What is Data Enrichment: Techniques, Types, Tools

In data-driven organizations, data is the most crucial resource used to formulate marketing, sales, and client service strategies. However, there are situations where the data collected by an organization may be inaccurate or outdated. This can be a huge challenge for businesses that want to leverage their data. 

This is where data enrichment comes into play. How and why? We will discuss that further with types, methods, examples, and best practices of data enrichment in this article. 

What is Data Enrichment? 

Data enrichment is the process of supplementing missing or incomplete data to enhance, refine, or improve the quality of raw data. It is a continuous process of adding new information to the existing dataset and verifying it against third-party sources to make data more reliable and accurate. 

Data enrichment starts with a quality check of the current data. If the information already present in your dataset is inconsistent, you can match it with other data sources to supplement what is missing. Once the match is deemed correct, you can use the information from the data source to enrich the existing data. 

Let's take an example to understand what data enrichment is: Suppose you have a customer list with their names and email addresses. Now, you want to send every customer personalized offers based on their interests. Data enrichment will involve adding the interest of each customer to the existing dataset by looking at recent purchases or browsing history data. This will allow you to deliver them offers with a higher chance of grabbing their attention. 

Overall, data enrichment enables you to harness the full potential of data assets by connecting different data sources and supplementing missing pieces of information.

Techniques to Perform Data Enrichment

You can perform data enrichment using multiple techniques. Let's explore some of the key ones: 

Data Appending

Data appending combines multiple data sources to create a more holistic data set. It includes sourcing internal, external, and third-party data sources such as demographic and geometric data and merging them into your dataset. Using this, you can make data more centralized for optimized analytics and accessibility. An example of this could be extracting customer data from financial systems, CRM, and marketing applications and bringing those together. 

Data Segmentation

Data segmentation is the process of dividing a data object, like a customer or product, into groups based on a common set of attributes such as age or gender. This segmentation is used to categorize and describe the data much better. Common examples of segmentation include demographic, technographic, behavioral, and psychographic segmentation.

Entity Extraction

In entity extraction, you take unstructured or semi-structured data and extract meaningful structured data from that element. With this technique, you can identify people, organizations, and places, as well as temporal expressions such as dates, currency amounts, phone numbers, and times.

Derived Attributes

Derived attributes are values that are not stored in your original dataset but can be derived from one or more fields. An example of a derived attribute is the value of a customer purchase, it is not stored directly, but you can calculate it based on purchase history and average transaction value. These attributes are useful because they contain the logic that is required for data analysis. By creating derived attributes in the ETL process, you can reduce the time it takes to create a new analysis and ensure accuracy. 

Types of Data Enrichment

There are a lot of forms of data enrichment that organizations use today. Some of the types are mentioned below: 

Geographic Enrichment

Geographical data enrichment is adding geographical information to an existing data repository. The geographic data involves postal codes, city names, geographic boundaries, and coordinates. This enriched data can be useful in several scenarios, such as deciding where to open a store, targeting customers, and even logistics planning. 

Socio-Demographic Enrichment

Demographic data enrichment adds demographic information to your dataset, such as marital status, gender, and income level. This enrichment can significantly optimize the sales and marketing strategies by allowing you to target the audience that meets certain demographic criteria. However, when you enrich demographic data, it's important to remember your end purpose in receiving a dataset relevant to your needs.

Temporal Enrichment 

This data enrichment involves including time-related information in your dataset. This could be past purchase information, the time and date of customer interactions, and so on. Temporal data enrichment is ideal for predicting future trends or understanding customer habits over time.

Behavioral Enrichment

This data enrichment aims to add information on customers' behavior. For an organization, this can include monitoring a client's past purchases, browsing patterns, or even interactions with marketing emails. Behavioral data enrichment can help you target marketing and create personalized user experiences.

Best Practices for Data Enrichment

While every enrichment process differs broadly because of the data you collect, there are common best practices that benefit all approaches. Below are some of them:

Strategically Implement Data Enrichment 

Here's an ideal way to implement the data enrichment process: define enrichment goals, identify data enrichment sources, and execute the process. While determining the goals, you must decide what information you want to add and how it will achieve your business objectives. After defining the goals, identify data sources by searching for external or internal sources that can provide updated or additional information you need. Lastly, execute data enrichment with steps and tools to collect, validate, transform, and append data from your sources to master data.

