Population Trends Causing Universities to Scramble?
March 12, 2008 | Leave a Comment
In a recent article featured on the front page of the Washington Post, Valerie Strauss looks at how major demographic trends are impacting the higher education landscape. These demographic trends include a drop in the number of high school graduates (starting with next year’s graduating class) and significant changes in the racial and ethnic characteristics of the high school graduates. It is projected that over the next decade the number of white graduates will decline by more than 10%, while the number of minority graduates will experience significant double-digit growth. Minority student enrollment at colleges and universities is expected to grow during the next several years, with some analysts projecting that it will be as high as 37% by 2015 (it was 30% in 2004).
So what do all of these demographic trends mean for colleges and universities around the country? First, the student population at most schools is probably going to look quite different in a decade. Just ask Stephen Joel Trachtenberg, president emeritus and professor, from George Washington University who is quoted in the article as saying:
“The majority will become the minority, there will be more Hispanics, more African Americans, more Chinese, Indians, Pakistanis, Koreans. I anticipate that the most common last name in the freshman class will be Kim.”
These trends will also significantly impact the way colleges and universities recruit students, deliver financial aid, and market themselves to prospective students. A significant trend discussed in the article is for colleges and universities to focus recruiting efforts out of state, in areas where the pool of high school graduates is not shrinking like it is most areas of the country. For example, George Washington University has set up recruiting offices in cities around the country (e.g., Los Angeles, Atlanta, Chicago, Boston, and northern New Jersey) to expand domestic recruiting and at the same time is devoting more time and resources to overseas recruiting. American University is pursuing a strategy of increasing recruiting in population growth areas such as Arizona and other western states.
At the same time, institutions are thinking about ways to reduce the financial burden of pursuing higher education. Actions highlighted in the article include increasing financial aid budgets and expanding scholarships for students that remain in-state to attend their public, state university.
It would be a very interesting study to expand on the findings in this article by talking to more institutions and finding out how they plan to deal with these challenges. Specifically, I’d like to know the following:
- Are all schools feeling the effects of these trends?
- To what extent are schools with different characteristics feeling the effects (e.g., geographical location, size, public vs. private, etc)?
- What are some innovative ways schools are dealing with these recruiting challenges? What about financial aid?
If you know the answers to any of these questions, or know of any additional studies please let me know.
Measuring the ROI of Social Networking as a Recruitment Tool
March 7, 2008 | 2 Comments
In a recent interview appearing in Campus Techology magazine, we hear from Brad Ward, electronic communcation coordinator for Butler University. Mr. Ward is employing some innovative techniques to reach out to potential students, all based on the power of social networking. One of the sites used extensively at Butler is Zinch.
Zinch is a social networking site that is currently used by more than 300,000 students and 475 colleges and universities. The site allows students to create detailed profiles that go beyond the standard test scores and GPA that seem to dominate the college admissions process. By buidling a profile that contains information about their extracurricular activities, passions, talents, and skills, students can show themselves as a unique applicant and not just a set of standardized numbers. Zinch even has a feature called Z-Folio which allows students to upload their artwork, videos, writing, and athletic highlights.
Admissions officers at colleges use Zinch’s Advanced Zeeker search tool to filter and query all of the student profiles stored on the site. They can use this functionality to locate and target students with specific characteristics and interests. Student data can be downloaded and loaded into the college’s communications management system.
All of this allows students and colleges to better target each other and connect in the recruitment process. This is evident from the message displayed on Zinch’s homepage:
Dear student, showcase yourself to your dream college.
Dear admissions officer, showcase your college to your dream student.
At Butler, Mr. Ward is seeing intial success with Zinch and some other innovative social networking techniques (e.g., YouTube video blogs, Facebook fan pages). For example, the “open rate” (percentage of mail messages that are opened by a recipient) seems to be about 3 times higher (i.e., 33% vs. 11%) for messages sent to students on Zinch, than they are for typical e-mail blasts.
While these intial figures are encouraging, Mr. Ward speaks to the real challenge in these efforts in his response to the last interview question:
[Another problem is], in terms of YouTube and sites [like it], there’s not yet a defined metric of what is successful. If we put these videos up on YouTube, was that worth it? There’s nothing to measure this stuff with yet.
That makes it a little tougher when we try and pitch these new sites and new ways to recruit. Hopefully, in the near future, we’ll all start to be able to define what success is and whether it’s worth it to be on Facebook and those kinds of sites.
