Data is the new gold. As organizations ingest more data daily, there is a need to make the best out of it. This can only be possible through data transformation. Data transformation is now made possible and much easier through the use of automated tools that transform your data in a short time by automating every process.

Businesses are becoming smarter...

More businesses are making data-driven decisions to improve their efficiency. A study shows that “businesses that rely on data management tools to make decisions are 58% more likely to beat their revenue goals than non-data-driven companies. And data-driven organizations are 162% more likely to significantly surpass revenue goals than their laggard counterparts”.

What is data transformation?

Data transformation involves the whole process of changing data to a better form for analysis, easy access, or use. Some of these processes could involve: merging, aggregating, summarizing, filtering, enriching, splitting, joining, or removing duplicated data.

It is the process of extracting good and reliable data from the source by converting data from unstable formats to a predefined structured format that can be used for making decisions. Data transformation is critical for data management (data warehousing) and data integration.

Challenges associated with data transformation

Even though we now subsist in a time of high technological advancement, the presence of automated tools for data transformation has not still made the process hitch free. Below are a few challenges encountered with data transformation.

  1. Lack of Expertise: There is a need for organizations to employ an expert to perform a data transformation process. Data engineers or data analysts without the appropriate skills will find it difficult to notice any error or malfunction in a dataset. The ability to handle large datasets requires high expertise. Also it is important for businesses to hire competent analysts so they can perform transformation on the right data needed for the business.
  2. Complexity and time-consuming: The process of transforming data can be very vigorous and time-consuming for organizations. Data engineers and scientists use more time in cleaning, compiling, and organizing data.
  3. High cost: Data transformation can be very expensive. The cost is completely dependent on the specific infrastructure or tools used to transform data.

Benefits of data transformation

Businesses benefit a great deal from transforming their data.

  • Data transformation will enable businesses to convert data from any source into a new structure that powers business intelligence and insights.
  • Data transformation will enable businesses to reduce every data error, incorrect indexing, and duplications which will help in improving the quality of the data.
  • It makes data to be well organized. Such data can be easily used for business intelligence purposes like data analytics, reporting, machine learning, and so on.
  • It promotes interoperability between several applications. The reason for creating other formats and structures in the dataset is for the data to become more compatible with other systems.

What are the stages of data transformation?

Stages of data transformation

When data is extracted from various sources, it is either raw or unstable. The entire process of transforming data is called ETL meaning Extract, transform and load. Through an ETL process, organizations can convert data to their desired formats.

There are several phases of data transformation that organizations must take into consideration which include the following :

  1. Data Discovery

The first step to data transformation is to identify and understand the information within the source formats. It is important to have a deep understanding of the type of data obtained from the source, such as structure, attributes, quality, and content of the source data. This is done to execute better data analysis and generate valuable intelligence about their data and business as a whole.

2.  Data Profiling

Data profiling involves examining data from the source to determine completeness, accuracy, and validity. When combined with an ETL process, it cleanses and enriches data. It helps to ensure that data moved to the target location is of high quality and is accurate. It also helps to identify issues of data quality that need fixing in the source and which data quality problems to fix during transformation.

3.  Data Mapping

Some of the most basic data transformations involve the mapping and translation of data. Data mapping is the process of extracting data fields from one or multiple source files and matching them to their related target fields in the destination. Mapping allows companies to extract business value out of data as the information collected from various external and internal sources for the data to be unified and transformed into a format suitable for the operational and analytical processes.

4.  Transform Data

There are several processes to be performed when you want to extract useful information from different sources. This is the crucial aspect of the data engineering process which allows you to have quality data to make decisions. They include:

a) Filtering, Aggregation, and Summarization

Data can be merged by filtering out unwanted fields, columns, and records. Data might also be aggregated or summarized depending on what you need the data for.

b) Enrichment and imputation

Data ingested from different sources can be merged to create denormalized, enriched information. Long or irregular fields may be split into multiple columns, and missing values can be imputed or corrupted data replaced as a result of transformations.

c) Indexing and ordering

Data can be transformed so that it is ordered logically or to suit a data storage scheme. In relational database management systems, for example, creating indexes can improve performance or improve the management of relationships between different tables

d) Anonymization and encryption

Data containing personally detectable customer information that could compromise privacy or security should be unnamed before distribution. Encryption of private data is a  requirement in many industries, and systems can perform encryption at multiple levels, from individual database cells to entire records or fields.

e) Modeling, typecasting, formatting, and renaming

Finally, a whole set of transformations can reshape data without changing content. This includes casting and converting data types for compatibility, adjusting dates and times with offsets, format localization, renaming schemas, tables, and columns for clarity.

Once this transformation is done, the data will change in structure over time and can be updated in case of changes. It makes the data easily accessible and understood by team members and organizations as a whole.

5. Send to Target Location

After transforming, send the data to the target location. Ensure you get the results you were hoping for so that your old data is presented in a new way and converted to a new format.

