Data wrangling is the method by which you unlock the value of data by cleansing, transforming and harmonizing it. But this process is not without its challenges - from inconsistent formats to missing values.
In this blog article, you will learn all about data wrangling, understand the crucial role of this process and how innovative technologies such as Konfuzio help companies to overcome data processing challenges.
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Data wrangling - definition
Data wrangling is the process of systematically preparing data for analysis. This includes data collection, selection, cleaning, integration, formatting and aggregation.
Tools such as Pandas or SQL help to prepare data for analysis. The challenges here are missing data, consistency problems and coping with large data sets.
Best practices include the documentation of steps, the use of automated processes and the validation of results.
Overall, data wrangling enables well-founded analyses and data-based decision-making.
The data wrangling process
The data wrangling process, also known as data cleansing or data preparation, is critical to transforming raw data into a usable form for analysis and modeling. The process comprises several successive steps:
- Data collection
When collecting data, information is obtained from various sources, such as databases, files or APIs.
Example: An e-commerce company collects transaction data, customer ratings and inventory data from various online platforms.
- Data selection
In this step, the relevant data that is important for the specific analysis objective or project is identified and selected.
Example: A market research company selects only the demographic and purchase-related data for a consumer survey in order to gain targeted insights.
- Data cleansing
The data cleansing ( Data Cleaning) focuses on correcting irregularities and errors in the data. This includes the handling of missing values, outliers and inconsistent data records.
Example: Identify and correct incorrect entries in a customer database to ensure consistent customer names and addresses.
- Data integration
Here, data from different sources is merged to create more comprehensive and coherent data sets.
Example: Integration of sales data from different departments of a company in order to obtain a uniform overview of overall performance.
- Data formatting
During data formatting, data structures, units and formats are adapted to ensure consistent presentation.
Example: Conversion of dates into a standardized format to enable simple temporal analysis.
- Data transformation
Data transformation includes operations such as conversions, aggregations or calculations. These steps are carried out in order to generate new findings or prepare the data for specific analyses.
Example: Calculation of the average shopping cart value from the transaction data for an e-commerce analysis.
- Data aggregation
By summarizing data at higher levels of abstraction, patterns and trends are identified. This step makes it easier to derive insights and helps to focus on relevant information.
Example: Aggregation of daily sales data into monthly sales totals for a better overview.
- Data validation
The Validation of the data is crucial for their reliability. Here, the data is checked for accuracy and consistency to ensure that it meets quality standards.
Example: Verification of inventory data by reconciliation with physical inventory data to ensure accuracy.
Detailed documentation of all steps carried out, transformations and decisions made ensures the traceability of the entire wrangling process.
Example: Creation of a log that comprehensively documents the applied filters, calculations and changes to the data.
The integration of automated processes helps to make the wrangling process more efficient and repeatable. Automation minimizes manual errors and speeds up the entire process.
Example: Setting up scripts or Workflow automation toolsto automate recurring wrangling tasks, such as the regular updating of data feeds.
Tools and techniques for data wrangling
Companies can use a variety of tools and techniques for data wrangling to prepare data for analysis and modeling. Here are some commonly used tools and techniques:
- Pandas (Python library): Pandas is a powerful Python library for data manipulation and analysis. It offers functions for data selection, filtering, aggregation and transformation.
- dplyr (R package): dplyr is an R package that facilitates data manipulation and analysis. It offers functions such as filter(), select(), mutate() and summarize() to wrangle data efficiently.
- SQL (Structured Query Language): SQL is often used for data manipulation in relational databases. SELECT, UPDATE and JOIN statements enable the selection, updating and merging of data.
- OpenRefine: OpenRefine is an open source tool for cleansing and transforming data. It facilitates the processing of large data sets through a user-friendly interface.
- Microsoft Excel: Excel is often used for simple data wrangling tasks. Functions such as sorting, filtering, pivot tables and formulas enable basic data transformations.
- Apache Spark: Apache Spark is a distributed data processing platform that also offers functions for data manipulation. Spark DataFrames enable similar operations to Pandas, but on distributed data.
- Python-Scikit-Learn-Pipeline: Scikit-Learn offers pipelines that make it possible to combine data preparation steps with machine learning. This promotes reusability and consistency.
The choice of the appropriate tool depends on the specific requirements, the amount of data and the skills of the team. Some companies may rely on a combination of different tools to meet their data wrangling needs.
