Challenges In Data Conversion How To Deal With

EM Admin
Oct 1, 2023
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Dealing with data brings a ton of challenges, especially when it’s about converting them into digital. This is simply because it involves a series of technical practices to take place for structuring, managing, enriching, cleansing, and standardizing details. These all tasks are not easy to go with. You have to be very accurate and consistent for converting datasets, which should be parallel to the associated objective.

In 1999, NASA lost a $125 million spacecraft . The reason was for sure a data conversion error. The luxurious spacecraft was structured to learn about the Martian climate. And, there was a thruster system in it to revolve it in orbit around the planet. The loss was indeed huge, which makes it crucial to think about conversion practices and their challenges

Challenges with Data Conversion

Now that you know how massive the loss can be, it’s important to learn how to deal with challenges in data conversion to make it accurately done. Let’s have look over the associated risks:

Data Conversion Challenges and Their Solutions

Data Loss

Data loss can be an unfortunate loss of precious inputs that could add vital information. It is like compromising a high-precision data to lower-precision datasets. Simply put, how wrong it will be if you convert a decimal value into an integer? Of course, this practice will lead to extracting precise decimal values, which configure accurate results in the end.

Precision and Rounding Errors

If you flip the aforementioned example, this challenge interferes. Yes, it is associated with the conversion of a negative or positive whole number or floating-point number’s conversion into a decimal value. This will certainly impact the outcome of two different types of numbers.

Compatibility Issues

Compatibility means closeness or match. In the context of data conversion, this error may occur when your system or software use a different data format, and you want your data to be intact. Let’s say, you have Microsoft SQL server-based data and want to create an Oracle database with it. The difference in their representation won’t allow you to do so. This incompatibility will certainly appear a big challenge.

Data Formatting Issues

Formatting represents the style in which a database is presented. Consider these formats of a date- 26th September, 2023 and 26-9-2023. However, both formats are fine and comprehensive. But, the presentation differs. This difference can result in incomprehensibility. Simply put, the user may get confused, understanding it as a code of an area. In essence, this could be a big risk to reach out to accurate information,

Methods to Defeat Data Conversion Challenges

The aforementioned challenges can be defeated with these methods, which are proven and tested before.

Data Profiling and Analysis

Data profiling refers to categorizing datasets under a specific profile. This kind of data conversion practices can make analysis a piece of a pie. With it, discovering potential issues in the quality of the data can be possible. This discovery will guide you to embrace the best practices to improve. With this method, one can easily come across precision and rounding values-related inaccuracies. Also, the loss of data can be minimal. Ultimately, you can take corrective measures for achieving accuracy.

Choosing the Right Data

There is no definition of the right data. But, such data that has exactness or match with the preset criterion can be accurate. Therefore, you should emphasize collecting the right set of details, whether in a decimal or whole or integer number. If there is no such possibility to find, convert them in the same format to draw the benefits of data conversion, which helps in battling out the precision challenge.

Handling Compatibility Issues

The compatibility, as aforesaid, is the matching or closeness. Here in the case of data transformation, you need to understand the objective and then integrate such sources and target systems/ software or application that are able to gel up consistently across different systems.

Data Mapping

Data mapping refers to matching datasets from different worksheets or sources. This can be difficult if you have an old version of Google Worksheet, for example. It allows you to automatically generate functions for conversion after an attempt. The old version may not be as efficient as it is. This is because of artificial intelligence algorithms that mapping won’t be like straining your head in shuffling between various worksheets. Nor do you need scripting skills to engage with APIs for automatically implementing various functions.

Testing and Validation

Testing and validation are connected, as validation is itself a part of auditing data. This process is also helpful in combating conversion challenges. Various data specialists choose methods as per their convenience and knowledge, such as unit test, integration test, and compatibility test to ensure seamless transformation of datasets. And hence, the result can be fruitful.

Wrap Up

Data conversion can infest your database with many errors or complexities, which emerge challenges. These threats should be addressed in time to maximize the value of data with conversion. This leads to transformation of your records, which conclude accuracy and integrity when you draw results from them. The knowledge of the aforementioned challenges can help your organization to overcome them. Various organizations can perform successful processes, ensuring data conversion projects and ensure the quality in datasets.