It’s easier than ever to collect and store data for your business with the rise of the modern data stack. However, raw data alone won’t do much to help you make vital business decisions. To understand your data, you have to transform and organize it, making it easier for humans and computers to comprehend and analyze. In the past, it used to be you would condense data before storing it, but since cloud storage in data lakes provides nearly infinite storage space, this is no longer necessary. Now, data analysts can set queries to convert your data automatically. Once the data has been extracted, it is then loaded and transformed in the last stage of the data transfer process, referred to as ETL or ELT.
You can transform your data in many ways to best serve your company’s needs. Different departments within your business may be better served with other variations of the same data. It’s imperative to develop a basic understanding of different types of data transformation to compare them and decide with your data analyst what will work best for your specific data needs.
The Basics Of Data Transformation
Data transformation essentially cleans and organizes raw data to make it more accessible. There are four basic ways that the ETL process will perform this task.
- Aesthetic Transformation – involves making stylistic changes to make the data more uniform. For example: standardizing street names or putting records from different sources into the same format.
- Structural Transformation – reorganizes data by moving, combining, or renaming columns.
- Constructive Transformation – raw data will be copied or added to fill gaps in existing data.
- Deconstructive Transformation – unnecessary fields will be deleted to clean data to make it more useful.
Before meeting with your data analyst, set clear goals for what you want to achieve with your data. The clearer you can be, the better they can set up queries to transform your data into a version that will most benefit your business in the foreseeable future.
Four Common Types Of Data Transformation And When You Would Want To Apply Them To Your Business
Once you’ve set clear goals for utilizing your data, your data analyst will help you set up individual layers of processing to modify the data to your specific needs. These
Mapping and Translation
Mapping allows you to combine data from multiple sources, making it easier for you. to see the whole picture. It can match codes and numerical information to the right column, which may come from another source. When you receive the information, it will be linked rather than you having to go through and match up data yourself.
Translation makes the process of matching data from different sources possible by converting raw data to a format appropriate for your systems. For example, raw data will come in a hierarchical structure. Data will be transformed and matched in near rows and columns.
This type of data transformation is advantageous when your company uses multiple customer-facing platforms. Through mapping and translation, you’ll be able to aggregate all your data charts your team can use to make critical business decisions.
Summarization and Filtering
The apps you use collect data on everything, but not all of that data may be necessary. Too much data can be a hindrance as it will take up storage space and slow down queries. Summarization and filtering allow you to reduce the amount of data to make it more manageable. You can use this type of data transformation to make your queries more specific.
Most businesses should employ some level of summarization and filtering to keep irrelevant data from slowing down your system’s processing power.
Anonymization and Encryption
Anonymization and encryption scrub the data to remove personal details. Many industries require this by law, so you should anonymize data before propagating it into your system. Encrypting personal data will protect you from data leaks and keep your customer’s privacy secured.
While most businesses will need some level of encryption, this type of data transformation is critical in the public health sector, including medical practices and fitness apps.
Enrichment and Substitution
Enrichment and substitution represent another way to uncomplicate data coming into your system from multiple sources. It helps you group data rather than look at each piece individually. It helps save storage space and decreases the cost by combining data from individual sources into a whole. Substitution allows you to standardize data and fill in gaps corrupted data may have left out.
For businesses who run online storefronts, enrichment and substitution can help you turn data from each customer into one reasonable chart to understand better and predict sales.
Hopefully, this outlines the common types of data transformation and allows you to develop a clear strategy with your data analyst. Choosing the proper layers of data transformation for your company can help you better implement the data you collect to achieve your business goals much faster.
