Over the past couple of decades, businesses have witnessed a rapid increase in the volume of data they manage. The data is generated from a variety of sources, including customer transactions, social media posts, and even sensor readings from machines in a factory. Managing it effectively is essential for businesses if they want to stay competitive and smoothly pave their way into the future. One way to do that effectively is to use data transformation techniques to make the raw data more organized and digestible.
What Is Data Transformation?
Data Transformation, also known as data wrangling, is the process of changing the form of data to make it usable in a new format.
The transformation process can include both simple and complex tasks like joining two tables together, formatting specific columns in a table, converting values in certain columns (e.g. from string types to numerical), etc.
Typically, during this process, an analyst will choose the structure, carry out data mapping, extract the data from the original source, execute the change, and finally store it in a separate database in a suitable format.
The transformed data is usable, accessible, and secure across a variety of applications. Organizations may also use the process to make the native data more compatible with other essential business information.
There are three kinds of data transformation:
- Structural – moving, combining, renaming columns in a particular database.
- Construction – copying, replicating, or adding data.
- Aesthetic – street names / standardizing salutations.
Why is Data Transformation Important?
Transforming the data allows business users to
(1) more thoroughly and more quickly grasp what they’re reviewing.
(2) also improve the performance of queries and reports.
(3) and reduce the amount of time you spend configuring your database.
By reducing the number of data sources that need to be queried, the time it takes to get the desired results is minimized too. This is especially useful when dealing with large volumes of information.
How Does Data Transformation Work?
Transforming complex information into new formats can be divided into two primary steps:
Mapping – transferring the information from an original source to a new destination. This may be done by copying and pasting all of the data from one database to another, or combining different datasets together. You can get the most out of mapping by extracting from its original source, cleaning and formatting and loading it into a new destination.
Transforming – processing the information using a predefined set of rules that help the way the data appears, is accessed, and used. However, you need to be applying transformation rules, checking for errors and resolving them, publishing the transformed data
Both steps are carried out by data transformation experts, who possess a deep understanding of the data and its relevance to the business.
Benefits of Data Transformation
- Improving data quality – ensured accuracy and consistency with new changes in the organization.
- Encouraging collaboration –eliminates barriers between teams by enabling them to work on the same dataset.
- Facilitating analysis – analysts more readily explore and understand the information.
- Improving performance –time to digest, evaluate, and analyze data is greatly reduced
- Bettering data management –reduces the risks associated with truncated, manual data management.
- Perfecting search queries – cleaner code and cleaner results reduces the complexity of data queries
Although there are several advantages of data transformation, there are a few obvious drawbacks.
For example, it may be resource-intensive and costly. And, if you’re working with inexperienced data analysts that don’t have the relevant subject matter knowledge, the entire process may encounter difficulties.
Overall, however, the benefits of data processing far outweigh the drawbacks.
How Procoto Improves Data Transformation
Procoto is a leading sourcing software solution that can make data transformation highly efficient and cost-effective. But how do we do that? Here are some of the practices we use to ensure the success and continual improvement of data transformation projects:
1. Define the Target
We work with our customers to identify the data source and what our ultimate goals are for the transformation.
2. Understand the Source
Once we know from where the data is coming, we digest its structure and format.
3. Choose Processes for Transformation
We outline the processes to transform the data into its final structure.
4. Execute the Transformation
Once we finalize the right tools, we transform the data accordingly. This will include reading it in from the source database or file, changing its structure to meet customer requirements, and normalizing it into the new format.
5. Monitor & Review
We review the results with our customers to validate that all objectives have been satisfied. Once done, we review the new process to ensure the ongoing data normalization functions smoothly.
Organizing, structuring, transforming data can be a highly resource-intensive and time-consuming project, but the benefits are indisputable.
If your organization is not already transforming data, it’s time you started. Procoto can help you there. Let’s chat!