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Data in its rawest form can be too vague to be useful. So depending on what is required, data has to be formatted specially to be useful. Essentially, data has to be transformed to fit the specific needs of a company or organization and typically there are a few types of data transformation. A more indepth look at that later, however, at the beginning data is stored in devices at their most basic level as binary values that employ two numbers: 0 and 1. The smallest unit of data is called a bit, and it represents a single value. Additionally, a byte is eight bits long. Memory and storage are measured in units such as megabytes, gigabytes and terabytes. Data can be stored in file formats using mainframe systems and other file formats for data conversion, processing, and storage, like comma-separated values. These data formats are currently used across a wide range of machine types. The data that is stored needs extra attention and restructuring in order to increase its utility and add value for a specific function or purpose.
Standard data processing is made up of three basic steps: input, processing, and output. Together, these three steps make up the data processing cycle. Data transformation could be said to be part of the data process in which, simply put, data gets converted from one format to another.
The process of data transformation can be handled manually, automated or a combination of both. There are several steps in the data transformation process: Data Discovery – this is where analysts work to understand and identify data in its source format. To do this, they will employ several data profiling tools. This step helps whoever is chosen to analyze to decide what they need to do to get data into its desired format; Data Mapping – at this step analysts are asked to perform data mapping to determine how individual fields are modified, mapped, filtered, joined, and aggregated. Data mapping is essential to many data processes, and one mistake could lead to incorrect analysis and ripple through your entire company or organization; Data Extraction – this is the step where analysts get around to actually extracting the data from its original source. These may include structured sources such as databases or streaming sources such as customer log files from web applications; Code Generation and Execution – Once the data has been extracted, someone needs to create a code to complete the transformation. Often, there are people to generate codes with the help of data transformation tools or platforms; Review – After transforming the data, there should be people employed to check the data to ensure that everything has been formatted to the liking of the company or organization; Sending -The final step involves sending the data to its target destination. The target might be a data warehouse or a database that handles both structured and unstructured data. These are probably the most rudimentary of the steps involved in the data transformation process; other customized operations, such as data filtering or data enrichment, may take place along the way if needed by the organization or company.
The data transformation process could be described as either: 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. There are several types of data transformation that are used to clean data and structure it before it is stored in a company’s data warehouse or analyzed for organizational intelligence. These will not all work with the data on hand, and sometimes more than one type of transformation needs to be applied.
Types of Data Transformation
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Deduplication: Duplicate records result in incorrect answers to queries, a common data transformation step is removing duplicates.
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Format Revision: Date/Time conversions, units of measure, and character set encodings are common
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Cleaning: Null handling, standardization on things like M/F for gender is critical for grouping dimensions, and getting correct summarization of metrical values.
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Key Engineering: Occasionally, the relationship between data stored in different databases is some function of a key. In these cases, key restructuring transforms are applied to normalize the key elements.
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Predication/Filtering: Only move data that satisfy the filter conditions.
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Summarization: Values are aggregated and stored at multiple levels as business metrics.
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Derivation: Applying business rules to your data that derive new calculated values from existing data
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Splitting: Splitting a single column into multiple columns.
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Data validation: Can be a simple “if/then” calculation or can be a multi-valued assessment
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Integration: Similar to Key Engineering: Standardize how data elements are addressed. Data integration reconciles different data keys and values for data that should be the same
Data transformation helps making it simpler for companies and organizations to understand and organize data sets, improves data’s usability and accessibility, enhances the quality of data acquired and thus enables companies and organizations to achieve specific goals by using the metrics and key performance indicators to help quantify their efforts and analyze their progress.