Key Takeaways
- Enrich your raw data with context to unlock its full potential and enable smarter, data-driven decision-making.
- Combine data enrichment and AI for more accurate predictions, personalized insights, and proactive strategies.
- To successfully implement data enrichment, identify current data gaps, choose the right providers, and leverage APIs and AI for maximum impact.
Modern businesses strive to gain stronger insights and make better decisions, fast. In fact, 76% of organizations rank data-driven decision-making as the top priority for their data programs. And yet, 67% admit they don’t completely trust their data.
Why is that? There’s certainly more raw data than ever, but the problem is that it’s often incomplete, siloed, or missing critical context. Without real-world context, even the most sophisticated algorithms can struggle to provide you with meaningful, contextually relevant results, let alone support informed business decisions.
Ultimately, better decisions rely on trusted data – and trusted data requires data integrity. To achieve data integrity, you need to go beyond raw data and add the crucial element of context. That’s where data enrichment comes into the picture.
Enriched data delivers the context that transforms scattered information into actionable information you can depend on – which is especially important in laying a solid foundation for successful artificial intelligence (AI) initiatives. Let’s take a closer look at data enrichment, valuable use cases, and how enriched data and AI can make an impact across your business.
Data enrichment has seen 22% growth as a data integrity priority among data and analytics professionals: from 23% in 2023 to 28% in 2024. Source 2025 Outlook: Data Integrity Trends and Insights
What Is Data Enrichment?
First, we’ll define data enrichment.
Data enrichment is the process of appending relevant context from trusted external datasets or other internal systems to your internal, first-party data. This added context deepens your understanding of the factors influencing your business activities, such as surrounding neighborhoods, demographics, environmental risks, and even nearby competition.
By enriching your data, you unlock hidden insights and reveal relationships that enhance its overall value, accuracy, and usability. For example, data enrichment gives AI models richer context on factors like demographics, location, or risk. This context ultimately helps the AI produce more accurate, contextually relevant results that you can use for better decision-making.
At Precisely, our data portfolio includes six main categories to help you achieve this:
These attributes enable you to create a holistic view of locations and customers, offering insights into where they live, work, and interact with the world around them.
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Valuable Real-World Use Cases for Data Enrichment
Data enrichment delivers tangible benefits across industries. What could it help you unlock at your organization? Here are three practical use cases to help spark inspiration:
- Improving insurance underwriting with location data
An insurance company seeking to assess property risk for underwriting can enrich basic information like property size and location with data like flood zones, crime rates, and historical weather patterns.
The result: a more accurate risk profile, which leads to more exact policy premiums thanks to the reduced risks of over- or underpricing.
- Enhancing customer profiles for targeted marketing
A retail company can enrich customer data (like names, addresses, and purchase histories) with demographic data including age, income, and regional interests.
The result: enriched profiles that empower hyper-targeted marketing campaigns that resonate with specific customer segments, which drives higher engagement and sales.
- Optimizing network planning with boundary data
For telecommunications companies planning network expansions, enriched location data – including boundaries for neighborhoods and metro areas – provides actionable insights for thorough analysis.
The result: these detailed insights ensure infrastructure is deployed in a way that captures market demand while maintaining cost-effectiveness.
As data integrity becomes increasingly critical for organizations seeking to make informed decisions, data enrichment – augmenting internal data with curated third-party data – is fast emerging as a key strategy. This is especially true for companies looking to ensure that initiatives, such as AI and machine learning, are driven by relevant data and accurate location insights.” – Dan Adams, Executive Vice President and General Manager Enrich.
A Dynamic Duo: Data Enrichment and AI
Now, let’s bring AI into the picture. Data enrichment and AI each have transformative potential on their own – but together, they’re complementary technologies that can unlock even bigger possibilities for your organization.
Combining enriched datasets with AI’s analytical power helps you uncover deeper insights that improve decision-making and drive innovation.
Enhancing AI with Enriched Data
AI models thrive on high-integrity data. Enriching your datasets with trusted external data adds context and relevance, enabling AI to deliver:
- accurate predictions: enriched data reduces ambiguity, allowing AI to offer more accurate outcomes.
- personalized insights: by incorporating diverse attributes like purchasing power and location, AI can tailor its outputs to individual needs.
- bias mitigation: enrichment introduces diversity and comprehensiveness to training data, which minimizes the risks of biased outputs.
Take an e-commerce platform, for instance. By enriching customer data with details like shopping habits and geographic trends, AI can deliver highly personalized product recommendations that boost overall customer satisfaction and revenue.
3 Key Steps for Data Enrichment Success
So, where do you start if you’re ready to embrace the benefits of data enrichment? You need a strategic approach that integrates enrichment into your AI models and workflows effectively.
Follow these four steps to achieve success:
- Identify data gaps
Start by evaluating your current datasets. Are critical details – like demographics, geographic factors, or market trends – missing? Pinpointing these gaps will guide your enrichment efforts.
- Choose the right data provider
Not all data is created equal. Partner with a provider that offers high-quality, curated datasets tailored to your industry’s needs.
- Use APIs for seamless integration
Modern APIs enable you to efficiently enrich your datasets. You’re able to pull in relevant data whenever it’s needed, without the burden of storing massive datasets locally.
Looking Ahead: Better Outcomes Powered by Data Enrichment and AI
As technology advances, the synergy between data enrichment and AI will continue to evolve – which means more potential applications that will benefit your business.
Automated, contextually aware systems will be at the forefront of future innovations, and we’ll see the real-time enrichment of data for instant insights. These capabilities will be crucial for stronger decision-making in use cases for healthcare to smart cities and everything in between.
Data enrichment is the key to transforming raw information into actionable information. Combined with AI, enriched data empowers you to:
- make informed, proactive decisions
- enhance customer experiences with personalization
- drive innovation and efficiency across your operations
With benefits like that, the time to embrace these technologies is now.
The good news is that it’s never been easier to discover, connect, and use the data you need most for AI and beyond. Read our eBook Bringing Insights to Life with AI and Data Enrichment and discover how these technologies can elevate your organization’s ability to make informed, strategic decisions.
