetings…and your quarterly meetings…

The question we’re constantly asking of higher education staff is: how well do you understand the issues behind your enrollment and retention trends? It’s not good enough to simply see these trends happening. You need to really dive into the details and understand what’s happening under the surface that’s causing students to drop out, or transfer, or not enroll in the first place. More importantly, you need to know why it’s happening so you can prevent it from happening again.

To achieve this, we offer the following guideline we’ll call “Pool, Predict and Prevent” and suggest that you challenge your university or college to follow this guideline to help you identify and use your organizational data to greatest effect. What is our guideline? Let’s look at each section in turn.


Also known as the technical hurdle. Pool your data sources together into one location so you can analyze trends campus-wide. Overcome this hurdle and you can finally analyze one set of numbers. But before you can pool, you have to address some common challenges. The answers you’re seeking—hidden in your university’s data—can be hard to consolidate for several reasons, some technical, some not. See if any of these sound familiar:

  • “The data we need is scattered throughout campus, often in different software platforms, always involving dozens, if not, hundreds of spreadsheets.”
  • “We have no data warehouse. Our data is never consolidated into a single location which would offer the ability to analyze trends across all data points.”
  • “It’s too difficult and cumbersome to squeeze trends out of our spreadsheets. We have complex formulas, tabs, updates, links, multiple contributing authors—introducing data error, calculation error, and out-of-date data problems. This result is a complete inability to rely on the data for any meaningful decision-making.”
  • “Data quality suffers. Starting from the source, data errors and inconsistent standards and definitions find their way all the way through our data management process and onto our summary analysis.”
  • “We have no time for extra analysis. We’re bogged down with requests from faculty and staff for data and analysis!”

I bet you could add 2-3 more items to that list. The point is that it’s not as easy as it appears to simply gather the data before you can analyze it. But pool you must, because the advantages of operating on a single set of numbers are too good to ignore.


Moving on, let’s imagine you can overcome this hurdle and have clean data in one centralized location, primed for expert analysis. Now what? Let’s go back to my earlier question: how well do you understand the issues behind your enrollment and retention trends?

Time to predict. Let’s call this the dashboard hurdle. Overcome this and you can finally see the trends. But how do you do this? Simple. Choose from any of the 1000s of business intelligence solutions out there and you can link that data to a host of colorful dashboards with all sorts of clicking, swiping, selecting and more. OK good. Now you can at least see the trends.

But is that enough?

Imagine you’re looking at your enrollment line graph. Does this solve your problem? Are you able to understand what’s happening? More importantly, are you able to prevent it? You need to start looking under the surface. Take that deep dive and start exploring.

When you’re focused on retention issues, you’ll likely start looking at what happened to the students that dropped out in the last 2 years. Are there trends in their GPAs? Were their GPAs falling in prior semesters? Did those students lighten their course load in the preceding semesters? Did they change the focus of their course load to easier courses or courses that didn’t support their major? Did their class attendance drop off?

Any of these trends might suggest dissatisfaction with the curriculum, or the faculty, or the campus, or some other element of their university experience. These are issues at the core of the problem—issues that you must address. If you could roll back time and engage those students, you might’ve been able to prevent some of them from dropping out. You should at least, learn from these trends and use them to help the cause going forward.

The idea is to put this experience to work for you. Consider having a magic wand that can sweep across your student performance history and scrutinize what happened over the past several semesters. Chances are that there were early warning signs that emerged in the data that foretold the outcome we wanted to avoid. Now that we’ve found them, we can watch for them and set triggers to help us prevent future problems.


We’ll call this the watchdog hurdle. Overcome this and you can finally prevent future problems before they happen. This requires the right tools and processes to be sure. Instead of waiting another 6 months only to find ourselves in this same situation—looking in the rear view mirror, trying to fix retention issues—we need tools that can “watch our back”, so to speak.

