Data transformation is the process of changing data from one format to another. It is a necessary step in data integration. It could include multiple activities like removing duplicates, converting data types, aggregating multiple sources, or more.
Why transform data?
One of the more common reasons to transform data is the comparison with data from other sources. For example, you might want to validate leads data from ad platforms, with actual signups on your platforms.
You can also compare the data from multiple platforms and campaigns to decide the best message-media mix.
How is data transformed?
Two popular ways of transforming data are:
- Scripting – it’s a manual process that uses SQL or Python scripts for data extraction and transformation
- Using ETL tools – it’s an automated process that Extracts, Transforms, and Loads data. You can host the tools on your company server or use vendors who provide Cloud-based tools. The former is expensive and requires in-house expertise. The latter uses the vendor’s infrastructure and expertise, making it more cost-effective.
Process of data transformation
Data transformation involves two stages.
Stage one is all about planning. It includes the following:
- Data discovery – identifying data sources and types.
- Determining the data transformation structure
- Data map to figure out how to map individual fields, which includes modifying, joining, filtering, and aggregating.
The second stage is about executing the plan. It includes
- Data extraction from a range of sources, including structured and unstructured.
- Aggregating the data, changing the format, editing text, joining rows and/or columns
- Storing data in a database or a data warehouse
Steps in data transformation
The data transformation process involves four steps.
1. Data interpretation
Establish the as-is and to-be. Understand what you have and what you need. A clear goal prevents you from getting lost in a whirlwind of data.
Dimensional modeling helps you do it better. Here you use two types of target tables:
Dimension tables set the context for the data answering who, what, where, when, why, and how. As they answer critical questions of your data transformation process, these are referred to as the soul of the data warehouse. Doing it right, sets the direction for the rest of the process. So, it is crucial to pay due attention to it.
Fact table stores the quantified metrics of the events. It could include
- Periodic snapshot: Summary of events over regular intervals
- Transactions: Recording of events
- Accumulating snapshot – including execution detail of a process in a single record
2. Data quality check before transformation
After identifying data formats and transformation goals run a data quality check. It helps identify issues like corrupt values or missing data.
Not doing it costs both time and effort. A thorough check helps avoid problems later.
3. Data translation
Data translation involves replacing each part of the source data and with a format that matches target data. At the end of it, you could end with a file with a new structure that better serves your purpose.
4. Data quality check after transformation
Look for inconsistencies, missing information, or other errors introduced during the data translation process. Even with high-quality data, it is likely for some errors to have crept in during the transformation process.
Invest in your data transformation and integration platform
To thrive in today’s data-rich environment, it is worth considering outsourcing data integration. Getting outside expertise is particularly helpful if your organization moves quickly and has an opportunity to avoid the common pitfalls noted above.
With Windsor.ai, you can onboard data from any source, build segments, and act on them in real-time. Try it for free.
Also, you can contact us for a demo of what our data integration services can do for your organization.