Data Terms Glossary

Data Management

Because businesses today—especially large businesses—are completely reliant on their data to make both day-to-day and strategic decisions, the quality of that data is of utmost importance. This means that the data should have the following general characteristics, among others:

– Every column in every data table contains only the kind of data intended for that column. For example, a “phone number” column contains only phone numbers.

– The data is accurate. For example, each phone number in a “phone number” column contains the complete, correct, and up-to-date phone number for each represented entity (customer, vendor, employee, or whatever).

– There is no duplication of data. For example, each customer is represented once and only once in a given table or database representing customer information.

In addition to data quality, enterprises must be concerned with issues such as:

– Availability: How soon after data is generated is it available for consumption? Are the systems that store the data operable when they are needed?

– Integrity: Do systems that collect, store, and transform data do so in a way that preserves the meaning of the data and avoids its corruption?

– Security: Is data—particularly sensitive data—available only to those personnel who need it? Are permissions in place, and enforced, that limit users’ ability to create, modify, and delete data?

The systems and processes put in place—and the people responsible for them—to ensure data quality, availability, integrity, and security are collectively known as data management (or data governance).

Because reports and information dashboards based on the company’s data are critical to the decision-making process, they warrant special data management attention (sometimes called BI data management). Is the report properly designed to show what was intended? Are the calculations correct? Is all the relevant data accounted for?

Some aspects of data management can be automated

Applications can ensure data quality by validating data entry fields. ETL processes can flag values that don’t make sense and are likely corrupted. Systems can enforce data security by preventing unauthorized access and direct manipulation of data objects.

Business decisions are only as good as the information on which they are based. There’s a human element too, of course, in how well company leadership interprets the data. But no amount of intuition will overcome poorly managed data that paints the wrong picture of the company’s status and direction.  The first step is establishing good data management.

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