Generative Data Intelligence

What is Data Mapping and How to map data efficiently?


Data mapping provides an organizational overview of an enterprise’s data and how it is used. It helps to identify areas of risk, optimize performance, and ensure the solutions comply with the company’s regulations.

It plays an important role in the effective management of large-scale applications.

When conducting a data mapping process for large-scale applications, it is important to consider a few key aspects:

Data mapping for enterprises helps them analyze all the information they have and ensure it works well and is safe. It also helps them organize their data in a way that can grow and work with other things they use. This can help leaders make better choices and save money and time.

Want to automate repetitive manual tasks? Save Time, Effort & Money while enhancing efficiency!

Data mapping automation

Data mapping automation tools are invaluable for businesses in the modern era.

These tools allow them to save time and money while improving the accuracy and efficiency of data mapping.

Following are some of the most famous data mapping automation tools.

  1. Nanonets
  2. Pentaho
  3. Talend
  4. Informatica
  5. Altova
  6. Salesforce
  7. IBM InfoSphere

The automation tool helps to automate the process of transforming data from one format to another. It makes it easier to share information across multiple systems and applications. You can use this automated process in multiple areas of business. Examples include financial reporting, customer relationship management (CRM), e-commerce, and more.

Nanonets for Data Mapping

Nanonets is an AI-based OCR software with advanced workflow capabilities. It is the perfect solution for businesses looking to maximize the potential of document data. Whether it is automating document data entry, document verification, document management, or document storage, Nanonets can handle every document-related task efficiently.

Nanonets connects with 5000+ applications, making it easier to quickly map data across platforms, sync data, and validate data points. Nanonets can map data from any document ( images, PDFs, word documents, CSV, excel) and sync it up with any software of your choice.

Why Nanonets? Well, here are some reasons:

Have a use case in mind? Reach out to our sales team or start your free trial!

How do Data Mapping Automation Tools Work?

These automation tools work by using algorithms and specialized software. They convert data from its original format into a different one (for better understanding).

With data mapping automation, businesses can automate many aspects of their processes. It includes data transcription, cleansing, merging, and integration.

However, some technical expertise is required to properly implement the automation.

Automated mapping tools provide helpful features like drag-and-drop interfaces, user-friendly wizards, and graphical representations of changes made on different fields or tables.

Advanced features like fuzzy matching are available, allowing similar pieces of information from different sources or documents to be combined while quickly preserving accuracy.

How are Data Mapping Automation Tools Beneficial?

Data mapping automation provides businesses with a great way of streamlining their processes. It does so while producing more accurate results in much less time than manual methods.

It surely requires some initial setup. However, this will ultimately pay off significantly. It increases the efficiency of the process, saves time, and improves accuracy when dealing with large volumes of complex data sets.

Additionally, automated mappings are always up-to-date. They will automatically adjust when changes occur in the source or target format, meaning you don’t have to worry about being out of date.

What are the best practices for data mapping?

The best practice for data mapping is to maintain a consistent, accurate, and up-to-date map of data flow across systems. It involves understanding and documenting the relationships between different elements in a data flow. It should also quickly and effectively identify any inconsistency or indifference. The goal is to ensure that data is properly managed throughout its life cycle, from generation to storage to analysis.

Planning the Data Mapping Approach

First and foremost, organizations should plan their data mapping approach ahead of time. It includes creating a roadmap of all the endpoints involved in the data flow, along with their respective connections and dependencies. This plan should be documented so that everyone involved can understand it. It’s also important to review this plan repeatedly to ensure accuracy and completeness. An up-to-date system map will help prevent unexpected issues down the line.

Ensuring the Quality Processes

Organizations should also focus on quality assurance practices throughout the process. It involves validating data accuracy at each step in its journey while ensuring any errors are quickly identified and resolved.

Implementing the Suitable Procedures

It is important to have a proper way of adding new information to the map and make sure it is recorded and everyone knows about it. This way, any changes can be tracked and understood by all users.

Adopting the Use of Third-party Analytics Tools

Organizations should also consider using advanced analytics tools for data mapping activities. These tools can automate many tedious tasks associated with manual mapping processes. These include tracking different versions of documents or verifying complex relationships between datasets. They can also uncover hidden patterns or insights within large datasets that are not noticed during traditional methods.

Automating the Data Mapping Process

Finally, having automated tools for data mapping can reduce overall time spent and maintenance costs for larger projects which require frequent updates over time.

What are the different use cases of data mapping?

Following are the main use cases of data mapping out of many.

Data Integration

Data integration aligns and combines data from two different sources into a single understandable format. It helps for more efficient analysis and reporting. This process involves the following steps

1.     Identifying common data elements across multiple systems

2.     Removing duplicate data

3.     Formatting the data in a single format

Data Migration

Data migration means moving data from one system to another. This process involves identifying and mapping data elements from the source system to the target system. It ensures that all relevant data is transferred and data integrity is maintained.

Data Transformation

Data transformation means changing the format or structure of data to make it more usable or compatible with other systems. This process involves identifying and mapping data elements from the original format to the desired format. And then using that mapping to transform the data.

We’ll also discuss the main techniques of data mapping.

Manual Data Mapping

As the name suggests, this technique requires manual labor. An IT professional does all the mapping processes manually.

Schema Mapping

The second technique is half automated and half manual. A data mapping software is used to establish the path between the source data and the target. The IT professional will then check it and do the required adjustments.

Automated Data Mapping

It is a fully automated data mapping technique that uses an automation tool for the whole process. Thus, it is the most accurate, efficient, and simplest way of data mapping.

Final Words

Data mapping is the process of creating a roadmap for your data. It is done so you can better understand where it comes from, how it flows through your systems, and where it goes when it’s no longer needed.

This guide provides an overview of data mapping, including its benefits and key steps to get started. By creating a data map, you can clearly understand your organization’s data landscape. Then you can make more informed decisions about managing and using your data best.

Optimize data processes with Nanonets. Map data from documents on 5000+ business software seamlessly. Give it a try.

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