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Data Manipulation: Techniques, Tips, and Examples

May 13th, 2024

From decision-making to day-to-day operations in business, everything is dependent on data. None of this is possible without transforming raw data into useful information, especially when a large amount of data and disparate sources are involved. This situation is where data manipulation comes into play. It translates data into the required format so it can be easily cleaned and mapped for extracting insights.

This blog will illustrate the data manipulation concept in detail and further explore the necessity of data manipulation tools for businesses. It’ll also share some tips and steps for optimizing the data manipulation process.

What is Data Manipulation? 

Data manipulation is changing or altering data to make it more readable and organized, generate new insights, or meet specific requirements. Effective data utilization necessitates implementing relevant data manipulation methods, which this blog also covers in detail.

Data Manipulation Examples

Here are a few examples of data manipulation:

  • Customer Analysis: Marketing teams use data manipulation to segment customers by purchase history, behavioral patterns, and demographical information. This segmentation allows marketing personnel to tailor their messaging, curate personalized customer experiences, and boost customer retention.
  • Financial Reporting: Finance and accounting departments manipulate financial data to create reports, income statements, and balance sheets. Organizations can evaluate performance and assess profitability by calculating key metrics and aggregating transactional data.
  • Healthcare Analytics: Data manipulation techniques in the healthcare sector enable providers to analyze medical research, clinical trials, and patient information. Aggregating patient data, determining risk factors, and predicting outcomes allow healthcare facilities to improve patient care and optimize treatment provision.
  • Sales analysis: Businesses in the retail and e-commerce sectors manipulate sales data to enable trend analysis. Data manipulation also allows vendors to identify their bestselling products and predict demand.
  • Website Management: Website owners and administrators can use web server logs to locate the most viewed web pages, traffic sources, and more. This helps them identify areas for improvement and refocus their optimization efforts as needed.

What is Data Manipulation Language?

Data Manipulation Language (DML) is a computer programming language used for inserting, retrieving, and updating data in its database. It can also make data more organized or readable through Data Manipulation Language or DML.  DML offers operations that enable such modifications through user requests, making the data easy to cleanse and map for further analysis.

DML implementation can vary between database management systems, but some commands are standardized. These commands are:

  • Select: This command instructs the database which data it should select for further manipulation and where it’s located. 

Example: `SELECT * FROM Customers WHERE RegistrationDate > ‘2024-03-20’ ; ` 

  • Insert: ‘Insert’ enables users to add new data within the database by providing the columns in which new values will be inserted and the values that will be inserted.

Example: `INSERT INTO Products (ProductID, ProductName, Category) VALUES (1, ‘XYZ Data Tool’, ‘Software’) ; ` 

  • Update: This allows users to update a database’s existing data. It tells the database which data needs updates, where the new data will go, and whether to update thee new data records individually or collectively. 

Example: `UPDATE Products SET Price = Price * 1.1 WHERE Category = ‘Furniture’ ; ` 

  • Delete: ‘Delete’ performs data removal by describing to the database what data to delete and where to locate it.

Example: `DELETE FROM Students WHERE Grade = ‘12th’ ; `

These four commands are also the primary DML commands in Structured Query Language (SQL), a commonly used data manipulation language. In SQL, these commands are paired with specific syntax and optional clauses to assist in updating and retrieving data in a relational database.

An image showing four common commands in data manipulation language.

Why Manipulate Data? 

Data manipulation holds substantial importance due to various factors, including the following:

  1. Data Quality Assurance

Raw data is prone to errors, missing values, and inconsistencies — but data manipulation helps clean, validate, transform, and standardize it. Without data manipulation, organizations would find it challenging to ensure their data’s reliability, completeness, and accuracy.

  1. Insight Generation

Data manipulation enables enterprises to derive meaningful insights and recognize patterns and trends in their data. Methods such as data aggregation, summarization, and visualization lead businesses to actionable information that guides their decision-making.

  1. Data Integration

Businesses today work with heterogeneous data sources that produce large volumes of data in disparate formats. Data manipulation is vital to integrating these distinct datasets. It provides a unified view of data assets by harmonizing data structures and standardizing formats — allowing smooth data integration and ensuring interoperability.

  1. Format Consistency

Data organized in a unified, orderly manner helps business users make better decisions.

  1. Historical Overview

Quickly accessing data from previous projects can help an organization make decisions regarding deadline projection, team productivity, budget allocation, etc.

  1. Improved Efficiency

A business can isolate and even reduce external variables by having more organized data to contribute to its overall efficiency.

The Data Manipulation Process

The step-by-step process for data manipulation is as follows:

  1. Data Collection and Acquisition

Data manipulation starts with raw data from sources such as Application Programming Interfaces (APIs), files, or databases. Raw data is stored in structured, unstructured, or semi-structured formats.

  1. Data Preprocessing

Preprocessing is necessary to eliminate inconsistencies, errors, and discrepancies from raw data. Processes such as deduplication, normalization, imputation, and outlier detection improve data quality, readying it for analysis.

  1. Data Transformation and Enrichment

The next step involves transforming and enriching the data. Data is aggregated, summarized, and filtered to accomplish this. Teams create new derived variables, calculate new metrics, and join datasets for data enrichment.

