Top 7 Toolkits for Developing Qualitative Data Analysis Software

Data-driven research, a product of using interdisciplinary scientific methods for extracting knowledge, is taking over the globe. According to McKinsey & Company’s Marketing Insights, data-driven organizations are 23 times more likely to acquire customers and 6 times as likely to retain them!

Also termed “the fourth paradigm of science”, data-driven research has created opportunities for big data analysis in science and other industries. Being a powerful factor in data-driven research, performing data analysis with effective software helps provide the best results through data management, preparation, visualization, analysis, reporting, presentation and collaboration. 

Software development tools have become increasingly important in today’s tech-driven world. As a result, software engineers and amateur beginners are turning to toolkits to develop their qualitative data analysis software. 

A software development tool or code editor is a program that helps developers write, test and debug code. Some common tools include syntax highlighting, code completion and error checking. Tools can also include version control integration and project management functionality. However, depending on the tool and the programming language it is designed for, the specific functionality of a programming tool can vary greatly. 

Qualitative data software helps researchers and engineers organize and analyze large amounts of unstructured data in the form of text, audio or video. The software typically includes various features that make it easier to categorize, code and identify patterns within the data. 

Identifying the right tools that would work best for you from an extensive array can be difficult. This post explores the top 7 toolkits for developing qualitative data analysis software which will help you save time, money and effort when building qualitative data analysis software.

How Does Qualitative Data Analysis Software Work?

Some common features of qualitative data analysis software include:

1. Text coding

The software allows you to manually assign codes or labels to specific segments of text, enabling you to organize and group the data in meaningful ways.

2. Memo-writing

This software provides a space to document thoughts, interpretations and insights as you analyze the data. 

3. Search and retrieval

The software allows you to search for specific terms or phrases within the data, making it easier to find relevant information. 

4. Data visualization

This type of software allows visual representations of data, such as word clouds or concept maps, which can help you identify patterns and connections. 

Components of Data Analysis Software

High throughput data analysis software is used to process and analyze data. It helps improve the accuracy of decisions and reduces time and cost while analyzing data. Moreover, software-assisted data analysis provides a better understanding of customer behavior in industry research. The key components in data analysis software typically include: 

1. Data import and management: Enables you to import, organize and manage large amounts of data from various sources, such as databases, spreadsheets or text files. Examples include SQL Server Management Studio, Talend, Informatica PowerCenter, Alteryx and DataRobot. 
2. Data cleaning and preparation: Provides tools to assimilate data, such as removing outliers, missing data or duplicate records. Examples include OpenRefine, Trifacta, KNIME and Talend. 
3. Data visualization: Provides visual representations of the data, such as charts, graphs or maps, which can help you identify patterns and relationships. Examples are Tableau, Microsoft Power BI, QlikView, D3 and R Shiny. 
4. Statistical analysis: Provides statistical analysis tools, such as descriptive statistics, hypothesis testing and regression analysis. Examples are MATLAB, Minitab, STATA, R language and SAS. 
5. Modeling and machine learning: Features tools for building and evaluating predictive models, such as decision trees, random forests and neural networks. A few examples include scikit-learn, TensorFlow, Weka and RapidMiner. 
6. Reporting and presentation: Enables you to create professional-quality reports and presentations with tables, charts and graphs. There are many options, including Tableau, Microsoft Power BI, Crystal Reports and SAP Lumira. 
7. Data security: Provides features that protect sensitive data, such as data encryption, access controls and data audit trails. A few options include Microsoft Azure Information Protection, Symantec Encryption, Trend Micro Deep Security and IBM Security. 

Collaboration and sharing: Provides features for collaborating with other team members and sharing data. Several options inlcude Slack, Google Drive, Dropbox, Trello, GitHub, JIRA, Basecamp and Confluence.

Tools to Build Data Analysis Software 

After conducting thorough research on defining the software product vision, building a product prototype, and approximately estimating the time and cost for building a data analysis software, we focus on the tools that could be used to build the software based on the tool’s functionality, scalability, security and price. 

