Incorporating a data-driven approach is pivotal for businesses seeking to make well-informed decisions regarding their expansion, products, and services. In the current corporate landscape, relevant data is readily available, making implementation relatively effortless. However, it is essential to ascertain the quality of the data, as it is just as important as the quantity; appropriate processing is required to optimise information usability.
Leveraging data analysis to a greater extent can enhance decision-making regarding the integration of omnichannel customer service, determining the ideal location for a new store, and devising new product offerings. Prior to formulating a data transformation plan, data analysts should ponder the primary goal of the data.
This post delves into the procedure of data transformation, examining its pros and cons, the different transformation forms, the accompanying methodologies and principles. We will also appraise the significance of data in contemporary society, and tackle effective approaches to modifying it.
The Beneficial Aspects of Data Transformation
Data transformation enables the creation of optimal conditions for fully harnessing a firm’s data collection and analysis capabilities. By transferring the modified data to a secure and readily accessible location, it becomes far more convenient to retrieve and categorise. Furthermore, “clean” data furnish a properly prepared and validated foundation for data appraisal, whereas “dirty” data may lead to flawed assessments.
Rendering superior verdicts can result in multiple gains, including but not limited to minimizing wastage, augmenting customer service quality, boosting profitability, and attaining a competitive edge. You may watch the video below for additional insights on how to configure your enterprise around data.
Risks Associated with Data Transformation
There might be hindrances that crop up during the course of data transformation. Converting data initially obtained for a specific purpose into content that satisfies a different objective implies an enormous financial burden on the concerned entity. This could encompass licensing costs, computer hardware expenses, recruiting competent personnel, and procuring supplementary processing power to handle additional operations.
Data can lose its value if errors occur during the transformation phase. Individuals lacking experience or adequate information regarding data transformation are particularly susceptible to making mistakes, particularly when dealing with financial data.
Data Transformation Categories, Techniques, and Principles
Two options are present for data transformation: batch and interactive modes. Developers must write code that entails transformation principles, which are then used to handle vast quantities of data. For quick data transformation requirements, this method offers a “micro-batch” subset approach.
Users can interactively transform data via an interface, which enables visualization and manipulation of extensive data through graphical display. The data can be viewed and edited to specific fields, allowing users to personalized, non-linear experiences with minimal training required.
Listed below are various manners in which data can be transformed by analysts:
Scripting. Analysts utilize Python or SQL tools to script code for data extraction and transformation, providing them with increased flexibility in the process. However, this method may be more laborious and has a greater probability of error. Moreover, the code will need to be rewritten whenever the process is repeated.
In-house ETL software. These software applications are housed in the company’s devices, and they streamline the task of data conversion. This technology is economical, and it offers features and the capability to scale for larger projects, in addition to producing data flow visualizations.
Cloud-hosted ETL tools. Cloud-based ETL solutions possess similar capacities as their on-premise equivalents in terms of automation of the data transformation process. Additionally, analysts can extract data from cloud sources and transmit it to a data warehouse owing to the cloud-hosted aspect of the solution.
Data transformation rules prescribe the measures necessary for altering data structure and meaning from one form to another. Semantic principles establish what comprises a complete transaction and offer definitions of other categories of data elements. Reshape rules outline how information is moved from one set of data to another, whereas taxonomy rules match values from the initial data set to their corresponding values in the new data set.
The Process of Data Transformation
ETL, an acronym for Extract, Transform, and Load, is a three-phase procedure that includes the complete data transformation cycle. The process is delineated as follows:
Data Analysis. Analysts utilize data profiling strategies to discover pertinent information and construct the requisite plans for transforming the data into an appropriate format.
Data Mapping. Data fields can be transformed, mapped, filtered, combined, and aggregated in several ways, as stated by analysts. To streamline the data, the process may require the removal of unnecessary information, such as fields, columns, or records.
Data Extraction. Analysts begin by retrieving the data from its source, such as a database or the log files maintained by online services for their users.
Safeguarding Confidential Data. The privacy concerns have necessitated encryption of personal information in numerous professions.
Programming. Subsequently, analysts utilize data transformation platforms or tools to create and execute the required codes.
Validation. Analysts then verify that every element is formatted correctly.
Apart from following the standard procedures, analysts may perform specialized operations such as filtering data by particular columns, supplementing it with supplementary information, erasing duplicate entries and merging data sets. After the completion of the process, the modified data is then transmitted to a storage system such as a database or data warehouse.
Inaccurate Data Impairs Sound Decision Making.
The data produced on one system may not be compatible with another in today’s corporate world. In order to make use of vast amounts of data and optimize its worth, it is necessary to transform it to fit the target system. This procedure is crucial for businesses to derive the maximum benefits from their data.
In the absence of data transformation, companies would be left with raw data.
- Errors, disparities, and duplications
- Unsatisfactory, erroneous, or missing information, or confidential data
- Numerical data that has not been plotted
- Unprocessed data that has not been consolidated
In such a state, the company is unable to leverage a vast amount of unexplored knowledge and potential that the data conceals.
