What Is Data Transformation? Types, Tools, and Importance

Data transformation is defined as the technical process of converting data from one format, standard, or structure to another – without changing the content of the datasets – typically to prepare it for consumption by an app or a user or to improve the data quality. This article explains the importance of data transformation, its different types and techniques, and the best tools you can use to transform data. 

What Is Data Transformation?

Data transformation is the technical process of converting data from one format, standard, or structure to another – without changing the content of the datasets – typically to prepare it for consumption by an app or a user or to improve the data quality.

Data transformation is known as modifying the format, organization, or values of data. Data can be changed at two points in the data pipeline when used for data analytics projects. Companies that utilize on-premises data warehouses often apply an ETL (extract, transform, load) methodology, with data transformation functioning as the intermediate step. Cloud-based data warehouses may expand computational and storage capacity with delay estimated in seconds or minutes.

Using ELT (extract, load, transform), organizations may load raw data straight into data warehouses without preload adjustments and convert it when a query is received. Data transformation might be used in various operations, such as data migration, warehousing, integration, and wrangling.

In addition, it is crucial for any organization that seeks to leverage its data to provide timely business insights. Organizations need a reliable method for utilizing data to put it to good use for their operations as the number of data has increased. Data transformation is a component of using this data since, when performed effectively, it ensures that the information is accessible, consistent, safe, and eventually acknowledged by the targeted business users.

Now, let’s go into the data transformation procedure’s steps: 

1. Discovery of data 

Identifying and interpreting the original data format is the first step. Typically, a data profiling tool is used to achieve this. This stage assists you in determining what must be done to the data to transform it into the required format. Data professionals apply data profiling techniques or scripts to grasp the data’s structure and features and determine how users should modify it.

2. Data mapping

The actual transformation process is planned during this stage—data specialists’ method of matching or connecting data elements from one resource to another. 

3. Code generation

For the conversion process to be successful, constructing a code to execute the transformation task is essential. Typically, a tool or platform for data transformation is employed to produce these codes. In this process phase, the computer code needed to transform the data is created through data transformation technologies or by data experts developing scripts.

4. Code execution

Code execution sets the previously planned and coded data transformation process into motion, transforming the input data into the desired output. Data is collected from the source(s), which may be structured, streaming, telemetry, or log files. The data is then transformed by operations like aggregation, format conversion, or merging as planned during the mapping stage. The data is changed at this point.

5. Review

The formatting of transformed data is examined to ensure accuracy. During this step, data professionals or end users check that the data flow fulfills the predetermined transformation criteria and, if not, address and correct any anomalies or mistakes. Additionally, not all data requires transformation; sometimes, it can simply be used as-is.

Notably, the meaning and process of data transformation can vary based on the techniques used. These include:

• Revising: Revising the data necessarily and adequately makes sure that the data supports the intended usage. This is accomplished in several ways. By removing duplications from the data set, dataset normalization improves the data. Data purification guarantees that the data can be formatted.
• Manipulation: This entails generating new values from old ones or modifying the state of the data through computing. Users can also transform unstructured data into structured data that machine learning algorithms can exploit through manipulation.
• Separating: Granular analysis entails segmenting the data values into their component pieces. By splitting, you generate separate columns for every one of the values in a column containing multiple values. This enables filtering based on specific values.
• Combining/integrating: Records from many tables and sources are integrated to get a complete picture of an organization’s operations. It combines data from different tables and databases, including information on couples.
• Data smoothing: The data set is cleaned up using this method by removing irrelevant, noisy, or corrupted data. Trends are best discovered by eliminating outliers.
• Data aggregation: This method collects raw data from many sources and transforms it into an analysis-ready summary form. Raw data that provides statistics like averages and sums is an example.
• Discretization: To increase the effectiveness and make an analysis of continuous data easier, interval labels are constructed using this technique. To convert big data sets into categorical data, decision tree algorithms are used in this process.
• Generalization: By employing the idea of hierarchies and building layers of sequential summary data, low-level data attributes can be converted into high-level attributes. Clear data snapshots are produced as a result.
• Building attributes: This approach expedites the mining process by permitting the development of a new collection of characteristics from an existing set.

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Types of Data Transformation

Depending on the technique used and the desired outcome, data transformation may be classified into six categories: 

1. Data transformation through scripting

Through scripting, data can be extracted and transformed by writing code in Python or structured query language (SQL). You can automate several processes using scripting languages like Python and SQL in software. In addition, they enable data collections to be mined for information. Scripting languages are less intensive since less code is needed than in conventional programming languages.

