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Why is transforming data important?

Data Transformation

The complexity of data in today’s marketing world is increasing day by day, making it difficult for organizations to drive actionable insights from the big data silos. Analyzing valuable information requires structured and accessible data for achieving the best results. In such a scenario, the concept of data transformation comes into the picture. With the help of the big data transformation tools, users can alter the structure and format of the raw data as required. In this article, we will learn about the proven useful data transformation technologies, but first, let take a look at the brief explanation of data transformation.

What is Data Transformation?

Data transformation is a process in which the raw data is converted into a single and easy-to-read format so that the same can be further used for analysis. It’s all about turning your data into something that makes sense. With such a transformation, you can drive valuable and actionable information from the data that helps you make the right decisions at the right time. 

Data transformation also goes by the name ETL (Extract, Transform, Load), the complete process of aggregating data from multiple sources, transforming it into a particular format, and loading it into the desired location. The data loaded into your preferred data warehouse powers the analysis and reporting processes. 

Data transformation is a two-stage process that includes the following:

Stage 1

  • Finding data and identifying the types and sources.
  • Understanding the structure and what transformations are required.
  • Creating data mapping that defines how the fields are mapped, modified, joined, filtered, and integrated.

Stage 2

In the second stage of data transformation, the data experts extract the data from the original source. The sources can vary based on their structure, database, and streaming services, like log files from web portals. The experts will also perform transformation and convert the data extracted from multiple platforms like social media, sales, etc. into a lucid format. The transformed data is then transferred into the target location, mainly a database or a data warehouse that can efficiently handle large volumes of data.

Benefits of Transforming Data

  • The primary benefit of transforming data is to make it organized. When data is converted into a more structured and organized format, it becomes easier to use by both humans and computers.
  • The data which is formatted properly and validated boosts the data quality and prevents applications from errors like unexpected duplicates, null values, incorrect indexing, and incompatible formats.
  • Effective data transformation facilitates compatibility between systems, applications, and data types. The data can be transformed into different formats to meet different purposes.

How To Transform Data?

Data Transformation can add rockets to your organizational efficiency of performing analytic and business processes and enable better decision making. The first phase of data transformation must include conversion and flattening of hierarchical data. This will help you structure data to improve compatibility with analytics systems. You can further implement transformations according to the layers of processing required. 

Here’s how data transformation is done:

  • Extraction and parsing: Data integration begins with extracting information from multiple data sources, accompanied by copying the data to the target location. Initially, the transformation is focused on formatting and structuring the data into something that ensures its capability with both the target system and data already stored.
  • Translation and mapping: Now come to the mapping and translation of data. Translation helps in changing the format of data when it moves from one system to another. The data is translated into rows and columns, making it easier to understand and useful.
  • Filtering, aggregation, and summarization: This step involves the consolidation of data by removing unnecessary fields, records, and columns. Those data sets that are not required for a particular study are removed for easier analysis. Data can also be aggregated by transforming a series of data daily.

BI tools can also be used to automate the process of filtering and aggregation to make the data more efficient before reporting tools to access it.

  • Enrichment and imputation: Data from multiple sources are merged to create enriched information. All the customers’ transactions are rolled up and added to the customer information table for instant analysis. 
  • Indexing and ordering: The transformation of data can be done logically to suit the data warehouse scheme. For example, creating indexes can boost performance or increase the management of the relationship between tables.
  • Anonymization and encryption: The data set that contains identifiable information that can risk privacy & security can be anonymized before propagation. The private data can be encrypted as per the organizational requirements by using systems that can perform encryption at several levels.
  • Modeling, typecasting, formatting, and renaming: At last, the entire set of transformations can restructure data without changing the content. The task will involve casting and converting data types for compatibility, dates, times, renaming schemas, columns, and tables for better clarity.

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