Understanding Data Enrichment- Importance, Benefits, & Ways

Data has become an integral part of our lives to the point where it defines everything we do.

As data continues to grow, businesses are seeking ways to harness its power to gain a competitive edge. But with the increase in raw data, making sense of it will require more advanced mechanisms.

We are creating data at an unprecedented rate, and by 2025, we are projected to create 180 zettabytes of data, which is a new high. As the speed, complexity, and scale of data continue to grow, we need to adopt mechanisms to simplify and make sense of this data.

Edited: According to Acumen research and consulting, the market for data enrichment services is expected to grow at a CAGR of 8.5% between 2022 and 2030, projected to be valued at $3.5 billion in 2030.

If you are interested in learning more about data enrichment and how to get started with it, then this blog can help you. Here, we dive into the key concepts of data enrichment and provide the information you need to enrich your data.

Let’s begin!

Data enrichment is the process of adding value to existing customer information by collecting, analyzing, and supplementing data from various sources.

This information can include personal details, purchase history, demographics, and online activity, allowing businesses to create more accurate and targeted marketing strategies.

Enriched data helps to identify potential customers and improve overall customer relationships. The most common techniques used in data enrichment include data mining, data matching, and data cleansing to ensure the accuracy and consistency of the data.

We have already touched upon how businesses of all sizes are constantly bombarded with new information and data. The sheer volume of data that needs to be extrapolated is often challenging for most brands, given the constraints when it comes to managing data assets.

From the infrastructure and talent to budget, businesses often struggle with storing, managing, and making sense of their data assets.

This is where data enrichment plays a crucial role. By enriching data, businesses can unravel meaningful patterns, identify hidden trends, and better understand their customers, markets, and competitors.

However, data in its raw format is often challenging to decipher, given the following reasons:

Raw data often suffers from inconsistencies, inaccuracies, and missing information.

As per the 2022 State of Data Science report, businesses can spend up to 37.75% of their time on data preparation and cleaning processes, with reporting (16%) and visualization (13%) also taking up significant time.

Data enrichment helps to cleanse, standardize, and verify data, ensuring that it is accurate, reliable, and complete.

Business data often resides in isolated silos, making it difficult to access and utilize effectively. This is because incoming data from multiple systems and platforms often comes in varied formats and is stored in a centralized repository like a data lake or data warehouse without any cleansing or refinement.

Data enrichment processes help to break down these silos, integrating data from various sources to create a unified and comprehensive view of information.

Raw data often lacks context, making it difficult to interpret and derive meaningful insights. Enriched data adds context to raw data by linking it with relevant information, giving it additional context and meaning.

Enriched data is easy to interpret and use for further analysis, and businesses stand to gain in more ways than one. Data enrichment provides several benefits, such as:

Data Enrichment turns raw data into actionable and valuable data, allowing companies to use it for further processing and reporting. Since this data is contextual, any anomalies or missing fields can be easily detected and corrected, improving the accuracy of the report or visualization that it is used for.

Enriched data enables businesses to create more precise customer profiles, allowing for targeted marketing campaigns, personalized product recommendations, and enhanced customer service.

Businesses can better tailor their sales strategies by understanding customer preferences and behavior. This ultimately leads to better lead generation, improved conversion rates, and higher sales and revenue.

Enriched data can be used to optimize supply chains, manage inventory levels, and reduce operational costs. It can also streamline processes, automate tasks, and improve decision-making efficiency.

There are several types of data enrichment that you can make use of, depending on the requirement and type of data that you are working with. Some of these are as follows:

Contact data enrichment is the process of enhancing the existing contact information by adding more details such as job titles, email addresses, company affiliations, background, and social media profiles.

This type of data enrichment is used for sales, marketing, pre-sales, or other related areas. The information empowers businesses to target their outreach efforts more effectively and engage with potential customers on a more personal level.

For example, contact data enrichment can be validating email addresses in a database to confirm the authenticity of the data and leads. Or enriching your lead lists with their contact details like email addresses, phone numbers, company names, etc from LinkedIn, the web, and other platforms.

Contact data enrichment helps sales and CRM teams improve form completion and data accuracy by using tools like HubSpot’s form shortener to auto-fill fields with enriched CRM and public data, which reduces friction while capturing key lead details behind the scenes, resulting in more complete, actionable profiles with less drop-off.

Geographic enrichment involves integrating geographic data, such as postal codes, city information, and demographic profiles, into existing customer data.

