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.
February 21, 2008 | 2 Comments
ASR Analytics issued the following press release on Thursday, February 21. The press release is also available via PRWeb.
ASR Analytics Introduces Business Intelligence Accountability Framework for Higher Education
Institutions are struggling with reporting for IPEDS, Common Data Set, Achieving the Dream, and other accountability initiatives in higher education. ASR Analytics introduces a framework for reporting through these accountability programs while helping institutions use the data for strategic reporting and analysis. ASR Analytics’ new Accountability Framework for Higher Education uses business intelligence methodologies to make a sustainable reporting foundation for decision making.
Potomac, MD (PRWEB) February 21, 2008 — Students, parents, and the larger college or university community are demanding that institutions be more transparent about graduation rates, the admitted class profile, and the success of their alumni. In fact, the U.S. Federal Government is requiring institutions of higher education to be publicly accountable for outcomes and results.
IPEDS is continually changing to reflect this new reality, while accrediting agencies and associations are responding to the call with new accountability reporting systems, including:
- Common Data Set
- U-CAN – University and College Accountability Network
- College Portrait – Voluntary System of Accountability
- Achieving the Dream
- Access to Success
With so many accountability initiatives in higher education it is becoming increasingly difficult for Institutional Research departments to keep up. Talented analysts are spending an exorbitant amount of time extracting often duplicate information from transactional systems to provide to these accountability initiatives in the proper format. Although the information produced through these initiatives is extremely valuable, it often goes unused by institutional decision makers as it is not stored in a format that makes it conducive for strategic reporting and analysis.ASR Analytics provides a unique solution to these problems by helping institutions establish a framework for accountability reporting using business intelligence methods and best practices. The goal of the Accountability Framework for Higher Education is to provide institutions with a system that facilitates strategic reporting and analysis using the same valuable data that needs to be reported to IPEDS, Common Data Set, and other important accountability initiatives.
“We’ve turned accountability reporting upside down,” said Graham Tracey, ASR Analytics’ Director of Higher Education Services. “First, we pull the data needed by the accountability initiatives out of the student information system and optimize it for strategic reporting and analysis, only then is the data layout prepared for submission.”
“It’s a bit like paying yourself before paying your bills each month,” said Tracey. “Why not use this valuable data for decision making before submitting it and effectively ‘throwing it away’?”
With ASR Analytics’ Accountability Framework for Higher Education, instead of worrying about extracting data and querying transactional systems, Institutional Researchers will be able to spend more time providing valuable insight and analysis to decision makers.
Deans, Department Heads, and Executives will also benefit through self-service strategic reporting that accurately reflects the reality that has been reported to governments, accrediting agencies, and voluntary accountability organizations.
ASR’s Accountability Framework: http://www.asranalytics.com/solutions/education/accountability/
ASR’s solutions for Higher Education: http://www.asranalytics.com/solutions/education/
ASR’s business intelligence blog: http://www.asranalytics.com/category/blog/
About ASR Analytics, LLC
ASR Analytics LLC (ASR) provides high-end business intelligence and analytic consulting services to clients in higher education. ASR aims to provide institutional decision makers with self-service decision support tools to help them be more effective in their recruitment, retention, and accountability initiatives. To learn more about our solutions visit: http://www.asranalytics.com/.
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.
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.
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.
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.