Implementing a Data Administration Function and Strategic Data Planning at the University of Michigan


Copyright 1993 CAUSE From _CAUSE/EFFECT Volume 16, Number 3, Fall 1993. 
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          IMPLEMENTING A DATA ADMINISTRATION FUNCTION AND STRATEGIC
                DATA PLANNING AT THE UNIVERSITY OF MICHIGAN
                  by Renee Woodten Frost and John Gohsman

ABSTRACT: An important role for information systems organizations on 
campus is to provide a framework and guidelines to support the use of 
institutional data for planning and decision-making. This article 
discusses the implementation of a data administration function and a 
strategic data planning process to accomplish this goal at the 
University of Michigan.


Rapid technological changes are creating many challenges for 
information systems organizations. One of the most pressing of these 
is responding to and supporting the growing number of users who have 
the technology and who want to retrieve increasing amounts of 
institutional data for performing business functions and for decision 
support. An important role for information systems organizations is to 
provide a framework and guidelines that will serve the diverse and 
expanding needs of these sophisticated users.

Data--the raw material for this new information age--must be 
formally managed like other institutional resources such as people, 
finances, and facilities. Data accuracy, consistency, and reliability 
need to be ensured. Data also become the key to integrating future 
systems. The University of Michigan University Information Systems 
(UIS), a unit of the Information Technology Division (ITD), has 
proactively adopted a data orientation toward its development of 
systems and its long-term systems planning. UIS established a data 
administration function to provide guidelines for some immediate 
assistance to users and to provide a long-term planning approach for 
integrating data and systems and mapping them to University goals and 
objectives.

This article provides an overview of the establishment of this 
function and its components, followed by a more in-depth presentation 
of the long-term strategic data planning process under way. The 
objective of strategic data planning, which is a component of the data 
administration function, is to define a data architecture and prepare 
a plan to develop and implement it.


DATA ADMINISTRATION

Technological changes affect the way systems are developed and 
implemented. More users at various levels of the organization are now 
involved in the development and use of new applications because 
technology is driving the distribution of data and processes, and 
information is being used increasingly as a competitive tool. 
Technology is, in effect, increasing our institution's appetite for 
data. This has resulted in a heightened need to enhance the 
accessibility, integrity, and usefulness of the institutional data and 
to promote the view of information management oriented to data as an 
integrated University resource rather than one that focuses on 
separate departmental processes.

To establish credibility for a data administration function, an 
institution must recognize the problems data administration can solve 
or the benefits it can provide. At the University of Michigan, it was 
a combination of both that led UIS to create its Data Administration 
group.

Specific data-related problems that currently face the University 
of Michigan, as well as many other institutions and corporations, 
include:

  *  interfaces and extracts which cause timing delays and loss of 
currency,
  *  redundant data entry,
  *  redundant data which are unplanned and unmanaged,
  *  diminished data integrity,
  *  inflexible systems,
  *  unwanted and unplanned restrictions on information sharing,
  *  lack of common, global understanding of information, and
  * i nconsistencies in definition and content of data.

Benefits of a data administration function to an institution vary 
depending on the audience. The following benefits are separated into 
four categories:

1. General audience:
  *  maximize or reengineer business processes by reusing data,
  *  result in flexible systems since systems are based on an 
institution-wide data model,
  *  result in maintainable systems, and
  *  promote controlled redundancy.

2. Information Systems management and staff:
  *  reduce politics in projects (common definition of business from a 
data perspective),
  *  escape from the maintenance quagmire, and
  *  establish data as a foundation (data tend to be more stable than 
procedures).

3. Middle management:
  *  enable sharing of data,
  *  reduce political barriers, and
  *  eliminate technology-related business difficulties within daily 
work routine.

4. Executive management:
  *  achieve competitive advantage,
  *  maintain and promote growth,
  *  support better product or service offerings, and
  *  improve quality.

