Data Transformation: Definition, Techniques, Solutions

Power your business with reliable, accurate insights that drive innovation, facilitate growth, and sharpen your competitive edge.

DEFINITION

What is data transformation?

Data transformation adapts raw data into useful, comparable formats for analysis (and other purposes). It involves cleaning, normalizing, aggregating, joining, and filtering data.

Types of Data Transformation Techniques


Aggregation
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Normalization
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Cleaning
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Attribute Construction
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Data Validation
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Discretization
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Smoothing
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Use Cases for Data Transformation

Explore data transformation examples regarding how businesses can leverage the data they transform.

Business Intelligence

Data transformation converts raw data into meaningful insights, helping businesses gain a holistic view of diverse data and improve data quality.

Corporate Data Integration

Data transformation streamlines mergers and acquisitions by combining databases into a single source. It also contributes to process automation and increasing efficiency.

Ecommerce

Data transformation reformats data from various systems, enabling data-driven decisions about customer acquisition, retention, and product development.

Cloud Migration

Data transformation ensures data is compatible with cloud platforms, streamlining migration and enabling businesses to leverage the benefits of cloud computing.

Combining Structured and Unstructured Data

Data transformation allows businesses to combine diverse data for analysis and insights, enabling real-time mining for applications, risk mitigation, and fraud detection.

Machine Learning

Data transformation prepares data in a suitable format for training, ensuring more accurate predictions and better model performance.

Challenges and Limitations of Data Transformation


Expense
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Expertise
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Resource-Intensive Nature
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Data Quality Concerns
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Integration of Transformed Data
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Data Security and Privacy
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Data transformation is a catalyst for business modernization, enabling organizations to harness the power of their data for actionable insights, competitive advantage, and sustainable growth.

19X

More likely to stay profitable

https://www.forbes.com/councils/forbeshumanresourcescouncil/2023/07/18/being-data-driven-is-likely-your-best-bet/

+8%

Increase in revenue

https://bi-survey.com/big-data-benefits

23X

More likely to acquire customers

https://www.mckinsey.com/capabilities/growth-marketing-and-sales/our-insights/five-facts-how-customer-analytics-boosts-corporate-performance

$3.9T (+56%)

Estimated global digital transformation spend (2024 vs. 2027).

https://www.statista.com/statistics/870924/worldwide-digital-transformation-market-size/

Data Transformation Solutions

Control-M

Control-M

Comprehensive data pipeline orchestration is just one powerful capability that keeps your business running smoothly, giving you confidence at every step.

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BMC AMI Datasteam

BMC AMI Datasteam

Real-time, cross-platform views of security event data, consolidated into a single console. Monitor critical mainframe systems, and stay in the know.

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ON-DEMAND

Watch an executive-level discussion on critical digital transformation drivers

Forrester: BMC named a leader in enterprise service management (Q4, 2023)

Learn More About Data Transformation

Article/Blog

Data Transformation in the Data Pipeline

Where and how does data transformation fit into your overarching data pipeline? Explore how data is used and how it gets from point A to point B.

Article/Blog

Intro to Data Management

Follow our step-by-step and best practices guide to data quality management (DQM). Learn how to leverage insights to improve customer experience, innovation, top-down decisions, and more.

Article/Blog

ETL Basics

Want to take a step back? Gain clarity about the “Extract, Transform, Load” (ETL) process, and solidify your understanding with helpful visuals.

FAQ


What is data transformation with an example?
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What is the difference between data transformation and data processing?
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What are common ways to transform data?
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What are the four types of data transformation?
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