Mr. Ward has hit the bullseye with this comment. The key to this recruiting effort is figuring out how to define success, measure results, and acheive some measure of ROI. Admissions officers need to get creative. For example, by creating something like a “Student Engagement” metric, colleges could measure and track the responsiveness of students to different types of communication. For example, higher scores could be assigned to students who join social networking groups, post or respond on a blog, or participate in an admissions online chat. When combined with the data from your student information system a full engagement picture emerges. This data can then be used to help forecast and predict enrollment more accurately and helps you target the most engaged students to help ‘evangelize’ on your behalf.
Given the increasing use of social networking tools by high school and college students this is going to become an important part of the recruiting efforts of colleges. While building this strategy, admissions professionals should also think about measuring outcomes.
Accountability reporting turned upside down
February 21, 2008 | Leave a Comment
A few months ago I wrote about the proliferation of accountability reporting initiatives within higher education. In conversations we’ve had with institutional researchers, one thing is clear; accountability reporting takes up far too much of their time and provides very little strategic value. But it doesn’t have to be that way. So ASR set to turn the problem upside down and approach it from an entirely different perspective.
Today, ASR is pleased to announce a new solution for accountability reporting called the Accountability Framework for Higher Education.
Moving Beyond BI Shadow Systems
February 16, 2008 | Leave a Comment
A recent post on The Data Doghouse, a Business Intelligence blog, explains that data shadow systems are still extremely prevalent across industries and will continue to be so for the foreseeable future. Data shadow systems are groups of spreadsheets and local, customized databases - often built in Microsoft Access or Excel - created by business groups to provide data for their users. An excerpt from the article shows just how prevalent these shadow systems are:
In a survey we conducted in the fall of 2007 we found that the medium number of data shadow systems at an enterprise was 30. According to the survey, data shadow systems are prevalent in all industries, companies of all sizes and throughout various business functions in the enterprise.
The presence of shadow systems is a trend we’ve seen with our own clients in education, government, banking, and insurance. For many of our clients, different shadow systems are providing business analysts with the data they need to answer important business questions, recognize trends, and make informed decisions. Obviously, these shadow systems are providing a valuable service. However, despite these benefits there is a downside to operating shadow systems. Let’s take a look at some of the negative aspects of shadow systems.
First, it is often time consuming to implement and maintain data shadow systems. Data shadow systems are typically cobbled together by business users. Let’s face it, designing these systems is not the strength of business users (this is something that is bettered handled by the IT department). This leads to a design framework that requires lots of labor-intensive maintenance activities and a lack of felxibility and extensibility.
Second and most importantly, these shadow systems do not enforce rigorous data management techniques and principles. For example, consistent business rules may not be used across the organization to clean and codify the same data. The result, data quality and consistency suffer and it is difficult to know how confident decision-makers can be in the numbers being reported.
So the question is, what can be done to move beyond the use of shadow systems in your organization. The best approach is to engage the IT department and use the organization’s existing Business Intelligence (BI) tools to tackle this problem. BI tools should be used to centralize data, implement consistent and documented business rules to clean data, and develop reports that meet the needs of business users and stakeholders.
Accountability and the Higher Education Reauthorization Act
February 13, 2008 | Leave a Comment
It looks like the U.S. Congress is getting much closer to passing the Higher Education Reauthorization Act after several years of negotiation and debate. Last week the House of Representatives passed its version of the bill, while the Senate passed a separate version last year. Now the reconciliation process between these two versions of the bill begins in earnest.
Will the reauthorization of the higher education act have an impact on institutional strategy and business intelligence? You better believe it. Among the many proposals that have come up for negotiation in these bills is a provision for the U.S. government to provide grant money to states for the establishment of ‘unit record’ systems for accountability reporting. In addition, new accountability measures have been proposed for inclusion to be reported to IPEDS. These initiatives could have a major impact on the strategies an institution employs in the future. At the very least, the measures by which institutions are evaluated may change and that will require an analysis of the institution’s measurement and reporting systems.
These and many other proposals in the bill have the potential to impact policy decisions at colleges and universities across the nation. To get the word out about the potential impact of this legislation AACRAO is providing a free webinar on Thursday, February 14.
I have worked with Barmak Nassirian, AACRAO’s Associate Executive Director and host of the Webinar, on several occasions and have found him to be knowledgeable about matters of public policy and issues on Capitol Hill. He is joined by two other ‘heavy-hitters’ in higher education so I would imagine this presentation should be very worthwhile.
Look for analysis of this legislation from a higher education strategy and business intelligence perspective on this blog in the coming days and weeks as this legislation moves through congress.
6 Best Practices for Successful Business Intelligence
February 11, 2008 | Leave a Comment
Business intelligence (BI) is not about technology. No doubt there is much technology involved, but a sound business intelligence strategy concentrates more on methods. The outcome of intelligence gleaned from a strategic reporting or decision support system should be an action or decision. The decisions made from business intelligence will likely lead to changes made in strategy and/or individual business process.
