Maximizing Your Data’s Potential: Best Practices for Streamlining Data Enrichment

Key Takeaways:

• Data enrichment is the process of appending your first-party data with contextually rich third-party data, enabling you to make more data-driven decisions.
• Traditionally, data enrichment can be lengthy and expensive, but implementing best practices for evaluating datasets and working with a data provider that offers data delivery options fit for your needs can accelerate time to value while reducing costs.
• Third-party data should be relevant, consistent, accessible, and trustworthy.

Organizations generate and use a staggering amount of data that details their operations, products, and customers, but alone, this raw data isn’t sufficient for optimizing decision-making. It doesn’t provide the full picture needed to make more informed business decisions, as it lacks relevant information about the places, competitors, people, risks, behaviors, and other key factors.

Simply put, it’s challenging to derive meaningful, actionable insights from first-party data without the context that external data sources provide.

But despite clear benefits, the process of combining proprietary data with external data sources can be slow and costly. Fortunately, it doesn’t have to be. The common challenges that delay time to value can be avoided by evaluating and selecting third-party datasets with the right criteria. Using these best practices for sourcing and integrating data, organizations can streamline data enrichment and rapidly unlock the full potential of their first-party data.

What is Data Enrichment?

Data enrichment refers to the process of enhancing your first-party data by adding supplemental information from external, often third-party, sources. This additional context transforms your data into a more valuable resource, improving its usability and helping you make more informed decisions.

For example, a retail company may collect extensive data about its customers’ purchase history but lack insights into external factors that influence buying behavior, like local economic conditions, household income, or social trends. By integrating third-party data, this company can enhance its understanding of customer preferences, optimize marketing campaigns, improve site selection, and forecast future sales more accurately.

Ultimately, data enrichment expands an organization’s perspective, enabling the discovery of key insights and critical relationships in the data that couldn’t be identified with first-party data alone.

Common Data Enrichment Challenges

Traditionally, data enrichment has been challenging for some organizations to do efficiently. Depending on their needs, infrastructure, and the external datasets they source, organizations can encounter obstacles with onboarding and integrating third-party data, leading to extended time to value and increased cost.

In the 2025 Outlook: Data Integrity Trends and Insights report, survey respondents were asked about the main difficulties they encounter when working with third-party datasets. The top answer? Cost (50%). Data formatting issues took second place (47%).

Enrichment will only provide value once the third-party data has been fully onboarded and integrated, but the process of ingesting, transforming, and storing the data can be resource-intensive and complex. As such the cost of onboarding data can sometime be even higher than the initial cost to license it. Potential delays and costs are further increased if there are issues with data formatting that require additional time and effort to resolve.

Cost and formatting are the two most common challenges, but organizations may also need to address issues related to data quality, coverage, delivery and more. However, these difficulties are by no means insurmountable or inevitable. By following best practices for sourcing and evaluating third-party data, you can sidestep these pitfalls and enrich your data with ease.

Best Practices for Streamlining Data Enrichment

To tackle common challenges and ensure efficient enrichment, third-party data should meet certain standards. Here are four key criteria to look for when sourcing and evaluating datasets – along with some questions you can ask to guide you:

1. Relevant: Data should be directly aligned to the business goals and challenges that you need to address. Linking third-party data with your first-party data often requires tedious, manual work, so focusing only on the data that’s relevant to your needs will accelerate the process.

Ask yourself:

• Do the data attributes provide the coverage needed?
• Does the data fully align with your business goals?
• Is data complete across pertinent geographies?

 2. Consistent: Data should have uniform quality and structure. These aspects can often vary from vendor to vendor, or even across datasets from the same vendor – making it challenging to seamlessly integrate.

Ask yourself:

1. Is the data format standardized?
2. How will data quality be monitored?
3. Will it be challenging to transform and normalize the data for analysis?

3. Accessible: Data should be easy to obtain, retrieve, and work with. It will require significantly more time and money to integrate if it is provided in a format that is difficult to access or if it resides in a system that is incompatible with the tools used by your business.

