Measuring Progress in the Iterative Data Conversion Cycle

This post was extensively updated and incorporated into my new book, The Data Conversion Cycle: A guide to migrating transactions and other records for system implementation teams. Now available on in both Kindle format for $4.49 and paperback for $6.99.

This is the last article in the series on the data conversion cycle.  As is frequently noted, you can’t manage what you don’t measure.  However, as both the Hawthorne experiments and Werner Heisenberg found in the 1920’s, the act of measuring a phenomenon influences the object under observation.  So the trick is to measure carefully, so that any influence your measurement has is at least neutral, and preferably desirable.  Consequently, I’m going to close out this (admittedly interminable) series on the data conversion cycle with considerations for assessing data conversion process quality, as the team “learns how to move data.”

As previously noted, a beneficial side effect of an iterative approach to data conversion is that the team eventually gets good at it.  But what constitutes “goodness?”  For most projects, “good” would be defined as error-free, fast, and predictable.  The trick is expressing those attributes in such a way as to make them measurable, without driving one at the expense of the others.  To that end:

  • Error rate: the number of number of corrections to be made in the target system subsequent to the load, divided by the number of records loaded.  This ignores the “learning” errors in mapping or extraction processes in order to concentrate on outcomes.
  • Extraction time: total time (as opposed to work hours) from copy of the source system to the extraction and formatting of records, to the transfer of the extracted data to the load team.
  • Load time: total time to load the formatted records to the target system.
  • Validation time: total time required for validation of the load.
  • Predictability: sum of Extraction time, Load time, and Validation time, divided by the predicted time required for them.  A value of 1.0 means that the process is absolutely predictable, whereas variances from 1.0 indicate the degree of uncertainty.

Plainly, the error rate is critical to the users of the system, as they will have to make any needed corrections.  Also, the more time it takes for the extraction, load and validation, the longer the users will be unable to enter transactions, and the more transactions will accumulate for entry once the target system is finally available to the users.  But predictability is vital to both the users and the conversion team, as a tight, accurate cutover schedule is in everyone’s best interest.  The ability to minimize the unknowns (read: risks) in the cutover to production is largely a function of the predictability of the process.

Tracking these metrics in each cycle will give the project team the ability to measure improvements, but also guide decision making on where to expend resources.  On most projects, improvements in the validation processes will reduce validation time, with the side benefit of improving predictability.  Driving automation of the extraction processes will usually produce the same benefits, frequently with the added benefit of a reduced error rate.  But in order to get the best return on investment, it is useful to analyze the metrics from each conversion, so that efforts to reduce the error rate increase the extraction and load times more than necessary.  Measurements allow for trade-offs, so you don’t go past the point of diminishing returns on any one metric.

Thanks for reading through all of these posts over the last two months.  As previously mentioned, I plan to consolidate these posts into a Kindle book.  Special thanks to Samad Aidane for the “blog a book” idea!

This entry was posted in Data Conversion and tagged , , , , by Dave Gordon. Bookmark the permalink.

About Dave Gordon

Dave Gordon is a project manager with over twenty five years of experience in implementing human capital management and payroll systems, including SaaS solutions like Workday and premises-based ERP solutions like PeopleSoft and ADP Enterprise. He has an MS in IT with a concentration in project management, and a BS in Business. In addition to his articles and blog posts, he curates a weekly roundup of articles on project management, and he has authored or contributed to several books on project management.

2 thoughts on “Measuring Progress in the Iterative Data Conversion Cycle

  1. Hi Dave,

    I love your articles. Very good job!!! Could you do me a favor and send me a sequence within data conversion using a payroll example. Specifically I am interested in what is dependency between the HR system of record and outgoing payroll vendor. What goes first? Master data, then YTD balances? Can you explain it?

    Thank you,


  2. Generally, you will need to load the employee records first, and after everything else is loaded, you load their payroll balances. Some systems, like Workday, require you to load the first quarter balances, run a process, and then load the second quarter balances, and so on.

    Note that if you are building an interface from your HR system to your payroll vendor, they will generally maintain the balances. You’ll send changes from your HR system, and possibly one-time payments. It depends on the systems. Payroll vendors generally provide implementation documentation for their system requirements.

Comments are closed.