These changes in strategy and business processes will necessitate changes to one’s enterprise resource planning (ERP) or other transactional systems. For example, new business rules or codes may need to be added to the ERP in order to operationalize a decision that was made. This will require an understanding of the business rules engine of the ERP as well as the implications from a historical measurement perspective of changing or adding codes to the system. People will need to be trained and constituents may need to be informed of new rules.
BI is much more about organizational alignment or people and processes around a common set of strategies and goals then it is about technology. To that end, follow these 6 best practices to move your organization from silo-based planning to one that is aligned around a culture of evidence:
- Define areas for exploration - What subject areas need to be studied? Not all can be effectively studied at one time - not at the start - therefore, you will need to prioritize. The organizations leaders will need to set the priorities based on the key strategies that need to be affected.
- Articulate problem statements - Now you have identified the subject areas to be studied. What are the problems in that area? Simple year-over-year trend analysis will often highlight where the problems are lurking. The problem should be stated as follows: [Subject Area] is down by 15% compared to last year.
- Identify causal factors - Perhaps one of the most overlooked steps in the process. Your problem statements only tell you what is happening. It is critical that you find out why it is happening. Statistical models need to be employed to determine the key drivers influencing the problem area. Identification of the key drivers in the area will help you isolate the problem and determine the factors causing the problem.
- Determine corrective action - Once the causal factors have been identified decisions can be made and corrective action taken. True evidence-based decision making.
- Align people, process, and technology - Decisions inevitably lead to change. Most often the change comes in the form of a new business process. The new business process will need to be codified in the ERP system and people may need to be reorganized and/or retrained. This will be the hardest step in the process toward a culture of evidence, yet it is also the most critical.
- Measure outcomes - Now that people, process, and technology has been aligned to solve the problem, you must measure the effectiveness of this action. Be careful that you allow a sufficient amount of time for the impact of the change. In fact, decision makers should agree about the length of time they will permit for the action to take hold and pre-determine a point in time for re-evaluation.
As you can see from the diagram above, the process is cyclical. As decisions are made and corrective action is taken, key drivers will change. This will constantly cause the organization to reevaluate and revisit its strategies and tactics over the course of time.
Colleges Take the ‘Business Course’
February 2, 2008 | Leave a Comment
In a recent article in the Chicago Tribune, Ann Meyer explains a growing trend in higher education. More and more institutions are facing an increasingly competitive landscape and are using business principles in their marketing and operations to attract students, improve their financial position, and maintain a competitive position in the marketplace. The importance of embracing business principles is summarized nicely in a quote from the article:
“The business side of all of higher education is increasingly important,” said Ronald Ehrenberg, director of Cornell University’s Higher Education Research Institute. Heavily dependent on tuition, many independent colleges are, as Ehrenberg puts it, “on the financial bubble.” They need to pay attention to larger trends and react by carving out new niches, he said.
The article highlights the accomplishments of two small independent colleges in Illinois, Elmhurst College and North Point University. Led by entrepreneurial college presidents, these institutions used marketing, communications, and sound business decisions to create new institutional identities and reposition themselves in the competitive marketplace. The results are impressive - increased applications, higher enrollment, revenue growth, and surging endowments.
The institutional changes made by these two institutions were no doubt made using one of the most powerful tools available in higher ed, data. The question is, how does an institution leverage its data to make the right decisions. The answer, by employing the right analytic techniques and business intelligence tools to identify improvement opportunities and measure success against strategic goals.
Let’s take an example from the article. Elmhurst College has been able to attract students with higher test scores and grades, and in the process boost its selectivity. How is this done? One way is to utilize business intelligence tools to closely monitor the institutional funnel (i.e., number or prospects, applicants, offers, and enrolled). At the same time, selectivity and recruiting goals must be set and progress should be tracked and reported annually against these goals. Higher powered analytic techniques can also support these initiatives. For instance, predictive models can be used to identify quality prospects that are likely to accept offers of admission.
This is just one example of how business intelligence and data are used to support strategic decisions. Businesses have been leveraging these tools for some time and now colleges are relying on them to compete in this increasingly competitive market.
Educational Goals: The Forgotten Factor in Measuring Student Outcomes at Community Colleges
January 22, 2008 | Leave a Comment
Achieving the Dream (AtD) released the November/December 2007 Data Notes recently and gave a thoughtful analysis of Enrollment Status and Student Outcomes for full- and part-time students. This is of particular importance since AtD reports that 46 percent of students enroll part-time. The authors report striking differences between full-time and part-time students when it comes to persistence.
