What is Data Quality?

Every extra place where someone types a record is another place an error can enter. If you do not block bad entry and clean the records you already have, campaigns and other outreach run on the wrong customer. Data quality is the name for whether a data set can still do the job you collected it for.

What is it?

Data quality is the ability of a data set to serve an intended purpose. That depends on how you process and analyze it, usually in a database or an analytics system. A company or a nonprofit cannot claim high quality without a concrete picture of what "good" looks like for that purpose.

A practical test from data-quality practice: data is high quality when it meets the requirements of its intended use. You can tell, because you can reach the people you mean to reach, see what clients need, and act on it. The test is broad enough for different products, markets, and missions. The requirements change. The question does not.

Dimensions

Accuracy. The record states facts about the client. Errors in contact data mean you cannot reach them, and you cannot grow the audience you think you have.

Completeness. The record holds the fields the job needs, and contact details are current. Without that, you do not have a full picture of what the customer needs, and the channel you use to reach them goes stale.

Consistency. People and reports can read the data the same way. A large database is useless if reporting and models cannot say what it means or how to reach the people in it.

Integrity. The record stays intact from the moment you acquire it through retention and later use. That is what lets the company keep and understand client data instead of watching it decay.

Relevancy. Accurate data that the business does not need is still the wrong data. Extra fields waste storage, and they can hide the customers you were trying to find in a report.

What to know

Valuable data is consistent and unambiguous. Quality problems often appear when databases are merged, or when systems are integrated, and fields that should match do not, because the schema or the format differs.

What the work involves

The activities are rationalization and validation. You also need them when applications are pulled together in a merger or acquisition, and when siloed systems in one organization are combined for the first time in a warehouse or a data lake. The same quality decides whether horizontal systems such as ERP or CRM can run without constant correction.

Uses that follow from that work:

• Raise the value of the data you already hold, and the number of places you can use it.
• Cut the risk and the cost of poor-quality data.
• Raise efficiency and productivity.
• Protect the organization's reputation.
• Profile, standardize, monitor, and cleanse the data.

In short

Data you can process and analyze quickly is what produces a decision you can defend. That is why data quality sits under business intelligence, other analytics, and day-to-day operations. If the record is unfit for the channel or the report, the analysis on top of it inherits the defect.