Types of Data Transformation

Types of data validation

Data transformation can be segmented into various types depending on how you want the result to be:

  1. Data cleaning is the process of identifying the incorrect, incomplete, inaccurate, irrelevant, or missing parts of the data and then modifying, replacing, or deleting them to increase the accuracy of the data. Data cleaning relies on careful analysis of data to remove all forms of incorrect, corrupted, incorrectly formatted, incomplete data within the dataset to generate meaningful insight.
  2. Data Deduplication: This is the process of extracting duplicate data from the dataset. It involves a data process where data that is duplicated more than once is removed from the dataset. This process analyzes incoming data and compares it with the data that is stored. If the data is there, the duplicated data can be deleted. Data has a monetary cost for its owner. There are storage costs for holding data, and then there are processing costs for querying data. As data volumes expand, data costs increase. and duplicated data has no value for the owner, yet it still costs money. In some circumstances, duplicated data can start to affect performance by slowing down query results.
  3. Data Filtering: This has to do with refining datasets into what the teams or users need without including any other data that can be repetitive, irrelevant, or even sensitive, it is a task performed to reduce data errors, amend reports, and query results.
  4. Data Integration: This is the process of combining different data sources relating to different departments and combining them together to provide organizations with a unified view of the data to perform business intelligence analysis or machine learning. It is a main component of the data management process.

According to the Smart Data Collective:

“If businesses want the right kind of data to underpin advanced analytics processes or to create multi-dimensional views of customers, data integration must be pursued as a strategic function that aligns with business objectives.”

5.  Data Joining and Data Union: It allows the joining of two or more columns together by their matching columns which indicate a relationship between multiple tables, which combines table data so as to query correlating data on the data. Data union is the coming together of two or more rows of tables together by matching rows that indicate a relationship between multiple tables.

6. Data Aggregation: This is the process of gathering, searching, and presenting data in a summarized format. The data may be gathered from multiple data sources with the intent of combining these data sources into a summary for data analysis. Data aggregation is useful for everything from finance or business strategy decisions to product, pricing, operations, and marketing strategies.

7. Data Splitting: This is the process of dividing data into different portions. It is used for developing, training and testing sets to be used for cross-validation. Data splitting is an approach to protecting sensitive data from unauthorized access by encrypting the data and storing different portions of a file on different servers.

8. Data Summarization: This is the process of presenting the summary of generated data easily and comprehensively. Presenting raw data is not advisable because it will contain a lot of data errors and also in a format that may be hard to understand. A carefully chosen summary of raw data would convey many trends and patterns of the data in an easily accessible manner.

9. Data Validation: This is the process of cross-checking for the completeness, accuracy, and quality of data before they are used for further processing.

Data Transformation Methods

Businesses can understand their data transformation through any of these transformation methods. These methods include:

  1. Transformation with Scripting

This involves the manual writing of code to perform the data transformation process from the beginning to the end of the transformation in R, Python, SQL, and other languages. It is good for customization but there are always unintentional errors that occur during writing and misunderstanding as the developer does not interpret the exact requirement accordingly.

2.  Transformation with On-site ETL tools

This involves working through an on-site ETL tool to extract, transform, and load data into an on-site warehouse. This tool is very expensive to set up and manage and organizations are now moving to advanced cloud-based ETL tools as a result of the volume of data.

3.  Transformation with Cloud-based ETL tools

The cloud-based ETL tool has helped to simplify the process of transforming data. Instead of working on an on-site server, it works through the cloud. This process helps the cloud-based platforms link with any cloud-based warehouse.

Using Data Transformation Tools over Custom Coding

Data transformation tools are cost-effective and efficient. It allows for performing ETL processes on time, not wasting resources and money, making the organization and employees to be more productive than custom coding that wastes time, wastes resources and funds, not cost-effective in nature

Data transformation tools reduce human errors, reduce stress, and give a better understanding of data in no time than custom coding that has a higher risk of human error and inefficiencies.

Data Transformation tools are designed to help to build the entire ETL process. It takes care of all the extraction, transformation, and loading process of your data to a centralized location quicker and faster.

Having good data transforms and speeds up your processes. It makes your employees more productive and improves decision-making. It will also make it easy to access up-to-date data leading to maximum usage of data to meet customer needs and achieve your bottom-line.

How does Voyance Manhattan DB help in Data Transformation?

Manhattan DB is a low-no-code data engineering infrastructure that helps in ingesting various multiple data sources together, transforming the data by cleaning and wrangling the data to clean, usable and quality data and load the data into a centralized system in order to make business decisions. Companies can perform all pre-processing like consolidating data, cleaning data, feature engineering to extract relevant information and overall data transformation.

Manhattan DB simplifies the ETL process by helping organizations to transform data through the cloud, connecting them to the cloud-based warehouse.  


  • With growing demands for data, businesses are seeing the need to transform data quickly. Only companies that invest in the right data can remain competitive. Data transformation allows your company to clean out the large volume of data and turn it into useful data analyzed for actionable insights. With the right data and the right tools in place, you can establish close relationships with customers and provide them with great experiences. You just need to have the right data at the right time about the right customer.
  • With data transformation, businesses can become more deliberate by providing personalized experiences. If you use the wrong data to target customers, it will affect your customer service and customer retention which will make customers seek competitors for better experiences.
  • If you’re running campaigns with the wrong data, your conversion rates will also suffer. Your sales team will be working with the wrong data and chasing the wrong leads or recommending the wrong products. Not transforming fast enough will also make you late to deliver what your customers want at the right time.

    Get smarter like other businesses by employing a software infrastructure  to help you transform your data seamlessly.