With the help of programming languages like Python, one can modify multiple jobs, allowing the company to manage schedules, jobs, and other business operations. Before writing an event to an XML file, transformation scripts are used to alter its contents or structure.

2. The use of on-premises ETL tools

Since on-premise ETL software is deployed internally, you have total control over its modification and security. Furthermore, the software enables you to conduct hundreds of jobs at once. On-premise software is quicker than cloud software since the data is first sent to the manufacturer’s server and then recovered in an alternate format.

It is favored by businesses that want to keep their data and need a certain amount of privacy and protection. These businesses, including banking and government, hold extra-sensitive information. One can use these tools remotely, but they frequently require third-party connectors to interface with mobile devices.

3. The use of cloud-based ETL tools

The ownership of data in a cloud-based environment is highly ambiguous, a problem many businesses have encountered. Accessing data in an unforeseen event will be challenging because encryption and data keys are stored with a third-party supplier.

However, many cloud-based ETL systems make it simple to access them from mobile devices, and some of them even include native mobile apps. But with increased accessibility comes higher security concerns, mainly if employees use mobile devices to access company files. Resources are housed and managed by the public cloud hosting service provider in cloud-based ETL tools.

4. Constructive and destructive data transformation

To fill in the blanks and standardize the data, constructive transformation involves adding, copying, and replicating raw data. The process may eliminate entire fields and records when data is cleaned and made more usable through destructive transformation. Programs are intricate, challenging to comprehend, and prone to errors because they have to be effective, according to the core premise of the transformation approach to program development. This entails structuring the creation of software as a development process, eventually sacrificing clarity for the economy.

5. Structural data transformation

Reorganization is another term to use for structural change. Data can be combined, divided, rearranged, and even generate bespoke data structures. Columns may be combined, moved, or given new names to restructure the database. Structural transformations can be categorized in four ways based on how they modify a dataset’s geometry, topology, and characteristics.

Transformation can be simple or complicated, depending on the initial and final target data. Columns may be renamed, moved, or combined as part of structural data transformation. Data transformation, at its most fundamental, is the process of transforming unusable data into usable data.

6. Aesthetic data transformation

Stylistic modifications, like standardizing street names or other values, are a part of aesthetic evolution. However, combining or moving columns is part of a structural restructuring. Following the aesthetic ideas that have arisen in every era, the way data aesthetics is seen has changed.

To comply with specifications or parameters, the transformation standardizes the data. Organizations can learn much about operational and informational tasks by transforming their data. Data transformation has become a crucial tool for businesses because of the enormous amounts of data from many sources they must manage daily.

See More: What Is Artificial Intelligence (AI) as a Service? Definition, Architecture, and Trends

Top 6 Data Transformation Tools

Companies may employ robust tools to accomplish the data transformation categories and procedures outlined above. Among the most effective data transformation technologies on the market, today are:

1. IBM InfoSphere

A straightforward tool for data integration, IBM InfoSphere DataStage can easily clean, edit, and convert your data. It offers real-time data transfer to data lakes, warehouses, etc. For practically any organizational use case, IBM provides several unique data integration capabilities for both on-premises and cloud implementations.

The on-premise data integration package contains both traditional and modern integration synchronization and data virtualization options. IBM also offers a comprehensive selection of pre-built connectors and functionalities. In the coming months, IBM will introduce more features to the market-leading cloud integration tool.

2. SAP Data Services

Data integration, transformation, profiling, and text data processing are all available on a single platform using SAP Data Services, an ETL solution. Through both ETL and ELT procedures, SAP Data Services, a product of SAP, combines and processes data from SAP or outside sources.

The data management platform includes a range of capabilities for data integration, quality assurance, and cleansing. You can create data-transforming applications on the platform. By connecting to new data sources, the software supports database management systems, applications, files, etc. It links to additional external data sources and interfaces with other SAP Business Suite apps.

3. Dataform

One can manage all their processes in cloud data warehouses with Dataform, a free and open-source data transformation tool (including Panoply, Snowflake, Redshift, and BigQuery). Someone familiar with SQL could use the command line to develop robust data transformation pipelines by utilizing SQLX.

As a result, data engineers may use the ref function to establish dependencies across tables quickly. They can now focus more on analytics rather than maintaining the data infrastructure. Dataform is a potent data transformation tool for data engineers who love SQL.

4. Azure Data Factory

One can clean, merge, and format enterprise data at scale with the aid of Data Factory by Microsoft Azure. The serverless tool Data Factory enables you to integrate data sources visually. You can therefore choose to construct ETL or ELT pipelines and allow Data Factory automatically generate the code in addition to writing your code.