Many factors contributed to motivating the University of Michigan 
to establish a data administration function: the strategic decision to 
establish a data orientation for information systems, the anticipation 
of distributed computing environments, the development of systems in 
relational database format, and, especially, recognition by our users 
of the importance of data integrity and consistency during our Data 
Access Project, a project created to improve end-user access to 
institutional data.[1] The first report from the Data Access Project 
recommended "establishing a data administration function." The report 
stated that this function was critical to the success of making 
institutional data more readily available. The report also stated that 
data must be managed across all institutional systems to ensure 
consistency and common definitions, and that the data administration 
staff must develop: (1) effective liaisons and communications with 
user groups, (2) an institutional data model, and (3) a standard 
format for data dictionaries. This report from the Data Access 
Project, the first to focus solely on data access, summarized the 
problems with accessing data and made specific recommendations on how 
to address some of the problems.

The Data Administration group was established within University 
Information Systems in November 1990 and has grown to include three 
data analysts, an administrative systems/data planner, and a support 
person.

Every day, a wide variety of data is collected and used to 
conduct the activities of the University. The philosophy adopted by 
the University is that data are institutionally more valuable when 
they are widely and appropriately used. Their value is diminished when 
they are misused, misinterpreted, or not accessible by people who have 
a legitimate use for them. This philosophy set the stage for the work 
of Data Administration and contributed to its mission and goals (see 
Exhibit I).

************************************************************************

Exhibit I: Mission and Goals of Data Administration

Mission:
Data Administration will promote data as any valuable shared resource 
by creating a data environment for the University of Michigan which 
will ensure the establishment, maintenance, and delivery of accurate 
and reliable institutional data.

Goals:
  *  Recognize and promote the importance of data as a valuable 
institutional resource.
  *  Promote data consistency and standardization throughout the 
University.
  *  Create a data architecture that supports the informational needs 
and business functions of the University.
  *  Minimize duplication in capturing, storing, and maintaining data.
  *  Encourage and facilitate data access and data sharing.
  *  Improve the quality, accuracy, and integrity of institutional data 
resources.
  *  Improve data management and access through the use of appropriate 
methods, tools, and techniques.
  *  Promote the use of the data resource in support of University 
decision-making and strategic planning.

************************************************************************

Data are a resource and should be managed as a resource. Managing 
any resource includes addressing the following life cycle stages:

1. Plan for the resource
2. Acquire or create the resource
3. Maintain the resource
4. Use or exploit the resource
5. Dispose of the resource

Data Administration in University Information Systems (UIS) is 
developing and applying a set of formal rules and methods to manage 
the University's data resources and maximize their value. Data 
administration is concerned with those data resources that are 
critical to the administrative functions of the University, regardless 
of whether the data are used or maintained by administrative, 
academic, or hospital clinical/patient care units.

Although administrative data may be stored in different database 
management systems and in different physical locations, all the data 
can be thought of as forming a single "logical" database, called the 
"institutional database." This terminology does not mean that 
information should reside in a single physical database. It means that 
no matter where the data are, the same principles of data management 
should apply to maintain the value of the data and ensure that they 
are used effectively.

If a data element satisfies one or more of the following 
criteria, it is considered University data and, therefore, part of the 
institutional database:

  *  A University administrative or academic unit needs it for an 
administrative or clerical function--functions such as planning, 
managing, operating, controlling, or auditing.

  *  It is generated as a result of clinical or patient care 
activities.

  *  It is generally used by more than one organizational unit. (Data 
elements used by a single department or office typically are not 
considered part of the University's institutional database.)

  *  It is included in an official University administrative report.

  *  It is used to derive another data element that meets any of the 
other three criteria in this list.

Data administration at the University of Michigan includes four 
essential aspects of managing this institutional data: data planning, 
data standards, systems development support, and data accessibility.

Data planning

Managing data resources with an eye to the future requires the 
definition of a data architecture and a systematic way of planning for 
the development of database applications and systems. Both are under 
way at Michigan. Planning is needed to provide a framework in which 
administrators can objectively determine the scope of each project, 
decide which projects should be initiated, and determine the order in 
which they should be developed. Strategic data planning, which is 
described in more detail later in this article, is the method the 
University of Michigan is using to plan for its long-term data and 
system needs.

