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For organizations that manage large volumes of data, leveraging maximum value from the information buried in the data can be a challenge. Breaking silos and collating data into a coherent set of information for processing will yield business benefits. Still, this is only possible once information is in a form enabling the application of analytical techniques. This is where data transformation can come to the rescue.
Simply speaking, the data transformation definition is the process of converting data from diverse sources into a standard format that supports its analysis. Traditional processes for data transformation follow the principle of Extract, Transform, and Load (ETL).
- Extraction is the process of acquiring data from various sources.
- Transformation is the process of converting the data into the required standard format.
- Loading is the process of warehousing the data in an accessible location.
The advent of cloud-based solutions has enabled a switch to Extract, Load, and Transform (ELT). The difference here is that warehoused data is in its raw form, with the transformation only performed on-demand following information access.
This approach has the advantage that the process from acquiring raw data to warehousing will be significantly faster, which can be beneficial when managing large volumes of frequently changing data. Another benefit is that this approach supports optimizing the data transforming processes all analytical processing evolves. Finally, where access requires small subsets of the data, this reduces the transformation processing overhead. One of the leaders in the space focused on data transforms is dbt.
The downside is that where large volumes of data are frequently accessed, the processing overhead of the transformation stage will be more significant.
Data transformations can involve a variety of actions:
- Structural transformations alter the format of records to adhere to a common standard.
- Constructive transformations add additional information to records, such as a status flag created by parsing the record's contents.
- Destructive transformations remove information from records, such as deleting personally identifiable information to comply with data protection laws.
- Localization transformations alter the content of records where the data format varies across geographic regions, such as the standardization of date records (DD/MM/YYYY vs. MM/DD/YYYY) or telephone numbers to include country codes.
- Aesthetic transformations alter the content of records for presentation purposes, such as standardizing titles for individuals.
There are many reasons why transforming data may be necessary. Typical reasons include:
- Change the format to enable aggregation.
- Change the structure of the data to support analytical processes.
- To validate data and improve quality.
- Include timestamp information to allow temporal sequencing, comparison, or association.
- To remove erroneous values and null entries that can disrupt processing.
- Add geolocational information to support processing.
The critical benefit of transformation is that it allows analytical applications to efficiently access and process all data quickly and efficiently by eliminating issues before processing.
An added benefit is that transformation to a standard format will make the manual inspection of data more convenient. Comparisons of data with a standard format can be significantly faster by the eye than for data presented in varying layouts.
Data transformation in data mining applications can also unlock valuable information. Data mining is the process of discovering trends and patterns and other helpful information that businesses were unaware they could access from existing data sets.
Processing large volumes of volatile unstructured data can be expensive in terms of processing resources, composed of infrastructure, services, and personnel.
Warehousing of large volumes of data can require significant storage requirements that can be expensive to maintain. As a result, transformation techniques that employ data duplication can be costly.
On-premises data transformation is significantly more costly to scale than cloud-based infrastructure, additional resources incurring both capital costs on the acquisition and recurring management and maintenance costs.
Transformation processes must efficiently integrate with the analytical methods that consume the data to realize the benefit of data transformation. Multiple modifications to cater to divergent consumer requirements will be inefficient and costly to the business.
If not correctly managed, complex data transformation of volatile data can consume significant processing resources that may impact other business operations.
Transformation processes require thorough verification and validation before operational usage to prevent the possibility of source data being inadvertently corrupted or deleted during transformation.
Data protection laws may impose legislative limitations on data transformation of certain data types in specific geographic jurisdictions, which imposes regulatory and compliance overheads to processes with the risk of financial penalties for any breach.
Extraction techniques move or copy raw data into the transformation environment, where the process of data transformation typically follows a sequential stage transition.
The first step in transformation is structural transformations that convert data to a standard format, typically including flattening hierarchical structures and adding missing records. To goal is to create a consistent and coherent dataset compatible with analytical applications and services.
The following steps will depend on the nature of the data and the requirements of its consumers. The following terms are all commonly used for types of data transformation processing, with some overlapping between definitions:
- Data aggregation is the combining of data records.
- Data anonymization permanently removes identifiable information from a data record to prevent any subsequent deduction of identity.
- Data casting is the structural conversion of the format of a data record.
- Data discretization transforms continuous data into discrete records.
- Data encryption protects the confidentiality of data records from any user or service that does not have access to the decryption keys for that data.
- Data enrichment is the merging of data from multiple records into new, more complex forms.
- Data filtering is the elimination of unrequired elements of a data record.
- Data formatting is the structural conversion of the format of a data record.
- Data indexing creates additional accessibility information for data records.
- Data mapping is the linking of matching fields within different data records from distinct data models.
- Data normalization range checks data and constrains values between the defined maximum and minimum limits.
- Data ordering performs a logical transposition of data to improve accessibility.
- Data pseudo-anonymization removes identifiable information from a data record to prevent any subsequent identity deduction without additional information.
- Data smoothing removes unwanted noise from raw data that can inhibit data mining techniques.
- Data summarization is the representation of a data record with a simpler or less granular version.
- Data translation is the structural conversion of records from one data format to another format.
An example of a typical data transformation process is converting data formatted using the extensible markup language (XML) into comma-separated value (CSV) format.
XML formatted records use tags that define data types and values that support complex, hierarchical data structures. CSV files by comparison simple list data values of a single format with individual elements separated using the comma character. These two formats are entirely incompatible with each other.
Conversion of XML format into CSV format requires data transformation to parse the XML file and create the equivalent CSV file.
Data transformation collates and organizes data to enable analytical processing that realizes the value of the information contained within the data.
Data collation from various sources can gather records in any format or structure. As a result, it can be static or volatile and may be incomplete or erroneous. Applying a range of data transformation techniques to this data can then enable the application of analytical methods to meet the business's goals.
Data transformation processes offer businesses the ability to improve data quality and extract maximum value efficiently to support decision-making business processes with increased confidence.
After data has been transformed, the next step is to then make that data actionable using a Reverse ETL tool such as Grouparoo. Grouparoo takes data from your data warehouse or data lake and syncs it to all the SaaS tools you and your team might us.
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See all of Stephen Mash's posts.
Stephen is a UK-based freelance technology writer with a background in system development and assurance, primarily focused on high-integrity applications.
Learn more about Stephen @ https://www.linkedin.com/in/steve-mash-exosure