Large language models have an impressive ability to generate human-like content, but they also run the risk of generating confusing or inaccurate responses. In some cases, LLM responses can be harmful, biased, or even nonsensical. The leading cause? Poor data quality. 

According to a 2024 poll of IT leaders by Gartner, poor data quality is the top obstacle companies face in their generative AI (GenAI) initiatives. This is especially true for organizations that use LLMs to interact with internal sources of knowledge, such as customer data, financial transactions, or private healthcare information. 

Fortunately, there are a number of solutions to poor data quality. In this article, we study one critical strategy: data enrichment. The data enrichment process provides contextualizing data to your retrieval-augmented generation (RAG) system to help LLMs work more efficiently. 

What is Data Enrichment?

Data enrichment is the process of enhancing your existing data by adding new information from internal and external sources. It’s a key part of artificial intelligence (AI) systems that improves the quality and value of your data, making it more useful for decision-making.

By combining your internal data with third-party data sources, you gain a more complete view of your customers, operations, and markets.

Suppose your organization uses an acronym that the LLM doesn’t understand by default or understands it as something else. For example, a LLM might incorrectly interpret the acronym “RAG” in the article title to mean the Red, Amber and Green color system used for traffic lights and data visualizations. 

In these cases, the LLM will fail to understand human’ queries and respond inappropriately. With robust data enrichment, you can provide the context the LLM needs to answer questions properly. 

How Data Enrichment Works

You enrich data by integrating additional data from internal and external data sources. This could include demographic, behavioral, business intelligence, or geographic information that enhances your internal records. Third-party data providers can offer valuable insights that you might not have access to internally.

Usually, this requires the help of a data enrichment platform like Shelf. Shelf’s data enrichment software allows you to connect from any source to a visibility layer to identify risks in your unstructured data. The Shelf enrichment features will help you understand the size and impact of your unstructured data gaps and identify the root causes of hallucinations and inaccurate answers.

Structured data enrichment sources include row-and-column spreadsheets and external service providers that provide structured data via an API or CSV exports. For example, ZoomInfo provides contact information for target job titles to update where your internal data may be incomplete or outdated. 

Equally important is data enrichment from files, folders, and Software as a Service (SaaS) applications. For example, your organization may have a library of PDFs, slides, emails, Slack messages, etc., any of which can offer important data enrichment especially to inform and update important context about your organization. 

Benefits of Data Enrichment

Data enrichment efforts offer several key benefits that improve the value and effectiveness of your data, making it more actionable for decision-making. Here’s a more detailed breakdown of each benefit:

Gain Deeper Insights into Customers or Markets

By enriching your internal data with third-party data, you can uncover patterns and trends that were previously hidden. External data sources like demographic information, social data, or market trends give you a broader perspective. This allows you to:

  • Understand the needs and behaviors of your ideal customers.
  • Identify new market opportunities or potential customer segments.
  • Predict future trends with greater accuracy.

Improve the Accuracy of Predictions and Models

When you rely solely on internal data, your predictive models may miss critical factors that influence outcomes. Enriching data with internal sources and external sources helps you:

  • Reduce data blind spots, leading to more reliable predictions and actionable insights.
  • Refine machine learning (ML) models with a more complete dataset, boosting performance.
  • Make better data-driven business decisions by having a fuller context.

Personalize Marketing and Communications

Personalization is key to improving engagement with your audience. Data enrichment platforms enhance your customer profiles, making it easier to create personalized messaging and campaigns. At the same time, data enrichment can help update customer profiles such as showing that a family recently moved to a new home. 

5 Point RAG Strategy Guide to Prevent Hallucinations & Bad Answers This guide designed to help teams working on GenAI Initiatives gives you five actionable strategies for RAG pipelines that will improve answer quality and prevent hallucinations.

With richer, more complete data, you can:

  • Segment your audience based on detailed demographics and behavior.
  • Create personalized marketing campaigns that resonate with specific groups.
  • Improve customer retention by delivering relevant content and offers based on external insights.

6 Data Enrichment Techniques

Let’s explore six techniques to enrich your unstructured data and semi-structured data with contextualizing information. Add these techniques to your enrichment workflow to improve the overall quality of your data.

1. Data Appending

Data appending is a technique used to enrich your existing datasets by adding new, relevant information from external sources. This process fills in the gaps where your internal data is incomplete or outdated. It enhances your records by appending missing fields, such as customer contact details, demographics, or behavioral information, allowing you to create more comprehensive data profiles.

How Data Appending Works

You start with a dataset that has missing or incomplete fields. External data providers are then used to match your data with their records and append the missing or outdated information. This helps you update and expand your data, keeping it current and relevant.

Benefits of Data Appending

Data appending increases the accuracy of customer profiles by filling in missing details. It keeps your data up to date, reducing the chances of working with outdated information. It also improves segmentation and personalization efforts by adding more data points to your records.

