Data transformation is a process whereby data converts from one format or structure into another. Typically, data transformation will consolidate information from several sources into a central pool of properly formatted records. The final result will be data that is in a format that you can analyze and create actionable business insights.
Stages of data transformation
There are two core stages of data transformation. To begin with, you will need to identify all your data sources, types, and formats. In doing so, businesses can discover the structures and data transformations that need to occur. A data mapping plan can then be created that defines how individual fields or attributes are modified, joined, and aggregated.
Only when a plan is in place should you move to the second stage, which involves extracting the data from all sources, performing transformations, and sending it to the target store, such as a data warehouse.
Why transform data?
There are several reasons why a business needs to transform its data. Generally, data transformation ensures system compatibility when joining it all together. For example, imagine you are in a large enterprise and acquire a smaller startup. It is highly likely that the small business uses different languages, databases, and systems to its parent. The date formats could differ, they may store NULL values in mandatory fields or have duplicate records. Each of these items will need to go through a data transformation process before you move the data across.
The top reasons for data transformation include:
- Changing your data storage solution such as going from on-premise to cloud.
- Mapping unstructured and structured data to analyze them together
- Data enrichment strategies that add new information to customer records
- Generating aggregate reports from different data sources
Limitations of data transformation
While data transformation can be necessary, like any process involving data, it comes with various challenges.
Firstly, you should not underestimate the time it takes to cleanse data from converting it to a new format. It can be very time-consuming and soak up many of your resources. For data scientists, as much as 60% of their time involves cleaning and organizing data, rather than writing scripts and building models. Moreover, 76% of data scientists say preparation is the least enjoyable part of their work, meaning it is a challenge to influence them to do it properly.
Some data transformation projects can be costly, depending on the systems and infrastructures involved. For example, if you are using lots of legacy systems, the formats can be hard to align. Data transformation tools come at a cost to automate the work.
Common data transformation use cases
In the corporate, business units will be scattered globally, often having distinct database management systems like Db2, Oracle, or SQL Server. Each of these requires a data transformation strategy to merge them into a master database.
E-commerce companies need data transformation to turn ERP and CRM data into a single source of truth. As the digital world grows, such a process is becoming pivotal for a better customer experience that spreads over several channels.
Cloud migration is perhaps the most common use case in the 2020s, as companies see the cost and scalability benefits of moving from on-premise services. Disparate systems will need to be converted into the same format before they can move to the cloud.
How to transform data
There are three main options for data transformation. Some companies will write code in SQL or Python languages to extract and transform the data. The reason for doing it this way is that it is easily customizable and well within their control.
The other two options are either on-premise or cloud-based ETL (Extract, Transform, Load) tools. The tools automate the process and will either be hosted on the company site or in the cloud. Cloud options are now preferable, where you can rely on the expertise of the vendor.
Finally, transforming data can go hand in hand in data cleansing. For data cleansing services, contact StrategicDB to see how they can optimize your data transformation.