What is Data Enrichment?: 4 Critical Aspects

Organizations collect a colossal amount of data to garner insights and make better decisions for business growth. However, gathered data by companies may be inaccurate, have missing gaps, or can be outdated. This becomes a bottleneck for companies that want to understand their audience better to formulate the right products and services. Therefore, relying solely on in-house information might not be enough for companies to differentiate themselves in the competitive landscape.

As a result, organizations collect data from multiple sources to enrich their existing information to offer better customer-oriented services. Data Enrichment helps organizations transform prepossessed data into a comprehensive profile that can support data analytics to obtain in-depth insights. With Data Enrichment, businesses can refine, improve, and reuse raw data for accomplishing business goals.

This article will help you understand what Data Enrichment is, why it’s necessary, and what its types are.

Table of Contents

• What is Data Enrichment?
• Why Data Enrichment is Important?
• What are the uses of Data Enrichment?
• What are the Types of Data Enrichment?
• What are the Methods for Data Enrichment?
• What are the Data Enrichment Techniques?
• The Process And Key Strategies for Data Enrichment
  • Ensuring Higher Data Quality
  • Creating a Robust ETL Pipeline
  • Performing Data Matching and Data Deduping
  • Performing Data Segmentation
  • Updating and Monitoring Data
• What are the Benefits of Data Enrichment?
• What are the best Data Enrichment Tools in the market?
• Conclusion

What is Data Enrichment?

Data Enrichment refers to enhancing existing information by supplementing missing or incomplete data with relevant context obtained from additional sources. In layman’s words, it is the process of improving, refining, and augmenting raw data.

This is achieved by merging third-party data from an external authoritative source with an existing database of first-party customer data. The key objectives of Data Enrichment are to increase data accuracy, quality, and value. 

Data Enrichment is achieved in the Big Data diaspora by including taxonomies, ontologies, and third-party libraries in the data processing architecture. When reliable and authoritative data is analyzed, organizations can offer improved business choices and personalized consumer experiences.

For example, for a food delivery company, the demand for two different zip code locations can be the same based on the in-house sales data. But, when the company enriches its existing data by gathering population data of the two locations from trusted public sources, it may witness a change in the percentage of demands because of the varying population count. Such insights can assist companies in making better marketing decisions to boost demand.

Businesses carry out Data Enrichment to improve the information they currently have so they can make better-informed decisions. Apart from that, it helps businesses perform the following operations:

• Define and manage hierarchies. 
• Create new business rules for labeling and sorting data on the fly.
• Investigate and process data that is multilingual and multi-structured.
• Process text and Semi-structured Data more efficiently.
• Reduce costs and optimize sales.
• Perform Predictive Analysis.

Why Data Enrichment is Important?

Data Enrichment prevents companies from facing data decay due to old and redundant data records. It enables them to improve the value and quality of their datasets and draw meaningful insights from them. Because it allows for smart automation, Data Enrichment also decreases the time and effort required to complete the task. 

Data Enrichment further helps brands create personalized services and boost the overall customer experience. This not only leads to customer satisfaction but also reduces the churn rate. In addition, it can save expenses by automatically evaluating data, combining redundancies, and removing errors while keeping updated profiles.

What are the uses of Data Enrichment?

Data enrichment refers to the process of adding additional information to a dataset in order to make it more comprehensive, accurate, and valuable. There are several uses of data enrichment, including:

1. Reduce the size of lead gen forms

The conversion rate is higher when the contact form is shorter and simpler. Data enrichment allows you to only request essential information, such as a name, email address, and company, on the initial form. Once you have obtained the lead, you can use data enrichment to add more detailed information, such as a job title, phone number, company address, number of employees, and sector of activity, to the profile.

2. Identify and reduce form fields that turn people away

Did you know that asking for a phone number on a lead generation form can decrease the conversion rate by 5%? Some leads may be hesitant to provide certain details, such as turnover, social media profiles, or address. Data enrichment allows you to obtain this information separately, rather than requiring it on the initial form. By removing form fields that may negatively impact the conversion rate, you can potentially increase the number of leads.

3. Segment and Structure Data

Data enrichment can be used to structure and organize messy or poorly formatted data. By prioritizing data quality and selecting relevant data points and sources, you can use data enrichment to create segments of leads based on shared characteristics. These segments can then be used to create targeted email lists and ad audiences, enabling you to run targeted outreach campaigns.

What are the Types of Data Enrichment?