Practical Examples of Data Appending

  • Appending Contact Information: If you have a list of customers with missing or outdated phone numbers or email addresses, you can append the latest contact details using external databases.
  • Adding Demographic Data: Enhance customer profiles by appending age, income, and household size information, helping you better segment your audience.
  • Appending Social Media Data: Add social media handles or behavioral patterns to your records to understand how customers engage with your brand online.
  • Geographical Data Appending: For businesses with location-based services, appending geographic data such as postal codes or regions helps in targeting customers more precisely.

Data appending ensures that your data remains rich and useful, giving you a more complete view for decision-making and strategy development. With regulations on protecting individual non-public data, a key part of data appending is to ensure that the data provider meets regulations applicable for your geographic region. 

2. Data Segmentation

Data segmentation is a technique that involves dividing your enriched dataset into smaller, more meaningful groups based on specific criteria. This allows you to target and analyze different segments of your data separately, leading to more precise insights and tailored strategies. By organizing your data into segments, you can focus on patterns, behaviors, or characteristics relevant to each group.

How Data Segmentation Works

You analyze your data and define categories or criteria to split it into segments. Segmentation can be based on demographics, behaviors, geographics, or any other relevant data points. Once the data is segmented, you can treat each group uniquely, leading to more targeted approaches in marketing, decision-making, and analysis.

Benefits of Data Segmentation

Data segmentation enhances personalization by allowing you to tailor messages and offers to specific groups, improves analysis by focusing on the unique characteristics of each segment, and increases efficiency in targeting efforts. It also provides clearer insights into customer or market behavior across different segments.

Practical Examples of Data Segmentation

  • Demographic Segmentation: Divide your customer base into groups based on age, gender, or income level to target marketing campaigns more effectively.
  • Behavioral Segmentation: Segment customers based on purchasing behaviors, like frequent buyers or one-time customers, to adjust offers and promotions accordingly.
  • Geographical Segmentation: Group your data by geographic location, such as region, city, or country, to focus on regional preferences or market trends.
  • Industry-Based Segmentation: If you serve multiple industries, you can segment your data by industry type, allowing for industry-specific outreach or product development.

Data segmentation helps you turn a large, general dataset into focused and actionable insights, leading to more strategic decisions and improved outcomes across your business efforts.

One aspect of data segmentation in the AI age is to apply Responsible AI. For example, it can be appropriate to segment your database by size of company or geographic location. It becomes problematic in most business use cases to segment by ethnicity as this can lead to bias. 

3. Derived Attributes

Derived attributes are new data points created by transforming or combining existing data fields during the enrichment process. This technique involves calculating or inferring additional information from your current dataset, adding layers of insight without needing external data sources. 

Derived attributes can be especially useful for analysis, modeling, and decision-making, as they reveal hidden patterns or trends not immediately visible in raw data.

Six Data Enrichment Strategies That Optimize RAG Performance for GenAI Readiness: image 3

How Derived Attributes Work

Derived attributes are generated through various operations such as arithmetic calculations, logical operations, or data transformations. For example, you might combine two or more data fields, such as sales figures and customer location, to create a new metric like “average sales per region.”

Benefits of Derived Attributes

Derived attributes add more context to your existing data, allowing for deeper analysis and enable you to create custom metrics and KPIs that better suit your business needs. They also reveal hidden patterns or relationships within your dataset and reduce reliance on external data sources by deriving new insights from what you already have.

Practical Examples of Derived Attributes

  • Customer Lifetime Value (CLV): Derive a new attribute by calculating the total revenue generated from a customer over their entire relationship with your business.
  • Sales Growth Rate: Create a new field showing the percentage increase or decrease in sales over time by comparing current sales with historical data.
  • Average Order Value (AOV): Combine total revenue and total orders to generate a new attribute that shows the average amount spent per order.
  • Engagement Score: Combine various customer interaction metrics (such as email opens, website visits, and social media engagement) into a single derived attribute that measures overall engagement.

Derived attributes allow you to turn basic data points into more meaningful insights, helping you gain a deeper understanding of performance, behavior, and trends.

For example, if customers in Japan tend to buy from your organizations for decades (and therefore have a higher CLV), versus some other geographies where many customers often churn after a year or two, that data enrichment can have important business outcomes such as for example to prioritize a bit more the Japan customers for customer loyalty offers. 

4. Data Manipulation

Data manipulation refers to the process of adjusting, organizing, or transforming data to make it more useful for analysis and decision-making. This technique involves reshaping, filtering, or modifying raw data to fit specific needs, improving its quality and relevance. 

Through data manipulation, you can correct errors, streamline datasets, and make the information easier to interpret.

How Data Manipulation Works

Data manipulation can be performed through various methods, such as:

  • Filtering: Removing unnecessary or irrelevant data.
  • Sorting: Organizing data based on a specific order, such as alphabetical or numerical.
  • Transforming: Changing the format of data (e.g., converting text to numbers or aggregating data).
  • Cleaning: Fixing errors or inconsistencies, such as removing duplicates or correcting inaccurate entries.

