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Data Warehousing >
Processes >
Observations >
How To Measure Success
Given the significant amount of resources usually invested in a data
warehousing project, a very important question is how success can be
measured. This is a question that many project managers do not think
about, and for good reason: Many project managers are brought in to build
the data warehousing system, and then turn it over to in-house staff for
ongoing maintenance. The job of the project manager is to build the system,
not to justify its existence.
Just because this is often not done does not mean this is not important.
Just like a data warehousing system aims to measure the pulse of the company,
the success of the data warehousing system itself needs to be measured.
Without some type of measure on the return on investment (ROI), how does the
company know whether it made the right choice? Whether it should continue
with the data warehousing investment?
There are a number of papers out there that provide formula on how to
calculate the return on a data warehousing investment. Some of the
calculations become quite cumbersome, with a number of assumptions and
even more variables. Although they are all valid methods, I believe the
success of the data warehousing system can simply be measured by looking at
one criteria:
How often the system is being used.
If the system is satisfying user needs, users will naturally use the
system. If not, users will abandon the system, and a data warehousing
system with no users is actually a detriment to the company (since resources
that can be deployed elsewhere are required to maintain the system).
Therefore, it is very important to have a tracking mechanism to figure out
how much are the users accessing the data warehouse. This should not be a
problem if third-party reporting/OLAP tools are used, since they all contain
this component. If the reporting tool is built from scratch, this feature
needs to be included in the tool. Once the system goes into production,
the data warehousing team needs to periodically check to make sure users
are using the system. If usage starts to dip, find out why and address the
reason as soon as possible. Is the data quality lacking? Are the reports
not satisfying current needs? Is the response time slow? Whatever the
reason, take steps to address it as soon as possible, so that the
data warehousing system is serving its purpose successfully.
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