#KEY FOB DUPLICATE CONSEQUENCES FOUND BY MANAGEMENT CODE#
For example, an empty Zip Code may cause the supporting Geocodes field to be left blank. Missing dependencies: Certain data fields are left blank since their dependent fields are empty.
Inconsistent formats and patterns: This relates to having the same data stored in multiple formats and patterns, rather than following the standardized format and pattern.Incomplete data: This relates to leaving necessary fields empty in your datasets.Duplicate data: This relates to storing multiple records belonging to the same entity.Ambiguous metadata: A lack of clear understanding about what certain data fields mean can cause you to store incorrect information.Human error: Typing errors, misspellings, and wrong data understanding are a few common reasons behind data quality issues.Outdated information: Data tends to change over time and must be revisited and revised periodically.Incorrect data: This is the data that does not conform to reality.Let’s take a look at the most common quality issues found in an organization’s datasets. The most common business risks incurred due to these data quality issues.The different types of data quality issues present in the main data assets of an organization, and.But just to help you fill up the template for your specific business case, I am listing the following aspects in this blog: The template mentioned above sets the stage for relating all kinds of data quality issues to estimated business risks. The Cost is estimated and can be measured at any periodic time, for example, monthly, quarterly, yearly, etc.The Quantifier is necessary to track down and there can be multiple quantifiers for a single Impact.The same issue can have more impact on these departments, or possibly more impact on other verticals, such as Sales, Marketing, Product, etc. In the table above, we saw how an issue had two impacts on Customer Service and two impacts on the Accounts department. A single Issue can have multiple Impacts on various business verticals.For example, a misspelled customer name or contact information can also lead to an incorrect customer in your database and losing the contact of an authentic customer. A data quality Problem can give rise to multiple Issues.There are a few things to note about this template: This template precisely summarizes the kind of impact a data quality issue (something as small as a misspelled customer name) can have on your business. $20,000.00 worth more staff time required ~500 less orders this year (as compared to estimated)Ĭannot trust estimated cash flow about 20% of the time $30,000.00 worth more staff time requiredĬustomer service: Decreased customer satisfaction Misspelled customer name and contact informationĭuplicate records created for the same customerĬustomer service: Increased number of inbound calls
This provides a periodic estimated cost incurred due to the business impact. This quantifies the impact in terms of a business measure. This is the impact the issue can have on the business. These are the various issues that can arise due to the data problem. This is the data quality problem that resides in your dataset. I have summarized the template in the table below: Problem Designing the data flaw – business risk matrixĭavid Loshin (in his book The practitioner’s guide to data quality improvement) introduces a very useful template for relating data flaws to business impacts and resulting costs.
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