Advantages and challenges
|Advantages of data wrangling
|The challenges of data wrangling
|1. Improved data quality: Data cleansing and checking lead to more reliable data.
|1. Complexity of the data: Different data sources can have a variety of formats, which makes integration more difficult.
|2. Better analysis options: Precise analyses and well-founded decisions are made possible by well-prepared data.
|2. Missing data: Dealing with missing or incomplete data requires special strategies.
|3. More efficient analyses: Faster analyses due to reduced time required for troubleshooting.
|3. Data overload: Large amounts of data can make the wrangling process time-consuming.
|4. Consistency in the data structure: Uniform structure facilitates analysis.
|4. Manual workload: Some tasks may require manual intervention.
|5. Automation potential: Automated workflows speed up repeatable tasks.
|5. Complex transformations: Complex data transformations often require programming skills.
|6. Combination of different data sources: Integration creates more comprehensive data sets.
|6. Data quality assurance: Ensure that wrangling steps do not lead to a loss of quality.
|7. Better visualization options: Well-prepared data makes visualization easier.
|7. Data history and traceability: Documentation in complex processes can be challenging.
|8. Flexibility for analysis: Good data enables flexible analyses and extended investigations.
|8. Data security and data protection: Data protection standards must be observed for sensitive data.
|9. Improved collaboration: Standardized data facilitates collaboration between teams.
|9. Maintenance costs: Adjustments to changes may require additional effort.
|10. Support for machine learning: Data preparation is crucial for successful ML models.
|10. Training and resources: Employees may need to be trained to use wrangling effectively.
Data wrangling enables effective data preparation, but challenges such as data complexity, quality assurance and manual effort must be taken into account.
Data Wrangling Use Cases
Below you will find 4 use cases that show you how you can use data wrangling profitably in your company.
Use Case 1 - Data wrangling with Konfuzio
Konfuzio is a Intelligent document automation solutionthat analyzes unstructured data and transforms it into valuable insights. The platform offers adaptive AI functions for existing processes, supports Low-Code- and per-code workflows and works in hybrid multi-cloud infrastructures.
A company has extensive data in different formats and from different sources, including Excel tables, PDFs and unstructured text data. The data is inconsistent, contains errors and must be cleansed and harmonized for reliable analysis.
Konfuzio provides crucial support in this data wrangling process.
The AI platform enables the extraction and transformation of data from various document formats. Using semantic analysis and intelligent input management, the application automatically categorizes data and brings it into a standardized format.
The flexible adaptability of the AI makes it possible to carry out even complex transformations without hard rules.
Before Konfuzio was used, the data was structured differently and contained errors, especially in table formats.
Konfuzio automatically recognizes tables, extracts relevant information and performs necessary data transformations. The company can now access consistent and cleansed data, which significantly improves efficiency in analysis and decision-making.
The use of Konfuzio enables the company to automate the data wrangling process and significantly improve the quality of data for analysis and reporting.
Use Case 2 - Customer analysis in a retail company
A retail company has collected customer data from various sources, including online purchases, in-store transactions and customer reviews. The data is inconsistent, contains missing values and needs to be cleansed and harmonized for in-depth customer analysis.
By using data wrangling techniques, customer data is checked for consistency, missing values are handled and merged into a standardized format. This enables a reliable analysis of customer preferences and purchasing patterns as well as the development of personalized marketing strategies.
Before data wrangling, the customer database was unstructured, with different spellings of addresses and names. After cleansing and integrating the data, the company can now precisely analyze which products are preferred by customers, which marketing campaigns are more effective and how customers interact via different sales channels.
Use Case 3 - Financial reporting in a bank
A bank has financial data from different departments and systems. The data contains inconsistencies, different currency formats and must be cleansed to create consistent financial reports.
Data Wrangling standardizes financial data, performs currency conversions and handles inconsistencies. This ensures that the reports are accurate and comparable.
Before data wrangling, financial data was stored in different formats and exchange rates were not applied consistently. After data cleansing and integration, the bank can produce more accurate financial reports that provide a better basis for management decisions.
Use Case 4 - HR management in a technology company
A technology company has HR information from different systems, including recruitment data, training data and performance data. The data needs to be consolidated and cleansed to enable effective HR management.
Data Wrangling standardizes employee information, fills in missing training data and handles inconsistent performance data. This facilitates the creation of meaningful employee profiles and enables data-based personnel decisions.
Before data wrangling, employee data was spread across different departments and some training data was incomplete. After cleansing and integration, HR departments can track exactly what training employees have completed, evaluate their performance and offer targeted development opportunities.
Conclusion - Data wrangling for an improved data structure
Data wrangling is an important application for giving structure to raw data and improving its quality. This crucial process creates the basis for precise analyses and well-founded decisions.
Data wrangling enables the integration of different data sources and creates consistent data structures that form a reliable basis for further analysis. The automation of repeatable tasks not only speeds up the process, but also minimizes sources of error.
However, the challenges, such as managing data complexity and ensuring data quality, require a well thought-out approach.
Companies that make clever use of data wrangling not only improve their data quality, but also create the basis for data-driven innovations and optimized business processes.
Do you have questions or challenges with cleansing and structuring your data? Contact us now and one of our experts will get back to you right away to discuss customized solutions for your data challenges.