What is Data Transformation?
Data transformation is the process of converting, cleansing, and structuring data into a usable format that can be analyzed to support decision making processes, and to propel the growth of an organization.
Data transformation is used when data needs to be converted to match that of the destination system. This can occur at two places of the data pipeline. First, organizations with on-site data storage use an extract, transform, load, with the data transformation taking place during the middle ‘transform’ step.
Organizations today mostly use cloud-based data warehouses because they can scale their computing and storage resources in seconds. Cloud based organizations, with this huge scalability available, can skip the ETL process. Instead, they use a transformation process that converts the data as the raw data is uploaded, a process called extract, load, and transform. The process of data transformation can be handled manually, automated or a combination of both.
- Constructive, where data is added, copied or replicated
- Destructive, where records and fields are deleted
- Aesthetic, where certain values are standardized, or
- Structural, which includes columns being renamed, moved and combined
On a basic level, the data transformation process converts raw data into a usable format by removing duplicates, converting data types and enriching the dataset. This data transformation process involves defining the structure, mapping the data, extracting the data from the source system, performing the transformations, and then storing the transformed data in the appropriate dataset. Data then becomes accessible, secure and more usable, allowing for use in a multitude of ways. Organizations perform data transformation to ensure the compatibility of data with other types while combining it with other information or migrating it into a dataset. Through data transformations, organizations can gain valuable insights into the operational and informational functions.
Given the massive amounts of data from disparate sources that businesses have to deal with on a daily basis, data transformation has become an essential tool. It facilitates the conversion of data, irrespective of its format, to be integrated, stored, analyzed and mined for business intelligence.
How is Data Transformation Used?
Data transformation works on the simple objective of extracting data from a source, converting it into a usable format and then delivering the converted data to the destination system. The extraction phase involves data being pulled into a central repository from different sources or locations, therefore it is usually in its raw original form which is not usable. To ensure the usability of the extracted data it must be transformed into the desired format by taking it through a number of steps. In certain cases, the data also needs to be cleaned before the transformation takes place. This step resolves the issues of missing values and inconsistencies that exist in the dataset. The data transformation process is carried out in five stages.
The first step is to identify and understand data in its original source format with the help of data profiling tools. Finding all the sources and data types that need to be transformed. This step helps in understanding how the data needs to be transformed to fit into the desired format.
The transformation is planned during the data mapping phase. This includes determining the current structure, and the consequent transformation that is required, then mapping the data to understand at a basic level, the way individual fields would be modified, joined or aggregated.
3. Code Generation
The code, which is required to run the transformation process, is created in this step using a data transformation platform or tool.
The data is finally converted into the selected format with the help of the code. The data is extracted from the source(s), which can vary from structured to streaming, telemetry to log files. Next, transformations are carried out on data, such as aggregation, format conversion or merging, as planned in the mapping stage. The transformed data is then sent to the destination system which could be a dataset or a data warehouse.
Some of the transformation types, depending on the data involved, include:
- Filtering which helps in selecting certain columns that require transformation
- Enriching which fills out the basic gaps in the data set
- Splitting where a single column is split into multiple or vice versa
- Removal of duplicate data, and
- Joining data from different sources
The transformed data is evaluated to ensure the conversion has had the desired results in terms of the format of the data.
It must also be noted that not all data will need transformation, at times it can be used as is.
Data Transformation Techniques
There are several data transformation techniques that are used to clean data and structure it before it is stored in a data warehouse or analyzed for business intelligence. Not all of these techniques work with all types of data, and sometimes more than one technique may be applied. Nine of the most common techniques are:
Revising ensures the data supports its intended use by organizing it in the required and correct way. It does this in a range of ways.
- Dataset normalization revises data by eliminating redundancies in the data set. The data model becomes more precise and legible while also occupying less space. This process, however, does involve a lot of critical thinking, investigation and reverse engineering.
- Data cleansing ensures the formatting capability of data.
- Format conversion changes the data types to ensure compatibility.
- Key structuring converts values with built-in meanings to generic identifiers to be used as unique keys.
- Deduplication identifies and removes duplicates.
- Data validation validates records and removes the ones that are incomplete.
- Repeated and unused columns can be removed to improve overall performance and legibility of the data set.
This involves creation of new values from existing ones or changing current data through computation. Manipulation is also used to convert unstructured data into structured data that can be used by machine learning algorithms.
- Derivation, which is cross column calculations
- Summarization that aggregates values
- Pivoting which involves converting columns values into rows and vice versa
- Sorting, ordering and indexing of data to enhance search performance
- Scaling, normalization and standardization that helps in comparing dissimilar numbers by putting them on a consistent scale
- Vectorization which helps convert non-numerical data into number arrays that are often used for machine learning applications
This involves dividing up the data values into its parts for granular analysis. Splitting involves dividing up a single column with several values into separate columns with each of those values. This allows for filtering on the basis of certain values.
