Navigating the Data Jungle: Your Guide to Data Mapping Techniques and Tools

In the jungle that is today‘s data landscape, organizations are dealing with an unprecedented variety and volume of data. According to IDC, the amount of data created, captured, copied and consumed globally is expected to grow from 64.2 zettabytes in 2020 to more than 180 zettabytes by 2025.

But it‘s not just the sheer quantity of data that‘s challenging. It‘s the fact that this data is often scattered across dozens or hundreds of different systems, databases and applications, each with its own unique structure and format. Gartner estimates that most organizations have data in over 80 different systems on average.

This fragmentation makes it incredibly difficult to get a unified, consistent view of key entities like customers, products, employees and more. Without this single source of truth, organizations struggle to answer basic questions, make informed decisions and deliver seamless experiences.

That‘s where data mapping comes in. Data mapping is the process of connecting and matching fields from different data sources to create a unified, 360-degree view of your core business entities. It‘s about finding the needle in the haystack – or in this case, finding the commonalities across haystacks to weave them together.

Why Data Mapping Matters

Effective data mapping is the foundation for all sorts of critical business capabilities and initiatives:

  • Customer 360: Combine customer data from your CRM, marketing automation, support desk, billing system and more to get a complete picture of every customer‘s profile, preferences and interactions. This is key for delivering personalized experiences and improving customer lifetime value.

  • Business Intelligence: Integrate data from various transactional systems and external sources into a central data warehouse to support cross-functional reporting and advanced analytics. Data mapping ensures that data is structured consistently for accurate, reliable insights.

  • Data Migration: Move data from legacy systems to modern applications as part of a digital transformation initiative. Data mapping helps ensure a smooth transition by properly matching and reformatting fields.

  • Application Integration: Connect data flows between different applications to automate business processes and eliminate manual data entry. Data mapping defines the rules for how data should be transformed and routed between systems.

  • Compliance: Demonstrate compliance with data privacy regulations like GDPR and CCPA by documenting what personal data you collect, where it resides and how it‘s being used. Data mapping is a key part of building an accurate data inventory.

The benefits of data mapping done right are immense. According to IBM, organizations that use data mapping and related techniques to create a unified view of customers are 2.6 times more likely to significantly outperform their peers in terms of revenue growth.

Data Mapping Techniques

So what does data mapping actually entail? At a high level, there are three main techniques that organizations use to map their data:

1. Manual Mapping

Manual data mapping is the most basic approach, where data analysts and engineers study the structure and content of different data sources and manually define the relationships between fields. This is typically done using spreadsheets, diagramming tools or custom code.

For example, a data analyst might compare the schema of a sales database and a marketing database and create a spreadsheet that specifies how fields like "customer_id", "first_name", "last_name", and "email" match up between the two.

Pros:

  • Allows for very granular, customized mappings to handle unique or complex data structures
  • Provides full control and transparency over mapping logic
  • Doesn‘t require specialized skills or tools (other than Excel and elbow grease)

Cons:

  • Extremely time-consuming and labor-intensive, especially for large volumes of data
  • Prone to human error and inconsistency, which can lead to data quality issues
  • Difficult to maintain and update mappings as data sources change over time
  • Not scalable to support enterprise-wide data integration needs

Manual data mapping can work for one-off projects with a small number of data sources. But in today‘s hyper-fragmented data landscape, relying solely on manual mapping is a recipe for perpetual inefficiency and inaccuracy.

2. Schema Mapping

Schema mapping is a semi-automated technique that uses specialized tools to visually "draw" relationships between database tables and fields. These tools typically have a graphical interface where users can drag-and-drop to connect related entities.

For example, a data engineer could use a schema mapping tool to create a visual map of how customer tables from three different databases (e.g. sales, marketing, support) should be combined into a single master customer table. The tool then generates the code to implement these mappings in the target database or data warehouse.

Pros:

  • More efficient and less error-prone than purely manual mapping
  • Provides a visual, intuitive way to identify relationships between data entities
  • Automatically generates the SQL code and documentation to implement mappings
  • Enables some automation, like the ability to infer likely mappings based on name similarity

Cons:

  • Requires a skilled technical user who understands database structures and query languages
  • Limited to mapping data between structured databases (not unstructured or semi-structured sources)
  • May require manual intervention to handle complex mappings or ambiguous matches
  • Still largely relies on human judgment to define the right mappings

Schema mapping tools are a good fit for data integration projects that primarily involve moving data between relational databases with well-defined schemas. But they fall short in handling the diversity of data types and structures that are common in modern data environments.

3. AI-Driven Mapping

The cutting edge of data mapping is using artificial intelligence and machine learning to automatically discover relationships between data entities. These intelligent tools analyze the actual values and patterns in the data to infer likely matches and mappings.

For example, an AI-powered data mapping tool could look at customer data across a dozen different systems and automatically identify matching records based on similarities in names, addresses, phone numbers, and other attributes. It would then suggest the statistically most likely mapping for each field and entity.

