Organizations gather massive amounts of data in order to gain knowledge and improve and augment the company’s growth. Collecting data has become crucial for businesses looking to develop the best products and services for their target market. So much so, that the worldwide big data analytics industry is anticipated to generate 68.09 billion US dollars by 2025.
However, the information obtained by businesses could be old, erroneous, or missing critical pieces. As a result, relying simply on internal data may not be sufficient for businesses to stand out in the market. This is the reason data enrichment is used. Data enrichment assists businesses in transforming existing data into a thorough profile that supports data analytics to produce in-depth insights. In addition, businesses may enhance, repurpose, and modify raw data via data enrichment to achieve their objectives.
What Is Data Enrichment?
There are several sources from which customer data can be fetched; it may be gathered directly from leads by asking them to fill out a form, seek a product demo, or schedule an appointment with a salesperson to get feedback. Research has shown that email-collecting forms have been the most effective at converting visitors, with 15% conversion in 2020.
Data enrichment is the process of augmenting customer-provided data known as first-party data with information from a third party. On many occasions, this process is also called data appending. Whenever data is enriched, first-party data from one dataset and third-party data from another dataset are analyzed. Third-party data includes data extracted from websites, social networking platforms, polls, subscription services, and more.
Data is available in many forms, so it becomes crucial to categorize them before you can perform enrichment. There are four popular forms of data enrichment.
Types Of Data Enrichment
Geographic Data Enrichment
An existing database that contains client addresses may be enhanced with geographic information by adding postal data and specifics like latitude and longitude. For example, let’s say a business wishes to attract as many consumers as possible within a certain range, say 20 miles, so they would need the geographic data of their target audience. After getting this data
Adding consumer behavioral patterns helps an organization identify the buying pattern of their customers. For example, by adding behavioral patterns to their user profile, you may determine a customer’s areas of interest and the steps they took to arrive at their final purchase choice. Enhancing behavioral data is crucial since it helps you evaluate the success of marketing efforts and save marketing expenses.
The process of adding additional demographic information to an existing customer dataset, such as income level or marital status, is known as demographic data enrichment. Getting demographic information will depend on the type of company you are in. If you are a credit card company, then you would like to consider income level, age, etc.
Contact enrichment is adding contact information like phone numbers and emails to an access dataset to create a comprehensive lead database.
Benefits Of Data Enrichment
Accuracy Of Algorithms
Algorithms are essential in creating high-quality training datasets, which are mainly used to train AI/ML models to recognize patterns and learn independent decision-making. Data enrichment guarantees the accuracy of datasets that make automated algorithm behaviors more exact.
Remove Extra Facts
Data enrichment also removes all extraneous and incorrect facts or statistics from the data sets to improve accuracy and dependability. Using the enhanced data to draw inferences and insights aids in appropriate decision-making.
Find Duplicate items
Data enrichment services assist in locating duplicate items and guide you in making the best choice on whether to keep them in the data sets or remove them. When employing such data in a crucial decision-making process, data enrichment can quickly identify duplicate items that might cause confusion during further processing or analysis.
Although manual lead scoring might be a tiresome task, it is essential for increasing conversion rates and creating a productive working relationship between the sales and marketing teams.
Utilizing information from other sources, data enrichment may assist firms in automating the lead scoring process. For example, consider a scenario where a lead has previously engaged with a company but had never joined a mailing list. The lead chooses to join the mailing list this time and enters the company database with the accurate first and last name they had previously used, but they ignore to include their address.
In this case, a socio-demographic data enrichment tool would be able to compare the input data to reliable postal records and instantly attach address data, potentially improving their lead score.
The Process Of Data Enrichment
Data Quality Check
It is a constant process to enrich data. As the needs of the consumers keep changing with time, data also needs to be updated accordingly. Businesses may create a continual enrichment process by gathering and scraping data from online sources.
Due to incorrect insights, companies might suffer great losses in terms of misunderstanding target audience needs leading to reduced sales. Additionally, the quality of the data must meet approved standards and be updated. To clean the raw data, companies have the option to choose data cleansing services so that the data gets scrubbed and organized quickly and affordably.
The data that has been cleaned and quality-checked is now prepared to be merged with the existing database. Extraction, transformation, and loading (ETL) procedures are used to do this and ensure that all manufacturing systems have access to the latest data. There are three phases in the ETL process:
- The information is extracted from the current database in the first step.
- The data is improved and organized during the transformation process.
- After being converted, the data is loaded into the necessary place and prepared for usage.
Before beginning the extraction step, evaluating the information in your datastore and any other data repositories is crucial. To get the desired results, you also need to determine whether the existing data needs to be corrected further and whether any more information needs to be filtered or not.
Massive amounts of data will be available to organizations, yet not all of this data will be pertinent to their operations. Therefore, to guarantee that data is rendered usable through data enrichment, ETL procedures are put into place.
Another step in the data enrichment process is extrapolating data. Using probabilistic reasoning approaches, engineers may squeeze out additional information from a stream of raw data. Finally, connecting data with existing copies is crucial after data cleaning. For this, many firms outsource data scrubbing services to the expert, who effectively cleans all the data within the stipulated time.
Probabilistic duplicates are among the hardest to detect duplicates. Data fields known as probabilistic duplicates may represent the same thing but have distinct features, such as multiple spellings for phone numbers, names, or even emails.
It is usually preferable to organize the data into distinct tags. Businesses can benefit more from data enrichment efforts if they can “tighten” and focus data sources on certain target categories. They must decide on their business and economic objectives before segmenting.
Monitoring The Data
Businesses must continuously execute data enrichment. Any information regarding an entity will inevitably change over time. Using old data might make corporate operations more difficult.
Consider the scenario where an online retailer uses consumer information from the previous year for a promotion. Customers will then receive inappropriate offers since the previously correct data could no longer be available.
Although there are many distinct ways that data enrichment might operate, its main goal is to enhance the value of the data. Every kind of data enrichment and data validation services is viable based on its business goals. It has thus evolved into a potent instrument in the various industries in the market to make smarter decisions that will eventually increase their sales, organizations need to opt for data enrichment continuously.