You’re probably using data enrichment daily without knowing it.
Google’s autocomplete feature, for instance, works with it: It takes raw data (the letters you type in) and enriches it to match it with an enormous database of (almost) all possible words. The result? A smarter tool that improves user experience.
But did you know about data enrichment (a.k.a. data augmentation or appending) is at the core of numerous online businesses these days? And did you know it’s easier than ever to get started with it?
In this post, we’ll go over the key concepts of data enrichment in a general context – as well as show why it’s such a must-have feature for online fraud prevention.
What Is Data Enrichment?
Data enrichment is a process that takes raw data points and merges them with similar data points in a larger database. The database can be internal or maintained by a third party service, or even a combination of open (OSINT) data and internal sources, or a combination of databases.
Through data enrichment, it becomes possible to gain deeper insights into individuals or entities, starting from just a single data point. For instance, by using someone’s email address or phone number, one can uncover additional information about them.
This technique is widely utilized across various industries, enabling companies to combat fraud more effectively, source alternative data for credit scoring purposes, identify high-value customers, and more.
Enter an email address or phone number below to observe how data enrichment draws from over 90 online platforms and social media channels, providing a comprehensive profile of the individual along with a risk assessment score.
Why Is Data Enrichment Important?
Data enrichment plays a vital role in obtaining additional insights about users without requiring extra information from them.
For instance, verifying someone’s identity can be as simple as asking for their email address. This approach reduces risk without adding user friction or impeding the user experience.
In the business domain, enhanced access to data facilitates more informed decision-making, which proves especially advantageous for companies navigating scenarios like:
- Entering a new market
- Keeping pace with industry trends
- Launching a new venture (e.g., transitioning from brick-and-mortar to online)
- Minimizing customer friction by collecting only essential information
- Enhancing targeting strategies
- Mitigating fraud risks
What Are the Benefits of Data Enrichment?
Data enrichment stands as a crucial competitive edge due to its ability to:
- Enhance understanding of users: Vital for risk and fraud mitigation.
- Minimize user friction: Eliminates the need for extensive form filling, allowing checks to be conducted seamlessly in the background.
- Decrease churn: Streamlines the user journey, reducing instances like cart abandonment.
- Enable real-time checks: A proficient data enrichment tool should provide instantaneous results.
- Facilitate expedited manual reviews: Unsure about a medium-risk transaction? Utilize a data enrichment module to enhance decision-making.
Examples of Businesses Using Data Enrichment
Automating data enrichment lies at the heart of the contemporary digital landscape, enabling businesses across various industries to thrive. Here are seven illustrative examples:
Lending
Credit scoring heavily relies on data enrichment, enabling banks and lending providers to access third-party or alternative databases for creating comprehensive customer profiles. This process is essential for identifying potential defaulting customers and effectively underwriting risks, especially in digital transactions where initial customer information is limited.
Fraud Prevention
Similarly, online businesses combat fraud by leveraging data enrichment to develop robust user profiles. By enriching single data points such as email addresses, devices used, or IP addresses, businesses can construct detailed user profiles, significantly reducing fraud rates over time. For instance, an email lookup tool can automate searches to gather precise information, including social media connections, domain validity, and age of the address, bolstering fraud prevention efforts.
Insurance
Insurance providers utilize data enrichment to categorize customers based on various data points and refine specific datasets to assess risk. With enriched data, insurance companies can offer tailored insurance products and deals based on individual customer risk profiles, enhancing segmentation and targeting strategies for improved efficiency.
Marketing
Data enrichment enables precise customer segmentation, empowering marketing companies to deliver more relevant offers and advertisements to their target audiences. Through data enrichment, companies gather insights into customer behavior and preferences, facilitating targeted marketing campaigns for enhanced engagement and conversion rates.
Retail
Retail giants like Amazon leverage data enrichment to offer personalized product recommendations, such as suggesting similar items to customers based on browsing history or past purchases. By aggregating and analyzing customer data, retailers can generate meaningful insights to drive upselling and improve customer experience, highlighting the pivotal role of data enrichment in modern retailing strategies.
How to Choose Data Enrichment for Your Business
Fortunately, an increasing number of companies offer data enrichment services nowadays. However, the challenge lies in selecting a provider that aligns with your specific requirements. Here are several factors to consider:
- Manual or automated: Determine whether you need manual enrichment for individual queries or automated solutions for large-scale operations. While some options excel for singular inquiries, others, such as third-party data providers or aggregators, are better suited for extensive data processing.
- Integration: Decide whether you prefer working through an API or purchasing a database to automate searches internally. Custom integrations may streamline processes for developers, although they might not always be available.
- Data quality and legality: Assess the freshness and legality of the data provided. Ensure that the company adheres to legal requirements, such as GDPR compliance, for data protection.
- Pricing: While pricing may vary slightly, most third-party data enrichment companies charge a micro fee for each check. Compare pricing models to find the most suitable option for your budget.
- Middleware options: Lastly, consider middleware solutions, which I’ll elaborate on further below.
Does Data Appending Affect Privacy Policies?
That’s an important consideration, particularly given the heightened focus on data privacy in recent years. With the persistent occurrence of data breaches affecting major organizations, governments have implemented stringent measures to safeguard user data, exemplified by regulations like GDPR and ISO 27001.
To ensure compliance with these regulations, it’s crucial that your data enrichment service accesses data from open and social sources. Failing to adhere to this guideline could expose your organization to regulatory violations in your region, potentially resulting in fines and unnecessary legal entanglements.
How Does Machine Learning Complete Data Enriching?
Obtaining enriched data is one aspect, while interpreting it is another challenge altogether. It’s essential to recognize that without proper training in data science, individuals are prone to making suboptimal decisions when confronted with large volumes of data.
Here’s where understanding the role of machine learning becomes valuable. Acting as a mediator between the influx of data and human analysts, machine learning technology can facilitate the comprehension of complex datasets.
For instance, if your data enrichment service offers a scoring mechanism, it’s imperative to grasp its underlying mechanisms and model construction. Eventually, adjustments and oversight may be necessary.
This highlights the fundamental distinction between opaque, or blackbox systems, and transparent, or whitebox systems. While the former provides only scores without insight into the underlying rules, the latter offers transparency through human-readable explanations. Merely receiving a score may leave you feeling at the mercy of algorithms without a comprehensive understanding of their functioning.
