The complete guide to data enrichment in 2026

In today’s digital economy, data empowers businesses.

Data gives you valuable insights that improve marketing campaigns, help you better understand your customers, and streamline operations. On average, businesses today collect and manage data from 400 different sources. However, the best insights often come from enhancing existing information to make it more valuable, rather than adding more raw data points to your datasets. 

In this article, we’ll delve into what data enrichment is and how it can benefit businesses. We’ll also provide some common data enrichment processes to help your organization augment its existing information sources.

What is data enrichment?

Data enrichment is the process of improving the value of internal, first-party data by enhancing, expanding, or improving it with additional information. In essence, the goal is to supplement existing data with more context to make that dataset more accurate, complete, and valuable. 

Typically, data enrichment involves one of two distinct processes:

• Adding third-party data from an external database to existing datasets: For example, you might acquire external demographic data and then combine it with internal customer data to deliver more personalized experiences.
• Merging internal datasets: For example, you might combine CRM data with customer support records. This method is useful for larger organizations with multiple departments, teams, or systems. 

Both data enrichment processes have one critical thing in common: They turn raw data into a strategic, valuable asset by adding context.

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The 6 types of data enrichment

Broadly speaking, there are six types of external information you can use to supplement and enrich your datasets. Each has its own use case, depending on the insights your business wants to uncover. Let’s take a look at what they are and how they turn data into intelligence.

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Why data enrichment matters more than ever

Data is now an organization's most valuable commodity. The quality and depth of the information your business possesses directly influence its ability to meet compliance standards, stay efficient, anticipate change, and make informed decisions. 

However, there’s a potential drawback to having an abundance of data: It can be unwieldy. Businesses often struggle with siloed information that’s spread across systems, teams, and tools, each with its own processes and formats. This can lead to incomplete records and blind spots, making it challenging to get a clear view of business performance. 

For instance, if marketing teams lack demographic or behavioral data, they’ll find it harder to create relevant, personalized campaigns. If compliance teams don’t have the right customer information, they may struggle to meet legislation during an audit. If CIOs don’t have a single source of truth across systems, decision-making can become fragmented.

Data enrichment helps businesses avoid these potential pitfalls by filling in the gaps and giving them a unified, contextualized view of the data they possess. All of this makes it easier to identify opportunities, improve efficiency, segment smarter, and make better decisions, resulting in sustained growth.

Data enrichment vs data cleansing: Is there a difference?

There’s a common misconception in data science that data cleansing is a subset of data enrichment, but they’re actually two distinct approaches with unique objectives. 

The purpose of data cleansing is to ensure data accuracy and completeness. This could mean finding and fixing errors and inconsistencies, deleting redundant entries, and updating outdated information like phone numbers and addresses. In essence, it focuses on purifying the data you already have so it’s reliable and accurate. 

Conversely, the purpose of data enrichment is to enhance the value of cleaned data by adding external information. It expands your dataset with new attributes, like demographics and behavioural insights, to add context and depth to what you already have.

So, while data cleansing corrects and standardizes your existing data, data enrichment enhances it with new information that wasn’t originally captured within your source dataset.

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How to enrich data: A step-by-step guide

To ensure their data enrichment efforts are effective, businesses must take a systematic approach. Here’s a step-by-step guide to data enrichment to help you achieve success. 

1. Evaluate internal data

The first step is to catalog your existing internal and external data and evaluate its current state. Once you’ve located all of the information at your disposal, you need to identify its quality and completeness and determine how relevant each dataset is to your business objectives. 

This step achieves two key things:

• It reveals the gaps and opportunities in your existing dataset, and it helps define which of these data sources is worth enriching. This will help you avoid wasting resources. 
• It allows you to locate external data sources like demographic, behavioral, and psychographic information that can be used to supplement the target dataset. 

After this initial assessment, you should have a clear idea of your existing data’s limitations and the data sources that can enrich your information. ‍

2. Cleansing existing data

Next, cleanse the existing data to ensure it’s ready for use. This step removes errors, duplicates, obsolete information, and redundancies. The aim of this process is to end with high-quality data that is primed to meet the business use case for data enrichment.  ‍

3. Segmenting data

Data segmentation involves organizing your cleansed data into logical groups based on business priorities, such as customer segments or geographic regions. You should also standardize formatting, such as addresses and date structures, to ensure everything is aligned. 

Segmentation helps to categorize and describe the data you want to enrich, allowing you to identify the types of external or internal data that will add the most value to each segment in the next step. 

