Navigating the Landscape of Data Wrangling for Strategic Insights
What is Data Wrangling?
Data wrangling is the process of transforming and structuring raw data into a desired format with the objective of enhancing data quality and making it comprehensible and valuable for analytics or machine learning. Data wrangling, also known as data munging. Data wrangling is a crucial step in the data preparation pipeline, ensuring that the data is accurate, complete, and ready for further analysis.
Why need Data Wrangling?
Data manipulation is at the core of data analytics. Suppose we scraped data from the web, carrying out statistical analyses, creating dashboards and visualizations (using raw script or BI Tool like — Apache Superset, Power BI) — all these tasks involve manipulating data. But before we can do any of these things, we need to ensure that our data is in a format we can use. This is where the most important form of data manipulation comes in: data wrangling. Data wrangling is the earlier stage of data analytics. The success of data analytics or machine learning algorithms depends on the source data purity.
The goal of data wrangling is to make data more accessible for applications such as business analytics and machine learning. The data wrangling process can involve a range of tasks. These include things like data collection, exploratory analysis, data cleansing, creating data structures, and storage.
Data wrangling Processes?
Data wrangling is time-consuming. In fact, it can take up to about 80% of a data analyst’s time. There are four broad steps in the data wrangling or munging process:
- Discovery
- Transformation
- Validation
- Publishing
1. Data Discovery
Data discovery refers to the process of identifying, accessing, and retrieving relevant data from various sources for analysis or business intelligence purposes. This phase is crucial in the overall data lifecycle, as it involves exploring data repositories, databases, files, or external systems to find the information needed to address specific questions, make informed decisions, or derive insights.
Identifying Data Sources: Firstly, we need to determine the potential sources of data that may contain relevant information. This could include databases, data warehouses, APIs, spreadsheets, logs, or external datasets.
Accessing Data Repositories: Establish connections and gain access to the identified data sources. This may involve obtaining credentials, permissions, or utilizing APIs to retrieve data programmatically.
API Integration: For web-based or cloud-based data sources, we need to integrate with APIs to programmatically extract data. This involves sending requests to the API and processing the responses.
Web Scraping: Web scraping is a technique used to collect data from websites by extracting information directly from the HTML source code. It allows us to automate the retrieval of data from websites, enabling us to gather large amounts of unstructured raw data.
Extracting Data from Files: If the data is stored in files (e.g., CSV, Excel, JSON), extract the necessary information using appropriate tools or programming languages.
Querying Databases: For database-driven sources, use query languages (e.g., SQL, NoSQL) to retrieve specific subsets of data based on criteria relevant to the analysis or reporting requirements.
2. Transformation
Data transformation refers to the process of converting raw data into a more suitable format for analysis, reporting, or further processing. It involves making changes to the structure, content, or representation of the data to meet the specific requirements of a particular task or system. Data transformation is a crucial step in the overall data preparation and analysis pipeline, ensuring that the data is in a meaningful and usable state. Data transformation is an iterative process, and the specific techniques applied depend on the nature of the data and the objectives of the analysis. We can termed the data transformation as exploratory data analysis (EDA).
Cleaning and Standardization: Data transformation often begins with cleaning the raw data. This includes handling missing values, correcting errors, and standardizing formats to ensure consistency across the dataset.
Data Inspection: Explore the dataset to understand its structure, identify missing values, anomalies, or outliers.
Data Type Conversion: We need to convert data types as our requirements. For example, converting date strings to date objects, or transforming categorical variables into numerical representations (one-hot encoding) for machine learning models.
Aggregation and Grouping: Aggregate data by grouping it based on certain criteria. This can involve calculating summary statistics (mean, median, sum) for groups of data points.
Encoding Categorical Variables: Convert categorical variables into a numerical format, such as one-hot encoding or label encoding, to make them compatible with machine learning algorithms.
Handling Imbalanced Data: For imbalanced datasets, apply techniques like oversampling, undersampling, or generating synthetic samples to balance class distributions.
Handling Outliers: We need to apply transformations to handle outliers, such as trimming extreme values, log-transforming skewed data, or using robust statistical measures.
Text Data Transformation: and transform text data for natural language processing (NLP) tasks, including tokenization, stemming, or vectorization.
3. Validation
Data validation is the process of ensuring that data is accurate, consistent, and meets specific quality standards. The goal of data validation is to identify and correct errors, inaccuracies, or inconsistencies within a dataset to ensure its reliability for analysis, reporting, or other purposes. Data validation involves checking the integrity and conformity of data based on predefined rules, constraints, or patterns.
Define Validation Rules: First, we need to set the rules and criteria that data must fulfil. These rules may include restrictions on data types, ranges, formats, and the connections between various data items.
Error Identification: Identify and flag records or values that do not meet the validation standards. Missing values, out-of-range data, and inconsistent formats are a number of common mistakes.
Data Profiling: We can analyze the overall characteristics of the data, identifying patterns, outliers, or unexpected distributions that may indicate issues.
Statistical Validation: Use statistical approaches to validate data, such as evaluating normality, distribution forms, and correlations.
Common approaches for data validation include format validation, range validation, consistency checks, completeness checks, and pattern matching.
4. Publishing
Data publishing involves sharing data in a structured and organized manner, often through online platforms, databases, warehouse or repositories.
Publishing Platforms: Choose appropriate platforms or repositories for publishing data. This could include internal data portals, government open data platforms, public repositories or domain-specific repositories.
Accessibility and Usability: We have to ensure and prioritize making the published data accessible and organized.
Metadata Creation: a good practice is create detailed metadata that describes the dataset’s structure, content, source, and any related documentation. Metadata improves the comprehension and usefulness of published data.
Anonymization and Privacy: To comply with regulations pertaining to privacy, ensure that sensitive information is anonymized or appropriately safeguarded. This is especially critical when posting datasets that contain confidential or private information.
Data Wrangling vs. Data Mining
Data mining is extracting useful patterns and information from data that has already been wrangled. The data is queried and studied so that you can properly understand the information it is giving to you. The correct conclusions can’t always be discerned by simply looking at the numbers.
Data Wrangling vs. Data Analysis
Analytics processes are the final step after wrangling and mining the data. This is where the information and patterns that have been discerned from the (now clean and queryable) data are studied to determine how they affect and relate to the original objectives of the business users.
What are the Challenges?
The main challenges with data wrangling are the time it takes and the limited amount of work that can be automated. While many Python libraries exist that can help streamline the wrangling process for common problems (i.e. standardizing date formats), the reality is that there will always be bespoke problems that require manual correction. Only a human can comprehend the semantic meaning of a non-standard format and change it into standard syntax that software can organize.
