What is Data Mapping? Definition, Methods, and Tools
Linking data from one source to another by visualizing correlations between data fields is called data mapping.
Data mapping is defined as the process of linking data from one source to another by drawing and visualizing correlations between their component fields and integrating them into a centralized schema or database for compliance, deduplication, and analysis. This article outlines the definition of data mapping, its various methods, and the tools used to achieve this.
Table of Contents
What Is Data Mapping?
Data mapping is linking data from one source to another by drawing and visualizing correlations between their component fields and integrating them into a centralized schema or database for compliance, deduplication, and analysis.
In the context of technology, data is a collection of facts and statistics used for referencing or evaluation. Through data analysis, one may gain significant insights. Almost all organizations use data-driven insights to expand their businesses. For every data analysis project to provide relevant findings, it is necessary to guarantee that the data has been transferred and mapped correctly. Data mapping becomes vital at this point.
Data mapping is the method of linking a data structure through one point of source to another source’s data field. This decreases the possibility of mistakes, helps standardize the material, and makes it simpler to comprehend your data, for instance, by connecting it with IDs. Data mapping is a crucial component of any data privacy strategy since manually locating and categorizing sensitive and personal information in bulk and comprehending how your organization utilizes and distributes it is neither entirely exact nor exhaustive.
One must understand that conventional methods cannot meet current privacy regulations’ data availability and compliance obligations. Companies require a consistent, dependable data mapping system to assist compliance initiatives. Companies must also comprehend customer data in the context of data subjects, as defined by the European Union General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). Further, data mapping helps utilize multifaceted information properly for business purposes.
Imagine that your company is gathering client data from desktops, mobile devices, and servers. One may use this information for several reasons, including sponsored advertisements, email marketing, push alerts, etc. Nevertheless, many of these systems employ a distinct and specific data language. This might make it challenging to utilize any knowledge gained fully.
However, data mapping functions as an interpreter to bridge the gap, allowing your data to be transferred, merged, or altered right from its point of origin, to its destination without interruption. It is also necessary to power complex data-driven models and applications like artificial intelligence (AI).
The industry uses a set of data mapping and data transformation standards to enable this degree of interoperability. X12 standards are general Electronic Data Interchange (EDI) specifications meant to facilitate data transmission between businesses of any sector. Certified Standards Committee X12 (ASC X12) maintains the standards, and the American National Standards Institute (ANSI) is accredited to establish EDI standards.
R2RML is a standard developed by the W3C for mapping relational database data. Future technologies based on semantic web languages, like Resource Description Framework (RDF), Web Ontology Language (OWL), and standardized metadata registry, will automate the data mapping process.
See More: What Is Deep Learning? Definition, Techniques, and Use Cases
How does data mapping work?
If data mapping sounds onerous, you may gradually find your feet in the task by following these steps. Implementing a reliable data mapping tool upfront with the data lifecycle will save significant time and guarantee that the material under observation is stable and trustworthy.
- Locate the data in question: This comprises creating the necessary tables, fields, and formats for a particular activity. Determine the structure of the columns and fields in the system of destination. When undertaking data integrations, you must also define the frequency of data mapping.
- Find interconnections and correlations: In this stage, you map the source data’s tables and fields to the destination data’s tables and fields. Determine whether transformations are required as well. If so, you must include the equation or algorithm used for data modifications.
- Evaluate the data mapping method: Test your mapping to ensure it functions as anticipated and forms the expected connections. Examine a tiny data sample to start with. This will offer you a clearer sense of the effectiveness of your mapping approach. If difficulties arise, make the appropriate modifications and retest.
- Implement data mapping: It’s time to implement the data mapping structure if you’re satisfied with how the mapping is performed in your testing and you’re convinced that everything is functioning as intended. You can use a data mapping tool at this stage.
- Outline a maintenance plan: Organizations must maintain and revise the mapping procedure as data sources evolve and new information sources are introduced.
See More: What Is Machine Learning? Definition, Types, Applications, and Trends for 2022
Pros and cons of data mapping
A data map is similar to an index because it allows you to quickly and easily locate where and how your organization stores its data. Therefore, if you need to access product usage-related material, the data map enables you to immediately pinpoint its position/location so that you may retrieve and evaluate it. Apart from speedy analysis, data mapping offers several other key advantages.
Mapping per specific appropriate legislation may help you maintain compliance with the law and standards for your data. Additionally, it enhances data exchange across divisions and guarantees that users can access essential information. A robust data mapping system enhances data management and protection. In addition, it provides clearer, higher-quality data, facilitating faster and more accurate decision-making. One can rapidly recognize and respond to new trends.
However, it is necessary to keep a few potential challenges in mind when mapping data. The first is inaccuracy. Because of the high probability of errors and ill-informed decisions, every human-led process has the potential to become detrimental. Incorrect, repetitive, or otherwise outmoded data is of little value to your organization’s various teams.
