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no. 173686
MAR
1986b
The Management of Data:
Preliminary Research Results
Dale
Goodhue
Judith Quillard
John
F.
Rockart
90s; 86-022
-
3 - 1990
c
/
I
The Management of Data:
Preliminary Research Results
Dale
Goodhue
Judith Quillard
John
F.
Rockart
90s: 86-022
May, 1986
CISRWP#140
Sloan
®
WP #1786-86
Massachusetts Institute of Technology
Manaqement in the 1990s
Sloan School of Management
Massachusetts Institute of Technology
^
MAR
Management
in the 1990s is an industry and governmental agency supported
research program. Its aim is to develop a better understanding of the managerial
issues of the 1990s and how to deal mosteffectively with them, particularly as these
issues revolve around anticipated advances in Information Technology.
Assisting the
work of the Sloan School
working partners
in
scholars with financial support
and
as
research are:
American Express Travel Related Services
Arthur Young and Company
British Petroleum Company, p. I.e.
BellSouth Corporation
Digital Equipment Corporation
Eastman Kodak Company
General Motors Corporation
International Computers, Ltd.
MCI Communications Corporation
United States Internal Revenue Service
Company
The conclusions or opinions expressed in this paper are those of the author(s) and
do not necessarily reflect the opinion of Massachussetts Institute of Technology,
Management
in
the 1990s Research Program, or
its
sponsoring organizations.
THE MANAGEMENT OF DATA
PRELIMINARY RESEARCH RESULTS
INTRODUCTION
1.0
Today, corporations are placing increasing emphasis on the management of
A number of factors, which include
data.
significantly more competitive
a
environment, more ubiquitous personal computers, rapidly improving software,
and
computer-literate
more
better
personnel,
Acknowledging
data.
this,
have
more
led
to
more
and
demand
a
companies
follow the exhortations of authors such as Diebold [1979],
and Appleton [1986] to "manage the data resource".
argue
information
that
achieving
and
organizational
data
play
a
performance
are
more
and
trying
Edelman
to
[1981],
These, and other authors
and
significant
therefore,
and,
for
growing
must
be
role
in
managed
proactively.
The
available
literature,
however,
contains
little
suggest
to
that
organizations have successful track records in managing data from a business
perspective,
about
the
resource",
companies.
rather
than
characteristics
we
studied
Almost all
17
a
technical
of
viewpoint.
effective
data
approaches
management
efforts
of these efforts were
information systems staff and user management.
order
In
to
in
viewed as
learn
to
"managing
a
set
of
successful
the
more
data
diverse
by
both
Our research focused on the
managerial motivations, planning processes, and types of outputs achieved.
The authors would like to thank Judith Green Carmody, William A. Gillette,
Lisa A. Johnson, Ruth E. Kocher, John S. Lively, and Judy E. Strauss, who did
their Master's thesis research in conjunction with this study.
They are:
Three major themes emerged from our study.
becoming increasingly important.
is
The management of data
Major business and technology trends suggest that, if anything,
this importance will increase.
improving
the
to
dominant
approach
single,
There
is
no
adopted multiple
firms
Rather,
have
management of data.
approaches, contingent upon business needs.
set of fundamental managerial issues must be considered
selecting the appropriate approach to data management.
A
The
rest
Section
of
describes
1
research
the
method,
in
growing
the
importance of data management, and some current approaches to managing data
Section
in the literature.
we
Section
saw.
several
presents
summarizes
3
managerial
key
2
they consider the options
framework and describes the choices
Section
framework.
the
that
issues
a
managers
4
explicitly
should
address
Section
suggested by our framework.
addresses
then
5
as
concludes
the paper by emphasizing the several major themes in our results.
1
Research Approach
.1
These
being
resulted
themes
conducted
the
by
Center
of interviews, were conducted
In
addition,
in
Information
for
MIT's Sloan School of Management.
1985.
first phase
from the
of
data management
a
Systems
Case studies,
Research
Figure
in
industries,
including
These
firms
1
with
at
each involving 4 to 6 days
six large corportions during the spring of
organizations to learn about their data management efforts.
In each firm, we
(CISR)
CISR reseachers spent 1/2 to 3 days in each of
summarized
study
disguised
electronics,
consumer
goods,
other
The companies,
represent
names,
5
range
a
insurance,
and
of
energy.
focused on one to four data management programs or projects.
discussed
with
us
the
data-related
policies,
processes,
controls, standards, and tools that were in place (or had been tried).
Also
discussed were the factors which motivated each organization to take action,
and the results which were achieved.
the
opinions
of
I/S
managers,
line
In
addition,
managers,
regarding the most important problems and issues
and use of data.
our interviews collected
and
staff
concerning
professionals,
the
management
-3-
Figure
1.
Firms
in
the CISR Data
Management Study
Description
Data Management efforts
cited in this paper
Blame Corporation
Personal care products; Fortune 500
Data access services
Crockett
Canadian subsidiary of Fortune 500
U.S. computer company
Integrated database for finance
and administrative applications
and managerial reporting
Diverse Conglomerate
Fortune 500
Disguised
Name
Among Top
Foothill
Computer
end users
Information database for use by
senior
Dobbs Insurance
for
10 in assets
Fortune 250
management
and new
Strategic data planning
systems for one division
1.
Information centers provide
data consulting
2.
Set of standard data definitions
established by corporate task
force
3.
Strategic data planning used for
order flow function
Global Products,
Inc.
Matrac Corporation
Sierra
Energy
Spectrum Electronics
Fortune 500 manufacturing
Subject area databases being
gradually implemented
Fortune 100 manufacturing
Data access services
Among
Information database
for financial reporting
lop 10 energy companies
Fortune 100
Fortune 500 diversified chemical
end users
Integrated manufacturing
dataoase
Waverly Chemicals
for
1.
company
in
one
Common
division
manufacturing
Systems largest division
2.
Common
3.
Strategic data
accounting systems
used by multiple divisions
done
for
l/S
modeling
planning
in
largest division
4.
Set of standard data
cfefinitions established
corporate group
Wmdsor
Products
Fortune 100
1.
2.
Several information
databases built
'Data Charting" effort
by
-4-
1
Managing Data
.2
A
rationale
Huber'
for
characterized
complexity
by
more
and more
,
importance of
growing
the
that
prediction
[1984]
s
Becoming Increasingly Important
Is
and increasing
environmental
changes,
organizations
to
knowledge
Huber
turbulance
suggests,
more
,
levels of aggregation.
organizations,
Thus,
of
all
at many
sources
need improved
survive, will
to
be
These
management
the
in
increasing
and
up-to-date information from multiple
have
will
(author's emphasis).
"
require
will
found
is
society
industrial
"Post
increasing
and
management
data
designs for information acquisition and distribution.
As the business need
increased,
has
so
has
deliver information.
Over the last twenty years,
declines
costs
hardware
in
gains
and
the benefits to be gained from timely,
the
costs
effective
decreased.
have
dramatic
been
Thus,
as
accurate information have increased,
manage
to
have
capability to
functionality.
illustrated in Figure 2,
As
organizations
for
there
software
in
technical
the
new
types
become cost
has
it
and greater quantities
of
information with computer technology.
Because
electronic
manage
the
problem
but
also
data
with
data
applications,
resource
data
which
were
access
development
accessible,
to
activities,
initiatives.
often
and
developed
even
led
timing
quality
information,
and
has
to
a
and
today
is
data
hinders
constrains
meet
the
in
widely
today
often
system
the
needs
in
to
in
attempts
to
underlying
difficulty
almost
problem of
This
data
of
isolated
of
definitions,
The
managerial
development
ability
only
not
dispersed,
problems.
limits
in
An
the
large corporation.
coordination
available
corporate
slate.
discrepancies
to
growing
clean
start from a
designed
now
is
is
Realistically,
organizations
in
originally
accuracy differences,
of
not
do
data
of
effectively
data
sub-organizations within
decentralized
lack
that
amount
complexity.
in
managing
integrating
autonomous,
managing
form,
importance,
increasing
rapidly
a
and
undertake
resulting
and
staff
maintenance
strategic
Figure
2.
HOW MUCH INFORMATION IS IT COSTBENEFICIAL TO
"MANAGE"
cost = benefit
20 years ago
cost = benefit
today
Due to decreasing costs and increasing benefits, the sets of data that have been
computerized in most corporations have expanded from accounting data to include a
complete range of operational activities, staff functions, and line managerial activities. In
addition to numerical data, increasingly, text is being stored. Efforts are underway to
through "expert systems", some of the knowledge previously available only in
people's heads. That much more data will be stored and accessed electronically in the
next decade is relatively certain
store,
-6-
1
Current Approaches to Data Management
.3
What means
manner which
which
have
should
best
been
use
firms
contributes
discussed
existing
the
in
objectives?
business
to
of managing
achieve the goal
to
in
a
approaches
Various
resource
data
data
management
(DRM)
literature can be categorized as follows:
Approaches with a technical focus.
