Did you know that marketing professionals alone use an average of 15 different data sources to collect customer information? While this may seem surprising, the predictions show that this number will increase to 18 this year, and that’s not even looking at other departments like customer service, sales, accounting, and finance.

The diverse applications used by different functions in an organization to gather information also make it difficult to review each source for accurate insights. These various tools tend to collect similar information, resulting in duplicates. Data merging is the solution to counter duplication issues, empowering organizations to access complete, accurate, and consistent data.

What is Data Merging?

Data merging is the process of combining two or more data sets into a single, unified database. It involves adding new details to existing data, appending cases, and removing any duplicate or incorrect information to ensure that the data at hand is comprehensive, complete, and accurate.

However, different organizational departments collect similar information using different tools and techniques.

Consider a company analyzing customer data:

• The marketing team uses surveys to gain insights regarding customer preferences, pain points, and opinions.
• The sales team uses customer relationship management (CRM) systems to gauge information such as past purchases, customer satisfaction, and preferences.
• The customer support team uses helpdesk software to create tickets and keep a detailed record of customer interactions, ensuring that customer concerns are promptly addressed.

Since these teams collect customer information with their specific objectives in mind, the data collected is often similar and needs to be integrated to avoid silos. Data stored separately includes several problems like:

• Scattered information makes it difficult for analysts to parse various data sets to interpret the data correctly and make the right decisions.
• Data may be inconsistent, inaccurate, or incomplete.
• Duplicate data can lead to wasted resources.

Combining disparate data into a centralized dataset will allow the company to generate a comprehensive customer profile to run tailored campaigns and create content that resonates with the target audience.

In response, data merging unifies the data sets and creates a single source of truth, offering benefits like:

• Resource Efficiency: By providing access to information in a consolidated framework, data merging expedites information retrieval, eliminates manual, repetitive processes, and enhances search capabilities. This centralization ensures that resources are allocated to strategic, value-adding tasks.
• Convenience: By combining multiple data sets into one, users no longer have to piece together information from several sources. The convenience of having relevant data in one place makes it easier to analyze the data and extract relevant insights.
• Improved Decision-Making: Data merging ensures that the information available is complete, accurate, and consistent, presenting a holistic and comprehensive view of what is happening within the organization—facilitating informed, data-driven decision-making.

When is Data Merging Needed?

Data merging is a technique that allows organizations to analyze data stored in diverse locations, spreadsheets, or databases. This approach is crucial in multiple scenarios. Let’s explore the key ones below:

Digital Transformation

Organizations embracing digitization must realize the importance of combining data sets. By leveraging digital technologies, data stored in disparate files such as Excel, CSV, and SQL can be consolidated into a unified and structured format and stored in a centralized data processing and hosting system.

Business Intelligence

Access to the right information at the right time is essential for data-driven decision-making. In today’s competitive landscape, businesses must ensure optimal resource utilization. According to Starmind, 50% of employees reported that spending long hours searching for data points hinders productivity and overall performance. Therefore, data residing in different applications (CRM, web analytics, social media insights) should be combined to gain actionable insights.

Mergers and Acquisitions (M&A)

When a company takes over or merges with another company, it must consolidate resources to operate as a single unit or organization. Data is a vital asset that must be combined and stored in a single repository for a complete picture of the merged entity’s operations.

M&A scenarios introduce new aspects such as customer profiles, demographics, supplier relationships, employee data, and more that encompass almost all facets of an organization. Therefore, data merging is crucial to ensure frictionless integration and enhance operational efficiency.

Stages of Data Merging: A Step-by-Step Process

1.   Pre-Merging

Profiling

Before merging the data, it is critical to know the current state of an organization’s data sources and the type of data they are working with. This comprises attribute analysis, which helps an organization understand how the merged data will scale, which characteristics the data will be joined on, and what additional information may have to be appended.

This step also analyzes the data values of each attribute concerning uniqueness, distribution, and completeness. By profiling the data, organizations can identify the potential outcomes of the merged data and prevent any errors by highlighting invalid values.

Transformation

Next, it is vital to transform the data (cleanse, standardize, and validate) into a usable format. This is done by replacing missing/null values, rectifying incorrect ones, converting data sets into a common format, parsing long data fields into small components, and defining conditions for data integration.

By harmonizing the data formats, an enterprise ensures compliance with legal rules and regulations, data accuracy, and consistency across various touchpoints.

Filtering

Data is often filtered when a subset of the data rather than the complete data set needs to be merged. In this scenario, the data can be segmented horizontally (data from a specific time frame is required or only a subset of rows meet the criteria defined for merging) or vertically (data consists of attributes containing unimportant information).

By filtering the data, the information is refined, and only relevant and accurate information is incorporated, enhancing the overall quality of the merged data set.

Deduplication

It is essential to ensure that the data sets have unique records. Duplicate information is a significant concern with data merging since often similar information is collected and stored separately by departments. Organizations should, therefore, conduct thorough data cleansing and deduplication to identify and remove duplicates. This helps to streamline the data merging process, ensuring that only distinct records are stored.

