Try to find a project plan for a new business/production system
implementation without a section dedicated to "data cleanup". You probably won't
be able to.
Large
organisations depend on accurate data for a variety
of mission-critical applications. If accurate data is so important, how is
it that users allow their data to become so "dirty" that they have to go through
a major cleanup exercise every few years?
Think about the last time you upgraded your home PC or your cell phone. If you
are like most people, chances are good that your upgrade was also associated
with a mini "data cleanup" exercise - you took the opportunity to get rid of
redundant data, clean up old records and to make a fresh start.
The same human dynamics that cause your PC, your cell phone address book (or
your tool shed, for that matter) to become cluttered also impact on
mission-critical business data. But while you can spend a Saturday afternoon
every few months to straighten out that tool shed, a world-class organisation
simply cannot afford to let mission critical data get compromised.
With the implementation of each new system, the expectation always arises that,
this time round, after the inevitable "data cleanup exercise", data will somehow
magically remain "clean". But data does not get "dirty" because of systems - it
gets that way because of what system users do and don't do. To be sure,
some systems with effective data validation and greater integration and
automatisation will contribute to cleaner data. But if sloppy data discipline
and poor compliance made the data in the old system unreliable and inaccurate,
it will continue to do so with the new system.
Given that data quality is still widely misconceived of as a system function,
rather than as a direct consequence of user behaviour, it is not surprising that
there is little line management focus on data quality.
Perhaps change practitioners working on ERP projects also need to do more to
help drive the point home. Are we doing enough in this regard?