These include tools and
data
techniques
such
database
management
systems,
as
dictionaries [Ross, 1981], data entity relationship modeling
[Chen, 1976].
Approaches with a focus on organizational
responsibilities
These include the establishment of organizational units such as
and
database
administration
data
administration
[Tillman,
1984], [Voell, 1979], and the formulation of administrative
policies and procedures covering areas such as data ownership,
access, and security [Appleton, 1984].
.
Approaches with a focus on business-related planning.
These
include planning processes and methods such as Strategic Data
Planning [Martin, 1982] and BSP [IBM, 1981] that link the
acquisition and use of data with business objectives.
It
increasingly evident
is
adequate
approach.
Coul son
that
[1982]
no
one
category provides
acknowledges
efforts
many
that
completely
a
straighten out data management problems through the implementation of
dictionary have failed.
Kahn
[1983]
to
data
a
evidence suggesting
presents empirical
most data administration groups have had little or no success in correcting
key data management problems.
Because
the
organizational
ultimate
units,
goal
well,
investments
discuss
BSP
however,
of
some
requires
time
reasons
and
put
to
many
require
place
tools
needs with
or
to
needs
the
create
of
the
These
significant resource commitments,
and
For example, Zmud [1983] notes that "to be
organizational
effort"
why
data
in
focused on the third type of approach.
are often not easy to undertake.
done
not
rather to link
but
business, much attention has
planning approaches,
is
(Chapter
large-scale,
efforts are difficult to accomplish.
10,
members
p. 281).
top-down
to
make
Section
strategic
considerable
2.2.1
data
will
planning
2.0
A CONTINGENCY APPROACH TO DATA MANAGEMENT
The
picture
emerges
which
from
our
"paths" to improving the management of data.
on
diverse in business motivation,
type of result obtained.
that
effective
are
several
Which path is selected depends
considerations.
organizational
is
Rather there
single clear pattern.
management efforts fit no
heavily
studies
case
Successful
organizational
scope,
Certainly one must be careful
efforts
are
planning method,
very
and
in generalizing from
less than twenty examples, but this variety (or contingency approach) in our
case studies
seems
also
to
be
borne out in
discussions which we
have
had
with a number of other companies.
In
analyzing
the
cases,
we
see
four
key
areas
which
represent
an
interlinked set of choices that organizations make as they undertake a data
management effort.
These areas are:
In the successful
The identification of a business objective
companies in our sample, data management actions were almost
always justified not by conceptual or technical arguments, but
by one of four compelling
business
needs:
coordination,
flexibility, improved managerial information, or I/S efficiency.
.
The scope of the data management project.
The firms we studied
had explicitly defined and limited the scope of their efforts.
Some focused on a functional area (such as finance), others on
a division, while some were corporate-wide.
The data planning method.
Top-down, in-depth strategic data
In
modeling was not the only data planning process we saw.
fact, there appear to be a number of obstacles in accomplishing
planning effort. The planning
a large-scale strategic data
processes utilized in our cases varied widely in terms of their
formality, their detail, and their emphasis on data models.
The range of options varied from strategic data modeling to
more limited planning approaches to no planning whatsoever.
The "product" of the data management effort.
Much of the
of
DRM
implementation
existing
literature centers on the
subject area databases [Martin, 1982].
In our case studies,
however, we saw five distinct "products", which were the end
results of the data management project team's work.
These
products are: subject area databases for operational systems.
conmon systems, infonnation databases, data access
and architectural foundations for future systems.
Figure
generalized
from
our
This
cases.
components
four
these
presents
3
simple
and
services,
shows
the
choices
a
starting
represents
framework
point for visualizing what organizations are actually doing with respect to
data management.
The next four subsections of the paper (2.1
-
2.4) discuss
We begin with the most tangible, the
each of the elements of the framework.
"products", and then in turn discuss planning processes, scope, and business
objectives.
Five Data Management "Products"
2.1
The
most common
"product"
subject
area
identified
databases
systems
as
databases
used
products
common
These
.
development.
identified
two
by
multiple
systems ,
are
addition
In
other
the
products
three
products:
foundations for the future.
to
data
This
DRM
existing
in
and
literature
operational
what we
similar
these
"new
access
system"
and
services ,
of
also
We
information
call
each
set
a
systems.
will
that
in
is
involves
new
products,
we
architectural
section discusses at some length each of
the products and presents case examples.
2.1.1
Subject Area Databases for Operational Systems
Subject area
databases
important
business
products,
rather
processing
or
entities
manufacturing
applications may share
SADBs.
described
four
In
as
of
SADBs
(both
the
for
subject
or
around
than
contain data
(SADBs)
individual
scheduling.
areas,
we
operational
such
Many
studied,
data.
is
In
data
the
the
customers
such
different
from
a
results
first,
around
organized
as
applications,
access and update)
companies
which
and
order
as
operational
single
can
set
of
best
be
strategic
planning was the basis for the effort.
Dobbs
Insurance Companies
is
one of the
nation's largest
insurance companies.
Dobbs has five major divisions which
operate relatively autonomously.
In the late 1970s, the Group
Insurance Division
(GID)
began to address issues
of
data
data
-9-
U
O
U
c
E
a\
C
03
ro
Q
O
g
E
ro
m
01
-10-
Due to a changing business environment and
resource management.
recent reorganization, the division wanted to reevaluate its
After a significant amount of preliminary
operational systems.
work, GID undertook a strategic data planning effort based on
From the resulting strategic data
James Martin's approach.
(including customer,
dozen subject area databases
a
model,
asset, and product) have been implemented as the foundation for
the division's applications systems.
a
Subject
area
databases
for
operational
systems
can
also
be
designed and implemented more gradually.
Global Products, Inc., had a history of using selected reference
files for static data elements such as customers, products,
Integration of application systems was
prices, vendors, etc.
achieved by passing transaction data elements from program to
In the mid-1970s. Global
program following a predefined flow.
Products
decided
to
replace
this
sequential,
applicationoriented approach with a subject area database approach for its
finance, distribution and marketing functions.
The goal was
achieved not by forcing rewrites of all existing systems, but by
offering new or rewritten applications access to the SADBs.
The
approach resulted in a growing set of integrated subject area
databases.
This
improved
the
accuracy
and
timeliness
of
operational data, and reduced system development and maintenance
effort.
The difficulty with such an incremental approach is that each
new system's data requirements may not be consistent with the
current design of the SADBs.
Where necessary, the set of
databases must be revised to incorporate the requirements of the
new system.
The advantage, however, is that the changeover can
be accomplished with minimum disruption to existing systems and
a preplanned movement toward a consistent set of databases can
occur, taking into account the resource constraints of the
organization.
Today, approximately 40-50 databases are shared
by 30 applications at Global Products.
Data management efforts began in the Consumer Products Division
of Spectrum Electronics more than ten years ago with the
creation of a centralized manufacturing database for its twelve
plants.
Pressed by Asian competition, the Consumer Products
Division, with annual sales of almost $1 billion, started by
developing and implementing a logical data model for five key
manufacturing applications.
The division then proceeded to
incorporate
remaining
manufacturing
the
all
systems
into
database, and now has subject area data for vendors, parts,
assemblies, etc.
-11
Common Systems
2.1.2
data files
A second type of data management "product" is the operational
or
databases which
developed
are
applications developed by
used
multiple copies of the system.
discussed
which
above,
applications,
Common
systems.
systems
are
single, most often a central, organization to be
organizational
multiple
by
a
common
for
Physically,
units.
opposed
As
developed
are
to
shared
be
can
subject
the
to
there
area
by
one
be
or
databases
of
range
a
common systems approach tends to focus on replacing existing
a
systems in one specific application area.
Many firms already have common systems in place, often developed not for
data management purposes, but rather to ensure common procedures or to lower
I/S
A
costs.
typical
example
can
although each division has its own
seen
be
at
Corporation
Blaine
products and sales
force,
where,
common order
entry and distribution systems are used by Its three major divisions.
As
data management may
at Blaine,
common systems effort.
However,
fact
in
definitions)
systems
is
will
not new,
a
be
only
a
by-product
of
a
common systems cannot be developed without
surfacing and resolving data definitional
old
be
discontinued.
disputes,
since
while
Thus,
the
old
systems
(and
concept of common
number of companies appear to be gaining major data
management benefits from implementing common systems and/or from using the
standard, sharable data in them.
