At ASR John has joined the Higher Education practice to implement a variety of Business Intelligence (BI) solutions for our growing client base.  Some of the projects currently underway include implementation and knowledge transfer of the popular SAP Business Objects Enterprise, SAS Enterprise BI, and Microsoft BI platforms.

Beyond the technology itself, many institutions are struggling with getting data across their numerous systems organized, defined, and delivered to internal and external constituents. Our “analytic accelerators” leverage the higher education expertise, data and systems knowledge along with predefined template designs for student enrollment and retention analysis, student outcomes analysis with course completion and success. Coupled with recommendations for Data Governance processes, this approach covers three key success factors — people, process, and technology.

One of the more interesting projects currently underway begins to address the void of information about a student once they leave an institution. Student “swirl” as it is called, presents even greater challenges for institutions trying to understand the ultimate success of a student in achieving their educational goal. This is particularly true for community colleges where it is common for a student to come for a couple semesters or years and transfer to a four year university. Or, they may have regular stop outs and take courses at other nearby institutions as convenience and demands of life dictate. 

By combining enrollment data with National Student Clearinghouse (NSC) enrollment and degree verification data, an institution can get a more comprehensive picture of the long term outcomes for their students after they leave. The results of this project will provide a whole new level of measurement of long term persistence and graduation rates while giving institutional leaders a broad range of student dimensional categories to help analyze their own student success and intervention initiatives.

To learn more about the services that ASR Analytics offers and how we can help with your reporting, analytics, BI and data warehousing needs, visit our Solutions page or use our Contact form.

Free as in “free puppies”, not “free beer”.

This is one of the best analogies I ever heard and I attribute to Linda Hilton from the Vermont State Colleges.

Apparently the institution presenting this session was surprised at the actual cost to pay for support, maintenance, and training. If you are in the process of selecting a BI technology, be sure you research the full cost picture as well as the functionality requirements.

Open Source may be a good option, but there are many commercial options such as Micro Strategy which give their full functionality for a given number of licences before having to pay license fees. SAP Business Objects online has a very low per user per month pricing where you don’t have worry about the hardware infrastructure. There are many possibilities to consider.

The specific example was a college student with a term registration status of “first time student” in the “2009 Fall” term (as opposed to “returning” which is assigned to subsequent registration terms),  yet the student had an entry term cohort value of “2001 Fall”.  The entry term cohort value didn’t match the term value  selected in my query of “first time” students registered in “2009 Fall”.

How could this happen? How could the data ended up this way in the data warehouse? A quick data validation query showed a clear discrepancy on about 10% of records where the student had a “first time” term registration status, but that registration term was not their entry cohort term.

Further research and discussion with the client revealed that their business process dictates that after certain periods of absence the student is required to reapply and therefore be treated as a “first time student” again, even though the student’s original entry term cohort value is not change manually or automatically. So what should be used to query for those entering for the first time? The Registration Status? or the Entry Cohort?  Each returns slightly different values. The question is now before the data governance team as to what to do with the “Entry Cohort” values in this case and what the impact is of making a business rule change given the apparent conflict of definition between these two data elements and the different purposes they serve, often for different business units at the college.

Ultimately, it raises the question about what to do about the data in the data warehouse. Should it be changed? Or should it be left as is? It is an interesting dilemma that could apply to even simpler scenarios where data may have been entered incorrectly like the student ethnicity and a week later it is corrected. The data warehouse will capture these data changes and show the student with the “incorrect” ethnicity for that period of time.  If a report showing the breakdown of students by ethnicity is requested for that very time period, the report would be “incorrect”.

This caused me to do some thinking and searching for other people’s wisdom on this topic. It seems there is no clear consensus on the best approach with pros and cons on both sides.  In the first example I came across, there is a way this type of discrepancy can be captured in data quality exception reports during ETL and either fixed to match an agreed upon business rule or left to the business users to review and implement manually in the source transaction system to flow into the warehouse during the next load.  The second example however has no automated way of correcting since there is no way for the system to truly know what the correct data value should be.

The decision to correct data anomalies of this nature depends somewhat on the business requirements and the general philosophy on data quality. There is a distinct argument for saying “leave it as is since the warehouse is meant to represent the historical state of transaction data and the errors will not create a variance to change decisions made by analysis of that data”.  Others argue that data warehouses are meant to provide as clean an inputs to business decisions and should be cleaned when known to be wrong.  This blog post has a good summary of the perspectives with thoughts from some of the founders of data warehousing Ralph Kimball and Bill Inmon.

What do you think? I’d be interested in your comments and experiences.