4. Appending and enhancing

This is the core element of data enrichment: connecting your clean, standardized dataset to external or internal data sources that add valuable context. 

First, you need to source quality data that links with whatever data repository you want to enrich. From there, you can take one of two general approaches:

• Use deterministic matching (exact identifiers like emails, addresses, or IDs) to match up records easily and precisely. 
• Use probabilistic matching (patterns or similarity scores) when deterministic matching isn’t an option. 

Modern enrichment solutions use APIs or automated connectors to draw this data continuously from CRMs or third-party services rather than importing it manually. An enrichment service can handle this step of the process if you lack internal resources.

Tip: It’s important to exercise caution and make sure that at least one common factor links any two distinct data sources. This common factor could be an email address or IP address, but you can’t enrich your data unless there is some linking factor that proves the third-party data source or internal dataset adds context to the data you want to enrich.  

5. Validate the datasets

After the integration, make sure you have a strict validation process to check that each integration is accurate, relevant, and valuable. You should also track the data flow and lineage to maintain compliance. 

6. Appending and enhancing

The final step (which completes a typical extract, transform, and load (ETL) pipeline) is to load the blended data into the system or location where it’s required for analysis. This makes the enriched data usable and accessible for end users to find better insights.

What are the benefits of data enrichment?

Here is a non-exhaustive list of the potential benefits to your business from data enrichment:

Increased likelihood of providing the right data for audits, legal proceedings and data subject requests  

For organizations managing large volumes of records within highly regulated industries, records are often needed for evidential purposes. It’s important to be able to identify and find records when needed.

Metadata that encompasses your end-to-end data landscape can help with data discovery.

Enriching records with metadata from systems external to the source system, or simply enriching typically metadata-poor content sources such as emails and google docs increases the quality of your records metadata. By increasing the quality of records’ metadata, it increases the likelihood of accurate categorization and providing the right data, at the right time for audits, legal proceedings, Freedom of Information (FOI) requests, or Data Subject Access Requests (DSAR).    

Streamlined data minimization and preservation  

Data enrichment adds context that helps businesses apply policies and processes like retention schedules more consistently. For instance, when a project manager closes a project, having enriched data makes it simpler to apply that closing date to all related records across systems.

Data minimization is important for situations like employee terminations. This typically occurs in a Human Resources Information System (HRIS), but related documents often sit in repositories like SharePoint. Enriching those documents with the HRIS termination data allows businesses to apply consistent retention rules and dispose of them defensibly when the retention period passes.  

Cost savings

Businesses around the world rely on data, but they don’t use 52% of what they have, which means they don’t know its value. With storage costs reaching $650,000 per petabyte, the cost of accumulating more data without extracting value from it continues to increase.

Data enrichment helps businesses uncover insights from this dark data, as well as identify errors and redundancies that can be removed. The result is a reduction in overall storage needs and costs.

Furthermore, part of the enrichment process is to evaluate existing data stores and discover redundancies, errors, or duplicate information. This evaluation not only primes data for enrichment but it also saves money through reduced storage space requirements.

Improved IT security

Beyond the more obvious benefits, data enrichment also has uses for improving IT security. One example is to improve threat detection by enriching an application’s security log files with additional sources of information about a user’s role, behavior, and access privileges from internal Active Directory or other Identity and Access Management databases.

More personalized customer communications

Improving customer experience and crafting meaningful customer relationships are crucial for recurring business. Data enrichment facilitates these improvements through more personalized customer communications. Armed with enriched customer data, you can more acutely predict and understand the preferences, wants, and needs of customers. Tailoring your communication strategy with this understanding drives better relationships and more recurring business.  

Enhanced operations

Ultimately, by giving businesses a more holistic, accurate view of all the data they possess, data cleansing can improve every corner of business operations:

• More effectively predict the wants and needs of customers, enabling better customer service and more personalized communication strategies to connect and engage audiences 
• Uncover patterns to make more evidence-backed decisions and respond with greater agility to market shifts and customer trends 
• Identify potential security, legal, and compliance risks proactively based on comprehensive, contextualized data 
• Automate data processes and workflow tasks to free up more time for teams to focus on strategic initiatives over tedious manual processes

All of this drives efficiency, reduces risk, and enables smarter, more profitable decision-making.

Data enrichment best practices to maximize results

Having a framework of best practices will keep your data enrichment efforts clear and focused. We’ve outlined six core principles data engineers should follow throughout the process.