A critical component of data planning is a policy and a set of 
guidelines for managing data resources. The policies and guidelines 
governing data administration are explained in two documents: 
"Institutional Data Resource Management Policy" and "Data 
Administration Guidelines for Institutional Data Resources."[2] These 
documents were prepared by an ITD/user group building on sample 
documents from both industry and higher education institutions, such 
as Virginia Tech and Indiana University. Input and revisions to these 
documents were solicited from major committees and/or individuals in 
all school/college/administrative units on campus. They have been 
extremely well-received and supported.

On an ongoing basis, Data Administration is responsible for:

  *  developing effective liaison and communication with the people who 
use the data,
  *  reconciling conflicts in data definitions,
  *  dealing with issues of data ownership, data redundancy, data 
integrity and accuracy, and data usage, and
  *  data migration strategies.

Systems development support/data modeling

For projects involving UIS, a University-customized systems 
development methodology (SDM) is used to guide the systems development 
effort. Tasks related to data administration are defined in the SDM 
and include:

  *  assisting with project estimates,
  *  assisting in new data requirement definition,
  *  building a logical model of the data (data modeling is generally 
considered the most visible service provided by Data Administration),
  *  reviewing data for use of institutional naming standards for 
entities and attributes,
  *  assisting in assigning sensitivity levels to data resources,
  *  mapping requirements against vendor models, and
  *  working with the database administrator on physical database 
design.

Data administration services in support of systems development 
are also available to projects outside of UIS and have been used by 
developers of departmental systems at UM.

Before a database system is built, the content and structure of 
the data must be known. Data modeling includes creating and validating 
a logical data model to collect data requirements prior to building a 
database system. A data model is an abstract representation of the 
structure and content of a set of data, independent of any database 
management system.

To be successful, data modeling requires carefully selecting the 
designers and system users who will participate in the data modeling 
session. A data analyst from Data Administration, trained in data 
modeling techniques, leads the group through a series of questions and 
discussions. These data-modeling sessions can last from a few hours to 
many days. The goal is to create a model with the simplest structure, 
a structure with the least amount of redundancy. Rather than 
displaying data relationships built for a specific application, a good 
model discloses the general nature of the data, allowing for future 
growth and expansion.

Eventually, Data Administration will consolidate the data models 
from numerous projects (bottom-up) and the strategic data planning 
(top-down) to create an institution-wide data model. This model will 
serve as a map of the institutional data, allowing the University to 
build new systems that can share accurate and timely data.

Data standards

Data users face a critical need to merge and analyze data from 
various administrative information systems to make informed decisions. 
One way to facilitate this process is through the use of data 
standards. Data standards comprise the rules for defining, 
documenting, and naming data. At the University of Michigan, standards 
are evolving and currently consist of various kinds of recommendations 
and approved lists, including: guidelines for defining data 
elements[3], major classifications of data, standard syntax for naming 
data, suggested formats for data, approved abbreviations, and 
guidelines for using and enforcing standards. These were developed in 
conjunction with data users.

Since institutional data need to be identified on a University-
wide basis, the standards for naming and defining data make data 
sharing easier and eliminate unintentional redundancy. After data have 
been identified in data modeling, they are named and defined according 
to the standards.

Data accessibility

ITD has begun investigating data-repository software that will 
store information about institutional data and how they are used. The 
ideal repository would automate the tasks of searching for data 
elements and comparing them to one another. The repository would 
provide a data directory that allows users to search the repository 
for data elements and identify their physical locations.

Data Administration's goal is to help authorized University users 
easily access data in the institutional database. This group was 
established to manage data across all institutional systems to provide 
consistency and integration. Data Administration has been intimately 
involved in data modeling efforts of the projects as well as with 
issues of data definition reconciliation and data ownership, 
redundancy, integrity, accuracy, and usage.


STRATEGIC DATA PLANNING

Strategic data planning means establishing a long-term direction for 
effectively using information resources to support an institution's 
goals and objectives. As the University of Michigan continues to 
review its investment in and reliance on information technology, it 
has implemented strategic data planning to build an institution-wide 
data model and to create an administrative information systems plan 
that supports University goals and objectives.