But what does “leading” really mean? The first time I heard the phrase my immediate reaction was: Huh? That sounds terrible. Are they not going to strive to be the best airline? Aren’t they trying to be #1 like most would assume is the goal of a merger? But by what measure? Size? Revenue? Fleet age? Service and satisfaction? Destinations served? Complaints? Lost bags?  Cost management? 

For an industry with dozens of closely watched measures of performance, creating a public marketing message to be “leading” is vague and pointless to me. It’s also a bit risky. After all, vague or non-existent goals will always make you successful, but maybe not in the way you intended. It is a safe way to go, though, if you are not sure what you’re doing or how things might go. Maybe the new United will be able to say by the end of next year, “We’re leading with the worst ontime performance of any airline!”. That’s nice.

We’ve all heard the phrase “for good measure.”   Hey, throw in some extra salt for good measure! Maybe you do it just in case what you’re cooking tastes terrible. It seems rather arbitrary. Why not taste it first? So, a more thoughtful approach to planning and success may be in order. I have worked with plenty of clients who do not understand how to make a good measure. Their five year plans are a wealth of vague, uncertain and impossible to measure goals, usually in an attempt to placate many differing views.

Think about this as you are setting goals for the new year for yourself or your organization.  Are the measures meaningful? Can they really be measured? Is the necessary data collected? How will you know you have achieved the goal? Is it actually a good measure people will recognize as success?

After all, if you are ”leading” the airline industry that still has a terrible overall reputation for service, you haven’t really set a good measure or accomplished much.

• I prepared a project proposal that was not accepted by the prospective client. Unexpectedly, another nearly identical request came along resulting in very little work to respond quickly. The first round was not wasted effort!
• I’ve beenworking with a client to resolve performance issues with their BI platform. The visibility of the project has imposed a lot of pressure on the team to make signficant improvements. Progress has been frustratingly slow. Yet, I am learning an incredible amount about the more detailed workings of this platform and how similar configuration improvements can be made elsewhere to benefit other clients.
• A coworker and I have had tense disagreement about the strategy for a proposed solution. We usually get on quite well. But the frustration, arguments, and counter arguments helped lead to a breakthrough “aha” moment as to how to proceed.

What are some examples you’ve experienced? Whenever something bad happens, look a little deeper… wait a little bit… look at another angle. There will very likely be something good to come of it.

Many of the clients we work with are asking the same kinds of questions proposed in the article. These questions are a logical extension of the research results. They want better insight into student behavior and achievement and what administrators can do to increase student success:

• Are students who place in a developmental Math or English course taking that course? Do they succeed in that course and then succeed in the college level course?
• Are students ignoring the developmental course requirement and do they succeed at any different rate than those who pass placement tests? (The article suggests students are quite good at self-selecting into the college level course and ignoring the test results!)
• What are the characteristics of students who do not succeed at developmental or college level courses?
• Are there specific courses or pathways that are more successful than others based on historical activity?

Of course, all of this really gets to the efficacy of the entire placement and developmental education system. Some states, with the help of research foundations, are attempting to address significant problems in misaligned curriculum between the developmental course and the college level course that should follow. Similarly, they are also applying new research in learning to redesign curriculum for greater real world application and effectiveness. The Developmental Education Initiative is one example.

Our job as BI consultants is to effectively understand the business questions and help institutions with the collection of useful data that can be presented in a way that informs the change process. We have several client engagements currently underway that are doing just that. I’ll be able to see for myself from exposure to specific institutions what the state of developmental education success is for their students.

First, let’s clear up one of the common misunderstandings that I have seen on various comment threads on LinkedIn and elsewhere. Despite the fact that John Speer, current CEO of Datatel, will lead the new combined company, this is not Datatel acquiring SunGard HE. Hellman & Friedman, a venture capital firm that already owns Datatel, is buying SunGard HE with plans to meld the two companies into one. As such, I would tend to consider the transaction a merger of equals. Hellman & Friedman clearly have a greater understanding of the higher ed. marketplace through the eyes of Datatel management – having owned them for the past several years – while SunGard HE brings the greater client base and broader product and service portfolio. In the end, I believe that we’ll see a blend of products and services from the two companies that it truly will be a fusion of the best talent from each that rises to the top.

But it is a long way from here to there. The first 18 months will be the consolidation phase. There is much duplication between the two companies that will have to be dealt with. While the two companies do essentially the exact same thing, I don’t think we’ll see much in the way of product or service consolidation from the outset. Early on we will see layoffs in areas such as accounting, human resources, corporate IT, sales, and marketing. I suspect that software and services will largely be untouched. If layoffs are to be had in these areas it will be more about productivity and utilization then it will be about product consolidation.

Now, let’s speculate about product and service direction.

