What Is Data Enrichment?

Data enrichment is the process of adding to or updating raw internal data with external information. Augmenting data in this way can enhance data accuracy and quality by filling data gaps and providing deeper context. Data enrichment is a popular procedure used for customer relationship management and business strategy, as it allows organizations to make more informed business decisions.

Understanding Data Enrichment

Also known as data appendaging, data enrichment may involve various steps or techniques for data collection and additions, such as:

• Supplementation
• Verification or validation
• Integration
• Analysis and application

With these enrichment elements, you source data that fills gaps in or complements existing data. Part of that process is verifying additions and merging the datasets for cohesive presentation and use. The validation or verification step ensures you incorporate accurate, high-quality insights, while integration aligns the data you add to your existing formats, structures, schema and other internal data standards.

Data Enrichment Benefits

Implementing data enrichment strategies can further these and other goals:

• Understanding of customers and targeted data sources, allowing you to create tailored experiences
• Contextualizing your data to your industry for smart business decisions
• Dataset accuracy and completeness

Implementing Data Enrichment

Every organization’s data enrichment processes and needs are unique, as the kind of data they collect varies according to industry, internal goals and other factors. However, there are some best practices and tools that will prove valuable in the majority of circumstances.

Data Enrichment Best Practices

There are standard practices you can use to make enrichment successful, including:

• Cleansing your preexisting or raw data beforehand to create a trustworthy foundation for enrichment insights.
• Establishing distinct, precise criteria for the data you collect.
• Setting clear enrichment goals and metrics and monitoring them, then reflecting on the impact of your supplemented data.
• Utilizing data visualization and observation tools to optimize impact analysis.
• Standardizing analysis rules and processes to apply to new data.
• Using validation and verification technologies to automate checks.
• Prioritizing integration processes to match your third-party data to your raw data.
• Making enrichment an ongoing practice that provides constant benefit to your organization.

Metrics to assess the success of data enrichment practices include accuracy, timeliness, applicability, relevance and completeness.

Data Enrichment Tools and Features

A few top tools and features to consider for data enrichment processes include:

• Data validation and integration: Anomalo
• Customer data management: Hubspot CRM
• Data quality management: Anomalo
• Data visualization and formatting: Anomalo
• Data cleansing and error detection: Anomalo
• Missing data identification: Anomalo
• Customer email address data intelligence: Reverse Contact
• B2B sales intelligence: Vainu