The term "strategic data planning" is something of a misnomer. 
While such planning strongly emphasizes defining data requirements, it 
gives equal attention to how the University functions. Strategic data 
planning stresses looking at how the University functions rather than 
how it is currently organized. It is concerned with what the 
University wants to accomplish in the future, rather than who is or 
should be doing it, or how it will be accomplished. Strategic data 
planning tries to answer the following questions:

  *  What business are we in?
  *  What things must we manage to conduct this business?
  *  What data do we require to manage those things?

With this information, the University can develop an institution-
wide data model and make objective decisions when determining 
priorities and allocating funds and other resources for system 
development activities.

One of many benefits of strategic data planning is that it 
increases the value and accessibility of the University's data 
resources. Over time, this should reduce the number of systems and the 
amount of data needed to run the University. Strategic data planning 
will identify administrative systems that can share the same data. In 
the past, many administrative systems were developed to automate the 
processes within a central administrative unit (sometimes referred to 
in the industry as "islands of automation"), and the data weren't 
easily accessible to those outside the central unit. As a result, many 
departments had to develop local systems to supplement the central 
one, which led to redundant systems and redundant data entry across 
the University.

The implementation of relational database technology is helping 
the University overcome this problem. Strategic data planning, when 
coupled with the ability to access distributed data through relational 
technology, will make it easier for users to access and manipulate 
administrative data to serve their particular needs, which should lead 
to streamlined business processes and procedures. Strategic data 
planning also contributes to institution-wide communication and 
education about the data and functions of the University.

Responsibility for coordinating and developing the strategic data 
plan is assigned to the Coordinator of Administrative System Planning. 
This position was established in the fall of 1991 as a joint 
appointment between the controller's office and Data Administration. 
The position reports to both the director of Data Administration and 
the controller and director of Financial Operations. At the direction 
of the vice president and chief financial officer, the controller and 
director of Financial Operations has responsibility to oversee 
planning for administrative systems development for business and 
finance and to coordinate those plans with other vice-presidential 
areas.

Methodology

During 1992, staff from many groups and departments on the 
administrative and academic sides of the University attended meetings 
where they learned about the University's intention to develop an 
institution-wide strategic data plan. Staff had a chance to ask 
questions and to provide their input to the planning process. 
Presentations at these meetings outlined the steps involved in 
strategic data planning, which include:

1. Planning the Plan

This step took most of one year to accomplish (about .5 FTE) and 
some activities still need to be addressed. The planning step includes 
setting the scope of the initial effort, communicating with those 
included in the scope to promote the effort and to identify 
participants, training for the Coordinator of Administrative Systems 
Planning and support staff, designing a methodology, purchasing a 
computer-aided software engineering (CASE) tool to support the 
methodology, and preparing a schedule of the order in which functions 
within the scope of the effort will be addressed.

With input from the vice president and chief financial officer 
and others, the initial scope of strategic data planning was set in 
this step to include the administrative functions that fall primarily 
within business and finance and academic affairs. Other areas of the 
University will be added in later phases. Units need to define each 
function for which they have primary responsibility; one person from 
each unit will lead the effort to define the functions that are the 
primary responsibility of the unit. Other participants will include 
staff and faculty administrators who have some role and responsibility 
in the function being defined.

Another part of the planning phase is developing a methodology 
(that is, customized procedures and techniques for implementing the 
plan). The methodology developed at UM is based on the information 
strategy planning phase of the information engineering methodology 
popularized by James Martin. Because the strategic data planning 
effort requires working with massive amounts of information that are 
too unwieldy to handle manually, a CASE tool is being used to help 
collect information and analyze the models that are to be defined. The 
planning tool from KnowledgeWare, Inc. was selected to support the 
strategic data planning effort.

2. Defining Goals and Problems

"If we don't change our direction, we might end up where we are 
headed."
                                               (Chinese proverb)

Administrative systems must be designed to meet the long-term 
goals of the University as well as resolve existing, ongoing problems. 
In addition to just being a good idea in general, a shared set of 
goals and problems will help provide direction to the strategic 
planning effort. Initially, strategic planners must work with 
representatives to identify and forecast the goals and problems that 
will have a University-wide impact during the next five years. A 
variety of methods will be used to collect goals and problems; Total 
Quality Management, currently under way at the University, is having a 
positive impact on this step. The results will provide the basis for 
the next steps--defining the function and data models.