1. Define your goals: Clear objectives will ensure you’re enriching the right data with the right external source. Aside from keeping your project focused, this will also save you from wasting effort, time, and money. 
2. Start with clean data: Enrichment won’t fix your data if the quality is poor. Tackle redundancies and standardize quality first. This foundation will ensure your new datasets will add value rather than amplifying the problems your dataset already has. 
3. Choose the right data sources: Quality datasets always beat quantity. Choose trustworthy sources that align with your enrichment use case, are regularly maintained, and meet compliance standards. 
4. Make data validation an ongoing process: Regularly update your datasets to maintain the relevance and accuracy of the data you’ve already enriched. Similarly, be ready to enrich your data again if new third-party data becomes available. 
5. Stay compliant: Keep compliance at the forefront of all of your data enrichment activities. To stay in line with regulations like the GDPR and CCPA, always obtain the right permissions and safeguard personally identifiable information (PII) throughout.
6. Rely on automation and AI tools: Modern data platforms, along with AI and machine learning (ML), can automate data matching and validation, improving accuracy while reducing manual effort. 

Collectively, these practices will ensure you can maintain trustworthy, valuable data that mitigates risk and supports your business outcomes. 

What data enrichment tools can do

Data enrichment tools come in many different forms. Some are simple plugins that enhance CRM data, whereas others are enterprise-grade platforms that unify and refine information across multiple systems. That said, each has the common goal of making business data more accurate and complete. Here are some key functions these tools can perform.

• Automating data collection: Data enrichment tools can automatically pull information from internal and external sources, like CRMs and HR systems, and then standardize it, saving teams time on identifying and organizing data manually. 
• Enhancing existing data: After gathering and standardizing data, enrichment solutions can locate contextual or complementary data in external databases and add missing attributes to enhance datasets. 
• Providing deeper insights: By combining and enriching data from multiple systems, enrichment tools help companies uncover trends and relationships that would otherwise be hard to spot. It delivers reliability, which can streamline the decision-making process. 
• Powering better workflows: The continuous loop of enriched data helps teams and departments work from the same, contextualized information, supporting collaboration and workflow optimization. 

The cost and time savings alone are a valid reason for businesses to invest in data enrichment tools. However, the real value is that they can transform raw information into a strategic asset autonomously, producing reliable insights consistently. 

If you’re considering enriching your data to gain a clearer view of your organization’s information, RecordPoint can help. Our solution can inventory your information and automate metadata enrichment across systems, improving governance and the overall integrity and quality of your data. 

How RecordPoint can help

RecordPoint allows you to inventory all of your data, both structured and unstructured, as part of a continuous inventory process. A big part of that process includes metadata enrichment.  

All data passes from your connected sources (e.g., SharePoint) through the RecordPoint platform’s intelligence engine. This engine detects metadata from the source system, such as data size, type, location, and author, as well as extra custom fields you may have set up in the source system.  

Source system metadata alone sometimes may not provide important context, allowing risk to creep in. RecordPoint has an External Metadata Enrichment module that adds consistent sets of additional metadata from external systems, which can be used for categorization and defining retention or disposal. It also allows you to enrich typically metadata-poor sources, such as Exchange Online, Teams, or Dropbox.

Listen to our EVP of Partners, Evangelism and Solutions Engineering, Kris Brown, explain how it works.

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RecordPoint can also enrich your records with privacy signals such as Personally Identifiable Information (PII) and Payment Card Industry (PCI). This is another way of enriching your records as it enhances existing information allowing you to better identify risk.  

If you’d like to find out more, schedule a 30-minute call today for a demo. 

FAQs

How could marketing data enrichment support my team?

Marketing data enrichment will add additional information to your customer profiles, helping marketing teams glean deeper insights into your audience. For instance, demographic data can reveal who customers are and how to segment them, while behavioural and psychographic information reveals what motivates them and how they interact with your brand. This helps teams predict customer needs and create personalized campaigns that resonate. 

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How does data enrichment lead to data quality improvement?

Data enrichment eliminates the blind spots and provides the missing context that can leave data incomplete and outdated. This makes your data altogether more useful and reliable, helping teams make smarter decisions and improve their overall operational efficiency. 

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Why does data need to be cleansed before it can be enriched?

Enrichment builds on your existing dataset, but it’s not a magic wand. If your data contains redundancies or inconsistencies, adding new information will only make these inaccuracies more apparent. Cleansing your data from the start provides a firm foundation that data enrichment can build upon.

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