by Peter Aiken, PhD with Juanita Billings MS/IS
It is rare these days to pick up a copy of Fortune, Forbes or the Wall Street Journal and not find an article about "big data", "data science" and read phrases like "Data is the new oil", "Data is a corporate asset", or "Data equals money". Dr. Peter Aiken and Juanita Billings, both of whom are noted experts in the data management profession, have offered case studies of eleven specific instances where improved data management practices result in direct monetary gain. They do this in their book “Monetizing Data Management” in “Part 2 - Bottom Line Pay-offs: Eleven Financial Cases.”
Here is a sampling of some of the ways that companies have used data for bottom-line payoffs:
A state government agency saved $10 Million dollars a year by improving data management practices for the accounting of employee time and leave-tracking. A $300,000 investment in improving data management practices eliminated 30% of the time required to manage time-and-leave data for 10,000 agency employees.
An international chemical company with more than $1 Billion dollars in annual sales found that highly compensated scientists were spending up to 80% of their time performing data management tasks instead of focusing on their actual jobs. They spent time rekeying data (thus introducing errors), manually moving files (sneaker-net), duplicating data, and manually manipulating data (cleansing in spreadsheets).
A large company successfully used good metadata management to make sound decisions about whether to invest $1 Million dollars into customizing a recent ERP implementation.
A large government agency used sound data management practices to save $5.5 Billion dollars in labor savings by utilizing a hybrid manual-automated solution decision.
A large healthcare provider spent $30 Million dollars on an enterprise data warehouse (EDW) solution. The effort had overrun its budget and had major data quality issues where 800,000 providers could not be retrieved from the EDW and 97% of the provider records had “dirty” ID numbers which made their data inaccessible!
After a migration to an ERP by a major financial institution, they found that they could only get analytical processes executed against 43% of the loan data in the EDW because of “…bad and missing data….”. After implementing better data management practices, the company increased the amount of trusted loan data to 88%.
Download this section of Peter's and Juanita's book to learn how our clients, when using a data-centric approach, begin to measure success differently than we have in the past.