What is data enrichment?

Data enrichment is a technique for improving data quality and usability by supplementing datasets with additional information from internal or external sources.

Organizations are collecting more data than ever before, but often that data lacks context or meaning. Data enrichment helps fill those gaps and improve understanding of existing data points, whether they’re in the form of raw data or a structured dataset. Augmenting data in this fashion can transform a dataset from inscrutable to enlightening, empowering organizations to make more informed decisions.

Data enrichment practices are often part of an enterprise’s data management and master data management programs. There are several types of data enrichment that organizations pursue depending on their business needs and data sources, such as demographic, firmographic and geographic enrichment. While data teams can manually perform data enrichment, artificial intelligence (AI) and automation help optimize data enrichment processes.

Common use cases for data enrichment are found within marketing strategy, but data enrichment processes can also play a role in areas such as cybersecurity, healthcare and urban planning. Data enrichment has also proven increasingly valuable in elevating the performance of machine learning models; it provides context and more complete data for more accurate predictions.

Imagine a canvas that’s only partially painted, its bottom half covered with blue brush strokes representing an ocean while a few curious, golden patches float in the middle. Once the painting is finished, however, it’s clear those patches are reflections of light—the completed painting depicts the sun setting over the water.

While an unfinished canvas can be a work of art in itself, it also has the potential to be something more. The same is true with datasets that are improved through data enrichment.

For example, when a table of customer data containing only names and phone numbers is enriched with email addresses, it becomes a more powerful tool for outreach. When a dataset of street addresses is enriched with geographic coordinates, it can provide deeper insights into a neighborhood’s land use.

As businesses continue to generate and collect massive amounts of raw and unstructured data, data enrichment has taken on a new urgency. More raw and unstructured data means more gaps and missing context within datasets. Through data enrichment, however, organizations can correlate this data with other datapoints that give it more meaning, driving greater return on investment on their data assets.

Data enrichment yields a variety of benefits, including:

• Higher data accuracy: Data enrichment can fill gaps in existing data, such as incomplete mailing addresses or missing professional titles.

• Greater trust: Seeing different dimensions of data—such as a dataset of business names enriched with industry classification codes—can give users confidence that they’re accessing the right datapoints for their purposes.

• Better AI performance: Artificial intelligence, including machine learning and generative AI models, functions best when fed high-quality, complete data.

• Insights for decision-making: Comprehensive datasets achieved through data enrichment can help businesses discover new patterns and opportunities related to market demands, pricing and more. For instance, customer insights can inform targeted marketing efforts based on customer preferences.

• Compliance support: Data enrichment, and in particular, metadata enrichment, can make it easier to track sensitive information subject to data privacy regulations and laws such as the General Data Protection Regulation (GDPR).

The terms “data enrichment” and “data enhancement” are often used interchangeably, but they are distinct processes. While both can improve data quality, data enhancement is focused more on working with the data at hand, while data enrichment centers on appending new, additional datapoints to a dataset.

In data enhancement, cleaning and updating data are core functions. Appending some new data may be necessary for the purpose of addressing missing values in a column or updating outdated information, but the amount of new data being introduced is not at the scale of data enrichment.

Through data enrichment, new fields are often added to existing datasets. As with data enhancement, data cleansing is part of the process but here, it is done in preparation for the addition of new information. (See “Key steps for data enrichment” below.)

Organizations commonly use one or more of the following types of data enrichment to append information to their existing datasets:

• Behavioral data enrichment: Data on customer behavior and engagement with products, services and various communications channels, including mobile apps and social media accounts.

• Contact data enrichment: Information for enriching contact lists, including phone numbers, email addresses, business affiliations and social media profiles.

• Demographic enrichment: Characteristics such as age, gender, ethnicity, marital status and income. Also referred to as sociodemographic enrichment.

• Firmographic enrichment: Details about a company, such as industry, size, revenues and location.

• Geographic enrichment: Information on an entity’s location, such as street address, zip code, country and geographic coordinates.

• Psychographic enrichment: Data on a person’s lifestyle, interests, attitudes and beliefs.

• Technographic enrichment: Data on the types of technologies used by an individual or organization, including applications, tools, hardware, software and IT infrastructure.

The data enrichment process can vary by organization, but there are a few common steps:

Organizations can perform data enrichment using their internal data, including first-party data (data collected directly from customers), as well as data from third-party sources.

Enterprises seeking to use data from internal sources may come across an obstacle: siloed data. Fortunately, they can break those silos using data integration, the process of bringing together data from disparate sources and transforming it into a unified and usable formats. For instance, an organization may enrich a customer dataset by integrating data from customer relationship management (CRM) systems and marketing databases.

Companies can also turn to external data sources, namely free, public data sources and third-party data providers. Public data sources include government datasets (e.g. census data, employment reports) while third-party data providers collect and sell a range of data, including contact, demographic and firmographic data. When selecting third-party data, businesses should work only with trusted sources and vendors so they can be confident data is accurate, timely and meets their quality standards.

Any data procured and stored as part of a data enrichment process should be managed according to rules governing data privacy and security, such as GDPR and the Health Insurance Portability and Accountability Act (HIPAA). 

With the growth of data-driven decision-making and AI-related data needs, demand for high-quality data and, by extension, data enrichment tools, has intensified. The global market for data enrichment solutions is projected to reach nearly USD 4.6 billion by 2030, up from roughly USD 2.4 billion in 2023.

While AI adoption is helping drive the use of data enrichment solutions, it’s also underpinning some of the most advanced data enrichment tools. Common types of data enrichment tools and solutions include:

• Data integration solutions: Data integration solutions support extract, transform and load (ETL) processes that include data enrichment as well as data cleansing and other data modifications. (It’s important to note that data integration solutions can also operationalize data after it’s been enriched, loading the enriched data into warehouses and other destinations for analysis.)

• Open data lakehouses: Leading data lakehouse solutions can automate the ingestion and enrichment of unstructured data and unify it with structured data.

• Data governance software: Advanced data governance software deploys large language models (LLMs) for metadata enrichment, enhancing technical metadata with additional context, labels or descriptions.

• Agentic enrichment workflow solutions: AI agents can further streamline data enrichment processes. In one model of agentic data enrichment, a user creates a spreadsheet, triggering an application programming interface (API) to find and ingest relevant real-time data from the web. The new information is processed by an LLM and then added to the spreadsheet.¹

Data enrichment has applications in a variety of fields and industries.

Marketing teams and sales teams are frequent users of data enrichment, particularly behavioral data enrichment, demographic enrichment and firmographic enrichment. They leverage enriched data to build customer profiles, support segmentation strategies, create tailored marketing campaigns and deliver personalized customer experiences.

High-quality spatial data is crucial for urban planning and development. A form of geographic enrichment known as geocoding derives latitude and longitude measurements from street addresses, helping urban planners identify locations with more precision.

Wearable devices, health and fitness apps and other health monitoring technologies are serving as new sources of information for enriching patient and research datasets. Such enrichment can help medical professionals improve patient care and aid researchers in discovering important patterns and insights.

Security event data can be enriched with information such as physical locations (geographic enrichment) and the devices being used (technographic enrichment) to improve the assessment of cybersecurity risks and vulnerabilities.