Make Consistent Processes

Building, designing, and implementing data enrichment processes should have adaptability, allowing it to be applied to various datasets. The processes should be reusable across different datasets to ensure consistency in results. For example, applying the same method of standardizing client address formats, regardless of data source, as every dataset follows a uniform structure for address. This way, you can simply reapply the function as necessary, and it also maintains uniformity in outcomes.

Scalability & Automation

You should design each data enrichment operation with scalability in mind. Therefore, every data source, resource, and timeline should be adaptable to accommodate the growth of your data over time. This can not be achieved if data enrichment processes are entirely manual, as you might face limitations in processing capabilities, and increased resources will be required with data expansion. To avoid these challenges, you should consider automating your processes using automation tools and integrating machine learning algorithms as much as possible. 

Data Enrichment is Ongoing

Data is continuously flowing into your organization's dataset, and your data environment is evolving constantly. Enrichment is critical to ensure you are getting the most value from your data sources. Therefore, you shouldn't treat data enrichment as a one-time process. It requires ongoing effort to ensure that collected data is relevant, timely, and accurate. 

Best Tools For Data Enrichment

As mentioned above, if your existing dataset lacks information, you can use third-party data sources to supplement that information. Here are three widely known data enrichment tools you can use as data sources that can help you in that process: 

Enricher.io

Specially designed for data enrichment, Enricher.io is one of the leading tools to provide this service. You can turn any domain or email into a full identity company or client profile. Enricher.io provides data enrichment solutions for your business, from basic requirements, such as data normalization, to advanced requirements, like deep company insights and predictive analytics. 

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Enricher's pricing plan is divided into Basic, Pro, and Enterprise. The basic package allows one user with ten credits, costing $279 monthly. The Pro package is allowed for three users with 50 credits and costs $879 monthly. Lastly, the Enterprise version has custom pricing. 

Clearbit

Clearbit is a marketing data engine that helps you use data to identify the people most likely to purchase your products or services. Data enrichment is one of the services Clearbit is known for. Using this tool, you can get customers' interests and demographics to focus on B2B lead data enrichment. In addition, Clearbit also allows easy integration with existing CRM systems' marketing platforms like Zapier, Hubspot, and Slack. 

The tool offers two pricing plans: Free and Growth. In the free plan, you get 25 credits each month. The Growth plan costs between $50 to $275, according to the credits you use. 

Datanyze

Datanyze is a sales and marketing company known for providing technographic data, meaning understanding the company's technology stack and usage. The tool allows you to collect client's data while browsing company websites and social media with its Chrome extension. 

Datanyze provides three pricing plans: Nyze Lite, Nyze Pro 1, and Nyze Pro 2. The Nyze Lite plan is free and provides ten monthly credits. The Nyze Pro 1 comes with a $29 monthly cost; you get 80 monthly credits with this plan. Finally, there is Nyze Pro 2, which costs $55 monthly with 160 credits. 

Streamline Data Enrichment With Airbyte

To perform data enrichment, there can be situations when you need to transfer data from multiple sources to supplement where it's required. SaaS tools like Airbyte can automate the data extraction, transformation, and loading processes from data sources to your existing dataset. With an extensive library of over 350+ pre-built connectors and transformation features such as aggregation, filtration, and manipulation, you can supplement data from any source you choose. However, if you do not find the specific connectors, you can build a custom one within 10 minutes using the Connector Development Kit. Automate the data replication process, and sign up for Airbyte today if you don't already have an account.

Conclusion

The above guide briefly mentions everything related to data enrichment, including its types, best practices, and tools. For organizations that aim to leverage data assets, data enrichment is necessary. It is a strategic tool to transform raw data into comprehensive, insightful assets to make data-driven decisions. 

By following the best practices mentioned above and using tools like Airbyte to automate the ETL process for data enrichment, you can harness the full potential of your data.