Top compliance organizations like CSA STAR, HIPAA, and HITECH have accredited Azure Data Factory. Your digital transformation initiatives can use the data transformation layer that Data Factory offers. It employs a pay-as-you-go strategy, simplifying it for you to scale up or down to your needs.

5. Qlik Compose

Thanks to Qlik Compose, an agile ETL automation and data transformation tool, data administrators are relieved of onerous and error-prone manual coding. Qlik Compose significantly cuts down on the time, expense, and risk of data transformation for business intelligence projects by optimizing the design of data warehouses and automatically creating ETL code.

With Qlik Compose for Data Warehouse, you can easily design, build, manage, and update data warehouses like Oracle, Amazon Redshift, and Azure Synapse. The process of sending consistently updated, reliable, and trustworthy data to big data platforms like Apache Hadoop is automated by Qlik Compose for Data Lakes.

6. Data Build Tool (dbt)

One can write code to immediately start data transformation and take command of the entire data pipeline. Version control, logging, modularity, and Git integrations are all supported natively. In well-known data warehouses like BigQuery, Redshift, and Snowflake, converting SQL queries into tables and views is simple because every data model is a straightforward SELECT statement.

The “ref function,” which enables people to reference one data model within another, is a neat feature of dbt. Additionally, this strategy aids in the automatic generation of dependency graphs. Therefore, their materialized tables in the graph are also updated in the proper sequence.

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Importance of Data Transformation

Why is data transformation so crucial in the age of AI and data-powered decisions? Well, the answer comes down to five key factors:

1. Analyzing global markets is made simpler by data transformation

One will probably need to use specific currency changes if a campaign spans international boundaries or serves customers in various nations. However, it’s critical to be cautious and strategic in this situation. This is one of those occasions where one can choose to drift off into the vast, black ocean depths or stay cozy by their figurative reef.

When dealing with money, it is essential to consider historical versus current conversion rates. It could be beneficial to change time zone data to reflect the synchronized global launch when reviewing success metrics in the past. This is just one example of how data transformation can affect financial success.

2. It can deliver altered data to someone’s intended recipient

One of the most widely used visualization tools available is Google Data Studio. It is robust, stable, and free. However, the fact that it can only gather information from one source is one of its key drawbacks. Due to this, people frequently observe that clients begin their data journeys by gathering all of their data into a sizable Google Sheet and then applying many data transformations to it before integrating it with an app. This is one of the key benefits of data transformation – i.e., it prepares data for consumption by an intended recipient.

3. Data usage becomes optimized

Data transformation can also simplify data used for both people and machines. Businesses are continually producing data from a growing number of human and machine sources. Different data types can be standardized to help firms manage their data more efficiently. Additionally, as data transformations make it simpler for users to get the data they require, applying this modified data for business results is frequently quicker.

Your data is kept in a source location after being modified and standardized, making it simple to access and retrieve. This can result in improved customer service, more accessible information sharing, and more precise and effective communication, to name a few benefits.

4. Data quality improves, leading to enhanced analytics outcomes

Businesses must keep an eye on the quality of their data because they frequently base crucial choices on it. Business executives or staff may overlook vital information that underpins effective outcomes if some data contains inaccuracies. Poor data comes with a lot of expenses and hazards. 

Data transformation can assist your company in getting rid of inconsistent data and other quality problems like missing values. Businesses may flag and eliminate issues like inconsistencies and complete any gaps in the data through data transformations, ensuring the data’s correctness and quality. When appropriately utilized, transformation tools can dramatically increase data quality and the efficiency of data analysis. 

5. The scope of data applications increases

More programs can use the data once you have transformed it. This is due to the particular formatting requirements that apply only to specific programs. 

For instance, a company gathering consumer data in one application might need to modify it before analyzing it in another application. As a result, data transformations enable greater data utilization since they make it more accessible to other applications. Even though corporations may continually acquire data, much of it remains unanalyzed. By standardizing and improving the usability of your data, the transformation makes it simpler to extract the most value from it.

See More: What Is Deep Learning? Definition, Techniques, and Use Cases

Takeaway

Data transformation is now an essential part of any IT practice. With so many diverse systems and multiple types of datasets at work, you need a degree of interoperability to utilize its full potential. Data transformation makes information assets workable across systems and platforms. It also enables greater standardization and quality enhancement for enterprise data so you can use it to generate exponential value. 

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