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Without realizing it, you most likely use data enrichment on a daily basis.
For example, Google’s autocomplete feature works with it by enriching the raw data—the letters you type in—with a vast database of (nearly) all potential words. What was the outcome? An intelligent tool that enhances the user experience.
However, did you know that the foundation of many modern web enterprises is data enrichment, often known as data augmentation or appending? Did you know that getting started with it is now simpler than ever?
In this article, we’ll discuss the fundamental ideas of data enrichment in a broad sense and demonstrate why it’s such an essential component for preventing online fraud.
What Is Data Enrichment?
The practice of combining unprocessed data points with related data points from a bigger database is known as data enrichment.
The database may be kept up to date by an external service, an internal source, or even a combination of databases and open-source intelligence (OSINT) data.
In this manner, we can start with a single data point and learn more about someone or something.
Through the use of a data enrichment company, Sterling helps companies uncover their most valuable customers, locate alternative credit rating data, and efficiently fight fraud.
Why Is Data Enrichment Important?
Because it allows you to learn more about your users without having to ask them for further information, data enrichment is crucial.
Asking someone for their email address, for example, is a simple way to confirm their identification. It lessens risk without making users more difficult to use or degrading their experience.
Businesses can make more intelligent judgments when they have access to more data. This is particularly true for businesses that don’t have access to important data, like when:
- Moving to a new market
- Trying to keep up with trends
- Starting a new business (like moving from brick and mortar to online)
- Trying to reduce customer friction by only collecting the essential info
- Looking to improve targeting
- Reducing fraud rates
What Are the Benefits of Data Enrichment?
A major competitive advantage is data enrichment since it facilitates:
- Gain more knowledge about your users: doing so will help to lower risk and fraud.
- Eliminate user friction by not requiring users to fill out a large number of fields: The user journey won’t be interrupted if the tests are conducted in the background.
- Minimize churn: Adding roadblocks to the user’s path often results in churn, as seen in cases like cart abandonment.
- Real-time checks: The output of a quality data enrichment tool ought to be available instantly.
- Review transactions with a medium risk more quickly: Uncertain? Use a data enrichment module on the data to help you make a more informed choice.
Examples of Businesses Using Data Enrichment
The foundation of the contemporary digital world is automated data enrichment. Actually, the following seven instances show how it is the process that makes it possible for firms to exist in many verticals:
Lending
Data enrichment is the foundation of credit scoring. By using alternative or third-party databases, banks and loan providers can build a comprehensive picture of the clients they serve—and perhaps weed out those who would default.
Indeed, without data enrichment, underwriting risk would be difficult as a whole, particularly when operating digitally. Trust alone won’t cut it, as you are initially in the dark about your potential consumers. You should try to arm yourself against shady characters by learning as much as you can.
Fraud prevention
Similar to this, by improving user profiles, internet companies can lower their fraud rates. To get a complete image of a user, one piece of data, such as their IP address, device utilized, or email address, can be enhanced.
Our email analysis module or email lookup tool is a prime example.
When a user inputs their email address during the onboarding process, we automatically conduct a search to gather extremely specific information, such as how old the address is, whether it is connected to social media sites, and so on.
As you may expect, over time, that straightforward procedure can significantly lower fraud rates.
Insurance
Insurance companies frequently use a variety of data points to classify their clients and enhance a certain dataset. Once they have all the information required, they can offer pertinent insurance offers or products depending on the customer’s risk.
In this way, data enrichment serves as a segmentation and targeting tool at the same time. It can help you improve your business procedures so that you can work more productively.
Marketing
This is another instance of how data enrichment improves the accuracy of client segmentation. By getting to know their audiences, marketing organizations are able to target individuals with more relevant offers and advertisements.
Have you ever wondered why so many businesses track your online footprint using cookies? Data enrichment is the solution; advertisers can use it directly or broker it for resale to other businesses.
Retail
The tool on Amazon that recommends related products serves as the clearest example of this. Amazon may only have basic information about you—the website you are now viewing—but by connecting it to their massive database of past consumer purchases, they are able to make astute recommendations for additional purchases.
The retail significant advantage over rivals in the internet space can be attributed in part to their capacity to compile data and produce insightful analyses for their objectives (upsell).
However, it should be noted that big data is becoming an essential component of the majority of internet retailers’ business models.
How to Choose Data Enrichment for Your Business
The good news is that an increasing number of businesses are offering data enrichment these days. The difficult part is figuring out which one truly suits your needs. So, let’s think about the following:
Manual or automated?
Certain data enrichment options are quite effective for particular queries. For example, if all you’re interested in learning more about is the strange loan applicant.
You will have to collaborate with a third-party data provider or aggregator for large-scale operations. This brings up the subject of the following:
Integration:
Would you like to use an API to work? Alternatively, buy the database and do the search yourself. A single point of contact simplifies things for developers when creating bespoke integrations, but it’s not always available.
Data quality and legality:
How recent is the information you are gathering? Does the business providing it also comply with regulatory standards such as the GDPR on data protection?
Pricing:
Since the majority of third-party data enrichment firms charge a tiny cost for each check, there shouldn’t be a lot of fluctuation in this case.
Does Data Appending Affect Privacy Policies?
That’s a valid question, particularly in light of the current attention that data privacy has received. Governments were forced to intervene and enact stringent measures to safeguard user data, such as the GDRP or ISO 27001, because the number of data breaches experienced by large corporations is increasing.
You should now source the data for your data enrichment service from open and social sources to make sure you abide by both of these rules.
If you disregard this advice, you run the danger of violating local laws in your area, which could result in penalties and pointless court disputes.
How Does Machine Learning Complete Data Enriching?
It’s one thing to obtain the enriched data. Its interpretation is different. In fact, one thing to keep in mind is that looking at a lot of data increases the likelihood of making bad decisions unless you are a skilled data scientist.
It’s iportant to comprehend the potential application of machine learning in this situation. As a mediator between the torrent of data you are about to receive and the knowledgeable persons who will interpret it, the technology is quite effective.
Because the models will eventually need to be adjusted and supervised, it is crucial to comprehend how your data enrichment service’s scoring system functions and how it is constructed.
This is the main distinction between a whitebox system, which allows you to view the rules in plain language, and an opaque system, sometimes known as a Blackbox system.
If all you get is the score, you may feel helpless in the face of the algorithms and never truly understand how things operate.
Final Thoughts
Choosing the right data enrichment service is crucial, considering factors like automation, integration, data quality, and compliance with privacy regulations. Leveraging machine learning further amplifies the benefits of data enrichment by providing actionable insights from large data sets.
As you explore the potential of data enrichment for your business, remember that a well-implemented data strategy can be the key to staying competitive and innovative in today’s data-driven world. Take the next step in transforming your business by harnessing the power of data enrichment.