There are several forms of Data Enrichment that are widely used today. Some of them are as follows:

• Contact Enrichment: The practice of adding contact information (valid business emails, job titles, and phone numbers) to an existing database in order to obtain a complete database of customers/leads is known as Contact Enrichment.
• Geographic Enrichment: The practice of adding address data, along with latitude and longitude data, to customer and contact information is known as Geographical Enrichment.
• Behavioral Enrichment: The practice of studying customer behavioral patterns, like previous purchases and surfing habits, is known as Behavioral Enrichment. This frequently entails tracking a user’s purchase behavior in order to discover significant areas of interest for each client. 
• Demographic Enrichment: The practice of adding value to consumer datasets by including information like marital status, family size, income level, credit score, etc., is called Demographic Enrichment.

What are the Methods for Data Enrichment?

There are several methods for data enrichment, including:

• Data Scraping: Data can be extracted from websites or other online sources using specialized software or scripts. This can be an efficient method for collecting large amounts of data, but it can also be time-consuming and may require some technical expertise.
• Manual Research: Data can be manually entered into a database or spreadsheet by a person. This is a relatively slow and error-prone method, but it can be useful for small amounts of data.
• Data Enrichment Tools: A data enrichment tool is a software or service that gathers, organizes, cleans, and formats data from third-party sources and aggregates it from different sources. Examples of data enrichment tools include Clearbit, Dropcontact, and Zoominfo. These tools can be effective, but they may have limitations in terms of the data points and sources available. Data scraping may offer more flexibility in terms of selecting data points and sources, but it may require more technical expertise and may be more time-consuming.

What are the Data Enrichment Techniques?

• Appending Data: By adding data to your data set, you combine data from several sources to form a set that is more comprehensive, accurate, and consistent than the sum of its parts. A greater overall image of your customer can be obtained than from any one system, for instance, by combining the customer data from your CRM, financial system, and marketing system.
• Data Segmentation: A data object (such as a client, product, or location) is divided into groups through the process of data segmentation based on a common set of pre-defined variables (such as age, gender, income, for customers). The thing is then more accurately classified and described using this segmentation.
• Derived Attributes: Derived attributes are fields that can be created from one or more fields but are not included in the original data collection. For instance, the column “date of birth” can be used to calculate age, even though it is extremely rarely kept. Derived attributes are quite helpful since they frequently include reasoning that is used repeatedly during analysis. You may shorten the time it takes to develop new analyses and guarantee the consistency and correctness of the measures used by creating them as part of an ETL operation or at the meta-data layer.
• Data Manipulation: The technique of substituting values for missing or inconsistent data inside fields is known as data imputation. The estimated value aids in a more accurate analysis of your data rather than considering the missing value as a zero, which would skew aggregations. For instance: If the order’s worth was unknown, it may be estimated based on the customer’s past purchases or the particular bundle of goods.
• Entity Extraction: The technique of extracting useful structured data from unstructured or semi-structured data is known as entity extraction. You can identify entities with entity extraction, including persons, locations, organizations, and concepts, as well as numerical expressions (dates, hours, quantities in money, phone numbers, and so on) and temporal expressions (dates, time, duration, frequency).
• Data Catergorization: It is the process of classifying unstructured information in order to make it structured and analyze-able. This can be divided into two groups. You can analyse unstructured text using either of these methods to better comprehend the data.
  • Sentiment analysis is the process of extracting emotions and feelings from text. As an illustration, was the consumer feedback negative, positive, or neutral?
  • Topication is the process of identifying the text’s “subject.” Was the text about sports, politics, or real estate costs?

The Process And Key Strategies for Data Enrichment

The various processes and key strategies for Data Enrichment are as follows:

• Ensuring Higher Data Quality
• Creating a Robust ELT Pipeline
• Performing Data Matching and Data Deduping
• Performing Data Segmentation
• Updating and Monitoring Data

1) Ensuring Higher Data Quality

Data Enrichment is a continuous process. It must be performed on a regular basis as the demands of the consumers change. Businesses may build a continuous enrichment process by scraping internet data sources and collecting data from them. A notable caveat is to ensure that the data is not inaccurate or incomplete.

Incorrect conclusions might be drawn due to inadequate data gathering, which users may not realize for a long time. This can cause immense loss to business organizations due to faulty insights. Also, the quality of data must lie within acceptable metrics and be up to date. 

The quality can be improved by following four simple steps:

• After accessing the data sources, profile data to identify and understand abnormalities.
• Define the guidelines (metrics) for data cleansing and standardization to guarantee that it is appropriate for the purpose. These guidelines can be on the basis of completeness, conformity, consistency, accuracy, integrity, and duplication.
• Apply the defined guidelines on data quality processes. Then verify the data.
• Data quality should be constantly monitored and reported against all goals and across all business applications.

The data collection for Data Enrichment processes can be direct (personal data), internal (CMS, ERP, digital services, or other production systems), or external (third-party). In consumer businesses, the direct source can also be referred to as first-party data as the information is collected from the customers directly.

