Discover Everything about Data Conversion Process

EM Admin
Oct 20, 2022
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At present, we’re generating around 2.5 quintillion bytes of data each day. Over 44 zettabytes of data are all in the digital space and almost 70% of that data is user-generated.

Certainly, it’s way more difficult than ever to manage such colossal data manually. This is why we have a cloud, multiple applications, devices, and operating systems.

But, are they able to convert the streaming data into a properly structured form?

A few organizations might be successfully managing it. But, a significant number of enterprises are still trying hard to manage. The first and foremost struggle is with data conversion.

Let’s go through the explanation of what data conversion is first.

Understanding Data Conversion

This is a technical process of digitising data from PDFs, manuscripts, image files, and other file formats. It’s self-explanatory that this is a critical step. To make it a little better experience and easier, businesses hire data conversion companies. Their support prevents any tussle with technology, data capturing, scanning, and OCR conversion.

Simply put, certified and trained data conversion experts use their expertise and tools to translate data into readable, editable, and searchable digitised files. Later on, you may search and alter anything using metatags and metadata. It won’t need you to scroll up and down to skim through the vast content and manually filter the data for different purposes.

Example of Data Conversion

A transcript of a video and audio file and PDF conversion into a doc file represents its finest and easiest examples. This can help in batch (data) processing, which is aggressively used for drawing patterns or models for machine learning and business intelligence from the collected databases.

Uses of Digitised Data (Converted)

It enables one-point digital data solutions.
You can store data for defining their accessibility at any time from anywhere.
It can make your communication more fact-based, as you can view and present statistics or proven facts to define feasible solutions.
It ensures online interaction or conference calls without any hassles.
It prevents an uncertain loss of data due to fire, natural calamity, or any other physical reasons.
Automation gets big support through this data conversion process, which introduces speedy and accurate data entry processing. Later on, seeing beyond lines becomes easier, which triggers inventions and evolutionary ideas.

Which Data Types Can be Converted?

To initialize the conversion process, it is necessary to understand different types of data. This is the foremost step that defines the rest of the conversion journey. It helps programmers or conversion experts to determine how to execute further and which records are useful. Almost all types of datasets can be translated.

HTML file conversion using compiler languages.
Symbols and characters are recognised electronically.
Specific operating system code pages can be converted.
And all document types like text, excel, audio, visuals, etc.

Real-Life Applications or Use Cases of Data Conversion

Telecommunication and networking companies like NETFLIX rely on vendor-agnostic input and output for translating media files.
Insurance companies use it during the medical billing process, which helps them to settle claims.
Digital marketing companies use digitised market research and their outcomes for discovering winning marketing strategies.

How Does Data Conversion Work / Process?

It’s a technically complex task. Professionals use tools or scripting to automate the data capture process. Here are the steps.

Step 1. However, scanning is its first step. It ensures the availability of source records. If it’s implicit, the process jumps to the conversion step (the last step) automatically.

If the records are explicit, the process moves to the next stage.

Step 2. It starts with directing the API browsers (receiver and sender) to interact with each other and allow crawling.

Step 3. Then, the captured data are recognised automatically. It all happens through satisfying codes or scripts. Once rendered, they avail databases.

Step 4. Upon recognition, the datasets are saved on servers where security should be strict.

Step 5. This is a cleansing step wherein enrichment, de-duplication, formatting, and validation take place.

Step 6. Finally, quality analysts get deep into validations if they show the right output (during testing). This is how the benchmark quality is produced.

Step 7. At last, useful records are loaded to the target location on a server.

These basic steps make the conversion journey really satisfying.

Here, take into account that every programming language is unique and uses a different set of scripts or codes for converting different types of datasets. In accordance with that, the rules are set. Simply put, strong typing languages and weak typing languages determine which rules (stricter or looser) to follow. The former language rules produce predictable results, whereas the latter language rules may not.

This is how the entire data conversion process takes place.

Conclusion

The data conversion process is typically technical and involves any conversion tools or programming languages. With a few specifications via scripting or codes and settings, the programmer translates any type of dataset. This is how the process of conversion takes place, which sets up the way for digitization.