Moreover, internal teams are already overburdened with responsibilities. To achieve a high degree of precision and certainty, they will need to spend time double-checking and reworking scripts and schemas. And improper field mapping might result in severe data loss and additional rework.
And remember that it is seldom possible to “set it and forget it” when using a data map. Specifications, reporting criteria, software procedures, and system modifications might occur at any moment, rendering any previous data map useless. Most businesses utilize sophisticated data mapping solutions that provide workarounds for these obstacles.
Data mapping objectives
Organizations typically engage in data mapping to carry out one or more of the following activities:
- Warehousing: Data warehousing consolidates all data into a single repository for analysis, searches, and reporting. The material in a data warehouse will have been through these three preceding stages.
- Migration: Migration is the single-time-only data movement from an existing system to a brand-new source. The originator is retired once relocated.
- Integration: It is a continual process of sending the data across systems, from one to another; this is often prompted by a particular event or part of a predetermined timetable.
- Transformation: Data transformation refers to changing unorganized (or disorganized) information from one earlier format to another, such as turning XML data into CSV. This includes reducing duplicates, eliminating nulls, refining the data, and modifying the data type.
Various use cases use data mapping (as well as sectors) to simplify data operations. In the healthcare business, for instance, source-to-target mapping facilitates interconnectivity for EHR (electronic health records) by trying to match source and target data.
See More: What Is Artificial Intelligence (AI) as a Service? Definition, Architecture, and Trends
Data Mapping Methods
Companies and IT departments may use the following techniques to map data:
1. The manual method
In manual data mapping, developers must manually code the links between the data source and destination architecture. Typically, the code is written in the computer language XSLT, which translates XLM documents into various formats. Eventually, as data systems expand and become more complex, human coders cannot keep up with the need for data mapping, and data teams will be forced to employ intelligent automation.
Due to the vast quantity of data presently accessible, this method is deemed obsolete and ineffective in contemporary commercial environments. However, it is still a practical mapping methodology for a one-time-only operation when the database is modest or not very complicated.
2. The semi-automated method
Semi-automated data mapping necessitates some understanding of coding and implies that the team will switch between conventional manual and automated information/data mapping methods. Data mapping software builds a link between the data sources, and an IT specialist manually adjusts the connections as required. This method is beneficial if your company is working on a limited budget. When dealing with a small quantity of data and looking to execute various migrations, integrations, and transformations, it is also recommended to employ this strategy.
3. Automated data mapping
In this approach, as the name implies, a tool handles all aspects of data mapping as your representative, which is incredibly beneficial if your team lacks a developer. The only requirements for these drag-and-drop tools are your attentiveness and, in most instances, a minimal membership fee.
Many individuals who are more comfortable with automation and wish to eliminate the chance of human errors choose automated data mapping. All procedures are performed by software, which eliminates the need for a programmer and enables the mapping of more extensive datasets. This strategy demands you investigate the structural dynamics of the program used.
4. The schema mapping method
Schema mapping is a semi-automated technique that uses software to link comparable schemas together with little manual effort. To create links, the program compares the sources of data and the destination schema. Then, a developer examines the design/map and makes any necessary revisions. After the data map is finalized, the schema mapping software creates the code (often in C++, C#, or Java) to add the info.
In addition to who maps data, it is also possible to classify data mapping methods based on where it happens.
5. On-premise data mapping
On-site data processing may seem safer, more accessible, and under control. But unless you want incredibly rapid accessibility to your internal data, on-premise is often too cumbersome and expensive in the long haul. This happens due to the acquisition and maintenance demands of equipment, programs, and other infrastructure. Large companies needing 100% control and observability over their data (e.g., a big bank) may opt for this method.
See More: Top 10 Open Source Artificial Intelligence Software in 2021
6. Data mapping on the cloud
Cloud-based data mapping technologies match the requirements of modern enterprises since they are designed to be fast, adaptable, and scalable. These solutions can readily adapt to evolving formats, neither decelerating nor losing data, and they are often supported by skilled installation and maintenance. However, cloud-based data mapping can lead to unexpected cost overruns if companies aren’t careful with their budgets.
7. Open-source data mapping
Cost-effectiveness is often a feature of open-source data mapping solutions. Utilizing the most recent code bases, these technologies are dependable and effective. However, they still need expertise and manual coding to be employed efficiently. They can be hosted on-premise or using cloud storage and are semi-automated or automated.
Top 8 Data Mapping Tools
Data mapping is an essential capability in today’s data-driven business environment. Here are eight tools that can simplify the process, reduce your efforts, and introduce new features when it comes to mapping multi-source data:
1. Talend Open Studio
Talend Open Studio (a free data mapping tool) supports over a hundred connections for diverse sources. Continuous integration reduces deployments and repository management costs. Its GUI or graphical user interface allows for the visual mapping of source data to target data types. With Talend’s GUI-led Master Data Management (MDM) feature, organizations may have a consistent and unified view of their information assets.