4. Combining/ Integrating
Records from across tables and sources are combined to acquire a more holistic view of activities and functions of an organization. It couples data from multiple tables and datasets and combines records from multiple tables.
5. Data Smoothing
This process removes meaningless, noisy, or distorted data from the data set. By removing outliers, trends are most easily identified.
6. Data Aggregation
This technique gathers raw data from multiple sources and turns it into a summary form which can be used for analysis. An example is the raw data providing statistics such as averages and sums.
With the help of this technique, interval labels are created in continuous data in an attempt to enhance its efficiency and easier analysis. The decision tree algorithms are utilized by this process to transform large datasets into categorical data.
Low level data attributes are transformed into high level attributes by using the concept of hierarchies and creating layers of successive summary data. This helps in creating clear data snapshots.
9. Attribute Construction
In this technique, a new set of attributes is created from an existing set to facilitate the mining process.
Why Do Businesses Need Data Transformation?
Organizations generate a huge amount of data daily. However, it is of no value unless it can be used to gather insights and drive business growth. Organizations utilize data transformation to convert data into formats that can then be used for several processes. There are a few reasons why organizations should transform their data.
- Transformation makes disparate sets of data compatible with each other, which makes it easier to aggregate data for a thorough analysis
- Migration of data is easier since the source format can be transformed into the target format
- Data transformation helps in consolidating data, structured and unstructured
- The process of transformation also allows for enrichment which enhances the quality of data
The ultimate goal is consistent, accessible data that provides organizations with accurate analytic insights and predictions.
Benefits of Data Transformation
Data holds the potential to directly affect an organization’s efficiencies and its bottom line. It plays a crucial role in understanding customer behavior, internal processes, and industry trends. While every organization has the ability to collect an immense amount of data, the challenge is to ensure that this is usable. Data transformation processes empower organizations to reap the benefits offered by the data.
If the data being collected isn’t in an appropriate format, it often ends up not being utilized at all. With the help of data transformation tools, organizations can finally realize the true potential of the data they have amassed since the transformation process standardizes the data and improves its usability and accessibility.
Data is continuously being collected from a range of sources which increases the inconsistencies in metadata. This makes organization and understanding data a huge challenge. Data transformation helps making it simpler to understand and organize data sets.
Better Quality Data
Transformation process also enhances the quality of data which can then be utilized to acquire business intelligence.
Compatibility Across Platforms
Data transformation also supports compatibility between types of data, applications and systems.
Faster Data Access
It is quicker and easier to retrieve data that has been transformed into a standardized format.
More Accurate Insights and Predictions
The transformation process generates data models which are then converted to metrics, dashboards and reports which enable organizations to achieve specific goals. The metrics and key performance indicators help businesses quantify their efforts and analyze their progress. After being transformed, data can be used for many use cases, including:
- Analytics which use metrics from one or many sources to gain deeper insights about the functions and operations of any organization. Transformation of data is required when the metric combines data from multiple sources.
- Machine learning which helps businesses with their profit and revenue projections, supports their decision making with predictive modeling, and automation of several business processes.
- Regulatory compliance which involves sensitive data that is vulnerable to malicious attacks
Challenges of Data Transformation
Data transformation is considered essential by organizations due to all the benefits it has to offer. However, there are also a few challenges that come alongside.
High Cost of Implementation
The process of data transformation is an expensive one. Depending on the infrastructure, the software and tools being utilized, the cost of the solution differs and tends to be on the higher side considering the extra resources who need to be hired, the computing resources and the licensing of tools that are used.
The process of transformation is a resource intensive one. A huge computational burden is created when transformations are performed in an on-premises data warehouse, which consequently slows down other operations. However, this isn’t an issue when a cloud-based data warehouse is used since the platform is able to scale up easily.
Data transformation also needs expertise from data scientists, which can be expensive and divert attention from other tasks.
Errors and Inconsistency
Without proper expertise, many issues can crop up during transformation which are likely to hamper the end results. Whether it is a poor transformation that results in flawed data, or a migration that fails and corrupts data, there are risks.
Data transformation helps in organizing data and making it meaningful, which improves the overall quality of the data. This compatibility between systems provides valuable support for functions like analytics and machine learning. Given the large volume of data that is being generated from new applications and emerging technologies, organizations are relying on data transformation processes to manage and handle it in a more efficient and effective manner. Data transformation not only helps organizations derive the maximum value from their data, but they also ensure that data can be managed in easier ways without feeling overwhelmed by the sheer amount of it all.
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