If you are running Banner or Colleague, don’t worry; neither of these systems is going away any time soon. In fact, I would expect the combined company to double-down and provide even greater focus to each of the platforms. Datatel has been increasingly looking to base its solutions on Microsoft technologies, while Banner is essentially married to the Oracle platform. Look for the company to further position Colleague around the Microsoft database and application eco-system as the technology will largely be the differentiator between the two ERPs.

New ERP sales in higher education are drying up. They have been for the past several years, with near 100% saturation of the target market. As a result, the company will need to eat into the client bases of the other remaining ERP companies. Look for Banner to be positioned against Oracle PeopleSoft customers, while Colleague will be aimed aggressively at the Jenzabar client base.

So what about PowerCampus? SunGard HE acquired this Microsoft based solution from ABT several years ago. After some initial investment in the student system early on, this application never seemed to gain enough traction in the industry. In the medium term, I would expect some kind of “upgrade” package presented to PowerCampus clients, designed to get them to move to Colleague.

From an overall product perspective, my guess is that new functionality will increasingly come from partner companies and software development efforts will largely be directed toward integration technologies. It’s likely the two companies will pool resources together to build common APIs and data transport technologies to help partner and 3rd party companies tie into each of the ERPs without having to do twice the work.

Business Intelligence has been a big focus for both companies in the last few years. The myriad of tool options between the two is dizzying. Expect for further consolidation around the Business Objects platform for Colleague clients and Cognos for Banner. The real action is in the data models, however. Each has a fairly well formed set of operational data models. I would not expect too much in the way of new models, as it is likely that the two will want to standardize on a single data warehousing model that works for either system.

So, what do you think? Poke some holes in my analysis above. Speculate some yourself in the comments. I’d love to hear more from some customers themselves.

When a Canadian friend of mine who happens to work for SAP Business Objects, forwarded this link to me that showcases their BI platform, I was ingtrigued. It takes full advantage of their analytics and data exploration technologies using hockey statistics. I mostly deal with higher education related data like student enrollment, retention, financial aid, and human resources. This was different and fun!

I took a look at how Washington stacked up against their second round rival Tampa Bay. Hmm…. Not such a good picture. Tampa Bay had higher average Goals For and lower Goals Against. Their offense and defense looked better by the numbers. I looked at the goalie save percentages. I compared some key individual players from each team. Everyone thought the Caps would keep winning and go to the finals. After exploring some of the data and visualizations, I wasn’t so sure of a spot in the finals. And, in fact, it didn’t happen. Sadly, the numbers seemed to support that outcome.  Certainly there is more to hockey than just numbers. Passion for play, pure skill, wanting to win, and luck sometimes create amazing upsets. That’s what happened in last year’s post-season. (And seemingly in every year’s March Madness for all of you college basketball fans!)

Of course statistics don’t always tell the whole story. Lots of other variables can come into play. And often good analysis includes domain knowledge with the human element to enhance any interpretation. But I can’t help thinking that those stats didn’t lie, and the results certainly bear that out. Now with Vancouver in the Cup finals and Tampa Bay winning tonight to force a game 6, It’s time to go back and do a bit more research and exploration!

Take a look at the site. Play around. Even if you don’t know much about hockey, it’s a good way to become familiar with some of the great analysis and visualization tools available in Business Objects. Maybe you can improve your chances of winning the office Stanley Cup pool!

What a great quote from Dr. Brene Brown at the University of Houston. You can see her TED Talk here for more about how she elevates the use of data to make change at her institution.

(Via Brand Autopsy)

And then it happened. We all were working together, as client and consultant team, to address the punch-list of items that needed to be fixed before “turning on the switch” and going live to the broader set of business users. I heard it. The language from this person changed. The ownership suddenly appeared. ”I have to research this data anomaly”  and “We have to finish the user cheat sheets and handbook”  and “I have to test this to be sure the security setup is working correctly.” 

To be honest, I am not entirely sure what triggered the change. Maybe it was the deadline of this new reporting environment being put out for all to use and either accept or reject.  And, naturally, someone deeply involved in that kind of project and commitment ultimately wants their positive imprint on a successful outcome. I know I sure did.  I suppose it is like the bird leaving the nest, or the child going off to school on their own for the first time, or maybe a teenager taking the car out for a drive by themselves after getting their full license.  Taking ownership or responsibility is perhaps a natural part of the human process of becoming more independent.

I am gratified to see this change andexperience the benefits of the client ownership in their evolving BI environment. Although  the details of this true story may seem to be unique and even readily identified by those involved, this happens in every engagement. It is not unusual, nor a cause for concern–unless, of course, the ownership never materializes .