The largest division of Waverly Chemicals,
Fortune
500
a
diversified chemical company, operates about a dozen plants.
Since about 1980, the division has emphasized the development of
common systems for manufacturing applications such as production
scheduling and spare parts inventory.
The objectives have been
to increase coordination among the plants and to reduce costs in
A
was
areas
such
inventory.
data
management
group
as
established as a key to the process.
Several
business
firms
area
have
which
have
found
in
the
instituted
era
of
common
fourth
systems
generation
they have derived significant value from the common data.
in
a
critical
languages,
that
-12-
Based on data from its common order entry system, which spans
manufacturing and distribution
divisions,
numerous
internal
able
to
provide
salesmen
with
has
been
Grand Distributors, Inc.,
customer
customer
basis.
information
on
a
by
complete sales
total
combined with an estimate of each customer's
This,
what
salesmen
not
only
vendors
tells
from
all
purchases
customers are buying from Grand, but what they are buying from
This
others and, hence, where the marketing possibilities are.
would not be possible if each division had its own order entry
In fact one
systems, since the data would not be compatible.
small division does use its own order entry system, and its data
can not be included in the information provided to salesmen.
Information Databases
2.1.3
A
third
new
"product"
system
is
information
Whereas
databases.
the
first two products provide data to be used by transaction processing systems
and
monitoring
for
real-time
aggregations of data which are
information
operations,
primarily
used
databases
staff analysis
for
and
are
line
management information.
Most
databases"
"information
for managerial
information.
In
can
be
general,
defined
as
subject
for data management purposes,
can be distinguished from subject area databases for operational
two
primary
existing
Information
ways.
systems.
Instead,
databases
"bridges"
systems to provide the appropriate
Depending
bridging
on
the
process
information
level
may
databases
external data.
of
be
data
are
to
compatibility
straightforward
usually
contain
databases
area
not
do
often
the
of
or
the
rewriting
from
existing
the
subject area
existing
quite
aggregated
systems in
necessitate
built
new
they
data
database.
systems,
difficult.
and,
the
Also,
sometimes,
[See Note 1.]
Windsor
Products,
management's
corporate
demands
for
information led to the development of several new databases.
However, existing applications have not been rewritten and the
operational databases on which the transaction systems depend
remain in place.
Automated bridges from the existing systems
populate the new "information-only" databases for customer,
product and shipment.
At
Sierra Energy Corporation has an information database for the
key financial information required by the corporate controller's
function.
This database is incorporated in a system called DMS
-13-
DMS was developed in the late 1970s
System).
data from divisions
to
flow of financial
geographic sectors and, then, to corporate, as well as to
A generic
bridging
increase data integrity and security.
mechanism to capitalize on the data collection processes in
existing systems was developed, along with download capabilities
Standardization of data
for forms and report structures.
definitions controlled by master tables in a data dictionary
helped create the data integrity feature of DMS.
(Data Management
to
improve the
next
The
case
illustrates
conceived
originally
as
an
effort
an
information
where
an
database,
is
integrated
now
database,
also
used
for
transaction processing.
computer
Crockett,
Canadian subsidiary of a major U.S.
the
corporation, has developed a single integrated database to support
all administrative applications, including accounting and order
It was
The effort began as a prototype within I/S.
entry.
originally aimed at removing the operational drain of running
report programs on the transaction processing systems, by creating
a user friendly managerial information database.
The system which encompasses the database has evolved to include
data capture and transaction processing as well as to provide
A cornerstone of the new
extensive end user access to data.
system is an active data dictionary, which is used to determine
how to calculate derived data elements and, by end users, as a
directory for data definitions.
Crockett has devoted considerable
hardware resources to the system, but feels it is benefitting from
the investment.
2.1.4
Data Access Services
The first three products discussed emphasize developing new databases or
files
study,
with
pertinent,
however,
accurate,
focused mainly
and
on
consistent
improving
data.
managerial
Some
firms
access
to
in
our
existing
data, without attempting to upgrade the quality or structure of the data.
Though the particulars of the efforts differ, each is centered around
small
cadre
of
personnel
whose
goal
is
to
better understand what
data
a
is
available in current systems and to put in place mechanisms to deliver this
data.
These efforts seem to be widely applauded by managers who are able to
"get their hands on" existing data.
questions
concerning
definitional
make use of the data provided.
They also have the promise of surfacing
inconsistencies
as
managers
attempt
to
-14-
mul ti -bil 1 ion dollar manufacturing finn, Matrac Corporation,
has put in place a "data warehouse" for its corporate end users.
Briefly, the data warehouse organization is a small group within
that
provides
access
organization
data
corporate
I/S
the
for
The
arranges
group
locates
data,
services
users.
to
of
the
in
user's
choice
delivers
data
the
extracts,
and
The group maintains copies of most of the data it
formats.
A
The next step for
provides and arranges for periodic updates.
data
management team is to develop a
data warehouse
the
directory designed specifically to assist end users by listing
Included for each
and cross referencing the data available.
data item will be key information such as update frequencies and
times, contact people for more detailed interpretation, accuracy
characteristics, etc.
Obviously,
provision
of
element
key
a
human
both
delivering
of
assistance
and
data
improved
to
end
users
for
tools
is
the
access
and
analysis.
At Blaine Corporation, corporate I/S provides informal
data
"consulting" services to help end users locate, access, and
interpret the data they need.
There are several
infomiation centers at Foothill
Computer.
Although
the
charged
with
centers
are
the
provision
of
analytical tools and support of end user computing, they also
devote significant time to the provision of data consulting
services.
Not surprisingly,
where
data
reasonable
of
is
Delivering data
data access services appear more helpful
in
its
quality
current
form
than
to
where
managers,
data
is,
in
companies
frankly,
however,
can
a
mess.
spur action
towards increasing data definition and control mechanisms.
2.1.5
In
Architectural Foundations for the Future
most of the
firms we
data serving a portion of
danger
business
that
by
studied, managers
the
approaching
corporation.
data
unit by business unit,
or
company leaves itself open to real
management
focused on
However,
in
a
a
there
function
subject area by subject
limited set of
clearly
by
is
the
function,
area manner,
a
problems if, in the future, it is desired
to integrate data across these boundaries.
15-
attempt
To
organizations
architectural
will
to,
have
these
avoid
to
on
focused
future
incompatibility
problems,
architectural
foundations.
developing
some
By
foundations we mean policies and standards which, when adhered
lead
to
a
well -structured
consistent
and
data
environment.
Managers allocating resources for architectural foundations are investing in
Our cases
the future without necessarily having immediate benefits in mind.
foundations:
emphasized two different types of architectural
wide scope
(1)
strategic data models, and (2) common data definitions.
One
data
view
model
to
development.
a model
of
a
data
firm's
serve
as
an
architecture
underlying
is
a
blueprint
strategic
corporate-wide
for
all
future
systems
James Martin's Strategic Data Modeling approach produces such
as one of its products
[Martin,
1982].
IBM's BSP methodology [IBM,
1981] and Holland's methodologies [Holland, 1983] are others that produce a
version of a data architecture.
strategic data model
Proponents of these approaches claim that a
provides an architectural
consistency of information,
foundation that will
more easily integratable
systems,
lead to
improved
and
productivity in system development and maintenance.
Only one of the ten firms we studied developed a
strategic
data model
primarily for architectural foundation purposes.
At Waverly's General Polymer Division a strategic data modeling
effort was begun for the entire division after the successful
implementation of common manufacturing systems.
The model has
been completed and will
be an input to the division's
I/S
strategic plan.
It is still
too soon to tell whether that
effort will eventually lead to benefits for Waverly.
A second approach to
definitions.
data
architecture
is
the
standardization of data
The choice of which data elements should have corporate-wide,
standard data definitions is an important architectural
there
are
some
data
elements which
are
so
basic
to
issue.
the
In
operation
any firm
of
the
business and which are the basis of so much shared communication, that it is
critical for all parts of the organization to refer to these elements in the
same way.
Presumably these data elements should be given global, mandatory
definitions.
Below the corporate level, it may make sense for a particular
•16-
division
standardize
to
that division.
a
Thus,
certain
on
additional
data
elements,
just
within
the standardization of data definitions can be seen as
hierarchical process.
Most of the new system "products" (Sections 2.1.1
firms
the
our
in
personnel
agree
elements,
as
several
a
on
was
the
required
however,
intended
that
precise
prerequisite
cases,
definitions
study
of
set
a
as
definitions
building
to
management
line
an
of
the
a
and
[See
technical
set
Note
of
2.]
corporate-wide
foundation
architectural
I/S
specific
product.
standardized
developed by
to 2.1.3)
for
data
In
data
unspecified
future systems development work.