2.   Merging

Once the pre-processing steps are performed, the data is ready to be merged. Aggregation and integration can be employed to combine data. Depending on the intended use, here are a few ways to execute this process:

Append Rows

When data is present in different databases and needs to be combined into one, this option is used. To implement this, it is essential that the data sets being merged have an identical structure.

For example, if an organization has monthly sales data stored in separate files, it can append the rows to create a consolidated data set covering multiple months to uncover trends or patterns.

Append Columns

When a company wants to add new elements to its existing data set, i.e., enrich it, appending columns is a suitable approach.

Consider a company that has customer data (demographics and contact information) in one database and purchase history in another. By appending the columns on a unique identifier (customer ID), it can have a comprehensive view of the customer profile and purchase patterns, enabling it to run targeted campaigns.

Conditional Merge

A company might have incomplete or missing records that need filling by looking up values from another database. In this scenario, conditional merge is a helpful approach. Therefore, information from the source database is selectively combined with the target database based on specific rules of alignment to ensure synchronization and accurate information.

For instance, a food chain’s restaurants are listed in one database, and the customer ratings are listed in another. To determine the average rating for each restaurant, the two data sets are merged by matching the restaurant names against the correct customer review and rating.

Note: In Conditional Merge, the lookup database (Source) should have unique values, while the Target database should have duplicates.

3.   Post-merging

Once the merging process is complete, organizations should conduct a final audit of the data, like the profiling conducted at the start of the process, to highlight any errors, inaccuracies, or incomplete records so that immediate action can be taken to correct them.

Challenges of Data Merging

While data merging is critical to high-quality data, enterprises should be mindful of the potential problems that could arise during the process. Some factors to consider include:

• Data Complexity: While merging the data, structural and lexical differences can introduce inaccuracies into the dataset. Structural heterogeneity refers to a case when data sets under consideration do not have the same columns present, while lexical heterogeneity is when the data fields have a similar structure, but the information contained within them is in a different format. To address this, it is important to invest in tools that define mappings between different data set structures and enable the transformation of data elements to a standard format.
• Scalability: When datasets are combined, they increase in size and complexity, resulting in tasks such as data matching, alignment, and aggregation becoming more resource-intensive. As data volume increases, storage capacity becomes an emerging concern. Traditional, on-premises systems lack the capability to scale, slowing down the processing time and heightening the risk of inaccuracies. To overcome this, organizations should migrate to cloud-based solutions to handle large volumes of data smoothly.
• Duplication: Combining different data sets can lead to duplicates, especially when each source might independently capture the same information. Duplication can lead to overlapping information in data sets, resulting in inaccurate analysis and, by extension, incorrect decision-making. To combat this, organizations should employ matching algorithms, perform rigorous data scrubbing, and enforce uniqueness constraints to identify and remove duplicates promptly.

Key Strategies for Ensuring Effortless Data Merging

• Evaluate data sources: Before combining data, organizations should analyze the nature of each data set. This includes understanding the types of variables, data formats, and overall structure. This aids in anticipating potential challenges during the merging process.
• Use visuals to understand data relationships: Visualizations like scatter plots, bar charts, correlation matrices, etc., provide an overview of the data and help select the right variables for merging. These visuals make it easier to identify patterns, outliers, and relationships within the data, ensuring the inclusion of relevant information.
• Clean and transform data: It is essential to clean the data by removing duplicates and handling missing values. This ensures the merged dataset is accurate and reliable, minimizing errors and inconsistencies.
• Choose merging methods carefully: The method of merging depends on the data’s structure and the intended goals. Different merging techniques, such as inner joins, left joins, and outer joins, have specific use cases. It is crucial to select the appropriate method to ensure meaningful data integration.
• Select the right merging tool: Organizations should conduct proper research and analysis to choose the right tool for their data needs. The tool should be equipped with data profiling, cleansing, and validation features and align with the data’s complexity and the user’s proficiency to simplify the merging process.
• Validate merged data: After merging, ongoing validation is vital. As new records are introduced in the data set, for example, customer transactions, it becomes imperative to regularly examine the merged data to identify any unexpected discrepancies and ensure that the final data set has up-to-date information.

Streamline Data Merging with Astera Data Pipeline Builder

Astera Data Pipeline Builder simplifies data merging by providing a unified, AI-driven platform that seamlessly integrates ETL, ELT, APIs, and data preparation. Instead of struggling with disparate tools and complex transformations, you can consolidate structured and unstructured data from multiple sources into a single, cohesive dataset.

With AI-powered semantic mapping and cloud-based data preparation, the platform automatically aligns and cleanses data, reducing manual effort and ensuring accuracy. Whether you’re merging customer records, integrating partner data, or consolidating financial information, Astera empowers your team to streamline the process efficiently.

Beyond just merging data, Astera’s automatic API creation and real-time processing capabilities ensure that your integrated datasets are instantly available for analytics, reporting, and decision-making. Teams with varying expertise can collaborate effortlessly, leveraging intuitive command-based interactions to build and refine pipelines.

With support for real-time, near-real-time, and batch processing, you can adapt to evolving data needs while maintaining consistency and compliance. By choosing Astera Data Pipeline Builder, you gain a scalable, future-proof solution that transforms raw data into actionable insights—faster, smarter, and without complexity.

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