Pros:

  • Dramatically reduces manual effort by leveraging pattern matching and predictive algorithms
  • Continuously learns and adapts mappings based on user feedback and new data
  • Can handle a wide variety of structured and unstructured data sources
  • Automatically detects schema changes and new data sources to keep mappings up-to-date
  • Provides a foundation for data governance by inventorying and cataloging data assets

Cons:

  • Requires high-quality data to train AI models effectively
  • May need human oversight to validate mappings and handle edge cases
  • Can be a "black box" in terms of exactly how the AI is determining mappings
  • Typically involves higher software licensing and compute costs

AI-driven mapping tools are becoming essential for organizations that need to map data at enterprise scale and keep up with the pace of change. By 2022, Gartner predicts that 60% of organizations will use AI-enabled data management solutions for tasks like data mapping and quality.

Choosing a Data Mapping Approach

So which data mapping technique is right for your organization? As with most things in life and data, it depends. Here are some key factors to consider:

  • Scale and Complexity: The more data sources and variety you have, the more you‘ll benefit from automation. AI-driven mapping is a must for large enterprises with hundreds of disparate systems.

  • Data Literacy: Manual and schema mapping requires skilled data practitioners. If you don‘t have that expertise in-house, an AI-driven tool can help fill the gap.

  • Use Case: Are you doing a one-time data migration or ongoing data integration? Schema mapping may suffice for the former while AI is better for the latter.

  • Budget: Manual mapping has the lowest software cost while AI-driven mapping has the highest. But don‘t forget to factor in the cost of manual labor and the risk and impact of mapping errors. Investing in the right tool often pays off in terms of accelerated time-to-value and better data quality.

At the end of the day, most organizations will use a combination of techniques depending on the specific use case and data sources involved. The key is to have a flexible, scalable approach that can adapt as your data landscape evolves.

Top Data Mapping Tools

To put these data mapping techniques into practice, here are three of the leading tools and platforms to consider:

1. Tamr

Tamr Data Mapping Tool

Tamr is an industry-leading data mastering platform that uses machine learning to automate data mapping at scale. It‘s particularly well-suited for large enterprises with complex, siloed data environments.

Key features include:

  • Automated data profiling and cataloging to identify relevant data sources
  • ML-driven entity resolution and record linkage to match records across sources
  • Intuitive UI for data stewards to review and provide feedback on mappings
  • Continuous learning to adapt mappings as data changes
  • Explainable AI to provide transparency into mapping decisions
  • Cloud-native architecture for scalability and flexibility

Pricing for Tamr is based on the volume of data and number of data sources. They offer a free trial and a range of professional services to help with implementation.

2. Informatica

Informatica Data Mapping Tool

Informatica provides a suite of data integration and management tools, including their flagship Intelligent Data Platform. Data mapping is a core capability within the platform.

Key features include:

  • Visual no-code mapping designer for both technical and business users
  • Automated schema drift detection to identify changes in source data
  • Pre-built transformations and parsing for common data formats
  • Collaborative mapping where multiple users can work on the same mapping project
  • Automated data lineage and documentation
  • Compatibility with a wide range of data sources and targets

Informatica offers both subscription and perpetual licensing, with prices that vary based on data volume and deployment model (cloud or on-premises). A free trial is available.

3. IBM InfoSphere

IBM Data Mapping Tool

IBM‘s InfoSphere platform provides a comprehensive set of tools for data integration, quality, governance, and privacy. The InfoSphere DataStage module handles data mapping and transformation.

Key features include:

  • Graphical drag-and-drop UI for visually designing data flows
  • Multiple transformation stages and operators to manipulate data
  • Built-in data quality checks and standardization
  • Automated job sequencing and scheduling
  • Version control and collaboration features
  • Scalable architecture for handling large data volumes

IBM offers both subscription and perpetual licensing for InfoSphere, with prices that vary based on deployment model and data volume. A free trial is available as well as extensive documentation and support resources.

Other notable data mapping tools include MarkLogic Data Hub, Talend Data Fabric, Oracle Data Integrator, and Altair Monarch. The "right" tool will depend on your specific data environment, integration challenges, and budget.

Mapping the Way Forward

As the volume and variety of data continues to grow, data mapping has evolved from a nice-to-have to an absolute necessity. Without a clear map of how data entities are related across different systems, answering even basic business questions can feel like wandering through a dense jungle without a compass.

The good news is that there are more tools and techniques than ever to help tackle this challenge. By leveraging data mapping best practices and picking the right tool for the job, organizations can go from data chaos to clarity.

Some key best practices to keep in mind:

  • Start with the end in mind: Define your key business entities and the specific use cases that data mapping will support. This will guide your choice of technique and tool.
  • Take an inventory: You can‘t map what you can‘t see. Use automated discovery tools to catalog your data assets across the enterprise.
  • Set data standards: Establish consistent naming conventions, data types, and formats to make mapping easier.
  • Make it collaborative: Data mapping is a team sport that involves both technical and business stakeholders. Look for tools that enable collaboration.
  • Measure and monitor: Establish data quality metrics to assess the accuracy and completeness of your mappings. Continuously monitor for issues and changes.
  • Think long-term: Data mapping is a journey, not a destination. Pick tools and approaches that can scale and adapt as your data evolves.

Data mapping may not be the most glamorous aspect of data management, but it‘s one of the most critical. By investing in the right skills, processes, and technologies to map your data landscape, you‘ll lay the foundation for better analytics, smarter decisions, and smoother operations.

So sharpen your machete, equip yourself with the right tools, and start exploring. With a clear data map in hand, you‘ll be ready to navigate the jungle of data and blaze a path to business success.