Customers, coworkers, and computer systems all need critical data completeness to communicate well with one another.
Businesses rely on data completeness, a data quality characteristic, to run well. Managers need correct names, addresses, and emails to build and maintain customer relationships. Also, IT professionals need enough data completeness and accuracy to make customer data available across many systems without errors.
Unfortunately, all too often, business projects frequently lack required data completeness. For example, while growing a college’s corporate relations department, colleagues provide contact information with missing addresses, telephones, and names. This patchwork of incomplete data not only hinders sending invitations to contacts successfully but also causes headaches when upgrading to a new interdepartmental database system.
So, before beginning your next marketing, sales, or IT initiative, you want to understand how data completeness will affect it. Read further to assess what makes data complete, see how incomplete data happens, and ensure the level of data completeness and accuracy you need to succeed.
We’ll explore and explain data completeness in detail, including:
Data completeness measures the availability of data on-hand, it is a data quality characteristic demonstrating data comprehensiveness. The data completeness example above, for Robert Johnson, shows 18 cells with content and two with missing data. Upon comparing the number of inputs against the total number of cells, you get over 80% data completeness.
Sufficient data completeness can be below 100% because it covers the critical data needed. For example, say Robert Johnson has a summer address in New York and a winter address in Florida. His record has two entries for the summer and winter address.
But not all your customers have two homes. Most people live in the same place year-round. In these cases, it would make sense that the person has only one address, leaving the other blank. Adequate data completeness would mean having at least one address per contact.
Just because an entry has content does not guarantee the data completeness and accuracy of that data. Whether Bob Johnson has an address of “6 East Bridge Drive” or “In Situ” would not matter. In either case, the data will be checked off as complete.
Data can become incomplete just by doing everyday business, for example:
By their nature, customers, coworkers, and computer systems cause incomplete data. Realizing and identifying this missing data and early-on saves a lot of headaches. Follow these steps:
Implicit and explicit business requirements will cue you in to whether incomplete data should be expected or considered random. For example, say you send marketing mailings domestically, only in Canada. There would be no need to investigate why country name entries remain empty.
However, say you plan on sending a pledge letter to prospects across North America (The US., Mexico, Canada, the Virgin Islands, Costa Rica, Belize, Greenland, etc.). Then you would want to have complete country information in your customer relationship management system.
Should some of the country data be completed and others left empty, you will have randomly missing data. At this point, the data may or may not meet adequate data completeness, but you will want to identify the missing data and follow the steps in the “How to Identify Missing Data” section.
If you have an email, a phone number, or part of an address, you may have enough data completeness with the missing country values. You could still contact people. However, you would need to assess whether that does meet business needs.
Data completeness and accuracy significantly impact data quality, determining whether customers, coworkers, and computer systems can communicate well to do business. Over time expect to see missing data in your system as part of doing business.
Early on, you want to identify and define critical data that needs to be available. Then, you want to profile your data to see about the acceptability of your data completeness.
Prioritize and resolve critical data completeness issues. Data quality software and automation tools that present and fix missing data well make dealing with incomplete data simpler. Be sure to periodically review your data completeness criteria and adjust them according to business changes.Find Out More About WinPure Data Quality Tool
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