In 1984,
Foothill
Computer formed a task force, chaired by
corporate I/S, to identify and define key data elements being
The members of the task
used in multiple areas of the business.
force came from the I/S groups in the various functions and
There were no immediate plans to implement the
divisions.
agreed upon definitions.
Rather, it was assumed that future
systems development work would conform to these definitions.
In
addition, it was established that any group supplying data to
another group within the corporation would be required to
if
deliver that data in conformance with the definitions,
asked.
After coming to agreement on definitions for over 200
data elements, the task force has refocused its efforts to
concentrate only on those elements for which a specific business
impact can be identified and pursued.
corporate
I/S
group
at
Waverly developed
rigorous
a
As of March, 1985,
methodology for data element definition.
approximately 150 data elements had been formally defined by the
Another 500 elements
data management staff using this method.
have
been
identified as candidates
for the
process.
The
definitions have been documented on a data dictionary but have
not yet been incorporated into applications development work.
The
Thus,
model
or a
as
these examples
set of
illustrate,
standard definitions can be
Their usefulness can be questioned,
to
either a wide scope strategic
however,
guide future systems development,
in either operational
or managerial
a
product in its own right.
unless the data model
or the definitions become
databases.
data
is
used
incorporated
-17-
A Variety of Data Planning Processes
2.2
We now focus on the planning processes used to select the "products" in
To many people, DRM is synonymous with a large-scale,
the previous section.
There are, however, other less
strategic data planning and modeling effort.
all -encompassing
planning
effective.
section
studies
This
four
into
approaches
categorizes
which
approaches; and no explicit planning.
equally,
be
from
more,
our
case
"80-20"
"targeting";
modeling;
data
not
if
processes
planning
the
strategic
types:
can
These approaches represent a continuum
of planning processes which assist the organization to identify the target
for
management
data
continuum
The
pursue.
relatively
action,
to
choose
the
action
from
scope
models
of
the
"product")
(or
methods
well-defined
ranges
broad
detailed,
and
enterprise
to
(producing
and
its
data),
through less formal and rigorous approaches, to no data planning at all.
2.2.1
Strategic Data Modeling
Section
2.1.5
discussed
strategic
developing a data architectural
modeling
data
foundation.
focus on using the approach in
we
section,
this
In
context
the
in
of
primarily
situations where the near-term objective is
to build new systems or databases.
The
—
underlying assumption of the
strategic
planning methodologies
data
that it is impossible to plan effectively if one does not know what the
business
contest.
is,
what
it
However,
of
and
does,
what
data
companies
eleven
it
uses
studied,
we
—
is
difficult
three
only
used
to
a
strategic data modeling approach, and as yet only one of those has succeeded
in
implementing
that model
many other firms have
actual
in
surfaced many
systems.
Informal
disappointments with
discussions
such
with
approaches,
and few successes.
The
diagram
in
Figure
4,
adapted
from
James Martin's
Planning Methodologies , is representative of several
planning
approaches.
The
left
side
of
the
Strategic
Data -
data-oriented strategic
diagram
shows
the
top-down
planning approach, leading to the bottom-up design shown on the right side
of the diagram.
-18-
Figure
4.
Strategic Data Planning
-19-
The
model,
(Box
in
1
with
begins
process
Figure
development
the
4).
The
of
enterprise
an
enterprise model
depicts
business
or
the
functional
areas of the firm, and the processes that are necessary to run the business.
next step
The
is
identify corporate
to
processes or activities,
enterprise model,
the
(Box
2).
leading
to
Data
the
entities and to link them to
data
requirements are thus mapped onto
identification
of
subject
areas
for
which databases need to be implemented, (Box 3).
In
general
only
selected portions of the enterprise model
Building the logical data
area databases are selected for bottom-up design.
is the first step.
model
The data model,
(Box 5),
of detailed end user and management data views,
the
previous
subsequent
entity
top-down
design
analysis,
of
application
in
Section
(Box
programs,
subject
and
results from
synthesis
a
(Box 4), with the results of
2).
(Boxes
Database
6),
design
proceed
and
from
the
logical data model.
As
mentioned
2.1.5,
developed a strategic data model
future
model.
systems,
but has
Waverly
Chemicals
for use as an architectural
not as yet implemented any
successfully
has
systems
foundation for
using
Dobbs Insurance is so far the only reasonably successful
the
data
example in
our study of this approach leading to actual systems development.
Dobbs did successfully use the top-down bottom-up approach in
its group insurance division, modifying the approach somewhat to
reduce the detail required.
Within a few years, the division
rewrote virtually all its systems to conform to the data model
it had developed.
Some departures from the model have been
necessitated on occasion to meet the requirements of short-term
deliverables.
Efforts to resolve these "nonconforming" systems
with the data model
are made at a later point in time.
While the
strategic
data modeling
approach
resulted in
new systems
Dobbs, it did not at Foothill Computer.
In the late 1970s, Foothill Computer recognized data management
as an important issue.
A group, composed primarily of senior
I/S professionals, began to study and then to develop top-down,
data-oriented methods for strategic systems planning.
After
much exploration, the group developed a strategic data planning
method that integrated
views of design.
both
data-oriented
and
process-oriented
at
-20-
In 1982,
the method was used to analyze one major functional
The project team
area, the flow of orders through the company.
worked for six months to define and get consensus from the
They also
worldwide business units on a logical data model.
Logical
reached agreement on data elements and definitions.
subject area databases were designed, and much effort was spent
The
systems,
those
databases with user views.
validating
A host of problems led to
however, were never implemented.
discontinuing the project.
These included the difficulty of
consolidating disparate user views, doubts about whether all the
key user views had been obtained, and not least of all, a
The course
multi -million dollar price tag for implementation.
of action finally chosen was to make extensive modifications to
an existing system to address its shortcomings.
There
studied.
proceed as
were
In
14
none
other
of
data
these
suggested by the
management
did
the
strategic
efforts
planning
the
in
and
a
firms
implementation
we
process
data modeling methodology.
we saw a variety of other approaches used successfully.
previously, depicts
eleven
Instead,
Figure 4, discussed
generalized strategic data planning process.
Figure
5
illustrates the actual planning process in four of the case study sites.
shown in Figure 5a and 5b, both Spectrum Electronics and
Waverly Chemical chose to develop manufacturing systems without
having used any top-down, data-oriented planning methodology to
arrive at that decision.
They started with a particular set of
applications (1), then developed a logical data model (2), and
finally designed the physical database and the applications (3).
As
Sierra Energy, (Figure 5c), started with a particular user's
view (l)--that of the corporate control ler--and designed a data
model (2) and physical data bases (3) from that perspective.
Methods for "bridging" data from existing applications were then
developed (4).
At Windsor
Products, (Figure 5d), the data management group in
corporate I/S consulted extensively with the distributed I/S
groups and with senior management to reach a consensus on what
new corporate-wide databases would have the greatest impact on
the business (1).
The data management group then consulted with
all users of that data as it built the logical data models for
those databases (2).
They essentially replaced the entire left
side of the diagram by wide-ranging deliberation within the
distributed I/S organization, then proceeded to build databases
and "bridges" (3).
In all
side"
or
of these cases,
top
down
the firms either skipped or abbreviated the "left
portion
of
the
top-down
planning
bottom-up
design
-21-
Figure
5.
The Planning Reality
Figure 5a.
Figure 5b.
SPECTRUM
WAVERLY
(General Ploymer Div.)
o
PHYSICAL
APPLICATIONS
DATABASE
PHYSICAL
APPLICATIONS
DATABASE
\
DATA MODEL
DATA MODEL
USER VIEWS
USER VIEWS
Figure 5c.
SIERRA
PHYSICAL
DATABASE
-22-
process.
In
fact,
they
followed
discussed in Sections 2.2.2
-
process
that do follow
reflection
more
there
firms
are
significant
have
appear
not
to
planning
processes
to
be
2.2.4.
Some companies, like Foothill,
planning
alternate
the
be
a
difficulty
number of
successfully
using
a
top-down, strategic data
implementing
reasons which
strategic
data
the
help
plan.
explain
planning
Upon
why
methods.
Among them are:
The strategic data planning process, done in detail and with
wide scope, can be very time consuming and expensive.
a
Also, for the process to be successful, key managers must
This commitment is often
commit significant time and effort.
difficult to obtain (and keep) from these busy individuals.
It is not always clear to the planners or top management
whether strategic data modeling is being done to create an
It
architecture or to immediately build new systems.
is
difficult to manage the expectations
of
involved
those
regarding the results and benefits of the process.
Total implementation of a wide scope data modeling effort can
be extremely expensive.
