On a high level, the cost of low-quality data can affect a company's profitability in two ways. First, the cost of scrap and rework, and second, the missed opportunities.
An example of scrap and rework costs is when an agent makes a mistake recording a customer's address details, resulting in a marketing premium being sent to the wrong address. Then the customer calls to complain. The complaint needs to be handled (extra call center time), then the address information needs to be entered a second time (reprocessing) and a second premium sent. The first premium was scrapped.
An example of missed opportunity costs would be a credit card that was not issued because the calculated credit score (incorrectly) fell below the threshold and the customer was rejected. When marketing costs are already incurred, the opportunity to make a sale is lost. In this whitepaper, I try to provide a comprehensive list of potential data quality costs. Cost Categories of Information Quality
Data quality costs can be divided into 3 categories:
- Immediate costs of poor quality data. This happens when the primary process gets corrupted due to bad data. Or, information Skrotpræmie and reprocessing, when immediately obvious errors or omissions in data need to be overcome to support the primary business process. For example, data entry of an invalid zip code requires back office personnel to redial and correct a product before shipping it.
- Information quality assessment or audit costs. These are the costs/efforts to (re)secure the smooth running of processes. Every time a ‘suspicious' data source is processed, the time spent seeking data quality assurance is an irrecoverable expense.
- Costs of information quality process improvement and defect prevention. Broken business processes need to be improved to eliminate unnecessary information costs. When a data capture or processing operation fails, it needs to be fixed. This is the long-term investment needed to prevent further losses.
- Immediate costs of poor quality data
Process error
For example, capturing incorrect customer data such as address, contact information, account information. Irreversible costs; For example. Premiums sent to non-existent customer addresses to no avail. Liability and exposure costs; for example, credit risk losses if data quality issues result in an incorrect credit being given to a customer whose creditworthiness is not accepted on the basis of self-supplied information. Recovery costs of unhappy customers; time spent handling complaints. Information Scrap and Reprocessing
– Excess data processing; It is customary for front and back office staff to keep small special “lists” of all kinds, as many transactions are “known” to rely on faulty data. They only serve as a backup or enhanced version of what is available in the primary database. Such activities are unnecessary and add no value, except that other issues such as ‘maintenance' and ‘recovery' are not possible for these custom lists.
– The cost of tracking missing information;
A field that is not properly filled or not filled in at all must be sought later in the process. Excess time and costs, inefficiency, and not in the least an aggravating factor. Time spent searching for missing information is not wasted on serving the customer better. Job rework costs; For example. Reissue of a credit card sent with a misspelled customer name. Workaround costs; when a primary key is missing or incorrect, laborious fuzzy matches are required to match records. This type of work is demanding and consumes valuable time from the most highly skilled database workers.
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