What is data mapping? | insightsoftware

by thebestworldevents.com 24 February 2024 · Business Intelligence

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The quick and dirty definition of data mapping is the process of connecting different types of data from various data sources. Data mapping is essential for integration, migration, and transformation of different data sets; it allows you to improve your data quality by preventing duplications and redundancies in your data fields.

Data mapping is a crucial step in data modeling and can help organizations achieve their business goals by enabling data integration, migration, transformation, and quality. It is a complex and challenging task that requires careful planning, analysis, and execution. This field guide to data mapping will explore how data mapping connects volumes of data for enhanced decision-making.

Why Data Mapping is Important

Data mapping is a critical element of any data management initiative, such as data integration, data migration, data transformation, data warehousing, or automation. Data mapping helps standardize, visualize, and understand data across different systems and applications.

Data mapping is important for several reasons. First, it reduces the potential for errors and inconsistencies in data movement and transformation. Second, it enables the smooth flow of data through different stages of ETL (Extract, Transform, Load) workflow. Third, it supports data-driven decision making by providing a holistic view and context for data analysis. Finally, it helps comply with data privacy regulations by identifying key data sources and personnel responsible for them.

What Does Data Mapping Involve?

At its core, data mapping involves establishing a connection between data elements across various sources and target data systems. This process ensures that data is correctly and meaningfully transferred, transformed, or utilized across different platforms or applications.

Key aspects of data mapping include:

Source-to-target alignment
Field-level correspondence
Data transformation rules
Data relationship understanding
Data quality considerations
Metadata documentation
Compatibility and standards adherence
Data governance and compliance

Data mapping is a foundational step that lays the groundwork for successful data management initiatives, ensuring that data is a valuable and reliable asset for an organization.

Data Integration

Data integration is the process of connecting data from one system to another, such as when synchronizing customer data between marketing and sales platforms. Data sources are crucial for reporting, analyzing, and acting on transactional and corporate data and connecting these sources in real time with various tools like connectors, ETL tools, mashups, Web services, and data source-neutral BI solutions is essential. ETL helps collect, optimize, and store data for better reporting and real-time analysis. ETL is beneficial for larger data volumes and diverse sources, and may be necessary for data architects, developers, and administrators considering factors like volume, source diversity, accuracy, and efficiency.

Data Migration

Data migration refers to the process of transferring data from one location or format to another. This can involve moving data between different storage systems, databases, or applications. The goal of data migration is to ensure that data is accurately and securely transferred, often to enable better efficiency, scalability, or to adopt new technologies.

An organization may choose on-premise migration or cloud migration, depending on its needs. An on-premise solution provides a high level of control and customization as it is hosted and managed within the organization’s physical infrastructure, but it can be expensive to set up and maintain. A cloud solution is highly accessible and scalable as it is not constrained by physical hardware limitations, but its reliance on an active internet connection can pose security concerns.

Data Transformation

Data transformation is crucial to the data mapping process; it involves converting data from one format or structure into another to meet the requirements of the target system or application. This process is essential when integrating, migrating, or consolidating data from various sources into a unified and meaningful format. Data transformation encompasses tasks such as cleaning, aggregating, filtering, and restructuring data to ensure its consistency, quality, and compatibility with the target system.

ETL tools play a pivotal role in automating and streamlining the data transformation process. The ETL process typically involves three main stages:

Extract: In this stage, data is extracted from multiple sources, which could include databases, files, APIs, or other data repositories. ETL tools provide connectors and mechanisms to efficiently extract data from diverse sources.
Transform: The extracted data is then transformed based on predefined rules and logic. ETL tools offer a visual or script-based environment where users can define and customize the transformation processes. This includes cleaning, aggregating, enriching, and restructuring data to fit the desired format.
Load: Once data transformation is complete, the transformed data is loaded into the target system, such as a data warehouse, database, or another application. ETL tools ensure that the data is loaded efficiently and accurately into the destination.

Because ETL tools allow the creation of reusable transformation logic, they’re able to automate the data transformation process, reducing the need for manual intervention and minimizing the risk of human error. Many ETL tools offer a visual design interface, enabling users to design and manage complex transformation processes without extensive coding. Data transformation is a critical step in data mapping, and ETL tools play a pivotal role in automating and facilitating this process, ensuring that data is effectively converted to meet the needs of the target system or application.

Data Warehouse

A data warehouse is a collection of business data from multiple sources that is optimized for reporting, analytics, and decision making. Data warehouse architecture extracts data from existing databases, transforms it using specified rules, and loads it into a central repository for easy access and control, providing a foundation for business intelligence and analytics.

