What is data standardization?

Data standardization is the practice of converting data values into uniform formats so datasets stay consistent, which makes them easier to analyze and manage.

Common examples include:

  1. Converting dates into a single format, such as YYYY-MM-DD
  2. Converting measurement units to a standard, for example miles to kilometers
  3. Translating multinational datasets into a single language

Why standardize data from different sources?

Data professionals treat standardization as essential work. It accounts for roughly 70% of the time spent on data preparation. Skip it, and three problems tend to surface:

  • Machine learning incompatibility: models can misread unstandardized data and produce biased predictions.
  • Analytics impairment: inconsistent data skews analytics results.
  • Integration complications: merging diverse sources becomes error-prone and incomplete.

Standardizing external unstructured data

External unstructured sources such as social media, blogs, news sites, and PDFs have no predefined format. That makes them harder to standardize than structured data, which already arrives in a predictable shape.

Steps to standardize data

1. Understand the data sources

  • Identify all data sources, including databases, CSV files, APIs, and manual records.
  • Assess data quality for missing values, inconsistent formats, and outliers.

2. Define standardization rules

  • Develop a data dictionary that defines standard formats and naming conventions.
  • Set data type standards that apply across every source.

3. Data cleaning

  • Remove duplicate records.
  • Handle missing data through imputation, deletion, or flagging.

4. Data transformation

  • Normalize data formats into their standardized versions.
  • Scale and normalize numeric data for machine learning applications.

5. Integration and consolidation

  • Merge data from multiple sources into integrated datasets.
  • Establish conflict resolution rules for when sources disagree.

6. Quality assurance

  • Validate that the standardized data is implemented correctly.
  • Maintain audit trails so the work stays traceable.

Tools to standardize data

The available tools split into two groups: pre-built options that handle much of the work for you, and lower-level tools that trade convenience for control.

Pre-built tools for data standardization

ETL tools (Talend, Informatica, Apache NiFi)

  • Offer out-of-the-box functionality that reduces manual coding.
  • Include user-friendly interfaces and pre-designed components.
  • Apache NiFi adds open-source flexibility.
  • Best suited to structured and semi-structured data.

Cloud-based pipeline solutions (Datastreamer, Google Cloud Dataprep, Azure Data Factory)

  • Feature built-in connectors and automated pipelines.
  • Datastreamer handles unstructured data with AI components.
  • Enable standardization with minimal manual intervention.

Data quality management software (Informatica Data Quality, SAS Data Management, IBM InfoSphere)

  • Comprehensive pre-built solutions.
  • Include data profiling, cleaning, and validation.
  • Focus primarily on structured data.

Tools that require more manual customization

SQL and database management systems (MySQL, PostgreSQL, Microsoft SQL Server)

  • Require manual SQL query execution.
  • MySQL and PostgreSQL are open source.
  • Designed primarily for structured data.

Programming languages: Python and R

  • Offer extensive data manipulation libraries.
  • Require substantial manual coding.
  • Both are open source with strong community support.
  • Can handle unstructured data through libraries like NLTK, TensorFlow, tm, and text2vec.

In-depth example: global financial services firm

This case shows data standardization applied to numeric and financial data.

Data sources to standardize

  1. Stock market feeds from multiple global exchanges
  2. Banking records from various countries
  3. Regulatory filings with jurisdiction-specific formats
  4. Market research data in different formats and languages
  5. Internal financial records, including budgets and forecasts

Standardization challenges

  • Currency conversion into a single base currency.
  • Aligning reports to standards such as International Financial Reporting Standards (IFRS).
  • Harmonizing financial metrics like interest rates, ROI, and debt-to-equity ratios.

Impact of standardization

  • Enhanced accuracy in financial analysis and risk assessment.
  • Streamlined regulatory compliance reporting.
  • Improved efficiency when consolidating international data sources.

Data standardization vs data normalization

These two processes are distinct yet complementary. Standardization transforms data from various sources into common formats and ensures consistency. Normalization organizes data to reduce redundancy by restructuring database tables and optimizes the database structure.

Aspect Data standardization Data normalization
Goal Consistency across datasets Optimized database structure
Method Convert values into common formats Restructure tables to reduce redundancy
Focus Data from various sources Data organization within a database