This is the third and final blog post in my “Charting a Course Through the Data Mapping Maze.” If you’ve been following the previous posts, thanks for joining me on this journey. Part one defined data mapping and outlined key components and why it’s essential. Part two explored how data mapping works, the common techniques used, and the top challenges to be overcome.

A few years ago, Harvard Business Review reported on a study showing a paltry “3% of companies’ data meets basic quality standards.” Bad data is bad business, and countless examples show the economic and reputational impact. Unity Software disclosed on a Q1’22 earnings call that it took a $110 million hit to its bottom line after “ingesting bad data from a large customer.” 

Selecting the right data mapping tool is an important decision to help maintain and improve an organization’s data quality.

Mapping Data Mapping Solutions

Data mapping is performed in three main ways: manual mapping (people meticulously match data elements based on their understanding), automated mapping (algorithms and tools match data elements, which is useful for large-scale projects), and semi-automated mapping (algorithms suggest the best matches based on preset rules and people fine-tune to the details). 

A recent IAPP report shows that 32% of privacy professionals use manual methods for data inventory and mapping, 28% use a commercial tool designed for the job, 22% customize their governance, risk and compliance software, and 16% develop their own system internally. Technology teams often turn to data mapping solutions to address the challenges associated with manual mapping and creating/maintaining DIY solutions. 

Data mapping is part of many tools and platforms teams are already using.

• ETL tools typically include data mapping capabilities in data integration and processing workflows. These tools help organizations extract data from source systems, transform data using mapping rules and transformations, and load transformed data into target systems. 
• Data integration solutions and iPaas platforms often offer features like graphical interfaces for designing data mappings, transformations and workflows. They also include connectors and adapters to easily connect to various data sources, applications, and systems.
• Emerging new data file exchange platforms provide robust and even AI-enhanced data mapping capabilities, particularly suitable for data conversion, onboarding and migration use cases that require a combination of automation and collaborative human review. They support files from many sources in various formats and enable in-line manual review, cleanup and validation before import is executed.
• Cloud-based data integration services offer data mapping solutions in a cloud-native environment. These services provide scalable and flexible data integration capabilities, including data mapping, transformation, orchestration, and scheduling. 
• Open-source data mapping tools offer cost-effective solutions for designing and executing data mappings. These tools offer features like graphical interfaces, support for various data formats and protocols and community-driven development.

To make the most of any data mapping solution, below are the best practices to follow:

Prepare and communicate across teams: Start by thoroughly understanding the project’s requirements, business rules, and objectives. Collaborate with data teams, business stakeholders, and IT teams to ensure mapping efforts align with business objectives.

Analyze, automate, and monitor: Conduct data profiling to analyze data quality, patterns and relationships before mapping data elements. Use mapping tools and platforms that help automate processes and reduce errors. Implement data validation checks to ensure accuracy. Continuously monitor data mappings, track data lineage, and perform regular maintenance to address needs as they evolve.

Document: Make sure to document mapping rules, transformations, and metadata for transparency and consistency.

It’s easy to become overwhelmed when tasked with a data mapping project, particularly during complex data conversion, migration, or onboarding projects that involve large sets of data and source-to-target relationships that are not intuitive and differ across data sources. The good news is today’s tools can automate much of the work and support workflows tailored to specific business needs. Some tools include AI-enhanced features that vastly accelerate and improve the process. As a result, data mapping improves data quality and compliance, accelerates data availability (sometimes even associate revenue), and significantly reduces costs.