What is Data Mapping

Data mapping is the process of setting a clear and structured relationship between data elements from different sources or formats.

Imagine you're planning a road trip to a new city. Before setting off, you gather information about the routes, landmarks, and attractions along the way. In this context, data mapping is like creating a detailed map for your journey as it entails the process of organizing and structuring data from various sources to create a unified view. 

Just as a map helps you navigate and understand the terrain, data mapping enables you to visualize and analyze information effectively. It ensures that data elements from different systems or formats are correctly aligned, allowing businesses to get new ideas, make better decisions, and unlock the full potential of their data assets.

Effective data mapping is crucial for businesses as it allows seamless integration and understanding of information from various sources.

For example, data mapping allows an e-commerce company to link customer purchase history with demographic data, enabling personalized marketing campaigns. 

It ensures data consistency, improves accuracy, and enhances overall operational efficiency. With clear and organized data mapping, businesses can unlock the full potential of their information, optimize processes, and drive growth.

Data mapping is used by various professionals in businesses, such as data analysts, IT teams, business intelligence specialists, and data engineers. It will be helpful for anyone seeking to integrate, transform, and analyze data from multiple sources.

These experts play a crucial role in interpreting data mappings and using them to support decision-making processes and optimize business operations:

Data Analysts use data mapping to establish connections between different datasets, enabling them to effectively analyze and derive insights from the data. This in turn helps support various organizational initiatives such as optimizing marketing campaigns, identifying customer behavior trends, or improving operational efficiency. For example, a data analyst might use data mapping to connect customer data from various sources, such as CRM systems and transaction databases, to get a comprehensive view of customer behavior and preferences.

Data Migration Analysts are responsible for transferring data from one system to another during system upgrades or transitions. For example, when migrating data from an old CRM to a new one, a data migration analyst maps the data fields between the two systems, ensuring that data is properly matched and transferred without loss or corruption.

Data Engineers need data mapping to define the structure and relationships between data sources and targets. It's like connecting the dots and ensuring that data flows smoothly from one place to another, making it easier to work with and derive insights from. For instance, a data engineer might map the data flow between different databases, data lakes, or data warehouses to ensure consistency and enable efficient data processing.

With data mapping, Data Warehouse Architects create an effective data structure within the data warehouse, allowing for retrieval, analysis, and reporting. It's like creating a blueprint or a roadmap that defines how data from various sources should be integrated and stored. For example, they map the fields from transactional databases to corresponding dimensions and facts in the data warehouse schema.

Data Modelers use mapping in their work to define the structure and relationships of data within a database or system. They create mappings that show how different elements connect and interact with each other. This helps them create a clear and organized representation of the data, making it easier for developers and users to understand and work with.

Data mapping is applied in various scenarios to address specific business needs. It is commonly used in data warehousing, data integration, data transformation, and data migration. Let's take a look at each of them.

Data warehousing helps bring together data from different sources and store it in a single location for analysis. When users run queries, generate reports, or do analyses, they pull out data from the warehouse. But before it gets there, the data goes through a whole process of moving, combining, and changing. That's where data mapping comes in handy as it ensures the data finds its way to the right spot in the warehouse, keeping it intact and ready for analysis.

Data integration is an ongoing process of regularly transferring data between different tools. For example, an e-commerce company may integrate its online store with its inventory management system to keep track of product availability in real-time, or the sales data from the CRM with the marketing cost data from advertising platforms. Data mapping is used to align the data fields between these systems

Data transformation involves converting data from one format to another. This includes simple tasks like standardizing date formats, converting multiple currencies into one, reformatting addresses, or more advanced like blending the advertising spent, collecting user behavior events into groups, attributing sales to clicks, etc. For instance, transforming "December 31, 2023" to "31/12/2023", converting prices from USD to EUR, or getting the single table for Facebook and Google Ads performance. Data mapping defines the rules and formulas, ensuring accurate and consistent data transformation. 

Data migration involves transferring data from one system to another in a single event. It typically includes static data that doesn't change. After migration, the destination becomes the new source, and the original source is retired. Data mapping facilitates this process by linking source fields to corresponding destination fields.

Data mapping includes a series of steps to ensure accurate data extraction, transformation & loading. These steps include defining the data to be mapped, mapping the data fields, transforming the data to the desired format, testing the mapping for accuracy, finalizing and deploying the mapping, and continuously updating it as needed.

Let's explore these steps in detail to understand how data mapping works.

The first step is understanding and documenting the data that needs to be mapped. This includes identifying the data sources, analyzing their structure and content, and determining the specific data elements that require mapping. Clearly defined data is the foundation for effective mapping and accurate data transfer.

Once the data is defined, the next step is to establish connections and relationships between the data elements in the source and target systems. Mapping involves linking corresponding data elements and creating a logical connection between them. This process ensures that data is correctly aligned and can be accurately transferred from one system to another.