3. Defining the Function Model

A function is a group of processes that supports one aspect of 
operating the University. Procuring goods and services, managing human 
resources, and admitting students are examples of functions. While a 
function is ongoing and continuous, processes are specific tasks that 
have a definable beginning and end. As shown in Exhibit II, a function 
model for the procurement of goods and services could include 
processes such as creating purchase requisitions, maintaining supplier 
information, creating purchase orders, recording invoices, and paying 
invoices.

A function model will be defined by working with a group of 
representatives in facilitated meetings. The function model helps the 
University clarify and communicate what it does independently of how 
it is organized. It also provides a tool to define the data needs of 
the University.

4. Defining the Data Model

Data modeling at this level of planning focuses on defining the 
major entities and relationships that support the functions and 
processes mentioned in the previous step. This type of data model 
establishes a framework for standardizing, integrating, and planning 
administrative information systems (see exhibit). A data model will be 
defined by working with a group of representatives (usually the same 
group used to define the function model) in facilitated meetings.

Staff in Data Administration will work with users to compare and 
consolidate these data models across functional boundaries to form an 
institution-wide data model, which will, as part of the data 
architecture, provide the basis for the data planning efforts of Data 
Administration.

5. Integrating the Function and Data Models: Data Source and Use 
Analysis

To integrate the function model and data models, strategic 
planners must determine the relationships between the functions and 
the data by identifying the data entities that each function creates, 
maintains, or uses (see exhibit). The integrated model can then be 
used to identify strategic projects and prioritize them as part of an 
overall administrative information systems plan.

There are a variety of analytical techniques that will be used to 
identify and prioritize projects. Affinity analysis is one example of 
a technique that will be used to identify strategic projects. Affinity 
is defined as a likeness based on a relationship or causal connection. 
In this step, affinity analysis measures the affinity between entities 
and processes based on the source and use information. This allows the 
planner to cluster processes and data together into natural business 
areas (or projects). Another technique, called the Northwest Rule, 
provides the planner with the proper implementation sequence for the 
projects. Clustered processes that create information should be 
implemented before processes that update or use information. If 
information is not available in electronic form, it makes little sense 
to automate a process or set of processes to use the information.

6. Architecture

In addition to the steps outlined previously, a technology 
architecture must be defined to indicate the hardware, software, and 
networking environment necessary to support the models. Given the 
constant evolution of technology, this step is difficult to 
accomplish, and any decisions can be rendered inaccurate or obsolete 
by new developments. At the University, a concurrent effort (outside 
the scope of strategic data planning) to define this environment for 
ad hoc reporting was just completed. Most of our current mainframe 
applications reside in IMS systems. The plan for enhancing the ad hoc 
reporting environment calls for this information to be moved into an 
Oracle RDBMS environment running on IBM RS-6000 servers. A separate 
effort to define a new application and technology architecture for 
operational systems is currently under way.

7. Preparing an information systems plan

Staff in University units and Data Administration will analyze 
the functions and data entities and identify the units that are 
responsible for them or use them in support of their mission. Also, 
UIS staff and users will inventory and evaluate current systems--
including systems developed and housed on the administrative computing 
mainframe and local systems developed by individual units--to 
determine how well they support the defined models. The information 
systems plan will be based on the results of the analysis of this 
information and the integrated model. The plan will provide objective 
planning information to those making funding decisions and 
prioritizing systems development efforts. More detailed systems 
project work can take place based on the models once the appropriate 
priority and funding decisions are made.

The intent is to update the information systems plan annually and 
incorporate it as an integral part of the University's strategic 
planning process. A plan that is not updated periodically to reflect 
changes in goals and objectives will quickly lose its significance.

Selling the approach to management

While leadership from Data Administration is critical to the 
success of strategic data planning, there must be a champion at the 
executive level for it to be successful. With competing priorities and 
demands for time, strategic planning of any sort can lose its focus 
and momentum without constant support. Since the vice president and 
chief financial officer wanted to see a plan for administrative 
systems and is interested in making information more available, 
selling strategic data planning was fairly straightforward in the 
business and finance division. It was much more difficult in the 
academic affairs arena where leadership changes were taking place at 
the same time we were trying to get this division on board. A number 
of months were lost because of the leadership changes. In retrospect, 
we should have moved forward on the functions in the business and 
finance area that have minimal direct impact on academic affairs 
functions without waiting for the academic affairs endorsement. 
Valuable momentum was lost; changes in management and delays are among 
the major pitfalls of a strategic data planning process.

