Before you analyze any data, it needs to be structured and accessible for the analytical systems. Data transformation is required to alter the structure and format of the raw data as per the standard set by the analytical algorithm.
Data transformation may include data changes like merging, summarizing, aggregating, enriching, filtering, joining, summarizing, or removing duplicated data. While data transformation is considered the most important step in the data flow, when the data is arriving from varied data sources. Transforming data creates consistency even if the data comes from varied data sources. It assists you to move your data efficiently and effectively across to an analytics system.
Organizations are increasingly relying on ETL or ELT processes, where E is Extract, L is Load and T is Transform. ETL is where the transformation is the middle step and used when organizations are using an on-premises data warehouse. While most businesses today have moved towards cloud databases that can be scaled based on computing and storage needs, where ELT is used. Transformation is used during all the processes of data integration, data wrangling, data migration, and data warehousing.
Data transformation may be constructive where addition, copying, and replication of the data take place; it can be structural– combining columns/ rows, renaming or moving may take place; or destructive when its deletion of fields and records or standardization based on value.
Businesses are now appointing Chief Data Officer (CDO) or Chief Information Officer (CIO) to manage data privacy and data governance demands, with teams consisting of data engineers, data analysts, and data scientists.
Pitfall 1: Unclear Business Benefits
Transformation is an expensive and extensive process. The cost of infrastructure, along with transformation tools and the need for data engineers there will be in transformation. You need to decide which of the transformation will be required based on the end goal of the data set. A technical roadmap can help you decide on the data flow and even derive a tangible goal that brings business opportunities.
The business opportunity could be anything from decision making to customer retention, or even increased revenue using AI-driven personalization.
Pitfall 2: Building a Monolithic system
Data transformation often ends up being a complicated system with the inclusion of various capabilities and creating a single switch over to the transformation process. A more agile approach can be where a single use case is developed initially and overtime capabilities are then added. Nexla, an ELT solution provider offers the benefits of automating the data flow where transformation can be managed based on varied use cases. It offers the benefit for the businesses having multiple data stores who want to transform their data based on analytical and operating system requirements.
Nexla completely automates the data flow, where the process of extraction, loading, and transformation can be performed consistently.
How to build a data transformation operation:
Data Transformation can be divided into three steps:
- Discover the data
- Map and Profile Data
- Transform Data
Step 1: Discover the Data
Determine what your actual data consists of and in what version it needs to be transformed. To have an accurate interpretation of the data, metadata must determine the correlation between the data set. Businesses need to determine data type and format, which will help in creating the roadmap for the transformation process.
Step 2: Map and Profile Data
Data mapping assists in building a complete roadmap for the migration process. Data mapping eliminates the data mismatches that might occur during the process, making the transformation process easier by understanding the data. Mapping will plan the merging, storage, and transformation process, enabling data quality to be maintained.
When the data is transformed from one format to another there might be data loss. Data profiling examines the accuracy, viability, and completeness of the data. Data profiling helps in determining the quality issues by enriching the data. Mitigating the loss will also need to be addressed under profiling.
Structure profiling helps to determine if the data is formatted in the right and consistent way. Structure profiling determines the format of each field. For example, if the data consists of a person’s name, there shouldn’t be any special characters or numbers.
Incorrect values of data will result in incorrect data relations. Discovering the data relations will help to create connections between the data sets. Data relation helps in validating the data based on the format required.
Step 3: Transform Data
In this stage you consider how to transform your data.Will you be using a written script or data transformation tool? The data from data stores have now arrived on a single storage platform, where you can perform the last steps of transformation.
Here are some transformation steps to consider based on your data:
- Splitting: Division of columns into multiple columns
- Enriching: Changing the data format. For example, if you want to make the initial character of first and last name capital.
- Filtering: Selectively remove the rows and columns based on the requirement.
- Aggregation: Fuse or add data from different data sets.
- Removing duplication of data.
- Binning: Binning helps when you have a large set of data and how to convert them into a specific range.
If you’re looking to build a scalable and repetitive data transformation process that can provide you with data privacy and governance features. Contact Us.