Data Cleansing vs. Data Enrichment: Key Differences

Data is the lifeblood of every organization, powering most business models and massively impacting business performance. A Forrester study reveals that 54% of organizations with advanced data and analytics have successfully increased their revenue, and another 44% have secured a substantial competitive advantage. This data-driven success is a beacon of hope for all businesses, showcasing the immense potential of data in driving business performance.

There’s no doubt that data can improve business performance. But what if the data is of poor quality?

Bad data doesn’t merely imply data full of typos; it can also be unstructured data scattered across the organization in various formats. CIO claims that 80-90% of all digital data is unstructured, presenting a significant challenge in converting this raw data into actionable insights.

So, how can you ensure that your business data is clean, organized, and accurate? The answer lies in two essential techniques: data cleansing and data enrichment. These are not merely options, but necessities for effective data management and decision making.

In this blog, we will explore both data management techniques and examine the differences between them to help you identify the best approach for your business.

What Is Data Cleansing?

Data cleansing is a technique of detecting and correcting all errors, discrepancies, and irregularities in your organization’s data. The idea is to make the data more accurate and improve its quality.

The data cleansing process fixes issues like:

• Incorrect data
• Irrelevant data
• Typos or missing information
• Duplicate data

Let’s take an example:

Say you are an e-commerce business owner who has gathered a massive customer database over time. This database includes information from various sources, such as website sign-ups, browsing history, social media campaigns, and promotional events. However, this data is duplicated and inconsistent. Entries include outdated contact details, varying formats of phone numbers, typos in email addresses, and other such irregularities.

The solution? Data cleansing. Data cleansing involves:

• Ensuring all formats are standardized
• Fixing typos or missing entries
• Eliminating duplicates
• Updating records

After the cleansing procedure is complete, you’ll have a precise, top-notch customer database that can be utilized for effective decision making.

Benefits of Data Cleansing

Data cleansing is an inherent part of overall data management.

Inaccurate data can lead to financial losses, misguided strategies, and reputational damage. Statistics show that poor-quality data puts a significant economic strain on the US, costing the economy as much as $3.1 trillion annually.

Let’s look at the notable benefits of data cleaning:

• Improves decision making through accurate data analysis
• Saves you the hassle of muddling through insufficient, inaccurate data
• Results in improved customer satisfaction with accurate insights tailored to each individual
• Simplifies compliance with data protection guidelines

What Is Data Enrichment?

Data enrichment is the process of improving the existing organizational data by supplementing it with additional data from other reliable sources. It allows business owners to perform more complex analytics and gain deeper insights than would be possible using just the raw data.

The data enrichment process typically enriches the database with three types of data:

• Demographic
• Behavioral
• Geographic

Let’s take an example:

Say you manage a CRM system for a software company. Currently, this CRM contains only basic information such as customer names, email IDs, and purchase histories. However, you wish to boost your sales team’s ability to connect with customers and refine targeting strategies. 

Data enrichment would involve integrating third-party sources to augment each customer’s profile. You can enrich the CRM data by adding each customer’s engagement history, job title, company information, and social media profile. This then enables your sales team to craft and deliver more personalized pitches.

Benefits of Data Enrichment

Now that you understand data enrichment, its importance may seem quite obvious. But there are certain advantages beyond the conspicuous benefits of having updated information:

• Create targeted customer segments for effective marketing campaigns
• Personalize your messaging to specific customer segments
• Gather significant customer behavioral insights for informed decisions

Data Cleansing vs. Data Enrichment: Key Differences

Now, let’s examine the critical distinctions between these two approaches to data management. This will help you understand them comprehensively and choose the right strategy for your business data.

1. Core Functions

The primary function of data cleansing is to eliminate incorrect and inconsistent data and update it with current information. It involves processing your data and rectifying any errors in existing columns and fields. The goal is to correct inaccuracies and enhance data quality for making strategic decisions. 

Data enrichment, on the other hand, involves combining data from additional sources into a single database to create a rich and diverse repository. It adds further information and fills in any gaps in your existing data. This provides access to more complete data for better analysis and insights.

2. Techniques

Data cleansing utilizes various techniques such as:

• Data standardization: Converts all raw, unstructured data into uniform data with consistent formats. This process makes it easier to identify and eliminate errors and irregularities.
• Data deduplication: Detects and eradicates all duplicate records in the database.
• Data validation: Authenticates the validity and accuracy of your data.

Data enrichment, on the other hand, uses the following standard techniques:

• Data appending: Uses external data sources to add missing information to your business dataset, most commonly, demographic, behavioral, and geographic customer data.
• Data verification: Ensures that the new information in the dataset is authentic and accurate.
• Data provider integrations: Collaborating with third-party data providers allows access to precise data to enhance your dataset.

3. Process

Both data cleansing and data enrichment are simple processes.

Data cleansing typically begins with inspection and profiling, where all the data is inspected and carefully reviewed to assess its quality level. Once all discrepancies are flagged, they’re rectified, and then the data is verified to conform with data quality rules and regulations.

Data enrichment starts with identifying your data goals and how enriched data will be used. You can then research potential third-party sources and evaluate their accuracy. Next, the external data must be carefully matched using advanced algorithms and identifiers. Finally, check the enriched data sets for anomalies and make sure to refresh the external sets frequently.

4. Benefits

The main advantage of data cleansing is enhanced data quality. Organizations can rely on their data by eliminating mistakes, redundancies, and discrepancies. Clean data results in more dependable analytics, more satisfactory customer experiences, and more efficient operations, which ultimately lowers the chances of costly errors.

The main benefit of data enrichment is increased data worth. Enriched data leads to greater understanding, enabling precise marketing tactics, improved customer grouping and better business decisions. It transforms unprocessed data into a valuable resource, allowing for tailored customer engagement and better strategic choices.

5. Challenges

One of the primary difficulties in data cleansing is the amount of time and effort needed. Working with extensive datasets can require a significant amount of resources. There is also the possibility of unintentionally deleting important information or simplifying data too much, resulting in missed insights.

Data enrichment presents the difficulty of guaranteeing precision and significance. Integrating external data sources may sometimes bring in incorrect or outdated information. Also, the enrichment process may come with a high price tag, so weighing the benefits of enriched data against the possible costs and challenges of handling it is crucial.

Which One Should You Choose for Better Data Quality?

The short answer is, both!

While enriching your organizational data is mostly about adding complementary data to strengthen your database, data cleansing eliminates inaccurate and outdated data.

Both data management methods are essential for maintaining the health of your company's database. Usually, data cleansing is done first to create space for new and updated data obtained through data enrichment.

Take the First Step Now!

Data cleansing and enrichment are core pillars of an effective data management strategy. It is important to prioritize both these processes throughout your data pipeline to facilitate advanced analytics and make informed business decisions.

Leading solutions like Savant offer various customizable, low-/no-code automation tools to support you in your journey toward effective data management.

Try Savant for FREE to optimize your data processes and drive better business outcomes.