As new data is being created, old data needs to be managed and organized to accommodate the new. 

This is where data enhancement comes in.  

In this article, you’ll learn what data enhancement is, how it works, and why you’ll want to consider investing in it.

What is Data Enhancement?

Data enhancement involves optimizing data accuracy and completeness by appending missing information, correcting mistakes in the data, and isolating inconsistencies. 

Marketers use data enhancement to transform raw data into tangible insights about their target audience.

This process of enhancing data is also called a data append, where you can expand your data by including external data pulled from outs sources.

This typically includes:

• Geocoding: You can add coordinates longitude and latitude) to data points to allow for visualization mapping and spatial analysis.
• Customer Profiling: You can create detailed profiles of your customers by merging internal data with external sources to generate insights into their preferences, behavior, and purchasing patterns.
• Text Analysis: This involves using topic modeling and sentiment analysis techniques to generate meaningful insights from unstructured data such as survey responses, customer reviews, and social media posts.
• Demographics: This includes age, gender, homeowner/renter, marital status
• Address Standardization: Correcting and standardizing address data to conform to USPS formatting guidelines, enabling better deliverability and geocoding.
• Phone Appending: Adding phone numbers (landline and/or mobile) to existing customer records to enable voice and SMS communications.
• Email Appending: Enhancing customer records with email addresses for email marketing and digital communications.
• Household Data Appending: Enriching records with household-level data like household income, home value, and length of residence.
• Interest/Lifestyle Appending: Adding data on consumer interests, hobbies, purchasing behaviors, and lifestyles to better segment and target audiences.
• Business Data Appending: For B2B data, adding details like company size, industry, revenue, and number of employees.
• Data Deduplication: Identifying and removing duplicate records to maintain a single, consolidated view of each customer.

What is the difference between data enhancement and data enrichment?

Data enhancement and enrichment are used to improve data quality and value, but they focus on different aspects of data improvement. 

Data enhancement primarily refers to cleaning and refining existing data to improve its accuracy and reliability, whereas data enrichment involves augmenting existing data with additional external information. 

This could include:

• Appending demographic information to customer records
• Integrating third-party data to provide more context
• Improving customer records with age, gender, income level, and education level data
• Adding household-level attributes like presence of children, home ownership status
• Merging purchased marketing data on consumer interests, lifestyles, purchasing behaviors
• Appending business firmographic details like company size, industry, revenue

While enhancement is about polishing and correcting the data you already have, enrichment is adding new layers of information to make the data more comprehensive and useful.

How Does Data Enhancement Work?

Data enhancement works to improve your existing datasets’ quality, accuracy, and usability. 

It involves identifying and correcting inaccuracies, such as typographical errors, inconsistencies, and outdated information. 

After this, deduplication eliminates duplicate entries that could skew analysis or inaccurate data. 

The next phase often involves data standardization, where data from various sources or formats is normalized into a consistent format, making it easier to aggregate, compare, and analyze. 

For example, dates might be standardized to a single format (YYYY-MM-DD), or addresses might be formatted according to postal standards.

Enhancement may also include data validation, where data is checked against known standards or patterns to verify its legitimacy, such as checking that email addresses fit a valid structure. 

When is the Best Time to Invest in Data Enhancement?

Data enhancement is necessary in specific situations where data quality directly influences the success of operations or your marketing efforts. 

Situations like: 

• Before launching a major marketing campaign: To ensure messages are targeted and relevant.
• During integration after a merger or acquisition: To unify disparate datasets into a cohesive system.
• Before implementing a new CRM or ERP system: To clean and prepare data for migration.
• When preparing for data analytics or machine learning projects: To ensure data quality supports accurate analysis and predictions.
• After identifying data quality issues in routine audits: To correct and prevent future inaccuracies.
• Before global expansion: To standardize and localize data across new markets.
• When regulations change: To comply with new data protection and privacy laws.

Best Practices of Data Enhancement

Start by auditing your database.

When you get started with data enhancement, you’ll first want to audit your database. 

This involves:

• Reviewing your data to pinpoint errors, duplicates, and outdated entries.
• Using data quality tools to automate the detection of discrepancies and inconsistencies.
• Looking at the current data structure to understand how information is organized facilitates targeted enhancement.
• Creating benchmarks that define clear, measurable goals for accuracy, completeness, and consistency.

Once you complete your audit, you can pinpoint inaccuracies and create a baseline for improving your existing database.

Make data hygiene an ongoing effort.

Routine data hygiene is almost necessary for data enhancement. 

Data hygiene will identify and resolve errors, inconsistencies, duplications, and missing values within your data.

This creates the foundation for enhancing your data. 

You can create better value from your database while mitigating risks caused by inaccurate or incomplete data.

Continuously Monitor Data Quality

You’ll want to monitor data quality as well. 

This involves measuring data quality against predefined metrics and rules. 

This process usually includes:

• Implementing data quality rules: Develop a set of rules operationalizing the defined quality dimensions. These rules can be expressed as logical conditions, constraints, or business rules the data must satisfy to be considered high-quality.
• Continuous monitoring: Implement automated monitoring processes that continuously assess the incoming and existing data against the defined data quality rules. This can involve scheduled checks, real-time validation, or integration with data pipelines to capture and flag quality issues as they occur.
• Measurement and reporting: Establish mechanisms to quantify and report on data quality metrics. This may involve calculating and tracking key performance indicators (KPIs) related to data quality, such as error rates, completeness scores, or compliance with predefined thresholds.
• Root cause analysis: When data quality issues are identified, root cause analysis is conducted to understand the underlying reasons for the problems. This analysis may involve investigating data sources, processes, or systems contributing to data quality issues.
• Remediation and prevention: Based on the root cause analysis, implement corrective actions to address data quality issues. This may involve cleaning or enriching data, updating data sources or processes, or implementing additional data quality controls to prevent similar issues from recurring.

This will allow you to proactively identify and address data quality issues as quickly as possible and ensure that your data is reliable, consistent, and fit for purpose. 

Having Clear Data Goals Set In Place

You should have a clear goal to get the most out of your data enhancement initiatives. 

Without well-defined goals, your data enhancement efforts can become unfocused and misaligned. 

First, you’ll want to identify the target audiences and specific marketing tactics that data enhancement will enable, such as:

• Customer segmentation
• Personalized messaging
• Campaign targeting

Your goals should center on measurable outcomes, whether that’s increased engagement rates, better conversion rates, or positive marketing ROI. 

This will allow you to focus and prioritize the areas of your data that are hurting your overall marketing efforts. 

Work With a Team That Specializes in Data Enhancement

Lastly, you’ll want to consider working with a team that specializes in data enhancement. 

They can help identify your datasets’ gaps, redundancies, and quality issues. 

Skilled professionals can help implement proven data cleansing techniques to standardize, deduplicate, and improve your data. 

They can also set up data governance and quality monitoring processes to maintain data integrity throughout the enhancement lifecycle. 

More importantly, they can take away the headache of trying to accomplish this on your own.

Getting Started with IMDataCenter’s Data Enhancement Solutions

IMDataCenter provides data enhancement services for brands across the US. 

We offer access to enterprise-grade append solutions for a fraction of the price.

Our services include:

• Marketing list-building to create impactful consumer audiences for effective email, direct mail, or digital marketing targeting.
• Real-time appending of demographic, phone, and email data.
• 1,000+ Demographics
• Custom digital audience creation for online marketing

To learn more about our data enhancement solutions, contact us today.