This enhanced data allows businesses to tailor their marketing campaigns to specific geographic regions, optimize pricing strategies based on location, and identify potential sales opportunities in underserved areas.

Unlike other data enrichment types, data standardization works not just on a particular type of data but on all types of information.

Data is standardized for consistency and uniformity, such as in data formats, structures, naming conventions, etc. This process is crucial in data analytics and reporting, as it helps integrate data from disparate sources.

Historical data enrichment involves adding historical context to existing data, such as customer purchase history, website analytics, and social media engagement metrics.

This enriched data allows businesses to identify trends or patterns in customer behavior, enabling them to make informed decisions about personalized campaigns, product development, and customer support strategies.

To enrich your data, you need to follow a systematic process. This includes the following:

The first step is to identify and evaluate potential data sources, both internal and external.

This is crucial because incoming data can be in different formats based on the system or source that it is coming from. By knowing which sources your data comes from, you can take the right steps to enrich it.

Internal data sources include customer relationship management (CRM) systems, enterprise resource planning (ERP) systems, and marketing automation platforms. Meanwhile, external data sources include public databases, industry reports, social media platforms, and third-party data providers.

Once data sources have been identified, the next step is to collect and integrate the data into a centralized data repository.

This may involve data cleansing, data transformation, and data standardization to ensure consistency and compatibility across different data sources.

For example, if you have a centralized CRM to track all your contact information, you will need to ensure that the fields for all your contacts follow a similar format. If you have a common field to store the birthday of each contact, make sure it follows the same format, i.e. DD/MM/YYYY or MM/DD/YYYY.

In case data from different sources follows different conventions, it needs to be converted into the right format to ensure no errors are introduced due to data centralization.

Data matching and deduplication are crucial steps to ensure all your data is arranged in the best way possible and no duplicate data is there to create confusion.

Data matching involves identifying and linking records that represent the same entity, while deduplication involves removing duplicate records to ensure data accuracy and integrity.

The core of data enrichment lies in enhancing and refining the data with additional information. This may involve adding new attributes like demographic data, industry insights, or social media profiles.

It may also involve enriching existing attributes, such as standardizing addresses, verifying email addresses, or confirming phone numbers.

It is essential to assess data quality throughout the data enrichment process. Data quality assessment involves identifying and correcting errors, inconsistencies, and missing values.

This helps to validate the accuracy and completeness of the enriched data to ensure its reliability for downstream applications.

By this stage, we have enriched data that can be used for analytics or reporting. However, data enrichment is an ongoing process that requires continuous governance and maintenance.

So, businesses need to establish clear data governance policies to ensure security, privacy, and compliance with their data process. It also involves making continuous updates to the enriched data to reflect up-to-date customer information, market trends, and business details.

To assist businesses with their data enrichment and maintenance procedures, Clearout provides key contact information and data insights about leads.

One of Clearout's key offerings is its LinkedIn Extension for enriching data. It helps you build a high-intent lead database and enrich their contact details.

You can easily gain insights into the prospect’s needs by extracting and enhancing their personal contact information, work experience, location, and company size directly from LinkedIn.

By setting up the LinkedIn filters to discover relevant accounts, you can then extract their details in one click directly from the search result pages or by visiting the lead's profile. You also get the option to automate your data enrichment process.

You can define the settings to extract the specific data points at first. For instance, you can choose to extract only email addresses and not phone numbers of searched lead accounts.

After building your lead list, you can further enrich the data in the future to refine, update, and add new information such as their updated contact details or company information using Clearout’s advanced data enrichment.

👉 Click here to get the LinkedIn extension

If you already have raw lead data, you can use Clearout’s Email Finder to enrich your database with their verified email address.

You also get additional insights like confidence scoring, email account type, etc to understand if it is safe to reach out to those leads and plan your email outreach campaigns better.

You can enrich data for individual leads or even use the bulk enrichment option to extract data for multiple leads in one go.

It is also important to maintain the quality and accuracy of your enriched data with time.

Clearout’s Email Verifier helps you validate your email lists so that you can remove the duplicate, outdated, and invalid data that can degrade your database quality and outreach campaign performance.

Once you run your email lists from the verifier it shows if the emails are valid, safe-to-send, catch-all, etc.

Hence, by investing in data enrichment and partnering with trusted tools like Clearout, businesses can harness the power of their data to achieve their strategic goals and thrive in the ever-evolving data-driven landscape.