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5 Golden Rules of Data Quality Management

13th Mar 2015
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Currently, companies have to deal with the great volume and variety of records that appear and get into the database with the lightning speed. Some business owners consider the more data they acquire, the higher increase will be in the conversion and revenue rates. Yet, without the well-executed data quality management, all attempts of marketing managers and sales representatives, as well as spent investments, fall by the wayside.

Data Assessment: Recognize the Quality

The high-quality data should be accurate and consistent, as well as relevant and valid. Some vendors may be disillusioned, but every database has eroded and misinterpreted records, duplicates and missing fields.

As a rule, such information appears after CRM migration without database revising or from the third-party applications and social networks. The quality of your reports and statistics, as well as decision-making processes, depend on the quality of your data. So, you should detect the records types that create a damaging impact on the company performance. Here are the 4 in-categories of dirty data.

  1. Incorrect records are the information that is determinedly false. As an example, the age of a client can’t be 120 years.

  2. Inaccurate data is a real information, yet such an information is incorrect. Usually, it is mistakes in zip codes.

  3. Inconsistent information is the redundant data, usually duplicated customer records. It happens when you have no single data entry rules and different departments store the same customer under different names.

  4. Incomplete records with empty fields of zip code, address or a phone number. Such occasions happen when data is misinterpreted or doesn’t enter into the system.

Firewall of Rules and Policies

Data quality management is a set of ongoing efforts and actions but not a project. So, in order to assure the effectiveness of your records and CRM solution, create a framework of rules for detecting and preventing dirty data issues from re-occurring.

Compile the list of requirements to the records entry and management in consistency to your company’s objectives and goals. You need to identify the areas critical for your business and determine its value to baseline processes. Besides, employees who gather records should have a single glossary for various data types. Clear definitions will shorten the misinterpretation incidents.

Key Facets of Data Quality Management

Being empowered with information, you need to find out how to use it for advancing your relationships with customers, as well as boost your service and productivity levels. There are 5 main requirements that drive the efficient data quality management.

  1. Completeness. Your entities should have all the records and rows with the information about a customer or transaction, etc. Make sure that conditions for referential integrity of different entities were set up.

  2. Correctness. All the attributes of your data  should correspond the validity rules.

  3. Accuracy. Records are the foundation of the decision-making process, so they should be consistent and store in the corresponding fields.

  4. Timeliness. To ensure the relevance of your data, enter it into the CRM system. The real-time records have a considerable influence on the reporting and forecasting processes.

  5. Availability. CRM users may have an ability to limit the access to their records what increases the appearance of copies. Company database should be accessible for the effective accomplishment of a daily round.

The structured and valid data facilitates the project and campaign planning, increase the accuracy of reports and successful/ineffective actions. However, the quality of records depend on the business rules you created and their obtaining by employees.

Natalie Khomyk is a marketing manager at Data2CRM.

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