Alexis Kestler
Want to organize, merge, standardize, and format large datasets to extract business intelligence? As a fellow data analyst, I‘m here to provide you with the ultimate guide on data transformation in the ETL process.
Based on my experience, companies rarely receive data in the ideal format for their business intelligence (BI) tools right away. The data connectors and repositories usually overwhelm you with raw, unorganized data initially. It‘s impossible to identify meaningful patterns from that kind of raw data.
You need a specialized process like data transformation to convert the data into a structure matching your business needs. It also uncovers hidden business opportunities within those inaccurate datasets.
In this guide, I‘ll break down data transformation from start to finish. By the end, you‘ll have the knowledge to successfully plan and implement data transformation projects. Let‘s get started!
What Is Data Transformation?
At its core, data transformation keeps the essence and content of the data intact while modifying its appearance. In most cases, data scientists make changes to these parameters:
- Data structure
- Data format
- Standardization
- Organization
- Merging
- Cleansing
The end result is clean, organized data. The final format depends on the BI tools your business uses. Formatting can also vary between departments because areas like accounting, finance, inventory, sales, etc. all have different data structures.
During modification, data scientists apply business rules to the information. These help analysts identify patterns and leadership make informed decisions from the processed data.
Data transformation merges models into a centralized database too. This facilitates comparison of products, services, sales, marketing, inventory, expenses, and more.
Types of Data Transformation
Transformation takes many forms:
Data Cleaning
People identify incorrect, inaccurate, irrelevant, or incomplete data sets/components. The data gets modified, replaced, or deleted to improve accuracy. Careful analysis ensures the cleaned data generates meaningful insights.
Data Deduplication
Duplicate entries create confusion and miscalculations during data mining. Deduplication removes all redundant data so datasets become duplication-free.
This saves money otherwise spent storing and processing duplicates. It also prevents performance impacts and slow query processing.
Data Aggregation
Aggregation collects, searches, and presents data concisely. It gathers information from multiple sources into one for analysis.
Highly useful for strategic product, operations, marketing, and pricing decisions.
Data Integration
Integrates data from different sources into one unified view. Anyone in the company can then access and utilize it for ML and business intelligence.
Considered a major data management component.
Data Filtering
With huge data volumes, not all information applies to every process. Filtering removes irrelevant, duplicate, or sensitive data, keeping what you need.
Minimizes errors and generates accurate reports/query results.
Data Summarization
Presents a summary of generated data. Raw data alone is unsuitable. It may have errors or an incompatible format.
Summarization creates a digestible data overview, making patterns and trends easily accessible.
Data Splitting
Divides data into different segments. Primarily used to develop, train, and test datasets for cross-validation.
Also protects sensitive data by splitting and encrypting it on a separate server.
Data Validation
Cross-checks accuracy, quality, and integrity. Essential before further processing to avoid downstream issues.
How to Perform Data Transformation
Let‘s explore methods and techniques to transform your data:
Choosing a Method
Consider these approaches based on your needs:
On-Site ETL Tools
Powerful for large, custom datasets processed regularly. Run on robust workstations managing big volumes fast. But cost of ownership is high.
Cloud-Based ETL Web Apps
Affordable option for small, medium, and startup businesses. Ideal for weekly/monthly preparation.
Transformation Scripts
Good for small projects with smaller data volumes. Use Python, Excel, SQL, VBA, Macros to transform data.
Transformation Techniques
Apply techniques as needed based on your raw data and desired outcome:
Integrating Data
Consolidate related data from multiple sources into summary tables. Like customer info from accounting, sales, marketing, social media, competitors.
Sorting and Filtering
Don‘t send unfiltered data to BI tools. Filter first to extract analyzable data, removing noise.
Data Scrubbing
Removes noise, corruption, errors, and irrelevant content like a deep clean!
Dataset Discretization
Adds intervals to continuous data without disrupting flow. Enables trend identification and long-term averaging.
Data Generalization
Anonymizes personal data to meet privacy regulations. Also simplifies large datasets for easy analysis.
Removing Duplicates
Eliminate duplicates that inflate warehousing costs and distort insights.
Adding Attributes
Introduce new fields, column headers, or attributes to better organize data.
Standardization and Normalization
Tailor data to your preferred structure, usage, and visualization models. Ensures cross-departmental usability.
Data Smoothing
Removes distortions that skew expected patterns. Prevents analytical pitfalls.
Steps for Data Transformation
Follow these steps to transform your data successfully:
Discover Data
Understand the dataset and model. Decide what changes are needed. Use profiling tools to preview data.
Map Transformation
Determine elements to review, edit, format, cleanse, and change. Identify reasons and methods.
Generate and Execute Code
Data scientists script transformations in Python, SQL, VBA. No-code tools allow raw data uploading and change requests.
Review and Load
Verify changes occurred as expected before loading the dataset into your BI application.
Benefits of Data Transformation
What are the major advantages of this process?
Improved Organization
Modifying and categorizing data enables separate storage and discoverability. Humans and applications can then easily utilize the organized data.
Higher Quality
Eliminating data issues reduces bad data risks. Less chance for misinterpretation, inconsistency, or missing information. Vital for major decisions requiring accuracy.
Streamlined Management
With growing multidimensional data, transformation simplifies management. Essential for organizations awash in information.
Wider Usage
Standardization maximizes data utility. The same datasets become usable for more purposes across the business.
Fewer Errors
Well-organized data lessens incorrect indexing, null values, and duplicates that trip up applications.
Faster Queries
Sorting and warehousing data enables speedy queries and BI tool optimization.
Lower Risk
Quality data minimizes the chance of financial/reputational losses from planning based on inaccuracies.
Refined Metadata
Skipping metadata chaos aids data management, searchability, and usage.
Top Data Transformation Tools
Let‘s explore some leading options:
DBT
DBT centralizes and modularizes analytics code. Also enables dataset versioning, collaboration, testing, and documentation.
Qlik
Qlik uses automation and agile methodologies to simplify transferring data between sources like BI tools, data warehouses, and ML projects. Minimal manual coding required!
Domo
Domo enables effortless SQL database transformations and merging through drag-and-drop interfaces. Makes data readily accessible across teams.
EasyMorph
EasyMorph simplifies manual Excel, VBA, SQL, Python transformations via visual tools. Automates where possible. Ideal for data scientists, analysts, and finance.
Final Thoughts
Data transformation plays a pivotal role in unlocking value across business units from information. It‘s a standard step in ETL and ELT processes for on-premise BI and cloud data storage.
The high-quality, standardized data generated enables critical initiatives like marketing, sales, product development, pricing adjustments, new units, and more.
I hope this guide provides you with a comprehensive overview of data transformation concepts and best practices. Looking for datasets to practice on? Check out this compilation of open data resources.
Let me know if you have any other questions! I‘m always happy to help a fellow data analyst learn more about mastering data transformation.