Leveraging tools here is the right idea. Let technology and your data work for you in the background, watching for specific early warning indications to arise. Do we have technology to do this? Leading business intelligence software solutions are learning that users need more than just dashboards, more than fancy ways to click through everything for hours on end. Users need real help finding answers, supporting decisions and taking action. Thankfully, advances in automation, report generation, data mining, monitoring, alerting, sharing and collaborating are helping business intelligence users lighten their workload, offload detailed data monitoring for critical conditions, and facilitate sharing of analysis tools to help alleviate the constant stream of requests.

So, to help prevent future retention issues, we can use this type of technology to detect and report so we can engage the students and address any concerns to counter falling enrollment trends. For example, we might want to watch for GPAs falling more than 5% among sophomores in our Geology program and automatically email the academic counselors when any single or group of students falls into this category. Of course, we could watch these numbers manually without too much effort. But in reality, the trends are more complex and more in number. Watching for a few key conditions is one thing. Watching for a larger list of targeted problem areas across university data at varying depths of detail is entirely a different problem, and one that could be very time consuming. But that is the challenge. That’s where the opportunity exists to address these issues—down in those details. So, to prevent in those cases we’d likely want to leverage automation and technology.

With the right tools, we can be given advanced warning and the opportunity to prevent students from dropping the program, or dropping out altogether. Stop our falling enrollment at its source. This is, at least, one critical approach to preventing known problem trends from repeating.

Pool, Predict and Prevent – Combined

Universities following this “Pool, Predict and Prevent” guideline can answer our question about understanding root causes of retention issues with a confident ‘yes, we can’. It’s not unreasonable to assume that—with this advanced preventative approach—they will be among the leading (or surviving?) universities in the future.

With the right technology at work, our scattered data sources consolidated into one location, early warning indicators watching our back, and the proper staff and faculty response procedures in place, universities can consider themselves far more equipped than ever before to be able to fully understand why enrollment falls at any time in any area—and be able to take preventative action to engage at-risk students and address issues that are behind those trends.

Authored by (SDD), a company that has been helping higher education professionals establish and maintain a clear picture of their organizational data, trends, outliers, problems and opportunities. SDD offers business intelligence workshops and consulting for higher education. To learn more about SDD’s workshops, please visit

SDD Mentors Business Intelligence Students in Tackling Issues in Higher Education

This November marks another chapter in SmartDataDecision’s (SDD) history of helping both students and higher education audiences.  Bob Scott, Business Intelligence Practice Lead at SDD and past President of Bilander Group, will participate in Temple University’s Information Systems and Technology (IST) curriculum.

Bob, along with adjunct professor, Dave Kelble, is on a panel of business intelligence experts in the program’s BI/Big Data course aimed at guiding students whose long term goals include system administrators, data analysts, database administrators, and possibly one day CTOs.

The panel is exploring real-world examples which challenge the students to interpret, and create analysis from, data sets representative of business applications they’re likely to find in the workforce.

Bob’s guidance is aligned with one of SDD’s key markets for business intelligence, higher education.  He is working with the students, engaging them on the challenge of using full life-cycle data of students and courses, activities, interaction with admissions, alumni information, and more to more effectively promote the college’s ability to influence successful student outcomes.

SDD offers business intelligence workshops and consulting tailored for the unique challenges present in institutional research departments in higher education.  The company also plans to continue its higher education outreach at the upcoming Northeast Association of Institutional Researchers (NEAIR) conference in Philadelphia, where Bob Scott will present to an audience of over 400 IR professionals the latest trends in technology and process maturity for the application of business intelligence to address leading issues like enrollment and retention—a hot topic of late for institutions facing falling student numbers.

Bob also serves on the Business Intelligence Advisory Committee for Philadelphia’s St. Joseph’s University, supporting a program that combines elements of operations research from the computer science department and business elements from the MBA program—involving both undergrad and graduate programs.  Bob has guest lectured at St. Josephs and maintains an active connection to the university.

For more information about Bob Scott or SDD, please visit