Data transformation is the most important step in the whole data mapping flow. It involves converting and modifying the data to match the format and requirements of the destination system. This may include data cleansing, standardization, aggregation, or other necessary adjustments to ensure compatibility and consistency between systems.

After the data is successfully mapped and transformed, testing is essential to validate the accuracy and reliability of the data transfer. This is all about verifying that the mapped data is transferred correctly, accurately transformed, and appropriately interpreted in the destination system. This step helps identify any discrepancies or errors that need to be addressed before the mapping is finalized.

Once the testing phase is successfully completed, the next step is to finalize the schedule. and deployment. At this step, you’ll typically need to document the mapping rules and implement them within the tool you use and processes that rely on the mapped data. This ensures that the data mapping is effectively integrated into the organization's operations.

Data mapping is an ongoing process that requires regular maintenance and updates. As business needs to evolve, systems to change, or new data elements to be added to the data schema, it is crucial to review and update the data mapping accordingly. By consistently updating the mapping rules, businesses can adapt to changing requirements, enhance data accuracy, and maintain the effectiveness of the mapping process over time.

When considering data mapping techniques, it's important to evaluate their relevance to your specific circumstances and the total cost of ownership (TCO) for the analytics platform performing data integration and transformation tasks. While some techniques may handle most of the work, a certain level of technical knowledge is still required in data mapping. There are three main approaches: manual, semi-automated, and automated, each with its own advantages and disadvantages.

Automated data mapping requires less technical knowledge and offers a low barrier to entry. Modern platforms leverage automation to allow both data experts and novices to complete data mapping without coding. Natural language processing is used to match data fields and provide insights, enhancing understanding and reducing assumptions.

Semi-automated data mapping strikes a balance between flexibility and effectiveness. It involves using graphical representations to visually map data links. Users can match data elements by drawing lines or using drag-and-drop functions. This technique requires coding knowledge and involves navigating between manual and automated processes.

Manual data mapping involves manually connecting data sources typically using SQL or other coding languages. This approach offers complete customization and flexibility but can be time-consuming and requires a certain level of expertise.

Investing in a data mapping tool empowers your organization to maintain transparency, improve data analysis, and effectively manage data mappings. It streamlines the mapping process, enhances data quality, and enables you to leverage the full potential of your data for informed decision-making.

A data mapping tool helps you understand the flow of data, ensuring transparency in the mapping process. It allows you to track data sources and destinations, transformations and mappings, providing a clear view of data lineage and, basically, how different data complement each other.

With a data mapping tool, you can enhance your data analytics capabilities. It enables you to connect siloed data, harmonize data formats, and create a unified and comprehensive dataset of business-ready data available for decision-making. By mapping data accurately, you can find new perspectives, identify patterns and trends, detect anomalies, and make informed business decisions.

Managing data mappings manually can be challenging. A data mapping tool simplifies the process by providing a centralized place for data modeling &  transformation so you can maintain data integrity, collaborate with other teams, and facilitate future updates or migrations.

When selecting a data mapping tool, prioritize those with wider format support to handle diverse data sources. Look for automation features to streamline the mapping process and scheduling capabilities for regular updates, ensuring efficient and accurate data flow.

Let's take a closer look at each feature:

A reliable data mapping tool should support a wide range of data formats, such as CSV, JSON, XML, and databases like Google BigQuery, MySQL, or PostgreSQL. It ensures compatibility with various data sources, allowing you to seamlessly map and integrate data from different sources.

Look for a data mapping tool that offers automation capabilities. This includes features like auto-mapping, where the tool suggests mappings based on predefined rules or patterns, saving time and reducing manual effort. Automation streamlines the mapping process, improves efficiency, and minimizes the chances of errors.

An effective data mapping tool should provide scheduling functionality. This allows you to automate data mapping tasks on a regular basis, such as daily, weekly, or monthly, ensuring that mappings are updated and synchronized with the latest data. Scheduling enables you to maintain data accuracy, stay up-to-date, and free up resources for other critical tasks.

With OWOX BI, you can collect all of your marketing data in a single storage. No coding is required. It's the simplest way to integrate data from popular advertising platforms like Facebook Ads, Instagram Ads, LinkedIn Ads, or Google Ads with user behavior data or data from your sales CRM. By merging these datasets, you gain ideas about which campaigns really generate profit and which ones don't, allowing you to optimize your marketing budget (cross-channel or cross-campaign) effectively.

OWOX BI goes beyond data integration by sessionizing the website user behavior data, allowing you to create and track cross-device user profiles, and numerous of other pre-made transformation templates. 

Once your data is collected in BigQuery, OWOX BI enables you to automatically prepare your data for visualization in Looker Studio (formerly, Google Data Studio) or use in other BI tools such as Google Sheets, Tableau, or Power BI.