The largest persistence gap between full- and part-time students was seen at the second term (21 percentage points) rather than in the second or third academic years (16 and 12 percentage points, respectively).
Part of this descrepency may be explained by the attainment of educational goals by part-time students. Not all part-time students are seeking credentials, and therefore may not “have the desire to” persist in order to achieve their educational goals.
Unfortunately for institutions that are part of the AtD initiative, educational goals are not part of the reporting requirement. It might be useful to explore how educational goals vary over time, and if these changes relate to persistence rates. Another limiting factor in the analysis of educational goals is that most ERP systems (from which the AtD data are derived) do not capture educational goals in a meaningful way - we discussed this in a recent post. Institutions may be capturing educational goals at the time of application, but rarely do they have these data follow students through the education life cycle.
While the AtD data are important peices of the persistence puzzle, they often won’t provide for a complete picture. Other data that are captured in the Student Information System (SIS) are critical for individual institutions. In order to make the most of both AtD and SIS data, institutions should identify automated ways to blend the two. One way to accomplish this is to create dimensional data models…but that’s a topic for another post.
Fair Lending Another Casualty of the Subprime Meltdown?
January 17, 2008 | 1 Comment
You’d have to do a lot to avoid discussion of the subprime mess and the effects that it is alleged to be having on the housing and financial markets (although we think there are more reasons for optimism than many, but more on that in another article). One area where we do have concerns is in the effect of the subprime cleanup on fair lending.
Fair lending compliance requires that regulated entitites (which includes essentially all retail mortgage lending institutions) provide government regulators specific facts on the mortgage applications they did and did not approve. These data are then examined statistically to determine if there is an observable pattern of discrimination in underwriting practices based on the treatment of members of protected groups.
In a recent article, the Washington Post describes the actions that lenders are taking to correct the underwriting excesses of the subprime boom. While the industry experts offer somewhat differing accounts of actions that are expected to be taken, all agreed that credit scores are going to lose some of their weight in the underwriting process, at least in the near term. To quote from the article,
But income matters now, and so does cash, said Sean O’Boyle, a vice president at SunTrust Mortgage in Chevy Chase. Lenders expect borrowers to have several months’ worth of mortgage payments in reserve and a steady job. ‘Job stability. Credit. Cash,’ O’Boyle said. ‘They’re all equally important. Not one of them overshadows the other.’
Unfortunately, moving to a broader set of measures of creditworthiness, while intended as a means of tightening underwriting, could achieve just the opposite, and cause fair lending compliance issues to boot. Remember that the pressure to maintain and increase loan volumes fueled the use of exotic mortgages to increase the number of eligible borrowers. In the same way there will be pressure to use additional information to cherry pick borrowers with borderline credit scores.
Here’s where fair lending compliance comes in – cherry picking borrowers based on any information not included in the compliance data provided to regulators introduces the risk that correlations will be found between protected group status and the probability of receiving a loan, even if no mortgage discrimination was ever intended. Is the value of cherry picking worth the potential compliance issues it might cause? We don’t think so.
Here’s a much less problematic solution to tightening underwriting requirements – demand higher credit scores from all borrowers and stick close to the information reported for fair lending compliance when making the underwriting decision. This would have none of the potential downside of including non-reported information and would require few changes to underwriting processes. It will be interesting to see if the pressure to maintain loan volumes or the need to assure regulatory compliance wins out.
Defining business rules in the business’ language
January 11, 2008 | Leave a Comment
One of the biggest problems with any business intelligence project is for all involved to fully understand the data model and it’s relationship to the business of the organization. Without an excellent business/data analyst, much will be lost in the translation.An article in DM Review addresses this very issue, however, largely from the technical person’s point of view. Since this blog is primarily aimed at business leaders and decision makers (information consumers) that require business intelligence to make informed decisions, I won’t go into gory detail summarizing the article.
The most useful tool mentioned in the article was this spreadsheet (pictured) to help put business language around a logical data model.
What’s a logical data model?
[Silence fills the air.]
…Exactly.
As a business decision maker you don’t really need to worry about it so much, however, your analysts and technical staff developing the business intelligence architecture sure do.
Essentially it is the relationship between and amongst the tables that represent the business data.
Without a rock solid understanding of the ‘business rules’ that describe the data, the business intelligence architecture will fail.
This tool is as good as any I’ve seen to help business decisions use their own language to precisely describe the relationships in the data model.
Think of this tool as the Rosetta Stone of BI. Okay. Never mind. I’m sure I’m overstating it. But it is useful nonetheless.