These three steps form the core of the data manipulation process. Once these steps are complete, analysts take the following actions:

  • They interpret and communicate their findings to stakeholders. Resources such as interactive dashboards, presentations, and reports are used to break complex insights into actionable recommendations. In doing so, analysts connect the technical aspects of data manipulation with the practical application of insights.
  • They review and fine-tune their analyses based on new data and stakeholder feedback. The iterative nature of data manipulation helps analysts improve consistently, practice adaptability, refine their data models as needed, and benefit from new insights over time. Ultimately, this leads to more effective data-driven decision-making.

Different Data Manipulation Techniques and Methods

Data manipulation involves wide-ranging techniques and methods to transform raw data into valuable insights.

Filtering and Subsetting

A user selects a data subset matching certain conditions or criteria in filtering and subsetting. These techniques help filter out unwanted or irrelevant data points, creating a more relevant dataset that helps generate more accurate insights.

Sorting and Ordering

Sorting arranges data in a specified order — such as chronological, alphabetical, or numerical — making it more organized and easier to evaluate. Sorting also simplifies further visualization and analysis since sorted data is more straightforward to plot or summarize.

Aggregation and Grouping

Aggregation combines data points into summary statistics like averages, counts, or totals through various functions. Grouping uses shared attributes or criteria to sort data into subsets. These techniques offer varying levels of granularity for summarization, analysis, and comparisons.

Joining and Merging

During joining and merging, data from numerous datasets or sources is combined into a single dataset. These techniques integrate data from different sources, resulting in more enriched and accurate datasets and enabling in-depth analysis that considers various data dimensions.

Pivot Tables and Crosstabulations

Spreadsheet software such as Excel frequently uses pivot tables and crosstabulations to summarize data and analyze it in a tabular format. These tools allow dynamic data manipulation and rearranging to aid in identifying trends, relationships, and patterns.

Data Transformation Functions

Data transformation functions can include date/time conversions, string manipulations, and mathematical calculations. Such functions let users derive new metrics or variables from existing data by transforming data from one structure or format to another. Transforming data into consistent formats, creating new features or variables, and converting data types enhance the reliability and quality of data.

Window Functions and Analytical Functions

Window functions and analytical functions are among the more advanced SQL techniques. They enable analyses and calculations over data subsets. Users can use these functions to rank data, compute moving averages, and perform more complex analytical procedures.

Text and Sentiment Analysis

Text and sentiment analysis techniques manipulate textual data to extract insights. Common kinds of textual data include survey responses, questionnaires, social media activity, customer reviews, and chat transcripts. These techniques leverage natural language processing (NLP) algorithms for analyzing text, identifying sentiment, and extracting the required information.

Data Manipulation Tools: How Do They Help? 

Data manipulation tools can modify data to make it easier to read or organize. These tools enable users to identify patterns in data that may otherwise not be obvious. For instance, a data manipulation tool can arrange a data log alphabetically so that discrete entries are easier to find.

Data manipulation tools can modify data to make it easier to read or organize. These tools enable users to identify patterns in data that may otherwise not be obvious. For instance, a data manipulation tool can arrange a data log alphabetically so that discrete entries are easier to find.

Different types of tools offer varying levels of data manipulation functionality, as shown in the image below:

An image listing the different types of data manipulation tools and their functionality.

Besides these tools, programming languages such as R, Python, SQL, and Java allow data manipulation with built-in functions, packages, and libraries.

However, coding is ultimately a manual process. Regardless of these languages’ flexibility and control, manual data manipulation is time-consuming and labor-intensive.

Data manipulation tools offer a faster and more convenient alternative, especially for manipulation tasks involving large datasets. These tools are widely used during integration to make data compatible with the target system.

Data Manipulation Best Practices

Data manipulation becomes more effective and beneficial when certain best practices are implemented. Here are five of the most important ones:

1. Understanding The Data 

Before manipulating the data, understanding its format, content, and structure is essential. This knowledge can help direct the manipulation process while minimizing errors.

2. Data Cleaning 

Cleaning data before manipulation removes errors, inconsistencies, missing values, and duplicates. This step ensures that subsequent analysis and processing use reliable and accurate data.

3. Data Standardization 

Standardizing data formats and conventions promotes compatibility and consistency across various systems and sources. The standardization process can include evaluating naming conventions, date formats, units of measurement, and other attributes.

4. Preventing Mutation in the Original Data 

Instead of modifying the original dataset, it’s better to create copies for storing the manipulated data as this maintains data integrity and enables easy debugging and verification.

5. Documenting The Process 

Comprehensive documentation for every stage of the data manipulation process should include the reasoning behind decisions taken and the transformations used. Maintaining such documentation boosts reproducibility and makes the manipulation workflow easier to understand for others.

Conclusion

Effective data manipulation enables data professionals to fully leverage raw data, improve its quality, and use it to obtain valuable insights. Data teams can rest easy knowing the data they’re working with is accurate and relevant.

Enterprise-grade tools like Astera simplify data manipulation by offering built-in transformations and source and destination connectors. They make it possible to easily manipulate data without writing a single line of code. The platform’s automation and workflow orchestration capabilities support set-it-and-forget-it scheduling.

Download the trial version of Astera or speak to our team to discover how the software helps businesses leverage their data.

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