Guiding Principles
Having worked on OLTP system projects, you are already aware
of sonic of the guiding principles of project management—do not give into
analysis paralysis, do not allow scope creep, monitor slippage, keep the project
on track, and so on. Although most of those guiding principles also apply to
data warehouse project management, we do not want to repeat them here. On the
other hand, we want to consider sonic guiding principles that pertain to data
warehouse projects exclusively. At every stage of the project, you have to keep
the guiding principles as a backdrop so Cial these principles can condition
each project management decision and action. The major guiding principles are:
Sponsorship. No data warehouse project succeeds without
strong and committed executive spon6orship.
Project Manager:
It is a serious mistake to have a project manager who is more technology-oriented
than user-oriented and business-oriented.
New Paradigm.
Data warehousing is new for most companies; innovative project man-agement
methods arc essential to deal with the unexpected challenges.
Team Roles. Team
roles are not to be assigned arbitrarily; the roles must reflect the needs of
each individual data warehouse project.
Data Quality. Three
critical aspects of data in the data warehouse are: quality, quality, and
quality.
User Requirements..
Although obvious, user requirements alone form the driving force of every task
on the project schedule.
Building for Growth.
Number of users and number of queries shoot up very quickly af-ter deployment;
data warehouses not built for growth will crumble swiftly.
Project Polities,
The first data Wa FCIIOUNC project in a company poses challenges and threats to
users at different levels; trying to handle project politics is like walking
the proverbial tightrope, to be trodden with extreme caution,
Realistic
Expectations. It is easy to promise the world in the first data warehouse
project; setting expectations at the right and attainable levels is the best course,
Dimensional data Marketing.
A we dimensional data model is a required to and blueprint.
External Data. A
data warehouse does not live by internal data alone; data from relevant
external sources is an absolutely necessary ingredient.
Training. Data
warehouse user tools arc different, and new. If the us do not know how to use
the tools. they will not use the data warehouse. An unused data ware-house is a
failed data warehouse.
