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Our connected age and continued advances in information technology and communication through ever-expanding channels bring both challenges and opportunities. It is easier than ever before to acquire data on your customers, making it simpler to design ad campaigns, fundraising efforts, and more tailored and personalized content. This, in turn, increases the odds of success, if the data is accurate.

On the other hand, the fact that there are many points of data entry on both the customer and company end increases the chances of errors being introduced into organizational databases. If steps are not taken to prevent this inaccurate data entry and to cleanse databases of incorrect client data, marketing efforts, awareness campaigns, and other outreaches to your users can actually be less effective.
To successfully meet the challenges of our data-driven age and take full advantage of the opportunities it offers, companies and organizations must strive for the highest data quality.

What Is Data Quality?

A basic data quality definition is this: Data quality is the ability of a given data set to serve an intended purpose.
Data quality refers to the overall utility of a dataset(s) as a function of its ability to be easily processed and analyzed for other uses, usually by a database, data warehouse, or data analytics system.

A company, nonprofit organization, or other entity cannot have the highest data quality without an accurate understanding of what quality data looks like. There are a variety of ways to define data quality, but all definitions have some important points in common.

According to data quality experts, data is of high quality when it satisfies the requirements of its intended use. In other words, companies know that they have good quality data when they are able to use it to communicate effectively with their constituents, determine clients’ needs, and find effective ways to serve their client base.
This data quality definition is broad enough to help companies with varying products, markets, and missions to understand if their data is up to standards.

A Short List of Data Quality Dimensions are:

Accuracy- Accurate data convey true information about a company’s clientele. If there are errors in client data, contact with customers is impossible, and it is extremely difficult to reach a larger audience.

Completeness– Data quality is also defined by its completeness. To get a full picture of customer needs, as well as maintain open channels of communication, a business must have data that includes all of the pertinent information and is up-to-date on customers’ contact information.

Consistency– The key to the data quality definition is the concept of whether the data is understood. Massive databases full of data are useless if reporting and modeling cannot understand what the data says about your users and how best to reach them.

Integrity– High-quality data can mean the difference between high profitability and a company that just barely gets by. Experian Data Quality is here to help your company acquire, retain, and understand client data.

Relevancy– Data should not only be accurate, but it must also be relevant to the needs and purposes of a business. Companies waste valuable storage space if they collect information that is extraneous to their purpose, and irrelevant data may also prevent key customer targets from being identified in reporting and analysis.

What Do I Need To Do Know About Data Quality?

Quality data is useful data. To be of high quality, data must be consistent and unambiguous. Data quality issues are often the result of database merges or systems/cloud integration processes in which data fields that should be compatible are not due to schema or format inconsistencies. Data that is not high quality can undergo data cleansing to raise its quality.

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What Activities Are Involved In Data Quality?

Data quality activities involve data rationalization and validation.

Data quality efforts are often needed while integrating disparate applications that occur during merger and acquisition activities, but also when siloed data systems within a single organization are brought together for the first time in a data warehouse or big data lake. Data quality is also critical to the efficiency of horizontal business applications such as enterprise resource planning (ERP) or customer relationship management (CRM).

A Few Uses of Data Quality are:

  • Increasing the value of organizational data and the opportunities to use it.
  • Reducing risk and cost associated with poor quality data.
  • Improving organizational efficiency and productivity.
  • Protecting and enhancing the organization’s reputation.
  • Data profiling.
  • Data standardization.
  • Data monitoring.
  • Data cleansing.

What Are The Benefits Of Data Quality?

When data is of excellent quality, it can be easily processed and analyzed, leading to insights that help the organization make better decisions. High-quality data is essential to business intelligence efforts and other types of data analytics, as well as better operational efficiency.

Want to learn more about PIM?

If you have any questions regarding Product Information Management, from PIM Selection to Implementation or how a PIM would fit in your IT landscape? Feel free to browse our Knowledge Base of articles on everything PIM related.

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