Data Wrangling: Turning Raw Data into Gold!

by Sambittin Data Analytics, Technologyon Posted on

As a data analyst, one of the most time-consuming yet crucial part of the workflow is Data Wrangling — preparing messy, real-world data for analysis. In this blog article, let us understand the key stages involved:

Data Exploration

It all starts with data exploration, to ensure that we can carry out the right approach towards data transformation and readiness of the data. Data exploration involves

  • Understanding the structure, types, and quality of the raw data.
  • Identifying missing values, outliers, duplicates, and data types.
  • Performing summary statistics and visualizations to detect patterns or anomalies.

Tools used in data exploration usually are Python (Pandas, Matplotlib, Seaborn), Excel, SQL, Power BI and Tableau.

Data Transformation

Data Transformation is where analysts usually spend ~40 to 50% of their time.

Data Transformation is a key and essential part of data wrangling and covers pretty much major part of the data wrangling. This is where the data is structured, normalized and / or de-normalized, cleaned and enriched. It involves the following steps:

Data Structuring

Data Structuring covers:

  • Converting unstructured or semi-structured data into structured formats (e.g., CSV, relational tables).
  • Re-formatting nested JSON, XML, or log files into usable tabular forms.

Normalization

Normalization includes:

  • Standardizing the values (e.g., units of measurement, date formats).
  • Scaling the numerical values for better comparability and model performance.

Data Cleaning

Data Cleaning is all about:

  • Handling missing values, typos, duplicate entries, and inconsistent labels.
  • Applying data type conversions and value imputation.

Data Enrichment

Data Enrichment ensures that:

  • Internal data are combined with external sources (e.g., demographics, geo-location).
  • New features are derived for deeper analysis (e.g., age computation from the date of birth).

Tools used normally are: Python (Pandas, NumPy, OpenRefine), Excel, SQL, Talend, Alteryx.

Data Validation

Now that the data is well formed, it is important to validate it.

Data validation activity:

  • Ensures data integrity through consistency checks, schema validation, and business rule compliance.
  • Confirms data completeness and detect any remaining anomalies.

Tools used: Python (Great Expectations, Pandera), Dataform, SQL constraints.

Data Availability

Finally, the cleaned data is stored in accessible formats and systems (e.g., data warehouse, cloud storage).

It’s also important to enable secure and governed access for analysis, reporting, or modeling.

The Tools used are: Google BigQuery, Snowflake, AWS S3, Azure Data Lake, PostgreSQL.

To conclude, remember that: “The magic of insights and AI starts with clean, structured data.”

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Image by Gerd Altmann from Pixabay

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