Data warehouses can be complex, time-consuming, and expensive. However, the right business intelligence solution will include a pre-built data warehouse that eliminates complexity, reduces costs, and decreases risk.

Advantages of Implementing a Data Warehouse During an ERP Upgrade

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Electronic Data Interchange

Data mapping transforms Electronic Data Interchange (EDI) files into a variety of formats, including Excel, JSON, and XML, and is particularly important in the process. Without writing a single line of code, the user can use a simple tool to extract data from various sources and apply pre-built transformations and functions to map data to EDI formats. It facilitates smooth B2B data transmission.

Automation

Automated tools significantly streamline and enhance the data mapping process in various data management initiatives. They empower users to focus on more complex aspects of mapping while reducing the manual effort required for routine tasks.

Organizations can use automation to:

Scan and analyze metadata from source and target systems, identifying data structures, relationships, and attributes
Match and align schemas between source and target systems, identifying corresponding fields, tables, or entities and reducing manual effort
Identify potential errors or inconsistencies in the mapping process, such as mismatches in data types or missing mappings
Perform real-time validation, error detection, and data quality checks
Leverage machine learning algorithms to improve efficiency and accuracy
Generate comprehensive documentation and reports for stakeholders, ensuring transparency and supporting compliance requirements

Data Mapping Examples

Source data is typically directed to a database, data warehouse, or file, and companies use a data mapping template to match fields. Source-to-target mapping integration tasks vary in complexity, depending on data hierarchy and structure. Business applications use metadata and semantic rules to ensure seamless data transfer without loss.

Mapping Customer Data

An organization decides to integrate data from its various customer touchpoints, such as sales, marketing, and customer support, into a unified CRM system. The company employs data mapping to align customer information from different sources (e.g., sales databases, marketing automation platforms, and support tickets) to corresponding fields in the CRM. This ensures that customer data is consistent, accurate, and accessible in a centralized location, enabling a comprehensive view of customer interactions.

Mapping Financial Data

A multinational corporation with subsidiaries in different countries needs to generate consolidated financial statements for reporting to stakeholders and regulatory bodies. Data mapping is used to integrate financial data from its various subsidiaries, to convert financial data denominated in different currencies to a common reporting currency, and to align financial statement line items (e.g., revenue, expenses, assets, liabilities) across subsidiaries, enabling the generation of consolidated income statements, balance sheets, and cash flow statements.

Mapping Operational Data

A retail company with an online presence and physical stores wants to analyze and report on sales performance across different channels and regions. They use data mapping to align product identifiers and categorizations and to integrate sales data from various channels, including online sales platforms, in-store point-of-sale systems, and mobile applications. Data mapping also helps categorize customers based on predefined segments (e.g., new customers, returning customers), supporting targeted reporting on customer behavior. Through effective data mapping, the retailer creates a comprehensive dataset for operational reporting, allowing stakeholders to analyze sales performance, identify trends, and make informed decisions to optimize business operations.

What are the steps of data mapping?

The first step of data mapping is defining the scope of your data mapping project. Focusing on objectives such as data integration, data migration, and data quality improvement creates the framework to consolidate data from various sources, ensures data integrity, and aids in identifying and rectifying errors, inconsistencies, or duplicates during the process.

Next, identify the data sources that will be involved in the mapping. Data sources can be databases, data lakes, files, applications, web services, or any other systems that store or produce data. Data sources can have different formats, structures, and schemas.

The third step is to define the data sets that will be mapped. Data sets are collections of related data elements that belong to a specific domain or context. For example, a data set can be a table in a database, a sheet in a spreadsheet, or a record in a file. Data sets can have various levels of granularity and complexity.

The fourth step is to match the data fields that will be mapped. Data fields are the smallest units of data that have a specific meaning and value. For example, a data field can be a column in a table, a cell in a sheet, or an attribute in a record. Data fields can have different types, formats, and constraints.

The next step is to transform your data, as it is likely not standardized or in the same format. Most tools simplify data manipulation with built-in data transformations and functions, including drag-and-drop, sort, merge, and join transformations. Complex transformations like tree join, normalize, and denormalize are also available.

Finally, test and automate your data mapping process. Start by mapping a small quantity of data and test and address any problems that arise. Once you have a functional process with no issues you can automate the rest of the data mapping process. Data mapping tools offer trigger-based task scheduling, allowing you to plan your process based on a specific time or event.