Benefits of Data Manipulation

  • Data manipulation ensures that your data is accurate, clean, and ready for analysis and allows for more detailed insights by transforming data into more useful formats. It improves the efficiency of data analysis by streamlining datasets and removing irrelevant information and enhances the quality of decision-making by presenting the data in a clearer, more organized way.

Practical Examples of Data Manipulation

  • Data Cleaning: Remove duplicate records, correct typos, or fill in missing data to ensure accuracy before running analysis.
  • Aggregating Data: Summarize large datasets by calculating totals, averages, or other metrics to simplify analysis.
  • Converting Data Formats: Change dates stored as text into date formats or convert currency values to a consistent format.
  • Filtering: Narrow down a large dataset to only show records that meet specific criteria, such as sales data from a particular region or time period.

Data manipulation is a foundational technique that ensures your data is clean, accurate, and organized, allowing for better analysis and informed decision-making.

For more on data cleaning, refer to the Shelf.io blog “How Implementing Data Cleaning Can Boost AI Model Accuracy”.

5. Entity Extraction

Entity extraction is a technique used to identify and classify key pieces of information — known as entities — within a larger set of unstructured data, such as text documents or emails. 

These entities typically include names, dates, locations, organizations, or any other defined category of data that is relevant to your analysis. Entity extraction helps you make sense of unstructured data by pulling out valuable information and organizing it into structured formats.

How Entity Extraction Works

Entity extraction uses algorithms and natural language processing (NLP) techniques to scan unstructured text and identify predefined entities. The process involves:

  • Identifying Entities: Recognizing specific types of information, such as names, places, or dates, within a dataset.
  • Classifying Entities: Categorizing these entities based on their type (e.g., a person’s name, a company’s name, or a geographical location).
  • Structuring the Data: Converting the unstructured data into a structured format, making it easier to analyze and integrate with other datasets.

Benefits of Entity Extraction

Entity extraction simplifies the process of analyzing large volumes of unstructured data. It makes unstructured data more usable by converting it into structured forms and helps you identify critical information quickly, allowing for faster decision-making. It also supports automation in workflows by automatically tagging and organizing important entities.

Practical Examples of Entity Extraction

  • Customer Feedback Analysis: Extract customer names, product references, and dates from unstructured feedback to organize and analyze trends.
  • Document Processing: Automatically extract key details like company names, addresses, and dates from contracts or legal documents to streamline data entry processes.
  • News and Media Monitoring: Extract names of organizations, locations, and individuals from articles or reports to track mentions and build summaries.
  • Email Parsing: Identify and extract key information, such as dates, names, and action items, from emails to automate task management.

Entity extraction is a powerful tool for turning unstructured text into structured, actionable data, allowing you to efficiently analyze large datasets and make more informed decisions.

6. Data Categorization

Data categorization is the process of organizing data into predefined categories or groups based on shared characteristics or attributes. This technique helps you streamline large datasets, making it easier to analyze, interpret, and apply the data. 

By assigning labels or categories to data points, you can quickly identify patterns, relationships, or trends within your data, improving both clarity and usability.

How Data Categorization Works

Data categorization typically involves defining a set of categories that are relevant to your goals, then systematically sorting or labeling data points according to those categories. This can be done manually, but automation tools and algorithms are often used to speed up the process, especially for large datasets.

Benefits of Data Categorization

Data categorization helps you manage and retrieve data more efficiently by structuring it into clear categories. It focuses on specific categories or segments to identify trends or patterns relevant to your business needs. This makes it easier to create reports, charts, or dashboards by grouping data into understandable sections. 

Most importantly, organizing data into categories clarifies relationships and insights, allowing for more informed data-driven decision-making.

Practical Examples of Data Categorization

  • Customer Segmentation: Categorize customers by their purchase history, demographics, or engagement level to tailor marketing campaigns and improve customer service.
  • Product Classification: Group products by type, price range, or sales volume to better understand product performance and inventory needs.
  • Support Ticket Categorization: Assign incoming support tickets to categories such as technical issues, billing, or customer feedback to prioritize and address them more efficiently.
  • Content Categorization: Organize content in a content management system (CMS) such as an external website or corporate Intranet by tags or categories like blog posts, tutorials, or case studies to make information easier to find.

Data categorization turns complex and varied datasets into structured, actionable insights, helping you and your team focus on what matters most.

Shelf AI automation includes humans in the loop to validate and apply data categories. This overcomes a key obstacle that organizations have faced when they install a data catalog or similar system but it has limited value because human employees do not validate and apply the data categories. 

Beyond Data Enrichment

Natural language processing (NLP) techniques such as Named Entity Recognition, keyword generation, topic modeling, and link contextualization are indispensable for enhancing the performance and effectiveness of RAG. 

By providing contextualizing information and structuring raw data, these strategies enable RAG systems to generate more accurate, context-aware responses, thereby improving user experiences and the utility of AI systems. 
As organizations harness the power of generative AI (GenAI), these methodologies are essential enrichment tools for unlocking the full potential of these technologies and driving innovation across various domains.