5 Pillars of Effective Data Management
Enterprises today are relying more than ever on having good data with which to work. Critical business decisions, from an organization’s strategic direction to the details of its everyday operations, depend on having information that is accurate, complete, and timely. Achieving that level of data excellence doesn’t happen automatically. It is the result of active and effective data management to ensure a high standard of quality.
The importance of data quality and data management
In a study by Gartner, responding companies estimated that inadequate data quality control was costing them on average $14.2 million per year. As data management expert Dr. Philip Russom says:
“Failing to ensure high-quality operational data may put many worthwhile business goals for operational excellence at risk.”
When the data a company depends on is inaccurate, incomplete, or inconsistent, the result is often a pattern of faulty decision making that can ultimately compromise the organization’s ability to remain competitive as market conditions change. On the other hand, when a company effectively manages its data to maintain a high level of quality, better decisions will likely follow. That typically results in significant improvements in productivity, agility, and security, as well as in customer, supplier, and partner relationships.
In addition, regulatory regimes such as the European Union’s GDPR and California’s CCPA impose a legal requirement that companies maintain a degree of data quality sufficient to ensure the integrity and security of records containing personally identifiable information.
Components of effective data management
How can you manage your data to ensure that it is of the highest quality? Take a brief look at five critical components of an effective data management program.
1. Data cleansing
Data cleansing is the process by which records that are inaccurate or corrupt are identified and removed. Common sources of such errors include mistakes in data entry, punctuation, or spelling. More generally, data cleansing is used as an umbrella term for the entire process of analyzing and correcting data records to ensure their accuracy, completeness, and consistency.
2. Data validation
Data validation assesses whether data records are accurate and complete. Checks are made to ensure that data items are of the correct type, that there are no blank, null, duplicated, or incorrect values, that values are within the range defined for that field, etc.
3. Data linking
With data linking, information from corresponding records in different internal datasets is combined to create a new master record containing the full scope of information available for a particular entity.
4. Data enrichment
Data enrichment brings in third-party sources to augment your records with external information, such as geolocation, demographic, and firmographic data, that is not available internally.
5. Data deduplication
Data deduplication is the process of removing all redundant information from your data pool. This ensures that each entity, such as a specific customer or product, is uniquely represented, thus eliminating inconsistencies between different instances of the same data.
How Precisely enables effective data management
Precisely offers a comprehensive suite of tools designed to address each component of the data management process and achieve high levels of data quality.
- Our data quality products allow you to implement data quality processes quickly and easily. These products efficiently cleanse data from varied sources and facilitates data validation and linking, allowing customer data to be synthesized into a single authoritative view. Our data quality products also include distinct data matching algorithms to facilitate deduplication.
- Trillium Geolocation features verification and consolidation of customer contact information such as age, gender, phone number, email, and mailing address.
- Our data enrichment products can even add worldwide postal and geocoding data.
Read our eBook “4 Ways to Measure Data Quality” to learn how you can improve the quality of your data.