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 However, gathered data by companies may be inaccurate, have missing gaps, or can be outdated.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 its necessary, and what its types are.
Table of Contents
- What is Data Enrichment?
- The Types of Data Enrichment
- The Process And Key Strategies for Data Enrichment
- The Need for Data Enrichment
- The Benefits of Data Enrichment
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 of 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, they 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 demands.
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.
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Let’s look at Some Salient Features of Hevo:
- Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects schema of incoming data and maps it to the destination schema.
- Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
- Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
- Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
- Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.
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.
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 the 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 a 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.
The Need for Data Enrichment
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 churn rate. In addition, it can save expenses by automatically evaluating data, combining redundancies, and removing errors while keeping updated profiles.
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 to Marketing and Sales because you are improving customer experience. Data Enrichment allows businesses to understand their customers better.
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.Visit our Website to Explore Hevo
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.
Hevo helps you directly transfer data from a source of your choice to a Data Warehouse or desired destination in a fully automated and secure manner without having to write the code or export data repeatedly. It will make your life easier and make data migration hassle-free. It is User-Friendly, Reliable, and Secure.
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