Ask yourself:

• Is the data compatible with existing systems?
• How will data be stored (on-prem, cloud, hybrid)?
• What’s the process for delivering data, ingesting it, and making updates?

4. Trustworthy: Data should be accurate, consistent, complete, transparent, up-to-date, and sourced from reputable and reliable sources. It’s critical to check these boxes – after all, trusted data is the foundation of more informed decisions.

Ask yourself:

1. How is the data sourced and how frequently is it updated?
2. Is the provider transparent about their data? Can it be sampled?
3. How are accuracy, precision, and data quality measured?

Keeping these criteria in mind will help you find a data partner you can depend on throughout your data enrichment journey.

2025 Outlook: Essential Data Integrity Insights

What’s trending in trusted data and AI readiness for 2025? The results are in!

Read the report

A Holistic Approach to Data Enrichment

With trusted third-party data sources that meet the criteria outlined above, you can confidently and quickly supplement your existing data with additional attributes that meet your business needs. At Precisely, our robust, always up-to-date data portfolio includes thousands of data attributes and hundreds of datasets, spanning six categories:

1. Boundaries
2. Demographics
3. Points of Interest
4. Streets
5. Risk
6. Address and Property

Whatever your use case, these datasets enable you to have a more holistic view of perspective of your data, powering more informed decision-making. This data is also accessible wherever you need it – whether on-prem/private cloud, or through cloud-native capabilities, cloud APIs, or cloud SaaS services.

Beyond providing comprehensive, curated datasets, Precisely also makes it easy to link data with our connected ID system, accelerating the enrichment process.

The PreciselyID is a unique and persistent identifier that facilitates seamlessly connecting data attributes to address data. It acts like a fingerprint for addressable locations, providing an accurate and consistent view of addresses, and is integrated across our entire network of connected datasets.

Moreover, it is automatically appended to address data that uses our geo addressing solutions. This means that organizations can rapidly and easily enrich their raw address data by leveraging the interconnectivity the PreciselyID provides.

The PreciselyID is also at the heart of the new Precisely Data Graph API, which allows organizations to access, explore, and harness our network of connected enrichment data with greater ease than ever before. This API delivers the exact data attributes needed through one access point with just a single query, cutting through the complexities of integrating third-party data and accelerating data discovery.

In tandem, the connected data, delivery options, and tools Precisely provides allow you to enrich your data with unmatched efficiency so that you can enhance the value and usability of your data.

Travelers: A Real-World Data Enrichment Success Story

What does rapid, efficient enrichment enable in practice? Here’s a real-world example of how streamlined data enrichment can accelerate decision-making and unlock better business outcomes.

Travelers, an industry-leading insurance company, wanted to determine if extending their hours of operation could positively impact their sales.

To find out, they needed a way to easily enrich their address data with additional attributes to conduct the analysis. The PreciselyID seamlessly connected their data, allowing Travelers to integrate their hours of operation data with their business addresses in just a few hours – rather than days or even weeks.

This was a massive time-saver for their data engineering team. Without tedious onboarding, data linkage, and integration work, they could test their hypotheses more quickly and make improvements without delay.

With rapid, efficient data enrichment, Travelers can now answer questions as they arise, enabling them to make more immediate data-driven decisions while saving valuable time and money.

Beyond insurance, the benefits of data enrichment can be seen across industries:

• In retail, enriched data helps businesses personalize marketing efforts, predict consumer behavior, and optimize inventory management.
• In financial services, enriched customer data aids in risk assessment, fraud detection, and compliance reporting.
• In healthcare, data enrichment enhances patient records by incorporating social determinants of health, leading to better outcomes and more targeted treatments.

Maximize the Potential of Your Data

Enrichment is essential to unlock the full potential of your data, but it doesn’t have to be a complex, lengthy process. By following best practices, you can streamline data enrichment and add valuable context to your data without extended time to value.

Once your data is imbued with context, you’ll be ready to make better decisions based on more accurate analyses. Find out what’s trending in data enrichment and the data integrity landscape at large – the 2025 Outlook: Data Integrity Trends and Insights report has the insights you need to build a strategy for success.