Software engineers use some standard programming languages for frontend and backend development, along with various tools for specific functionality. With the right tools, data analysis software can be developed quickly and efficiently. From the various tools and technologies used to build data analysis software, here are the top 7 toolkits for developing qualitative data analysis software:

NVivo

NVivo is a text analysis software tool used for analyzing qualitative data via coding, memo-writing and assisting in searching large amounts of text data. Some of the features of NVivo are:

• Coding and classifying data by assigning tags or labels to segment text, audio or video data 
• Performing advanced queries and searches on your data to identify patterns and themes
• Creates visualizations of your data, such as word clouds, network diagrams and concept maps to help understand the data
• Allows organizing, storing and managing your data in a centralized location
• Assists in exporting data and findings in various formats, such as PDF, Excel and Word

Python

Python is preferred for backend development and supports multiple programming paradigms such as object-oriented, procedural and functional. This simple and easy-to-learn syntax is a great choice for beginners with remarkable features as follows: 

• A large and active community of users, which means a wealth of resources and support is available
• Numerous data visualization libraries, such as matplotlib and seaborn, allowing the user to create visual representations of your data
• Several machine learning libraries, such as TensorFlow and scikit-learn, which can be used to build and train machine learning models
• Provides several libraries for natural language processing, such as NLTK and spaCy, which are used to extract meaning from text data
• Libraries for web scraping, such as Beautiful Soup and Scrapy, used to scrape data from websites

CoreNLP

CoreNLP is a natural language processing (NLP) toolkit developed by the Stanford Natural Language Processing Group. Some of its features are given below:

• A wide range of text analysis tools, including part-of-speech tagging, named entity recognition, sentiment analysis and conference resolution
• Supports multiple languages, including English, Chinese, French, German, Spanish and Arabic
• Sentence parsing to extract grammatical structure and dependencies, allowing you to analyze the synthetic structure of a text
• Highly customizable and useful for developing your own NLP pipeline by choosing which annotators to use and in what order
• Designed to be highly scalable and can process large amounts of text data in parallel

ggplot2

ggplot2 is a data visualization library created for the programming language R. Some of its features are as follows:

• Built on the “grammar of graphics” philosophy, which allows you to create complex visualizations by combining simple building blocks
• Highly customizable and creates a wide range of visualizations, such as scatter plots, line plots and box plots
• Provides a set of themes that control the overall appearance of the plot, such as axis labels, font size and background color
• Allows you to layer multiple visual elements, such as points, lines and text, on top of each other to create complex visualizations
• A large and active community of users who have created several resources for assistance

Azure 

Azure is a cloud computing platform and service created by Microsoft. It provides interesting features such as:

• Allows you to scale your data, based on your needs
• Provides services that include virtual machines, storage, databases, analytics and machine learning, allowing you to build and deploy a wide range of applications
• Offers several services and tools for automating tasks, integrating with other systems and managing your workloads
• Offers services and tools to build and deploy IoT solutions, such as Azure IoT Hub

MongoDB

MongoDB is a free open-source document-oriented database that is popular among web developers. It uses JSON-like documents with optional schemas for the storage and retrieval of data with scalability and flexibility. MongoDB provides numerous other features such as:

• Stores data in a format called BSON (binary JSON), which allows easy storage and retrieval of complex data structures
• Handles large amounts of data and high levels of read-and-write operations by adding more nodes to a cluster
• Automatically distributes data across multiple servers to ensure that it can handle large amounts of data
• Provides a rich query language that allows you to query your data in various ways, including filtering, sorting and aggregation
• Automatic failover mechanisms that ensure your data are always available, even if a server goes down

MySQL

Another open-source relational database management system that is widely used for web-based applications is MySQL. It is a high-performing database that provides better scalability, usability and reliability. 

• Built-in support for high availability through replication and clustering, which allows you to ensure that your data are always available
• Runs on a wide variety of platforms, including Windows, Linux and macOS
• Supports stored procedures and triggers, which allow you to perform complex data processing tasks and automatic specific actions based on certain events
• Provides various security features, including user authentication, access control and encryption to help you protect your data
• Built-in tools for backing up and restoring your data, allowing you to easily protect your data integrity and consistency

In Closing 

Developing software for qualitative data analysis is an invaluable resource. With an increasing need for data-driven research, cost and time-effective methods to get accurate data play a vital role in developing secure software.

However, developing a software program also requires streamlining troubleshooting queries. Retrace helps software developers proactively improve their software by identifying the bottleneck of the system and constantly observing the software application while performing improvements.

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Author

Academy Enago

Enago Academy is the knowledge arm of Enago — a global leader in academic research and publishing services. Our team of subject matter experts, academics, trainers and IT project managers are passionate about helping researchers and IT professionals build successful careers and enjoy enriching our communities with insightful and highly useful content.

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