Data often gets pushed back on that never-ending to-do list as other day-to-day business operations take priority. However, your business’s information is only as good as the data you use behind it.
Incorrect data: This is the data that does not conform to reality. It usually happens due to:
Outdated information: Data tends to change over time and must be revisited and revised periodically.
Human error: Typing errors, misspellings, and wrong data understanding are a few common reasons behind data quality issues.
Ambiguous metadata: A lack of clear understanding about what certain data fields mean can cause you to store incorrect information.
Duplicate data: This relates to storing multiple records belonging to the same entity.
Incomplete data: This relates to leaving necessary fields empty in your datasets.
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.
Missing dependencies: Certain data fields are left blank since their dependent fields are empty. For example, an empty Zip Code may cause the supporting Geocodes field to be left blank.
Different measurement units: This relates to storing the same data field in multiple measuring units, causing you to lack a standardized unit scale of measurement.
Poor-quality data causes inefficiencies in those business processes which depend on data from reports to ordering products and just about everything in between for which facts are required. These inefficiencies may result in very expensive rework efforts validating and fixing data errors, instead of focusing on core duties.
Poor-quality data leads to poor decisions. A decision can be no better than the information upon which it’s based, and critical decisions based on poor-quality data can have very serious consequences. This is another reason why you should make sure that your data actually represents reality.
Poor-quality data creates mistrust. Especially in industries where regulations govern relationships or trade with certain customers, such as finance. Maintaining good-quality data can be the difference between compliance and millions of dollars in fines. If the data’s wrong, time, money, and reputations can be lost, reflecting adversely on your business and lowering customer confidence.
Poor-quality data can lead to lost revenue in many ways. Take, for example, communications that fail to convert to sales because the underlying customer data is incorrect. Poor data can result in inaccurate targeting and communications, especially detrimental in multichannel selling.
A business may miss a lucrative opportunity for new product developments or customer needs that a competitor with a more advanced understanding of data may capitalise upon.