Data transformation: an essential part of data preparation

What is data transformation?

Data transformation is the process of modifying the data’s values, structure, or format. On-premise data warehouses are typically used in an extract, transform, and load (ETL) process, with data transformation being the middle phase. The majority of companies now employ data warehouses that are cloud-based, which can increase computation and storage resources in a few seconds or minutes.

Advantages and disadvantages of data transformation

Data transformation has several advantages:

• Data is organized by transforming it, and organized data is easier to deal with in the analytics process.
• Ensuring that data is structured correctly increases data quality and protects programs against possible anomalies such as null values, duplicates, inaccurate indexing, and formats that are incompatible.
• Data transformation facilitates the collaboration of applications, systems, and data types.

However, there are some obstacles to effectively transforming data:

• Data transformation is costly. The tools, infrastructure, and software that are used to process the data determine the cost. Recruiting appropriate employees, licensing, and computing resources—are all examples of expenses.
• The methods of data transformation can use a lot of resources. Performing transformations after importing data into a data warehouse that is on-premises—or processing data before putting it into apps—can bog down other operations. You can conduct the conversions after loading if you utilize a data warehouse that is cloud-based since the platform can expand to meet demand.
• Businesses can carry out modifications that do not meet their requirements. A business might convert data to a certain format for one application solely to convert it back to its initial state for another.

General types of transformations

Extraction and parsing

In the present ETL process, data ingestion begins with the extraction of data from its original source. The first transformations aim to shape the data’s formatting in order to make sure it’s interoperable with the target system. The extraction of fields from the log data that is comma-delimited and importing them into a database that is relational are examples of this type of data transformation.

Translation and mapping

Two of the key fundamental data transformations are data mapping and translation. For instance, a column having integers indicating error codes can be connected to the proper error explanations, making the column easier to comprehend in a client-facing application.

The data is transformed from one state’s format to that of another. Web data may come in the format of XML files or even hierarchical JSON after processing, but it must be converted to row and column data before being stored in a relational database.

Filtering, aggregation, and summarization

The goal of data transformation is usually to condense and make data more manageable. Data can be consolidated by filtering away unnecessary fields, columns, and records. Omitted data includes numerical indexes in data intended for dashboards and graphs, as well as information from business lines that aren’t pertinent to a certain study.

Data can be compiled or summarized as well. An example of this is transforming customer transactions’ time series data into daily or hourly sales figures.

Although BI systems can conduct this aggregation and filtering, it may be more efficient to perform the transformations before using a reporting tool to access the data.

Enrichment and imputation

Denormalized, enriched data can be created by merging data originating from multiple sources. A client’s transactions can be grouped into a total and kept in a table for use by customer analytics tools or easy reference. Freeform or long fields can be broken into multiple columns, with damaged data or missing values imputed or substituted as a result of the changes.

Ordering and indexing

Data can be changed to make it more logical or able to fit within a certain schema. Indexes can enhance effectiveness and end up making table maintenance simpler in relational database management technologies, for example.

Anonymization and encryption

Before being disseminated, data comprising personally identifiable information (PII) or other information that could jeopardize security or privacy should be anonymized. Many industries require data encryption, and systems can encode data at numerous levels, ranging from individual database cells to complete records or fields.

Modeling, typecasting, formatting, and renaming

Finally, to modify data without impacting the content, a range of transformations can be applied. This includes using offsets and format translation to change dates and timings, as well as renaming of columns, tables and schemas for clarity.

How is data transformation used in businesses?

Because corporate firms generally store data in multiple databases, data transformation is required. For instance, a major corporation might use multiple databases for various departments: financial data might be saved in a PostgreSQL database, whereas employee data might be housed in a MongoDB database. As a result, a large number of transformations and queries must be performed between the databases each day.

The primary benefit of data integration and transformation is that it improves the quality of data and prepares it for analysis. The goal behind standardizing different sources of information is that organizations will have analyses that are reliable. If you’re a business owner looking to make smarter decisions, you’ll want to learn as much as possible about your data. Any discrepancies could have a major influence on your financial planning and forecasts. Businesses can start trusting their analyses if they have effective data preparation practices in place.

Data integration and data transformation solutions enable businesses to stay ahead of emerging trends while being nimble enough to pivot when necessary. Forward-thinking companies are continuing to transition their business applications into such a state where data flows effortlessly between different IT platforms. Using a full-stack business intelligence tool to perform data transformation tasks can make this crucial step fairly easy. Consider the advantages of using a BI software tool to assist in your data transformation efforts.

Conclusion

Data transformation is a key part of the data preparation phase. In order to conduct reliable analyses, a company’s data must be correctly cleaned and formatted. By investing the time and resources to properly transform data previous to analyzing and visualizing, businesses can ensure that the insights drawn from the data are dependable and genuinely valuable.