The leadership changes in academic affairs finally settled down. 
However, the new leadership wasn't easily convinced of the benefits of 
strategic data planning. Additional marketing efforts were requested 
with various representatives from academic affairs. The results of 
these marketing efforts (presentations and opportunities for feedback) 
were documented and presented back to academic affairs leadership. A 
meeting to discuss the feedback resulted in an agreement to develop 
the data component of strategic data planning methodology as a test 
case. This effort was completed and the High-Level Student Data Model 
was created, a project which was very successful in meeting its goals 
and in selling the benefits of strategic data planning. This success 
resulted in an agreement with academic affairs to complete the 
remaining components of strategic data planning to implement the 
student data architecture.

Selling the approach to lower-level management is equally 
important. These are the individuals primarily responsible for the 
functions we are trying to define. By definition, staff at these 
levels of management are inherently more narrowly focused than people 
at the executive level. Selling basic data modeling services for a 
single project is a much easier process because it is focused on a 
specific problem or project. Some, however, find it difficult to 
achieve and maintain a broad vision of the institution from a systems 
and data perspective, and thus they have trouble understanding the 
need for and benefits of strategic data planning. Hence the need for 
support from the executive level. The executive officer can resolve 
issues, commit resources, and set priorities when necessary. Some 
staff will understand the need and benefits but may identify issues 
that must be addressed in order for data administration and strategic 
data planning to be successful. Documenting these issues and getting 
executive management to address them contributes to the creditability 
of the effort. In summary, ongoing communication is a key element in 
selling the approach to the institution.

Issues

During the presentations to the units within the initial scope of 
the strategic data planning effort, several issues were identified.

     Concerns about the impact on budgets and project funding

Some participants were worried about budget cuts and being asked 
to do more with the same or fewer funds. Some participants suggested 
that funding for developing strategic and tactical projects should be 
addressed separately from maintaining and operating current systems. 
The budget situation at the University of Michigan, while not unique, 
is a sensitive and very real issue.

Efforts are under way to review how priorities are set and 
funding is allocated for administrative information systems 
development, maintenance, operation, and access. It is expected that 
these efforts will address most budget concerns to some degree. The 
approach must strike a balance between strategic, tactical, and 
operational decision-making, control, and funding by departments and 
central units. The strategic data planning effort is centrally funded 
and intentionally separate from an individual unit's budget process 
and is meant to be a tool for those individuals and groups making 
decisions about administrative information systems.

     Concerns about the impact on current projects, unit-specific 
     enhancements, or mandated changes to operational systems

Mandated changes will continue to be a reality and must be 
addressed on a timely basis. Until the first information systems plan 
is complete, it would be difficult to understand the impact on current 
development projects. Extensive, ongoing development that does not 
adhere to an overall architecture can negatively impact a strategic 
data planning effort. In one recent case, we were able to convince 
people who were working on several related projectsto reconcile their 
data models by participating in the creation of a high-level data 
modeling effort. Another way to address this is to encourage 
communication and cooperation among projects. Units should ask 
themselves what other units might be affected by any proposed system 
changes and include those units in the project planning process. The 
Coordinator of Administrative Systems Planning is working with others 
to facilitate this process.

     Questions about the amount of effort required by units

Many units were concerned about the amount of time they would be 
required to invest in this effort. Units will need to be involved in 
every step of the strategic data planning methodology; this is 
ultimately a business plan rather than a technology plan. One of the 
major pitfalls of a strategic data planning effort is not involving 
the right individuals. If someone is not involved, that individual may 
not be supportive of the results.

