When kicking off data management initiatives a large and key component is establishing the data stewards that represent the data that is collected, managed, and leveraged in business intelligence. By having these data stewards, and subsequently a data management committee, companies feel safe that the proper data governance practices are going to be put in place. Not so. Ownership (=Stewardship) does not equate to governance.
Many factors contribute to governance and business boundaries can quickly be broken down if you approach governance in business silos. As you walk through your process of data collection you’ll quickly find that what is considered the preferred source of data may not be generated by the team that determines what should stay, what should be modified, and what should go. In fact, depending on how you view the data, conflicts arise as to what is considered accurate, appropriate, of the contributing factor in decision and business point of view.
This is something I’ve run into recently when building a business intelligence solution for web analytics. Even within my own department of advertising executives, views of what transactional data should be considered the record of source is up for grabs depending on who is the recipient of the information and how it is used. Levels of accuracy vary depending on when data is needed, how it may be used for marketing optimizations, or if it will be used to actualize spending for billing. Throw into the mix that data feeds coming from vendors are constantly changing as they actualize transactions over the course of days, weeks, and even months, and finding the truth in the data becomes a challenge that defies religious opinion on the subject.
Sorting through the challenges of governance to determine what makes data reliable requires looking at a variety of factors and allowing for multiple views and uses.
- Reliability of source
- Time of collection
- Actualization
- Business process affected/use of data in decisions
- Degree of accuracy required
If you will notice, I do not include ownership. This is the artificial governance. Ownership in establishing governance only serves to create a framework around the above factors that creates credibility. Ownership, and then the transformation to stewardship, serves to continuously monitor, enforce, and improve governance around data needs.
Start your data management off on the right foot, don’t confuse ownership with governance.
Filed under: business intelligence, data quality, data governance, data management, data quality, data stewardship
Well said!
I would state it this way: just as Ownership is not DG, neither is Stewardship DG, or is DQM DG, etc. But, in order for DG to work, you must address topics such as ownership of data, stewardship of data, policies, processes, etc. Otherwise, your DG will be an exercise in futility, creating “shelfware” more often than not.
Ownership is critical b/c without it you’re trying to solve problems of data quality, sharing, etc. with no one who’s ultimately responsible for getting it right. If some data are truly critical business assets, as we want to claim, then it makes sense that some person should own responsibility for some of those assets.
But you’re right – ownership does not equal governance. It’s just an essential part of governance imho.
Ownership certainly is critical. It affords accountability. Great points. I’ve seen DG turn into “shelfware” and it pulls down the entire project either keeping it from moving or killing projects altogether after they have kicked off. It requires resolve and discipline and a good amount of continued PR to keep things on track. Thanks for the feedback.