There is a tendency to avoid these
new costs, especially if many of the existing systems which
represent a huge Investment are still effective.
When implementing only a subset of the plan, it can be
difficult to bridge the gap from the top-down plan to
bottom-up design.
If proposed and existing systems interface
along different boundaries, it may be hard to isolate and
replace a subset of existing systems with a subset of
proposed systems.
The use of application packages also
creates interface and boundary problems.
Because of the upfront effort needed, organizations face a
longer and more expensive development process for the initial
Line managers
systems developed with this planning method.
do not like to see project schedules lengthened.
Similarly,
I/S managers, who have incentives to deliver quickly and
contain costs, may resist the additional effort involved.
Often the business will
developed and implemented.
change
while
the
plan
is
being
The methodology requires new I/S skills.
Thus,
process
for all
and
these reasons,
manage
expectations
it
is
difficult to gain commitment to the
regarding
the
outcome
of
strategic
data
-23-
We
planning.
will
discusses several
return
some
to
these
of
items
Section
in
4.0
which
issues affecting the implementation
fundamental managerial
of data management efforts in general.
2.2.2
Targeting High Impact Areas
Most
corporations
that
abbreviate
or
skip
methodology do not act without a plan.
the
top-down
planning
There are a variety of alternative
planning processes which can be used.
The most conrion process
other
business
area.
the
is
companies
some
In
"targeting" of
a
important
particular function or
or
problem
opportunity
areas are quite clear without an extensive analysis.
Sierra Energy, the controller knew from his experience that
needed, (and did not have), consistent, accurate data for
At Windsor Products, it was quite
corporate reporting purposes.
clear that consistent and accurate customer data and product
data needed to be shared to support changing business demands
and, after some discussion, it became clear that shipment data
At Spectrum Electronics, the head
was also critically needed.
of the I/S department was certain, and was able to convince the
manufacturing vice president, that an integrated manufacturing
database and common systems were the answer to coordination
problems and high systems costs.
In
he
none
In
But,
improved
data
rigorous
a
key managers who
there were
all,
in
these companies was
of
quality.
each
In
case,
data
could visualize
data
a
method
planning
used.
benefits
the
management
program
of
was
undertaken which was limited in scope, but was effective and implementable.
"80/20" Planning Methods
2.2.3
In
some
firms,
without
planning
data
there
necessary to carry out
aim
in
these
implemented
global
cases
is
(bottom-up),
planning
is
desire to get the major benefits of global
a
having
invest
to
full-scale
a
to
zero
while
(top-down)
in
strategic
quickly
reducing
phase.
amounts
the
This
the
on
of
data planning
the
amount
key
of
and
time
The
process.
"products"
effort
type of planning can
money
spent
be
to
in
be
a
termed an
-24-
"80/20" approach, after the adage
that for many undertakings,
80 percent of
the benefits can be achieved with 20 percent of the total work.
Windsor Products, having carried out its first round of data
management in a quick targeted manner (as shown in Figure 5d),
found itself uncertain as to the next subject databases to
implement.
Management decided, however, that a full strategic
What it needed
data model was not necessary for its purposes.
identify
fairly
rapidly
planning
process
means
to
from a
was a
It
the next round of candidates for subject area databases.
therefore developed its own abbreviated planning approach which
This involves identification of the
it calls "Data Charting".
the
corporation and the groups which
major data aggregates in
The use of this method took less than six man months
used them.
of effort, and involved reviews by line managers in all parts of
the business. This "Data Chart" is serving as the basis of
planning for the next round of information databases.
A
different
type
"80/20"
of
approach
was
used
to
identify
key
data
elements that should have common corporate-wide data definitions at Foothill
Computer.
In
Foothill
1984,
put priority on developing
common
data
definitions to promote data consistency, as described in Section
A Key Data Task Force was formed to identify and resolve
2.1.5.
high impact data definitional problems.
The method used was to
ask distributed I/S groups to each nominate 50 important, cross
functional data elements.
There was a 30 to 40 percent overlap
among the twelve lists of data elements nominated.
Work then
proceeded on the "product" of developing common definitions for
the identified critical data elements.
The problem with strategic data planning approaches is the investment of
time and dollars needed to obtain results.
probable
the
inconsistencies
that
Drawbacks of "targeting" include
arise
will
from
multiple
targeted
projects, and the fact that, in some companies, the most appropriate targets
may not be evident.
An
"80/20" approach, while not providing the detail
strategic data planning or the quick hit of targeted approaches,
of
does offer
the major benefits of both previous approaches.
2.2.4
No Planning Process
There are also data management actions that can be taken without doing
any data-oriented planning.
For example,
if
a
decision is made to provide
-25-
better access to existing data,
without addressing changes
that data, then no data planning methodology is needed.
form of
the
in
This was
the case
at Matrac with its data warehouse approach, described in Section 2.1.4.
Bounded Scope
2.3
our
In
study,
corporation;
all
attempted
firm
manage
the
data
used
by
the
limited the focus of the effort in one or more ways.
An
no
to
all
important factor in the success of data management efforts is that the scope
of the effort be carefully
defines
which
part
of
selected.
As
organization
the
described
to
is
this
in
section,
included
be
the
in
scope
data
management effort.
Most firms
which
focused on
functional
a
targeted manufacturing
corporate
targeted
finance
management efforts was
Section
(see
Section
(see
divisional
An
.
such
as
Spectrum Electronics
2.1.1),
or
Sierra
The
scope
of
some
case
of
the
area,
2.1.3).
example
the
is
Energy
which
data
Group
Insurance Division of Dobbs, described in Section 2.1.1.
In
some of our cases,
However,
small
in
set
all
of
but
two
standard
staff.
small
In
within
instances,
definitions.
data
selection of corporate-wide
data.
these
of
planning effort was corporate
scope of the
the
subject areas:
the
product was
Windsor
Products
customer,
relatively
focused
and
product,
on
a
shipment
Matrac focused on making data available to the corporate headquarters
Thus,
while corporate
these efforts,
in
were
scope,
limited
to
a
subset of the total data used by the corporation.
addition
a
to
functions
corporation.
and
Among
districts, and product lines.
divisions,
these
are:
other
suborganizations
groups,
show,
the
scope
of
the
sectors,
exist
geographic
Some organizations may choose one or another
of these units as a locus for data management efforts.
will
a
.
data
determined by the business objective.
management
As the next section
effort
is
substantially
-26-
Business Objectives, Not Conceptual Justifications
2.4
The proponents of data management
either
soundness
conceptual
the
of
data-centered
underlying
rationale
far too often base their arguments on
viewing
data
systems
design.
as
resource
a
or
assert
They
the
that
processes change while data is relatively stable, and therefore data should
the
be
element
key
management
in
the individual
a
plan
that
global
data
developing
before
the pieces built one at a
plan,
be entrenched
time
inconsistencies in
While these arguments are appealing,
engender action in the pragmatic,
not
argue
global
a
and the result will
myopically designed systems.
they
Or,
needs
one
Without such
pieces.
not fit together,
will
planning.
because
essential
is
I/S
they
do
cost-conscious world of the business
manager.
The
for
successful
most
the
exploiting
problems
part,
an
and
data
at
management processes we
solving
clear
a
opportunity.
This
opportunities
which
section
observed
specific
and
discusses
motivate
have
been
problem
business
the
executives
kinds
aimed,
of
business
consider
to
or
more
intensive management of the data resource.
2.4.1
Coordination within or across Business Units
A major motivation
better
coordination
business units,
of
for data management action is the perceived need for
activities,
or across them.
either
within
specific
Improved coordination requires
ability to communicate within or between organizational units.
terms this means more than the rapid sharing of data.
be
shared
usefully,
it
must
be
functions
in
a
language
In
understood
enhanced
an
In
or
practical
order for data to
by
all
involved.
This implies common data definitions and common codes for key data elements.
There are a number of clear examples of coordination as
a
motivation in
our case studies.
Both Spectrum Electronics and Waverly Chemical felt the need to
standardize their manufacturing systems so that many plants
could be coordinated more effectively.
In both these cases.
27-
Better
led
to
significant
benefits.
have
efforts
their
coordination between the production schedules of separate plants
has led to reduced in-process and inter-plant inventories; the
coordination of spare parts inventories has led to smaller
inventories and reduced downtimes; and coordinated purchasing
and
operations
has
brought
economies
of
scale
special
arrangements with vendors.
Sierra Energy is a different type of example.
In order to meet
needed
better
external
reporting
requirements.
Sierra
to
coordinate the financial information being fed to corporate from
Again the need was for a
diverse decentralized divisions.
coinnon "language" so that critical information could be rapidly
and accurately available to the headquarters units.
