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What is Important in Data Transformation? Featured

What is Important in Data Transformation? Jurica Koletić

Data has become one of the critical components of any business in the modern era. It is for this reason that you keep hearing some conversations around big data and big data analytics. With this, data transformation has also gained fame. It is the process where data is analyzed, reviewed and converted by data scientists from a given format to another. This process is essential for organizations, especially at a time when data integration is required for an effective running of operations and security. It might involve the conversion of large amounts of data and data types, elimination of duplicate data, enriching data and aggregating it. Here is how data is transformed:

  • Extraction and parsing

In the modern data-driven world, the process of extraction starts with gathering information from the data source. This is followed by copying data to a desired destination. The transformation process is aimed at shaping the data format and structure to ensure that it is compatible with the source and destination. At this stage, different sources of data will vary depending on the structure, the streaming service or the database that the data originates from. After data has been gathered, it is transformed and changed from the original form to another; for example, aggregate sales data or customer service data is changed into text strings. The data is then sent to a target place, such as a data warehouse that can handle different varieties of data, such as structured and unstructured.

  • Translation and mapping

For data to be compatible with other sources, be moved easily to the other location, joined with data other data and added to additional data parts, it must be transformed accordingly. This is the second crucial part of data transformation. This step is important because it allows data from different departments in an organization to be made compatible and joined with other data. Some of the reasons for the transformation of data include allowing data to be moved to a new store or cloud warehouse, adding more fields and information to improve information, joining structured and unstructured data, and perform aggregations to enable comparisons.

  • Filtering, aggregating and summarizing

Transformation of data is the stage where data is made manageable through proper listing. At this stage, data is consolidated through filtering the unnecessary fields, records and columns. On the other hand, data such as numerical indexes, in data that is needed for graphs or records from business regions that are not of interest and are omitted. Data is also summarized and aggregated by transforming those regarding customer transactions to either hourly or daily sales counts. With the business intelligence tools, filtering and aggregation can be done efficiently before data is accessed using reporting tools.

  • Data enrichment and imputation

Data enrichment and imputation entails merging data from different sources to form denormalized and enriched information. With this stage, transaction data can be added into the table that has information about the customer to allow quicker reference. Enrichment entails splitting fields into many columns and the missing or corrupted values can replaced due to such transformations.

  • Data indexing and ordering

Indexing data is the first step before other operations are undertaken. Indexing entails creating an index file that references records. During indexing, data is transformed so that it can be ordered logically. Doing so also suits a data storage scheme. Indexing improves performance and management of relationships.

  • Anonymizing and encrypting

The data that has personally identifiable information (PII) or other critical information which if exposed can compromise privacy or security of individuals must be anonymized before sharing. This can be achieved through encryption in multiple levels ranging from individual databases cells to the entire database records or even fields.

  • Modelling

This stage is crucial because it entails casting and converting data types to enhance compatibility, adjusting dates and times and formatting. It also involves renaming database schemas, tables and columns to enhance clarity.

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Scott Koegler

Scott Koegler is Executive Editor for Big Data & Analytics Tech Brief

scottkoegler.me/

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