Ziff Davis Enterprise Researchjust released their survey of IT professionals. Not surprisingly the CIO’s agree on the need to spend feverishly on a number of fronts. Three of the top four business priorities included: providing better service to customers, improving business processes and cutting costs.
The technologies judged to offer the biggest impact included: business intelligence, collaboration and systems/data integration.
Their top technical priorities for implementation? Strategic applications, infrastructure changes and build-out to keep up with business growth and way down at the number 5 of 10 listings was improve the quality of information.
These results seem a bit out of alignment. These CIOs control billions of dollars which they are aimed at infrastructure and at the applications dependent on data in order to serve customers, improve processes, cut cost. Yet fixing the data, specifically product information, is ,once again, is far down the list. Further, the definition of “fixing it” is broad enough to encompass NIS, PIM, MDM, Content Management and CRM systems all of which have little to do with improving the actual quality of the information which enters this infrastructure and applications.
This issue with incorrect product data, specifically in the CPG business, goes back many years. In part this issue still exists because Industry folks placed their bets on a variety of “silver bullet” systems to deal with the data. The hope was to take the data, distill it, homogenize it, harmonize using systems to force formatting, detail inclusion, uniform abbreviations and the like. The billions spent on the systems mentioned in the paragraph above, coupled with those spent on data exchanges, standards organizations and efforts, data pools and catalogues, data cleansing and data harmonization have had little effect in the improvement of operational Product Information in the CPG space.
Spending money has never been the problem in the quest to serve customers better improve business processes and reduce costs. Spending money on solving the data quality issues needs to be a higher priority or it will continue to stunt the impact of all these expenditures, as it has done for many years. You cannot make bad data good.