The “Do Nothing” Option

When it comes to writing proposals and setting scope for data intensive projects like data integration, master data management, CRM, and data warehouse, project managers often find themselves struggling to justify the additional funding needed for information quality. But I recently picked up a neat trick when I was talking to the folks at BT. If you recall, BT has a huge implementation of enterprise-wide information quality and a great data governance story. If you want to read the details, they are written up in a 2006 Gartner report (See " Strategic Focus on Data Quality Yields Big Benefits for BT" on Gartner.com). Also, you can hear it in BT’s own words in a webinar on Trilliumsoftware.com.
Project managers, can I share a secret with you? Let’s keep this secret just between us, OK? Let’s not let upper management know you’re pulling this stuff on them.
The key is to swaying the approval process, according to BT, is to always calculate what will happen if you don’t do anything about the data quality. They call it the “Do Nothing Option”. Sounds simple, but I suspect it’s something that is missing from many proposals. In your scoping documents, make sure that you state in black and white both the ROI of improving data quality processes and the potential risks that you run when you ignore it. In short, if you invest resources in information quality, all will be right with the world. If you do nothing, anarchy and chaos will ensue.
For example, you’re installing a new CRM application. The new CRM application will likely have ROI on customizing workflow, tracking customer interactions, efficiently contacting customers and prospects, and reporting, among many other benefits. However, if you do nothing with information quality, what are the risks and what are the costs associated with the risks? Be specific about what kinds of problems are likely to occur and the costs associated with them. Are there mailing costs? Are there process costs associated with chasing down duplicate accounts and customers? Are there billing efficiencies that can be mitigated with information quality? Any inventory inefficiencies? Will reports show incorrect information requiring more process and problem chasing? Will all forms of corporate compliance be met with the current data, or are there risks of fines associated with compliance? Will data management issues instead have to be handled by the call center and if so, at what cost? What is the long-term impact of not managing the data to the corporation? Etc. What’s the cost of doing nothing?
If you’re still having trouble with management, even after explaining what will happen if you do nothing, feel free to contact me. I’m very interested in the process that project managers must endure in order to do the right thing and take on the responsibility for managing data.