Survey results are released from time to time that estimate the effort required to implement cross-functional entity data solutions, such as master data management (MDM), customer data integration (CDI) or product information management (PIM) solutions. The effort estimates vary, but all indicate that solving data quality is as significant, if not more significant, than software and governance. A key to successful data quality improvement initiatives is the enlistment of a strong executive sponsor, who has either the direct authority or the power of influence to cut across organization boundaries. Lets look at why you need an executive sponsor from the business to drive data quality enhancements.
Although entity data is occasionally created without human intervention (e.g., visits to a Web site can be tallied by user ID), the great majority of the data you will be rationalizing and maintaining will require people within your organization to do things differently or better. People are prone to making errors: they enter data incorrectly, dont bother to enter data in fields not directly tied to what they are doing, they delay entering the data for long periods of time and use their own definitions and notations. Furthermore, as I have pointed out in previous columns, the data that humans add to their own systems may be adequate for the needs of their particular function, so they will have little interest in changing their practices to meet the needs of some centralized data effort.
There are three ways to try to change peoples behavior within an organization in order to deliver the entity data you need for your cross-functional project:
- You can ask them to modify their data collection and quality assurance practices to meet your needs for a corporate-wide entity data solution.
- You can use the power of peer pressure to ensure that people actually change their data quality practices.
- You can modify data collection practices by changing compensation plans to focus on achieving data quality goals.
In thirty-plus years in the information industry, I have never seen option number one work. People will often say that they intend to help your project, but they dont deliver because of the stresses of their current job. Nobody likes change, so they wont reliably alter their data quality practices to help you when time pressures or other job-related tasks take priority.
Peer pressure and compensation, on the other hand, are great motivators when you need to change behavior. Metrics and an executive sponsor (or, in large organizations, an executive steering committee) are the keys to implementation. Lets first look at metrics.
There is an old line in management that what gets measured gets managed. In order to change peoples behavior across the organization, you need to start with a baseline of current data quality metrics and then establish goals, preferably monthly or quarterly. The goals should be achievable, but they should also be challenging, or stretch goals. Anyone who is creating or maintaining entity data should have a very clear set of goals for their particular activity.
These goals should be widely communicated, and achievements against the stretch targets should be reported on broadly throughout your organization. This is the peer pressure component of successful data quality enhancement. Most people like to achieve goals, and no one likes to have their colleagues know that they are regularly failing to achieve their data quality targets. Now, many organizations do not regularly share internal data quality metrics. The fear is that end users will find out about the state of the databases. Trust me - they already know because they use the data. You will, however, need a powerful executive sponsor to ensure that communications about successes, or lack of successes, compared to data quality goals are actually communicated.
These goals will also form the basis of compensation decisions. Every enterprise manages goals and compensation differently, but compensation is the most powerful tool available to drive the necessary behavioral changes to modify how data is collected and maintained. Bonuses rarely work and can, in fact, do more harm than good because paying people extra to do what they should already be doing sends the wrong message. You will need a strong executive sponsor to help you navigate through the compensation process.
Key takeaway: It is highly unlikely that the people working on implementation of central entity data systems (MDM, CDI, PIM, etc.) can achieve compliance to new data quality standards across the organization. You need to enlist a powerful executive sponsor or executive steering committee to pull the peer pressure and compensation levers to guarantee success.
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