Data Mapping Techniques

There are three data mapping techniques; prioritize the method that best fits your situation and objectives, as well as the overall cost of ownership of the analytics platform that will handle the data integration work. Data mapping requires a certain amount of technical expertise, even though some of these methods take care of most of the work for you. Data mapping techniques range from fully automatic to entirely manual, and each has its own advantages and disadvantages.

Manual Data Mapping

Manual data mapping involves connecting data sources and documenting the process using code, typically in coding languages like SQL, C++, or Java. Because each field must be hand-coded and manually mapped from the data source to the desired destination, it requires data professionals for complete control.

While manual data mapping is completely customized to your specific needs, it is also labor-intensive and error-prone, putting a significant load on IT staff. Manual processing is not the ideal solution given the large amount of data that businesses produce these days.

Semi-automated Data Mapping

Semi-automated data mapping offers a balance of flexibility and effectiveness but requires coding knowledge and the ability to navigate between manual and automated processes. Also known as schema mapping, it uses graphical representations of data links and can be created using drag-and-drop functions or smart clustering in software like Tableau Prep.

While schema mapping is less resource-intensive than manual mapping, your IT staff will still need to check the connections made by the schema mapping tool and make any required adjustments.

Automated Data Mapping

Automated data mapping tools make data mapping processes accessible to anyone, regardless of technical ability. Featuring a low barrier to entry, automatic data mappers use natural language, drag-and-drop tools, and no-code visual tools to quickly and easily extract, transform, and load data from multiple sources.

Automated data mapping platforms offer advantages like instant data preview, reduced error risk, faster insight, and user-friendly control, making these solutions an excellent option for organizations that want to streamline data mapping without burdening IT staff.

Data Mapping Tools

Taking advantage of data mapping tools offers benefits like flexible deployment, low barriers to entry, and easy scaling to significantly improve your data mapping processes. These tools allow users to take charge of their data-driven initiatives without relying on IT teams.

A data mapping tool will simplify your data mapping by providing a visual and automated interface for data from source to target systems. It will support various data sources and targets, ensuring seamless integration.

When evaluating which data mapping tool is best for your organization look for:

Visual Interface: Create, edit, and visualize data mappings with an intuitive, visual interface that includes easy-to-use templates.
Connectivity: Enable seamless data integration with support for a wide range of data sources and targets, including databases, file formats, APIs, and cloud services.
Data Transformation: Ensure compatibility and consistency with advanced transformation functions, scripting capabilities, and data manipulation features to transform data during mapping.
Data Validation: Verify data accuracy and compliance with predefined validation rules and data quality checks.
Complex Transformations: Handle a variety of data mapping scenarios with support for complex data transformations, aggregations, calculations, and conditional logic.
Error Handling: Capture, manage, and resolve errors with robust error handling mechanisms.
Version Control: Manage changes to data mapping logic and maintain a history of revisions.
Automation: Accelerate data mapping and eliminate manual intervention with automation and job scheduling features.

Actionable business strategy begins and ends with accessible financial data. As the most comprehensive provider of solutions for the Office of the CFO, insightsoftware offers a number of tools to simplify and streamline your data mapping processes. With solutions across financial planning and analysis, accounting, and operations, we transform how teams operate, empowering leaders to make timely and informed decisions.

Logi Symphony is a powerful embedded business intelligence and analytics software suite that empowers Independent Software Vendors and application teams to embed analytical capabilities and data visualizations into their SaaS applications. With an emphasis on seamless integration, flexible customization, and composability with rich user experience, Logi Symphony grants users an overall level of mastery to data connectivity they won’t find anywhere else; it enables users quickly and easily complete critical data mapping processes with in-app contextual analytics and self-service analytics.

Bizview is an integrated, web-based budgeting, planning, and forecasting solution with an Excel-like interface that offers users solid, self-service data mapping, reporting, dashboards, and analytics. Because Bizview connects in near real-time to all your data sources, it simplifies and automates your data mapping processes to drive smarter decisions from more accurate data. Bizview allows you to break down silos and create planning transparency, shorten cycles to support continuous planning, and increase planning accuracy.

Spreadsheet Server is an intuitive Microsoft Excel add-in, that integrates with 140+ on-premises and cloud-based data sources to provide users with access to their live data within Excel for reporting and analysis. By directly integrating with your ERP, Spreadsheet Server fully automates your data mapping and empowers your team to create self-service reports right in Excel. By bridging the gap between company data and insightful reporting, Spreadsheet Server eliminates manual processes and enables efficient reporting across multiple data sources, saving your team valuable time with streamlined analytic processes in a secure environment.

Learn more about how insightsoftware’s data mapping tools streamline your processes and allow you to connect data faster and enable advanced data integration, migration, and transformation.