data mapping

Data mapping forms the core of every field’s data management, be it a tech company or a healthcare center.

It is the only way to make processing the bulk of data easier and simpler for companies. You can have a huge decision-making team. And, they would still not be able to make the best decisions for your company without the data mapped out.

Are you feeling confused already? Well, do not worry because we have explored the topic in detail below! Read more!

Data Mapping-What is it?  

The term data mapping refers to the practice of organizing data from different sources into one homogeneous set.

In simpler terms, it is an organizational technique. It combines a variety of information into one table or set based on certain mutual data. 

Modern Data Mapping Techniques

From manual to automatic, there are several different modern data mapping techniques in the market. Each one has its specific pros and cons. Let’s evaluate them in detail!

1. Manual Data Mapping

As the name suggests, manual data mapping involves constructing the tables and doing everything yourself.  Data mappers use coding to connect sources and process documents.

They may use coding languages, such as Java, SQL, and C++. Manual data mapping consumes a lot of time as everything gets done from scratch. 

It is an intensive and demanding task.  However, you get the benefit of complete customization and flexibility.

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2. Semi-automatic Data Mapping

In Semi-automated, companies prefer a blend of manual and automatic techniques. The data analysts use both coding and software to create data tables.

So, you can get the benefits of both types.  However, it also means you’ll have to tackle the cons of both too. 

3. Automatic Data Mapping

Today, everything has become automated.  So, it was no surprise when automation took over data mapping too. 

You can find several innovative data mapping tools in the present times, such as Ethyca. These applications visualize the data and organize it accurately.

Hence, analysts have a transparent and clear understanding of the data. Plus, you get to save all that time spent on coding. These tools also convert data formats, which conserves more time.

Moreover, data mapping tools are usually cloud-based.  It means the risk of data loss is minimal to none. Perhaps, the mere drawback of automatic data mapping is the requirement of highly skilled staff to operate it. 

Significance of Data Mapping in Tech World

Data mapping is indeed playing a prominent role in all fields, ranging from business to medicine. However, let’s talk about the tech world specifically.

While the central purpose of data mapping is management, there are countless minor processes in the tech world where we use it.  These include:

  • Merging Data
  • Transforming Data
  • Data warehouse creation
  • Transferring data from one place to another

All these tasks help maintain a smooth flow of a company’s data. Consequently, the organized information enables the decision-makers to make better decisions.  And, so the company thrives and prospers. 

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Let’s evaluate the first two in detail to understand data mapping’s position better.

Data Integration

It refers to the practice of integrating data into a data warehouse or workflow. Usually, the data is in a different form and format than the warehouse.

Hence, data mapping is necessary to evaluate the incoming data and its features. So, the experts can integrate it into the warehouse in the best way possible. 

Data Transformation

Unlike integration, data transformation refers to the conversion of data’s current format into another.  It is a pre-integration process where experts prepare the data to get combined in a warehouse or workflow. Here, data mapping assists in identifying the interdependent connections and links.