What is Data Quality?

Data quality has been defined as a data that is fit for use and able to meet the purposed set by data user. High data quality could help an organization to formulate better business strategy and unveil business pattern for decision making. Failure in providing high data quality to the organization have brought various issues such as false decision due to incorrect data, high cost of operation and lack of customer satisfaction.

Six common dimensions of data quality standards:

• Completeness / Comprehensiveness: Data is complete when all necessary value pertaining to the data exist. For example, employee with unmarried status would leave the spouse name field blank. In this case, the null value in spouse name cannot be considered as incomplete data. It is important to understand that data may carry null value and the existence of null value in which it is appropriate should not be consider as incomplete data.

• Consistency: Consistent data reflects a state which the same data represent and the same value with standard representation are used throughout the system. For example Data representation such as currency unit, month and year should be represented in the same manner as long as it represents the same value.

• Accuracy: Precision and free-of-error data are the main characteristic of data accuracy . In order to justified the precision and the accuracy of data, comparisons towards real-world data can be made.

• Timeliness: Data timeliness referred to the age of data. Data timeliness is very important as the most current data has more potential to be consider as high data quality

• Validity / Integrity: This criteria looks as whether a dataset follows the rules and standards set. Are there any values missing that can harm the efficacy of the data.

• Uniqueness: This points out that there should be no data duplicates reported. For example, we may have in our database two customers that were registered as Tom Adams and Thomas Adams, which in fact are the same person, but the latter has the latest details. Now this situation poses the risk that a Customer Service representative may access outdated information under Tom Adams and will not be able to contact the client.

Advantages of Data Quality

• More Informed Decision-Making: Improved data quality leads to better decision-making across an organization.

• Better Audience Targeting: Data quality also leads to improved audience targeting. Without high-quality data, marketers are forced to try to apply to a broad audience, which is not efficient. When you have high-quality data, you can more accurately determine who your target audience should be.

• More Effective Content and Marketing Campaigns: In addition to improving targeting, data quality can also help to improve your content and marketing campaigns themselves. The more you know about your audience, the more reliably you can create content or ads that appeal to them.

• Improved Relationships with Customers: High-quality data can also help you improve your relationships with customers, which is crucial for success in any industry. Gathering data about your customers helps you get to know them better.

• Competitive Advantage: If you have better quality data than your competitors or use your data more effectively than they do, you gain a competitive advantage. Better data quality means that you can discover opportunities before your competitors do.

• Increased Profitability: Ultimately, high-quality data can lead to increased profitability. It can help you to craft more effective marketing campaigns and increase sales numbers. It also decreases ad waste, making your marketing campaigns more cost-effective.

Conclusion

Data in small or large company is no longer limited to what are stored in the database. Various data sources such as website and social media have become important to organizations in marketing and building relationship with their targeted customers.