Do you have a data problem? Don’t feel bad, most companies do. It seems there is either too little data or you are swimming in meaningless mounds of data. Maybe you have data but can’t access it, analyze it, or report on it easily. If you want to improve your data you’ll undoubtedly be faced with the need to make a business case for data quality. This can be tough to justify and unfortunately you will have to make choices about which problems to fix and which to leave for later. In order to make the best decisions, you will need some tools to express the impact that poor quality data has on your company. Here is a quick guide to help you get started.
Begin by attempting to quantify the impact that a data quality issue could have on your business. Consider both the area of the company that is affected by the data you are investigating and the number of users impacted. Considering both dimensions will help you assess the severity of data quality impacts to your business.
- Data that impacts legal and/or compliance reporting or management should be marked critical.
- Financial reporting data, which should also be rated critical regardless of the number of users affected. Data that impacts your GL reporting always warrants a critical rating.
- Operational data (used for daily management and reporting) is certainly important, but the severity of the impact can range from slight to critical depending on the number of users or business areas impacted. Consider the case when a few users are impacted and maybe their job is less efficient because of the issue, but the business does not grind to a halt. This would be rated a slight or possibly moderate impact.
- Analytical data used for forecasting, modeling, scorecarding, etc. generally would have slight or moderate severity impact on your business. However, in the case where a large percentage of the users or quantity of data has been affected (say greater than 5%) the impact could be rated serious.
By understanding the severity of the impact that data has on your organization, the areas affected, and the number of users impacted you will identify which data you can trust and which data need stronger controls and possibly clean up.
