What is data transformation?


 The conversion, cleansing, and structuring of data into a usable format that can be analyzed to support decision-making processes and propel an organization’s growth is known as data transformation. When data needs to be transformed to match that of the destination system, data transformation is used. Two locations in the data pipeline may experience this. An extract, transform, and load process is used initially by businesses with on-site data storage, with the data transformation occurring during the middle “transform” step.

Cloud-based data warehouses are becoming increasingly popular with businesses due to their rapid capacity expansion. The ETL procedure can be skipped by cloud-based businesses due to the extensive scalability available. Instead, they employ extract, load, and transform, a transformation procedure that converts the data as it is uploaded. Data transformation can be handled either manually or automatically, or a combination of the two.

How does FutureAnalytica’s data engineering boost the quality of the data and verify its accuracy?

It’s possible that the data you have won’t always be enough to build the right AI models. FutureAnalytica data enrichment applications can enrich data for the construction of high-end AI models and uncover deep data insights by utilizing data from a variety of sources. The IT task of managing data takes a lot of time. With the help of FutureAnalytica data management apps, end users can manage data from various sources and integrate it into the platform, facilitating seamless collaboration and the development of new Artificial Intelligence models.

Why it’s important to have good data: If bad data is used, businesses could lose a lot of money. Bad data is frequently blamed for incorrect analytics, incorrect business strategies, and operational difficulties. Data quality issues can have a number of negative effects on the economy, such as increased costs when products are shipped to the wrong customer addresses, missed sales opportunities when customer records are inaccurate or incomplete, and penalties for reporting errors in financial or regulatory compliance.

What uses are made of data transformation?

The straightforward goal of data transformation is to extract data from a source, transform it into a format that can be used, and then deliver the transformed data to a destination system. As a result of the data being pulled into a central repository from various sources or locations during the extraction phase, it is typically in its unusable raw original form. The extracted data must be transformed through a series of steps into the desired format to ensure its usability. Before the transformation can take place, the data may also need to be cleaned in some instances. Inconsistencies and missing values in the dataset are resolved in this step. There are five stages to the process of transforming data.

1. Discovery- Using data profiling tools, the first step is to identify and comprehend the data in its original format. Identifying all of the data types and sources that need to be transformed. Understanding how the data must be transformed in order to fit into the desired format is made easier with this step.

2. Mapping- During the data mapping phase, the transformation is planned. This involves figuring out the current structure and the necessary transformation that comes with it, as well as mapping the data to understand how each field would be changed, joined, or combined on a fundamental level.

3. Code Generation- In this step, a data transformation platform or tool is used to create the code needed to run the transformation process.

4. Execution- The code is used to help convert the data into the format chosen at the end. The sources, which may be structured, streaming, telemetry, or log files, are used to extract the data. Following the mapping phase, data undergoes transformations like aggregation, format conversion, or merging. The transformed data is then transferred to the system of destination, which may be a dataset or a data warehouse.

Depending on the data being transformed, some examples include: Enrichment, which fills in the fundamental gaps in the data set, Filtering, which aids in the selection of columns that require transformation, Splitting, in which a single column is divided into multiple or vice versa, Removal of duplicate data, and Joining data from various sources Examine The transformed data are evaluated to ensure that the conversion produced the expected formatted results. It is also important to keep in mind that not all data will require transformation; in some cases, it can be used as is.

Conclusion

Data transformation has the potential to improve data-driven decision making and improve the efficiency of analytical and business processes. Things like data type conversion and flattening of hierarchical data should be part of the first phase of data transformations. Data is shaped by these operations to be more compatible with analytics systems. As required, data analysts and data scientists can implement additional additive transformations as individual processing layers.

FutureAnalytica.com offers “no-code AI,” a next-generation technology that lets people create cutting-edge Artificial Intelligence/Machine Learning solutions with no prior knowledge of data science or coding. Contact us at info@futureanalytica.com if you have any queries or want to set up a demo.

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