2.4.2
Organizational Flexibility
A second category of motivation
greater organizational
for
data management
is
desire for
the
flexibility to allow either an internal
restructuring
of the organization, or a refocusing of the organization due to changes
in
the environment.
Waverly Chemical merged two large manufacturing divisions in the
mid 1970s, and faced major problems with accounting systems
which had been designed and implemented separately in the
The company faced similar problems in the
original divisions.
late 1970s when it again reorganized, this time combining five
old divisions into two new ones.
It was quite clear to senior
management that there would be other reorganizations in future
As long as each division had its own accounting systems
years.
with much incompatible data, the problems would persist.
In
faced
regards
with
to
refocusing,
important
changes
a
in
number of companies
the
marketplace
we
which
studied
have
have
put
been
intense
competitive pressures on them to change from a product focus to a market or
customer focus.
At Dobbs, each product line in the Group Insurance Division sold
its
own
customer
directly
to
customers
and
maintained
information.
Each product line had its own set of customer
As the competitive environment changed, the inability to
codes.
present a single face to a customer interested in many types of
services put Dobbs at a disadvantage.
-28-
flexibility
Organizational
have
designed
been
but which
support
often
hindered
data
by
particular applications
flexible enough to be able
not
are
to
is
to
which
structures
suborganizations,
or
provide
strategically
new
important "views" of the business.
2.4.3
Improved Information for Managers
A third motivation
improved
information
for more
data management
effective
senior managers, middle management,
for
These information consumers want two things:
personnel.
the
is
and
for
need
key
staff
improved access to
data and improved data quality.
Top management at Windsor asked for a list of their fifty
They were told that because the data was
largest customers.
organized around product lines with different customer codes
within each product line, it was very difficult to respond to
their request.
At one bank, the quality of computerized management information
on such items as loan position and bankers acceptances was so
poor that managers
relied on manually
prepared,
redundant
reports.
Since it took almost three months before reported
errors were corrected, some managers stopped notifying systems
personnel about errors and only used the manual reports.
When
problems
such
these
as
become
important
enough
to
management,
strong motivations arise to improve data quality and access.
2.4.4
I/S Efficiency
As backlogs
adopt
methods
productivity
considered
standard
avoid
to
and
having
definitions.
increase,
and
or
be
tools
reducing
a
way
stabilized
to
and as
that
I/S costs climb,
show
maintenance
addressing
of
Reduced data
both
"reinvent
redundancy
increasing
of
Improved
costs.
environment,
data
constantly
promise
there is a clear need to
of
these
wheel"
should
also
of
management
problems.
programmers
the
data
should
data
trim costs.
development
In
be
a
more
able
structure
is
to
and
Presumably,
reduced inconsistencies will make it easier to access information for either
new applications or new reports.
-29-
few of the cases we studied was
In
for taking action.
contrary,
To the
efficiency a primary motivation
I/S
number of firms felt that the data
a
management actions they were undertaking would involve greater,
not
lower,
I/S costs in the short run.
Some firms did look
for
improvements
in
efficiency as a
I/S
secondary
benefit, and some have achieved it.
Spectrum eliminated separate programs and programming staffs in
data processing and using a
its plants by centralizing all
centralized database and common software for all manufacturing
The company also claims to have reduced maintenance
systems.
costs by eliminating not just redundant data, but the programs
that updated redundant data, and the programmers who maintained
those programs.
Crockett claims a 40% reduction in development costs through use
of its active data dictionary, the refocusing of the development
process on eliciting business rules rather than programming
procedures, and user generation of their own reports.
2.4.5
Summary of Motivations
What
linked
these
all
the
to
factors.
None
motivations
them
in
reflect
They
business.
of
have
draws
on
the
common
competitive,
to
The
they
better manage data
and
cost
that
data
managerial,
companies
tightly
are
justification
conceptual
should be considered a corporate resource.
our research are taking steps
that
is
participating in
for concrete
business
reasons.
In
a
few cases,
these motivations to better manage data emerged from a
line manager's need to respond to changes in his
departments,
successful
however,
have
also
efforts we have seen.
had
a
key
role
or her environment.
I/S
initiating many
the
in
of
Many line-sponsored efforts have occurred
after months, or even years, of education by I/S managers.
-30-
FOUR CRITICAL COMPONENTS FOR DATA MANAGEMENT: SUMMARIZING THE CASES
3.0
The
previously
Figure
in
for
These
discussed
framework
Figure
for
each
in
area
together with
presented
is
provide
a
rough
a
corporation.
choices
actual
planning
framework
Section
In
In
presented
data
scope,
separately.
the
action,
shows the filled in framework.
6
section
made
by
the
the
case
under
alternative
the
used
it
in
each column the firm's name
In
major
a
management
data
subscript to distinguish between the efforts.
a
we
2,
this
effort.
Where a firm had more than one major effort, its name appears several
with
for
A few evident patterns emerge.
study firms.
appears
choices
the
objectives,
components
thinking about data management actions
management
data
business
are:
3,
"product".
and
process,
components
critical
four
times,
Refer back to Figure
1
for capsule descriptions of each effort.
observations are evident from the figure.
Several
from
a
coordination
planning process, to
reflects
been
able
functional
a
existence
the
see
to
flexibility
or
of
business
objective,
to
managers
in
one
is
specific
benefit/cost
functional
ratio
targeted
a
scope, to a variety of products
positive
a
there
This path, with 4 companies,
especially prominent path through the diagram.
is
First,
The path
.
who
areas
their
in
have
area
of
responsibility.
Second, another path that stands out (with 3 companies involved)
an
is
from
objective of improved management information , with various scopes, to no
planning
process
to
,
a
"product"
improved
of
data
access
This
.
is
an
important option, with real benefits at minimal cost.
In addition to these two prominent paths.
choices,
firm,
several
predominantly
guided
and
its
unique
appropriate
are not the only
application
by
situation.
planning
the
Figure 6 shows
particular
Strategic
processes.
data
business
rich array of
objective
modeling
Likewise,
a
subject
is
only
area
provide
useful,
usable
data;
information
one
the
of
databases
"product" through which benefits can be delivered.
files
of
Common
databases
-31-
c
CD
Q.
E
o
u
c
o
§
a;
E
-32-
"bridged"
application
existing
from
contain
systems
data
useful
for
managers; and even the improvment of data access services can have important
benefits.
There
is
Multiple
corporation.
organization
path
improving
to
options
contingent
are
organization at
4.0
single
no
readiness
the
data
of
choices
correct
The
exist.
upon
management
the
a
any
for
that
of
needs
and
in
particular point in time.
a
FUNDAMENTAL MANAGERIAL ISSUES AFFECTING DATA MANAGEMENT IMPLEMENTATION
Our Interviews with I/S and line managers, and our own reflection on the
concerns
seem
to
raised,
have
underlie many
suggested
of
fundamental
five
affecting
problems
specific
the
effectively implement data management efforts.
managerial
These are
which
issues
ability
the
trade-off
the
(1)
to
between short-term deliverables versus long-term plans or architectures;
the tendency to centralization inherent in data management;
new
organizational
roles
responsibilities;
and
the need for
(3)
impact
the
(4)
(2)
data
of
management on I/S culture; and (5) the process of effective introduction of
innovations
into
thoughts
these
on
organization.
an
issues
as
a
In
means
section
this
laying
of
the
we
present
groundwork
our
for
early
future
research.
4.1
The Trade-off between Short-term Deliverables versus Long-term
Architectural Foundations
Managers
trade-off
considering
between
foundations.
The
data
short-tenm
development
management
actions
deliverables
of
a
data
today
architecture
and
often
diffuse.
A
fact
short-term focus of line managers.
architecture
demonstrable
are
in
results,
direct
of
life
in
is
with
a
architectural
long-term
and
infrastructure decision, like the building of roads.
term
faced
are
in
essence
an
The benefits are long
American
business
is
the
Decisions to allocate resources to data
conflict
with
managers'
near-term earnings per share,
and
focus
on
this year's
quickly
return
-33-
efforts reported in
Not surprisingly, most of the successful
on investment.
this paper have tended toward the short term end of the spectrum.
The danger of limiting the scope of data management action to areas with
a
near-term payoff is that this may result in problems later on if the local
"architecture"
Efforts
usually
are
each
however,
inconsistencies
data
models
data
with
continuum,
the
of
prevent
to
strategic
end
incompatible
prove
ultimately
solutions
also
corporate-wide
developing
by
many
and
the
to
problems.
its
has
expensive,
very
Moving
other.
not
have
succeeded.
What
is
needed
is
a
extensive,
less
expensive
less
architecture than strategic data modeling.
