Top Benefits of Data enrichment for A Business


Business is uncertain. The only thing that can bring stability is making the right decisions, and those decisions must be driven by real-time statistics. Now that data is extremely significant and widely used, improving the quality of that data becomes a necessity. This is where data enrichment plays a big role.

Did you know a predicted compound annual growth rate of nearly 30%, 2023?  It clearly indicates that organizations are embracing data-driven insights rather than intuition. And without data enrichment, you cannot go ahead and make accurate decisions.

Introduction: Data Enrichment

Data enrichment is a technical process that completes the missing parts of any piece of information. These parts can be extracted from third-party records, which can be from external but trustworthy sources.

Considering these third-party sources, they can be various platforms like social media, mobile applications, IoT devices, etc. This can be any piece of data that you require and is missing from your database.

Why do businesses require data enrichment services?

Well, the reason is obvious behind adopt data enrichment solutions. It is to get into insights. These insights can be related to customers, productivity, revenues, the back-office team, pricing, logistics, or anything else. These are basically the components of any business. Every entrepreneur likes to learn whatever is happening in his corporation. In other words, he requires insights into what the business is gaining, losing, or where it stands. These insights enable him to make further decisions about doubling or multiplying profits. Precisely, these are the reasons why every organization leverages enriched records.

·       Knowledge discovery

However, this term conflicts with machine learning. It is related to understanding insights and gaining value from them to further improve decisions related to commercial activities. In the present scenario, data scientists enrich databases to make them accurate and filled with consistent and relevant entries. And data mining experts rely on it to prepare data models to further gain knowledge and make decisions that actually prove effective in the end. 

·       Targeted marketing

The very next reason can be targeted marketing. However, marketing already exists. But to make it more precise and result-oriented, enriched customer data is required. It may consist of information related to customer intent, the web journey, comments and reviews, carts, location, pricing, etc. The insights from these metrics can help in understanding what customers want and, hence, tailor messages or offers so that they can be converted into consumers. 

·       Better customer experience (CX)

The very next benefit behind data enrichment can be personalizing communication and services. Let’s say a customer frequently explores your website, puts things in the cart, and tries to make payment, but bounces out. This can be due to the pricing or shipping charges. The product in the cart indicates the willingness of the customer to buy, but for some reason, he leaves it unsold. The deep analysis can help in discovering missing information like zip codes, locations, or email IDs. Like this, there may be hundreds of such visitors who intend to buy but leave without investing. Collecting their data and completing missing pieces of email IDs or locations can help in drafting lucrative offers. These offers can encourage them to come again and purchase. So, it can help in personalizing communication about offers, which drives customers to invest. So, this practice can help win customers’ hearts by offering intended offers.

·       Insights about new opportunities

Insights refer to complete knowledge. A new market can guide the addition of new opportunities. This can be possible with a complete database of the potential market, its customers, and competitors. Once discovered, developing the right products and services is like a walkover. These right decisions may be related to the unmet needs of prospective customers. Overall, enriched details can guide in forming the right vision, which attracts customers, revenues, and recognition.

·       Increased productivity

Every business focuses on enhancing its productivity. Making random decisions won’t do any good, but the correct and complete datasets can. Such data can show customers’ pain point, interest, purchasing capacity, and average selling cost.

With data enrichment, these details can be at your fingertips. It means that you need to waste time looking for vital information to complete the collected database for customer, intent, competitor, and supplier's analysis.

·       Better data quality

Like every mortal thing, data decays. And it is also true that it happens quicker than anything else happening around. So, before it’s too late to overcome, you need to enrich your incomplete data with vital elements from trusted sources. Also, proactively test their quality by verifying and validating the collected data to narrow down the possibility of bad and impractical decisions.

Also, refresh or update databases from time to time to avoid bad decisions that can cost you heavy penalties. Enriching addresses, phone numbers, and updates can increase the accuracy of that data, which will save and improve the customer experience via exclusive data quality. 

How does data enrichment work?

After reading a long narrative on the benefits or uses of enrichment, let’s figure out how this process works. Basically, it can be split into three segments.

·       Aggregation

The very first phase is aggregating data from a variety of sources. This process requires external sources like social media, directories, etc. to extract and collect data from.

·       Extract, Transform, and Load (ETL)

This phase formally ensures extracting data from reputed sources to collect and integrate with the existing data. Once extracted, the data may appear in XML or any other format. It requires cleaning, which is to extract the useful parts from the raw data. And then, convert it into a useful piece of information that relates to an existing database. Once cleaned, the datasets are loaded.

·       Appending Data

The final phase is about appending or adding contextual pieces of information to the existing data.

Considering the methods, there are two ways. The first is the manual method, and the second is automation. For sure, manual processing is a lengthy procedure and has the risk of erroneous results. In comparison, automation can be faster and free from the risk of inaccuracies.


So, it’s up to the business which method it relies on. The ultimate goal is to gain value through contextual and enriched databases. The process is helpful and often ends up attracting benefits through complete and accurate data-driven solutions.

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