The amount of time required will depend on the size and 
complexity of the functional area being defined. A six-to-eight-week 
duration for each function is considered reasonable. In addition, 
units and technical staff will be asked to inventory the current 
systems. A tentative schedule, based on functional areas and 
identifying potential participants, will be published for review. 
During the planning stage for each functional area, a project plan 
providing detailed estimates of the necessary resources will be 
prepared.

     Need for total involvement and 
     cooperation from all units within the 
     scope of the effort

Most units believed that this planning effort would be successful 
only if all of the units within the scope of the effort participate in 
the process. Some units believed that the models must be used by 
management to make decisions about future administrative information 
systems development projects for strategic data planning to be 
considered successful.

Participants with an opinion felt strongly that academic affairs 
should be included in determining the initial scope of the effort, as 
the vice president and chief financial officer originally suggested. 
Many business and finance functions are tightly linked with academic 
affairs. As was mentioned earlier, efforts to include academic affairs 
units took significantly longer than efforts to include business and 
finance. Other participants from outside of academic affairs and 
business and finance are identified and invited to participate on an 
as-needed basis.

The following recommendations were documented and communicated to 
management in response to the previously listed issues:

  *  Address project funding and budget issues.
  *  In the interim prior to the completion of the strategic data 
planning effort, focus on communication, coordination, and "customers" 
with all current administrative systems projects.
  *  Produce a schedule, identifying sequence and participants, for the 
strategic data planning effort.
  *  Ensure commitment from all parties within the initial scope of the 
strategic data planning effort.

     Three pitfalls

There are three other "pitfalls" to be aware of that didn't 
necessarily come up in the presentations to staff. Many times, 
strategic data planning identifies a need to reorganize. The 
integrated function and data models produce "natural business areas" 
that may be different from the current organizational structure. While 
this doesn't mean an institution must reorganize, it does make 
management aware of the possibility.

"The art of progress is to preserve order amid change and to preserve 
change amid order."
                                              (Alfred North Whitehead)


The second pitfall is migration. Migration is a critical 
implementation component of strategic data planning and it is 
essential that the methodology and resulting information systems plan 
produce a realistic migration plan.

The last pitfall is a fear or lack of understanding of a data-
driven methodology. Strategic data planning is a new approach to many 
people. Some find it difficult to make the transition from more 
traditional planning and development methodologies. Continuing 
education and communication directed at this issue is important to the 
overall success of strategic data planning.

Current status and experiences

To date, strategic data planning has been completed in two areas 
and plans are being made for other areas. One area that is complete is 
the function "procure goods and services." It was accomplished as part 
of a Total Quality Management Quality Improvement Team's (QIT) 
efforts. The QIT had already defined its goals and problems. The 
Coordinator of Administrative Systems Planning came in at this point, 
at the group's request, to create a strategic data plan based on those 
goals and problems. This effort resulted in the creation of one short-
term project, a QIT for small orders, and a project looking at a long-
term reengineering effort. The other area is the High-Level Student 
Data Model that was discussed earlier. Several student-related 
projects were in progress, and a discussion on strategic data planning 
disclosed a need to define a high-level data model to guide the 
individual projects. In addition to completing strategic data planning 
for the student area, plans are also being made to do strategic data 
planning for the personnel, development, and finance areas in an 
accelerated fashion (a six-month effort for all three areas, once it 
is approved by management).

Those involved in strategic data planning will quickly learn the 
value of flexibility. While having a methodology is important, it is 
equally important to know how to get results using many different 
techniques and in different sequences. Each group's situation is 
unique. It is the responsibility of the strategic data planning 
coordinator to recognize the needs of the group and adjust 
accordingly. Obviously one will get better at this with experience and 
training. An approach to this problem is to contract with a consultant 
with experience in this type of planning. Much of the research 
indicates that one should not attempt strategic data planning the 
first time without a consultant. (Of course, the consultants write 
most of the articles and hold the seminars that provide this advice.) 
We decided to move forward on our own for political and monetary 
reasons. We did, however, pilot the methodology on a low-risk , low-
visibility project prior to trying it for "real."

Some consultants indicate that it takes up to five iterations to 
get a solid plan in place. Given the fact that we expect to update the 
plan annually, we expect it may take up to five years to have a solid 
strategic data plan with the necessary buy-in from the whole 
University.