2) Creating a Robust ETL Pipeline

After the data quality check and data cleaning, it is now ready to be combined with the current database. This is performed through ETL (Extract, Transform, and Load) processes to guarantee that all production systems have the most recent data. The ETL process can be broken down into three stages:

• The Extraction phase: The existing database is used to extract information.
• The Transformation phase: The data is enhanced and converted into a more usable format.
• The Loading phase: The data is ready for use after being transformed and is now loaded into the location where it is needed. 

Before the Extraction phase, it is critical to assess the data in your Data Lake and any other data repositories. You also need to check if the available data needs further correcting and analyze if there is any need to add any further information to obtain the desired results.

Organizations will have access to a massive amount of data, but not all of this information is relevant to the business. Hence, ETL processes are implemented to ensure that data is made usable through Data Enrichment.

3) Performing Data Matching and Data Deduping

Extrapolating Data is another process of Data Enrichment effort. Engineers can extract more information from a raw data source using fuzzy logic techniques. After data cleansing, it is important to match data with existing duplicates.

One of the most challenging duplicates to catch is probabilistic duplicates. Probabilistic duplicates are data fields that may reflect the same entity yet have different attributes like spelling names, phone numbers, or even emails. To establish whether these data are reflecting the same object, one must perform regular matching overall records.

Therefore, maintaining a high accuracy during data matching is a must. Higher accuracy would mean that the data is representing the same entity. 

Simultaneously, Data Deduping should also be performed. This is needed because when an entity’s information is updated, there is a chance of the accidental creation of a separate record instead of actually updating the existing one.

This is why the duplicates or redundant data records must be purged in the Data Deduping process. Only when these duplicates are dealt with, and records are clean can Data Enrichment be successful.

4) Performing Data Segmentation

It is always better to group or catalog the data into specific tags. The more businesses can narrow and “tighten” data sets to focus on certain target segments, the more Data Enrichment operations will pay off. Before segmenting, they must determine their target market and business goals.

Then they can evaluate which processes will be aided by the enhanced data that can help achieve the set goals. Lastly, after segmentation, businesses need to connect up a data flow between all their tools. This is where data becomes an actionable asset. 

5) Updating and Monitoring Data

Businesses must run Data Enrichment consistently. Any data about an entity is bound to change with time. Using outdated data can hamper business practices. For example, suppose an E-Commerce company uses year-old customer data for its sale campaign. In that case, customers will receive irrelevant offers since the data (like residential addresses) used may no longer be accurate.

Customer purchasing patterns change rapidly, which is evident since the onset of the COVID-19 pandemic. Hence, data should be regulated such that it is periodically cleaned and validated to ensure consistent and continuous data quality in the future. Data Matching and Deduping help achieve this accuracy in the long run.

Another way of doing this is by data monitoring. Monitoring data entails setting up controls that it is compliant with data quality and business requirements. Also, while updating data, businesses may purchase access to other databases to search for extra information on their clients/customers, which they can then add to their own database.

What are the Benefits of Data Enrichment?

A few benefits of Data Enrichment are listed below:

Improves Data Accuracy

A single dataset is not sufficient to build a perfect customer view. Data Enrichment allows companies to make their raw data useful. It allows businesses to add additional as well as missing data to the original data set to make it more useful.

Aids in Customer Engagement

Data Enrichment helps in maintaining the data up to date so that companions can personalize Marketing Campaigns. It helps you segment the customers and make more accurate decisions by managing every segment of customers separately.

Improves Your Customer’s Experience

Dealing with customers using personalized messages delivers a great impact on Marketing and Sales because you are improving customer experience. Data Enrichment allows businesses to understand their customers better.

What are the best Data Enrichment Tools in the market?

Data enrichment tools are software or services that help businesses gather, organize, and format data from a variety of sources. These tools can be used to add additional data points to a dataset, such as contact information, demographics, or behavior, in order to make it more comprehensive and valuable. Data enrichment tools can be particularly useful for improving data quality, enhancing data analysis, personalizing experiences and recommendations, and targeting marketing campaigns. There are many different data enrichment tools available on the market, and the best tool for you will depend on your specific needs and requirements. It’s important to carefully evaluate the features and capabilities of different tools to determine the best fit for your needs.

Conclusion

Though Data Enrichment works in many different ways, its key objective is to add value to the data. Depending on the business goals, every type of Data Enrichment is viable. As a result, it has become a powerful tool in the data-driven consumer industry. This article provided you with an in-depth understanding of what Data Enrichment is, its types, why it’s needed, and the key strategies that can be leveraged to implement it.

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Most businesses today, however, have an extremely high volume of data with a dynamic structure. Creating a Data Pipeline from scratch and performing all Data Enrichments operations for such data is a complex process since businesses will have to utilize a high amount of resources to develop the ETL pipeline and then ensure that it can keep up with the increased data volume and Schema variations. Businesses can instead use automated platforms like Hevo.

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