2. Pentaho
Kettle operates the very popular Extract Transform Load (ETL) tool Pentaho Data Integration. The architecture of the platform is user-friendly and very versatile. You can efficiently manage data conversions and generate reports with relative simplicity. The tool’s ability to accept inputs from various forms — including CSV, Excel, text, JSON, Hadoop, etc. — is one of its finest strengths. Pentaho Data Integration allows the ingestion, blending, mapping, cleansing, and diversification of data from any source.
3. InfoSphere
IBM InfoSphere offers an extensive suite of data mapping capabilities, encompassing ETL, master data, and quality management for data. In addition, it employs end-to-end integration to detect, cleanse, and convert all incoming data before beginning the data mapping process. IBM InfoSphere may be deployed on-premises or via a server in the cloud. This tool enables experts to analyze vast quantities of complex data and work on metadata projects.
4. AtomSphere
Boomi AtomSphere is a multi-tenant, on-demand cloud-based integration platform that synchronizes and replicates data across applications, software as a service (SaaS), and other databases. It provides a cloud integration framework that combines several apps using a repository of pre-built apps or bespoke solutions for more specific needs. This platform enables the construction of cloud-based integration procedures, known as “Atoms,” to move data between cloud-based and on-premise applications.
5. HVR Software
This program can handle enormous volumes of data and sophisticated ETL specifications. It’s intended for large to medium businesses. This data integration solution is loaded with capabilities that make it simple for administrators to manage diverse architectures, interact with various data sources, and provide meaningful statistical reports. HVR is a technology for on-the-go, real-time data mapping and duplication that can manage large volumes of data. One may use it for cloud-based mapping operations between data lakes and databases.
See More: What Is NoSQL? Features, Types, and Examples
6. Astera
Astera is corporate data analysis and management software that employs visual interfaces to transform, map, and evaluate data structures automatically. Users can develop, debug, and manage complicated data integration jobs using the tool’s drag-and-drop functionality. Astera connects natively to several database providers, notably SQL Server, Oracle, and DB2.
There are data cleaning, analysis, and mapping solutions to guarantee that the material is of the best standards. In addition, Astera’s built-in optimizations eliminate data duplication, fill in missing data, and remove redundant data. If your data records do not adhere to high-quality data standards, users will be identified and notified through emails.
7. Clover
CloverETL is another free Java-based data mapping as well as an integration solution. It is capable of transforming, mapping, and manipulating data. It allows customers to utilize it as a standalone program, command-line tool, server application, or embedded application. CloverETL enables businesses to rapidly build, test, deploy, and automate the source-to-destination data loading process. It equips developers with visual and code interfaces for mapping and transforming data.
8. MapForce
Altova MapForce is a solution that maps content between combinations of XML, databases, Excel Spreadsheets, JSON (JavaScript Object Notation), EDI, XBRL, flat files, and web service. The MapForce Platform is simple and easy to set up. Furthermore, the framework is adaptable to various applications and requirements.
This utility supports MapForce Server’s automatic mapping execution. This facilitates the automation of business procedures involving iterative data conversions. MapForce offers several solutions for organizing and viewing complex mapping projects and individual mapping, as well.
See More: What Is Finite Element Analysis? Working, Software, and Applications
Choosing the best data mapping tool
Choosing the appropriate tool for your project relies on its precise specifications. Nonetheless, the data mapping app should offer at least the following features:
- Code-free or partially automated data mapping capabilities
- Integration and transformation of data
- Integration with standard database management systems (DBMS) support for varied forms of organized and unstructured data
There are numerous other features available; however, these three are vital. One has to identify and evaluate a tool with all three features. Utilizing timeframe and event-specific job scheduling to synchronize a complete workflow is an excellent attribute of a data mapping tool. This automation reduces manual labor, increasing efficiency and saving time.
See More: OLAP vs. OLTP: Understanding 13 Crucial Differences
Takeaway
As businesses become increasingly more data-driven, data mapping is crucial for most organizations. Further, in AI fields and computer programming, maps are data structures storing key-value pairs used to run data operations. Using the right tools to automatically or semi-automatically draw correlations, you can make the most of the information assets available.
Still have a doubt or query about data mapping? Tell us on Facebook, Twitter, and LinkedIn. We’d love to speak with you!
MORE ON ARTIFICIAL INTELLIGENCE
- What Is General Artificial Intelligence (AI)? Definition, Challenges, and Trends
- What Is Super Artificial Intelligence (AI)? Definition, Threats, and Trends
- Top 10 AI Companies in 2022
- What Is Artificial Intelligence (AI)? Definition, Types, Goals, Challenges, and Trends in 2022
- What Is Narrow Artificial Intelligence (AI)? Definition, Challenges, and Best Practices for 2022