SUMMARY

"The more people realize that strategic planning can be quite real in 
its consequence, the more seriously they will take it." [4]

Rapid technology changes are creating many challenges for information 
systems organizations and, at the same time, providing new 
opportunities to information systems users. The increased need for 
access to institutional data is driving the need to manage more than 
ever the institution's data resources. The University of Michigan is 
meeting this challenge by having established a data administration 
function to ensure the accuracy, integrity, reliability, and 
accessibility of the data resource.

As the University's investment in information technology 
continues to climb, it stands to reason that the University should 
incorporate information technology in its decision-making equation and 
focus its investment on those projects that contribute directly to the 
goals and objectives of the University. Data administration and the 
strategic data planning effort are key factors in developing a plan to 
meet these goals and objectives.

========================================================================

Footnotes:

1 Peggy Bennett, "Improving Access to Corporate Data: Users Remain 
Partners in Experimentation," in CUMREC '92 Proceedings. This document 
is available from the CAUSE Exchange Library (CMR-9231, $2.60). E-mail 
orders@cause.colorado.edu or phone 303-449-4430.

2 These documents are in draft form and not yet available from the 
CAUSE Exchange Library. For information, e-mail: jgohsman@ umich.edu 
or phone 313-747-3165.

3 UIS Data Element Naming Standards is a 16-page document available to 
members from the CAUSE Exchange Library (CSD0777, $3.20 to cover 
shipping and handling). It is also available as an ASCII text file and 
may be retrieved free by sending mail to:
search@cause.colorado.edu with the one-line message: get csd0777 
access_code where access_code is your personal access code for 
retrieving documents from CAUSE. If you do not know your access code, 
send an e-mail message to:
search@cause.colorado.edu with the one-line message:
help access_code

4 John Bryson, Strategic Planning for Public and Nonprofit 
Organizations. San Francisco: Jossey-Bass, 1988.

===============================================================

Bibliography:

Cheung, Steven C.K., and David R. Ells. "Tips, Tricks and Traps in 
Building an Enterprise Information Model." Database Newsletter, 
September/October 1987.

Data Administration and Data Dictionaries. Seminar materials published 
by Barnett Data Systems, Rockville, Md., 1990.

Data Resource Planning (Y6311). Seminar materials published by IBM 
ABI/Los Angeles, 1991.

Durell, William. Data Administration--A Practical Guide to Successful 
Data Management. New York: McGraw-Hill Book Company, 1985.

Extended Relational Analysis. Birmingham, Mich.: Relational Systems 
Corporation, 1988.

Holcman, Samuel B. "A Review of Three Information Strategy Planning 
Tools." Database Newsletter, July/August 1988.

_________. "Requirements for an Information Strategy Planning Tool." 
Database Newsletter, March/April 1988.

Information Strategy Planning. Seminar materials published by 
Performance Development Corporation, Princeton, N.J., 1991.

JAD Across the Life Cycle. Chicago: Guide International Corporation, 
1989.

Martin, James. Information Engineering Book II Planning & Analysis. 
Englewood Cliffs, N.J.: Prentice-Hall, Inc., 1990.

Pinghera, John M. "Strategic Data Planning at AT&T." Database 
Newsletter, November/December 1987.

Ross, Ronald G. Entity Modeling: Techniques and Application. Boston: 
Database Research Group, Inc., 1988.

_________. Data Administration and Information Resource Development. 
Houston, Texas: RGR and Digital Consulting, Inc., 1990.

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This article is based on a paper presented at the 1993 CUMREC 
Conference.

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Renee Woodten Frost is Director of University Information Systems at 
the University of Michigan, responsible for administrative systems 
development and operations. Prior to this position, as Data 
Administrator she established the data administration function at the 
University. She has given many presentations at CUMREC, CAUSE, and 
EDUCOM, and has served on the CUMREC Board of Directors and various 
CUMREC and CAUSE committees.

John Gohsman joined the University of Michigan as a programmer in 
1983. He currently holds a joint appointment in University Information 
Systems/Data Administration and the Office of the Controller, where he 
is responsible for coordinating the planning of a University-wide data 
architecture and for developing an information systems plan for 
administrative systems.

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