Data Mapping That Simplifies Data Analysis

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When it comes to data, it’s easy to get lost in all the data that you have to work with. Data mapping allows you to more easily find and use the data you want, allowing you to spend less time searching through large amounts of information, and more time creating work.

The data mapping process involves first identifying the data sets that could be used, then organizing them into an appropriate structure, and finally ensuring that they are properly linked to one another.

The basics of data mapping are rather simple. It’s about establishing relationships between different sets of data. Any business that handles a large amount of data will benefit from having a formalized system in place for how they store the data and how it relates to other information within and outside their organization. As such, every single department of the company will benefit from a solid data-mapping program.

For instance, Let’s imagine a corporation has two databases with customer information, and the data analyst doesn’t want the analysis tool to count the same data twice. This will result in inaccuracies in the analysis, so here comes the data mapping! Data from one database can be mapped to another using data mapping to obtain accurate and relevant insights from the existing data.

By implementing data mapping into the existing database or developing a new database altogether, organizations can make sure all of the data is on one page, and it can also simplify internal processes like reporting and analysis.

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Why is data mapping important?

Data is what’s driving almost every decision made in companies. Whether it’s making sure a signup process is functioning correctly or setting KPIs for a marketing campaign, data can be found behind almost everything. Data helps identify where the company is spending its time and resources.

With data mapping, you can track where the data is going and ensure that it is stored in a way that makes sense. By making sure all the data storage is intentional, you have an opportunity to discover new insights about the business. It can help to become more aware of any gaps in the current system that may lead to loss or errors as well as opportunities for potential improvement.

Data mapping is important because it increases the quality and usefulness of the data. It makes data more accessible to analytical and business processes. A single mistake in data mapping can result in repeated errors and, eventually, faulty analysis. To prevent this, organizations must refine and map different sets of data correctly.

When extracting valuable information from a piece of data from one dataset, it’s possible that another group of data can provide more significance or correct facts. So the organization must accurately map this data from many sources to use it as a whole and get deeper insights and accurate meaning.

Data mapping is the first step toward making data migration, data integration, and other data management tasks more simple.

Data migration

Data migration is the process of moving data from one point to another, either from one database to another or from one system to another. In data migration, data mapping helps to map the data source field to the destination field.

Data integration

Data integration is the process of transforming data from one form to another. This process is usually done in batches, and it can be done for any type of information. Data mapping helps to match source fields with destination fields in the data integration process.

Data Transformation

Data transformation is the process of converting data from one format to another. It also includes removing duplicates and more. Data mapping is used in data transformation to create data links and to determine the relationship between different datasets.

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Types of data mapping

Manual Data Mapping

Manual data mapping can be completely custom to the exact needs but it is not feasible for large complicated datasets and even coding is required. It is time-consuming and resource-intensive. In these cases, businesses need to migrate to an automated solution.

Automated Mapping

For automated mapping, less technical knowledge is required and It’s code free. With automated data mapping, organizations can map data fields and records without having to know how they were designed. Even a non-technical person can carry out the mapping with automated mapping.

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Steps of data mapping

To create a successful data map, it’s important to be familiar with all steps of data mapping. You can break down data mapping into five primary stages: Data identification, Mapping the data, Transformation, Test and deploy, Maintenance and update.

Data identification

Identify the data that needs to be mapped and ensure the accuracy of that data.

Mapping the data

Map data from source to destination field and watch closely for any errors.

Transformation

Data should be transformed from the source field to the destination field and can be stored and used efficiently later.

Test and deploy

Run the test to check how it works and make any required modifications. After the test has been completed, migrate data to the data store.

Maintenance and update

As new data is introduced, it becomes more important to maintain and update the data mapping process.

How to choose the best data mapping software?

Deciding which data mapping software tool is best for your organization can be challenging. Many companies are confused about how different tools work and what features are important to their business. Before you make a decision, here are some of the things you should consider when selecting a data mapping software platform.

Look for a tool that supports most sources and destinations – The tool should be able to work with different format data. Organizations want a solution that doesn’t impose limitations on data processes.
It should be easy to use, able to work quickly and efficiently (saving time), affordable and supportable by those who have used it.
The tool should be automated enough so that it can be set up fast, but still gives enough control to fine-tune it based on the specific map requirements. The ability to auto map fields will save time and effort while saving flexibility at the same time.
The tool should be able to create a comprehensive mapping workflow by scheduling mapping tasks that are triggered by the calendar or an event.

Data mapping has gained a lot of momentum in recent years. Why? Because it can streamline data analysis processes to improve company operations, make informed decisions, and help with important business outcomes.

Even with these benefits, not all companies are using data mapping effectively. Many companies still struggle to collect and organize their data before they begin performing analysis. Today a huge amount of data is generated and by the year 2025, 175 zettabytes of data will be produced everyday. Now is a crucial time to implement data mapping successfully in the business.