In
approach
data
to
order to develop such
new
a
approach, we need to understand better what aspects of current data design
might
with
conflict
efforts
in
future
constructive,
what
and
needs,
minimally
principles
restrictive
planning effort at Windsor Products seems
a
The
ways.
very useful
current
guide
can
move
"80/20"
in
data
right
the
direction.
4.2
The Centralizing Tendency of Data Management
Underlying
organization
greater
any
effort
reality
the
is
toward
centralization
of
more
that
decision
effective
improved
data
in
an
lead
to
standardization
of
management
data
Increased
making.
management
can
data does facilitate increased central control.
For
example,
standard
systems are developed.
executives,
If
data
the
definitions
may
be
on
this
instituted
as
common
resulting data is made accessible to senior
they may have a vastly enhanced ability to compare operational
details of business units under their jurisdiction.
act
established
data.
to
In
fact,
facilitate
several
"central"
of
the
There is a tendency to
systems
coordination
and
described
control.
increased centralization is not a design objective, it may be
a
here
Even
result.
were
if
-34-
Data management thus presents corporations with
a
particular business
unit
environment,
unique
its
in
there
is
make explicit choices concerning the balance of centralization
flexibility that will
importance
greater,
to
best serve the business as
recognition of the
the
is
the implementation of actions which
shift real
to
versus local
inevitable
almost
need
a
Of equal,
whole.
a
For
familiar dilemna.
a
not
if
resistance
or perceived power in the
organization [Markus, 1983],
4.3
Responsibilities
New Organizational
Data
management
organizations.
established
is
It
is
changing
old
apparent
that
creating
responsibilities
new
units
organizational
new
data-related
various
handle
to
and
activities.
being
are
addition,
In
policies are being set that describe the responsibilities of personnel
respect
areas
to
such
as
collection,
data
access,
with
Perhaps
security.
and
in
most significantly, the new responsibilities have an impact on not only the
I/S department, but also the roles of line managers.
In
addition
which
groups,
companies
some
access
groups".
responsible
sometimes,
as
an
highlighted
are
1985],
administration
database
to
A
for
few
study
firms
had
development
the
architectural
important
our
in
the
in
part
of
the
administration
groups"
organization
end
"data
groups,
guidelines,
plans,
supporting
and
planning
DRM
emergence of data
The
[Gillenson,
1981]
DRM
corporate
corporate
data
[Ross,
"corporate
created
foundations.
of
literature
had
and
groups
access
and,
groups
computing
user
deserves to be emphasized.
For the end user,
assistance with hardware and
software
Increasingly,
user
is
consulting.
the
organizational
responsibilities
appropriate
redesign
must
also
organizational
effort.
be
of data owners,
data suppliers,
To
assigned
administrative policies and procedures
clarified.
end
support
includes
data
(See Section 2.1.4.)
Establishing
the
enough.
not
units
achieve
for
just
effective
establishing
related to data
data custodians,
is
part
of
implementation,
and
quality.
and data
one
executing
The
users need
roles
to
be
-35-
administrative
the
clear assignment of
how
of
One
example
from
Diverse
quality
data
comes
helped
responsibility
information
database
used
by the
extensive
an
Conglomerate, where
chief executive was fed by many operational systems from many
The quality and consistency of the CEO's database
divisions.
depended upon coordinating the update data from the divisions so
that the database reflected a view of the corporation at a single
In order to manage what was becoming a difficult
point in time.
problem, the group in charge of the CEO's database developed a
"master data input calendar" for critical data files coming from
The MDIC specified the schedule of updates from
the divisions.
the person responsible for data arriving on
and
each division,
formalized
the operational coordination problem.
Thus it
time.
The Key Data Task Force at Foothill Computer, mentioned in Section
200
data
for
some
definitions
standard
issued
has
2.1.5,
As part of the process, the task force identified a
elements.
The "controller" of a data
"controller" for each data element.
element is responsible for its definition, but not necessarily for
The
the acquisition, updating, or accuracy of the physical data.
Not all of the
typical "controller" is a senior business manager.
the
readily
took
on
controllers
data
selected as
managers
responsibility.
the organizational
At this point in time,
to data management are still
the
cases,
these
fledgling
Significant thought must be given to
evolving.
establishment of appropriate
roles
must
groups
responsibilities with respect
be
and
organizational
given
strong
units.
sponsorship,
In
many
and
be
protected from the vicissitudes of corporate politics.
4.4
Impact on the I/S Culture
A fourth managerial
culture.
DRM implies
the
issue is
impact of data management on
the
I/S
significant changes in the ways systems planning and
design are carried out.
As Durrell
[1985] points out,
"data administration
challenges the basic process-oriented approach that has been employed during
the last 20 years.
This
can
be
many long-time DP professionals"
disconcerting
and
sometimes
insulting
(p. ID/29).
The new data-centered system used at Crockett (Section 2.1.3),
has had a major impact on the work of programmers and analysts.
The major activity now of these people is working with business
managers to define the business rules governing the meaning of
into
the
data
data
rules
elements,
and
entering
those
There is really no such thing as an applications
dictionary.
to
-36-
Not surprisingly, this has had
programmer at Crockett anymore.
a
significant impact on the attitudes and turnover of the
A great many programmers found
programmer/analyst staff there.
their skills of no value in the new environment, and many left
While the level of excitement of
during the transition period.
those who remained is high, Crockett personnel admit that they
have more luck hiring recent graduates than they do bringing in
experienced programmers to fill the new applications development
role.
problem
The
professionals
new
support
move
the
incentive
not
is
skills.
of
There
must
also
programmers
reward
schemes
one
data-oriented
toward
for
information
teaching
only
implementing
systems
to
current
example,
For
design.
and not for conforming to, or petitioning for changes to,
data
changes
organizational
be
systems
schedule
on
the data model
or
Without changes to these incentive structures, when system
standards.
deadline
pressures
develop
their
become
own
programmers
high,
structures
data
local
have
a
strong
rather
tendency
enter
that
time-consuming negotiations with the data administrator for changes
to
into
to
the
corporate data model.
The
cultural
changes
organizational
and
data-oriented approach to
systems
implied
development are
adopting
by
important
a
anticipate.
to
The incentive structure appropriate for programmers and the skills needed by
them
two
are
obvious
areas.
apparent as organizations
of
new
methods
and
tools
Other
important changes will
gain more experience in this area.
reflecting
approach to systems development will
a
major
change
not be successful
to
become
probably
The adoption
traditional
the
if the organizational
change is not carefully managed.
4.5
The Process of Effectively Introducing Innovations into the Organization
The
viewed
Implementation
as
the
process
of
of
initial
effectively
methods or tools of unproven value,
that
in
general
the
data management
diffusion
of
introducing
innovations
is
the relative
can
usefully
be
innovations,
into the organization.
things, on five characteristics of the innovation.
(1)
efforts
new
i.e.,
Rogers suggests
dependent,
among
[Rogers, 1962]
other
These are
advantage of the innovation over its alternatives;
(2)
the
-37-
observability of the results;
the compatibility of
(3)
existing values, past experience, and perceived needs;
the
and
innovation;
trial ability,
its
(5)
the
or
innovation with
the
the complexity of
(4)
extent
which
to
the
innovation can be experimented with on a small scale, low risk basis.
Rogers'
argument can help explain why it has been difficult
theoretical
First of all the
to implement data management actions in many corporations.
advantage
relative
most
of
results to observe
Where results are available,
.
it
current
to
there have been few
in most organizations,
To date,
practice is not known.
compared
actions
management
data
extremely hard to
is
separate, and quantify, the impact of data management from other related (or
unrelated) actions.
As
noted
Section
in
4.4,
data-focused
a
approach
to
I/S
not
is
compatible with the existing application/procedure/project focus, where the
systems to specification on time, rather than to
is to build individual
goal
create
a
architecture
data
data management
applications
involves
a
functions, and organizational
management actions
efforts.
risk
financial
have
not
DRM
Instead,
meet current and
great
down
torn
are
to
and
interrelationships
units are examined.
been
has
presented as
been
sold
the
between
systems,
wery often data
Finally,
low
scale,
small
basis
the
walls
between
trial able ,
on
Certainly,
needs.
complexity as
of
deal
future
that
major
a
investment and top to bottom commitment in the organization will
be needed to achieve results.
If one does adopt Roger's perspective on the diffusion of innovation,
is
in
easy to understand the difficulties that organizations
implementing
large-scale
data
management
have
This
efforts.
it
encountered
perspective
argues for more limited approaches where the relative advantage is clearer,
the impacts more observable, and where complexity is lessened.
limited approaches can be viewed as trials or experiments.
trial
is
successful,
the
ambitious second trial,
infrastructure".
organization will
probably
be
and ultimately for significant
In
addition,
When the first
ready
for
investment in
a
more
"data
-38-
CONCLUSION
5.0
from our study.
Three major themes emerge
research
ability
The
managing
that
operations,
data
is
managerially
access
of
firms
to
reorganize,
to
significant
a
or
change
to
First,
issue
it
corporations
in
relevant
their
clear from our
is
data,
to
strategic
today.
coordinate
can
focus
be
severely limited by poorly managed data.
there
Second,
range of options
exist
depend
heavily
scope.
As
can
tailored to
be
particular
the
on
needs
the
data
in
management
and,
A wide
data.
of
a
particular
"product" to deliver
objective
business
demonstrated by our case examples,
considered
be
that
managing
to
The appropriate planning process to use and
business.
to
clear-cut approach
one,
no
is
organizational
and
there are many contingencies
multiple
thus,
paths
take.
to
Another view of these contingencies is that data management actions can have
an impact on
three aspects of a corporation's data:
its infrastructure,
its
content, or its delivery.
infrastructure we mean design or definitional
By
current
flexibility
local
future.
Enforced
definitional
development of common
set
data
of
which
can
contribute
tend
to
to
is
a
be
of
flexibility
global
standards
are
one
of
many
the
infrastructure.
data
difficult
and
data
management
Actions
expensive,
the
in
example.
with their implicit enforcement of
another
both
greater
coding
and
systems,
definitions,
infrastructure
favor
in
standards which limit
to
build
and
the
a
The
single
actions
a
data
benefits
from such actions are most often realized only in the longer term.
Content
refers
to
the
choice
of
what
data
policies that address the accuracy of that data.
more detailed data,
actions
which
to
maintain,
data
content.
These
also
to
Systems to capture new or
and decisions to purchase external
affect
and
efforts
data are examples of
tend
to
be
moderately
expensive, with benefits in the middle term.
Delivery
it.
refers
to
making existing data available to managers who need
Data consulting services, extract policies, and the provision of fourth
-39-
generation reporting tools are examples of mechanisms
improve
to
delivery.
Actions in this area tend to be less expensive, and have short term benefits.
The choice of where a firm should best allocate its resources
infrastructure, content, and delivery
between
-
depends ^ery much on the willingness
-
Current
and ability of senior management to invest now for future benefits.
systems,
resource constraints,
management style,
strategic
vision,
and
the
evolution of the industry are all important factors.
The
third
theme
is
number of managerial
order
to
affinity
and
successfully
for
that
no
matter which
organizational
implement
short-term
data
results,
path
is
issues which must
management
management, the need for new organizational
efforts.
Treating
introducing
a
tendency
managerial
of
data
and
present issues that, if
severely inhibit the effectiveness of data management
initial
innovation
The
roles and responsibilities,
the impact of data management on the I/S culture all
not managed well, will
are
addressed in
be
efforts.
centralizing
the
there
chosen,
can
forays
help
into
data
managers
practical in their organizational environment.
management
understand
as
which
a
process
efforts
of
are
-40-
NOTES
NOTE
1
.
The two diagrams in Figure 7 show alternate visions of the relationship
In
between operational
databases and information databases.
the first
diagram, transaction processing (TP) systems are segregated from end user
information databases to allow better tuning of the high volume TP
applications and databases.
Only selected
and usually aggregated
data is
downloaded to the information database, making the latter smaller and more
easily tuned to the very different load and demand pattern of end user
queries.
Thus, the TP database has complete data, but only for a limited
time horizon.
The information database has only selected and aggregated
data, but for a longer time horizon.
,
This two block model
requires either that we know
detailed historical data managers will be interested in, or
to the information database all detailed data purged from
or that we accept that some future questions will have to go
,
advance what
that we download
the TP database,
unanswered.
in
The three block model, pictured in the second diagram in Figure 7,
recognizes
that
the
questions
of
interest
to
the
managers
querying
information database will change over time, in ways that cannot be predicted
in advance.
Therefore, it adds the historical database which is intended to
carry al 1 data about company transactions at the lowest level of detail
available.
This is obviously a large database but one with very lenient
efficiency demands.
Extract programs to feed the information database can
change as needs change, and if it is necessary to view data in a new
configuration, both current and historical data can be put into that form.
extreme example of problems with historical data comes from
Southern
Cross
Products,
which
consumer
and
gift
sells
products.
There is currently a major push at Southern Cross to
develop
better
product
line
sales
forecasts
by
including
economic
indicators
in
the
anlysis.
Thus,
the
marketing
analysts want data on past sales to as far back as 1950.
Unfortunately, the data processing group maintains data for one
year on tape, then discards it.
The solution has been to locate
"old timers" who have been with the company for years and might
have old printouts of sales data hanging around on a shelf
The data which the marketing analysts have found in
somewhere.
this manner has been rekeyed to create a patchwork database for
the forecasting analysis.
An
more typical
example comes from Boston Mutual.
In
this
insurance company, a front-end policy preparation system records
all agents who share in the credit (and compensation) for any
particular policy.
policy
takes
force,
When
the
actually
however, the preparation information passes to an information
A
-41
Figure
7.
Conceptual Views of Operational and
Informational Data
Applications Applications
Applications
Information
Transaction
processing
Database
Database
End Users
Applications Applications
End Users
End Users
Applications
Transaction
Processing
Historical
Information
Database
Database
Database
End Users
End Users
End Users
-42-
Recent
database which carries only the first agent listed.
business pressures have forced Boston Mutual to begin to dock
agents for policies which are not maintained beyond the first
year, but the data to do this accurately is not available.
The two models in Figure 7 illustrate an important architectural choice
Depending on the business demand for data and on
that firms must make.
firms may make different choices for different
technical
constraints,
For instance,
detailed sales-related data may be
categories of data.
captured using the three block model with an historical database because of
the likelihood of having to reaggregate in different forms as marketing
requiring
real
Detailed
manufacturing
time
change.
data
structures
accuracy, but with no presumed historical significance, might be purged from
the transaction processing database without loading it into the historical
database.
NOTE
2
.
Establishing data definitions was important in many of our
definitions and codes are key standards for a consistent data
The process of resolving specific data definitions, however,
While as many as 50 percent of the data elements in a given
defined without any disagreement, those that remain may be >/ery
resolve.
cases.
Data
environment.
is not easy.
area may be
difficult to
mentioned in the section on conmon systems, data management
began at Waverly's General Polymer Division as part of an effort
to
develop
Today,
common
manufacturing
systems.
the
standardization of data definitions is viewed as central to GPD'
data management program.
The cornerstone of the program is an
internally-developed data dictionary, with over 5,000 total and
500 standardized data elements. A committee composed of the data
administrator,
systems
analysts,
and
user
representatives
facilitates the definitions of standard data elements.
These
definitions are then used by all new applications.
As
Many of the companies we studied, including Waverly mentioned above,
used a task force of both I/S and user managers to resolve definitional
problems.
I/S personnel are familiar with the technical aspects of the
definition, including how implementing the definition might affect other
systems.
User managers can see the impact on their business practices from
different definitions.
A technique we saw at Foothill
Computer for resolving definitional
disputes is to refocus the dispute from "what should the definition be" to
"who has final authority for deciding".
This person or position is then the
"owner" of the data element, and has both the right to decide on the
definition, and the responsibility of responding to others who are unhappy
with the current definition.
-43-
Metadata standards add structure to the definition process by explicitly
setting out what a data definition must include to be complete.
Several of
the companies in our study had formal metadata standards to improve the
quality of data definitions and the productivity of the definition process
These standards ranged from being data-type
[Symons and Tijsma, 1982].
classification schemes to being format "languages" with syntax rules for the
structure and completeness of definitions and a controlled vocabulary of
permitted terms to be used in definitions.
In spite of the importance of developing and managing standard data
definitions, tools in this area are not yet technically adequate.
Most data
dictionaries on the market were designed to support a particular software
environment, and thus are extremely cumbersome or unworkable in the typical
corporation with data on multiple hardware and software systems.
This has
forced several of the corporations we studied to invest in designing their
Satisfaction of the
own data dictionaries to be able to meet their needs.
corporations with their proprietary data dictionaries varies, but it is
clear that there is an unfulfilled need here.
-44-
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Coulson, Christopher J. "People Just Aren't Using Data Dictionaries",
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,
Diebold, John "Information Resource Management--The New Challenge",
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,
Durrell, William "The Politics of Data", Computerworld . September 9, 1985.
Edelman, Franz "The Management of Information Resources: A Challenge for
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Holland, R.H. "Tools for Information Resource Management" Paper presented
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Scott, Foresman and
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