Mining insights from large amounts of qualitative data requires some special tools. Sure you can mine for insights using free quantitative data analysis software but the tools are different. For qualitative data, you need qualitative data analysis software. It’s ok if you’re not familiar with the term ‘qualitative data’ because it just means any type of information that can’t be measured numerically, but rather with words. Quotes, observations, complaints, these are all examples of data that is qualitative, not quantitative.
Qualitative data analysis is a research approach that creates detailed descriptions and interpretations of qualitative data. What’s qualitative data? It’s any kind of information that can’t be quantified, such as descriptions of behavior, interviews, surveys, videos or photos. Qualitative data has no numerical value — it’s simply categorized and described. The interpretive nature of the qualitative analysis is what makes it different from other forms of data analysis. Qualitative analysis is subjective because it’s based on human understanding and judgment. Qualitative researchers do not use statistics; they use personal observations to create narratives that convey the meaning behind the data collection. These narratives help designers to better understand users and products or services that may not otherwise be obvious. So to
Qualitative data analysis is a research technique used to gain a deep understanding of the meanings and significance of the data obtained from a survey or interview. Unlike quantitative data, qualitative data focuses on expressing meaning through concisely written text or images.
Qualitative data analysis software is a set of tools, with a simple and easy to use, with which you can proceed to the analysis of text or verbal information. In any case, qualitative data analysis software is an indispensable tool for qualitative analysts. Qualitative data analysis is the analysis of unstructured, natural language data and talk to data. There are various qualitative data collection methods which can generate text and speak to data that will require qualitative analysis. These qualitative analysis software tools help in better analysis and interpretation of such texts.
1.Microsoft Power BI
Microsoft Power BI is a top business intelligence platform that offers support for dozens of data sources. This Data analytics software allows users to create reports, displays, and dashboards and post them. For quick delivery, users may combine a group of dashboards and reports into a Power BI app. The Power BI helps users create and implement automatic models by applying Machine Learning with Azure Machine Learning.
2. SAP BusinessObjects
SAP BusinessObjects provides a suite of Data analytics tools for data discovery, analysis, and reporting. The tools are designed for novice technical users but also for carrying out complex analyses. BusinessObjects incorporates Microsoft Office products, enabling Business Analysts to easily reverse and switch between applications, like Excel and reports from BusinessObjects. It also enables self-service predictive analytics.
3. RapidMiner
RapidMiner is the next tool on our list. Being named a Visionary in 2020 Gartner Magic Quadrant for Data Science and Machine Learning Platforms, RapidMiner is a platform for data processing, building Machine Learning models, and deployment.
Products
The products of RapidMiner are as follows:
- Studio
- GO
- Server
- Real-Time Scoring
- Radoop
All these products have sub-versions that differ by features offered by them and pricing options.
Companies using:
Companies such as BMW, Hewlett Packard Enterprise, EZCater, Sanofi use RapidMiner for their Data Processing and Machine Learning models.
Recent Advancements/ Features:
Recently RapidMiner has launched RapidMiner 9.6 which has extended the platform to full-time coders and BI Users. It is a fully transparent, end-to-end Data Science platform that enables data preparation, Machine Learning, and model operations.
4. Tableau
Tableau is a market-leading Business Intelligence tool used to analyze and visualize data in an easy format. Being named as a leader in the Gartner Magic Quadrant 2020 For the eighth consecutive year, Tableau allows you to work on live data-set and spend more time on Data Analysis rather than Data Wrangling.
Products
Tableau Product Family include the following:
- Tableau Desktop
- Tableau Server
- Tableau Online
- Tableau Reader
- Tableau Public
Out of all, Tableau Public is a free Tableau software that you can use to make visualizations with but you need to save your workbook or worksheets in the Tableau Server which can be viewed by anyone.
Companies using
Multinational organizations such as Citibank, Deloitte, Skype, and Audi use Tableau to visualize their data and generate meaningful insights.
Tableau is coming up with frequent updates to provide users with the following:
- Fast Analytics
- Smart Dashboards
- Update Automatically
- Ease of Use
- Explore any data
- Publish a dashboard and share it live on the web and on mobile devices.
5. Sisense
Sisense is a Data analytics software aimed at aiding both technical developers and the Business Analytics process and visualizing all of their business data. It offers a wide variety of drag-and-drop software and interactive dashboards for collaboration. The Sisense platform’s unique feature is its custom in-chip technology, which optimizes calculation to utilize CPU caching instead of slower RAM. This can lead to 10-100 times faster computation for certain workflows.
6. TIBCO Spotfire
TIBCO Spotfire is a Data analytics software that provides natural language search and AI-powered data insights. This is a comprehensive platform for viewing reports for both mobile and desktop applications. Spotfire also offers point-and-click tools for predictive analytics models.
7. Thoughtspot
Thoughtspot is a Data analytics software that allows users to explore Data from various sources through reports and natural language searches. The SpotIQ, its AI-powered system, automatically seeks insights to help users discover trends they didn’t know to search. It also enables users to automatically link tables from various Data sources to break down Data silos.
8. Jupyter Notebook
Jupyter Notebook at a glance:
- Type of tool: Interactive authoring software.
- Availability: Open-source.
- Mostly used for: Sharing code, creating tutorials, presenting work.
- Pros: Great for showcasing, language-independent.
- Cons: Not self-contained, nor great for collaboration.
Jupyter Notebook is an open-source web application that allows you to create interactive documents. These combine live code, equations, visualizations, and narrative text. Imagine something a bit like a Microsoft word document, only far more interactive, and designed specifically for data analytics! As a data analytics tool, it’s great for showcasing work: Jupyter Notebook runs in the browser and supports over 40 languages, including Python and R. It also integrates with big data tools, like Apache Spark (see below) and offers various outputs from HTML to images, videos, and more. But as with every tool, it has its limitations. Jupyter Notebook documents have poor version control, and tracking changes is not intuitive. This means it’s not the best place for development and analytics work (you should use a dedicated IDE for these) and it isn’t well suited to collaboration. Since it isn’t self-contained, this also means you have to provide any extra assets (e.g. libraries or runtime systems) to anybody you’re sharing the document with. But for presentation and tutorial purposes, it remains an invaluable data science and data analytics tool.
9. Apache Spark
Apache Spark at a glance:
- Type of tool: Data processing framework.
- Availability: Open-source.
- Mostly used for: Big data processing, machine learning.
- Pros: Fast, dynamic, easy to use.
- Cons: No file management system, rigid user interface.
Apache Spark is a software framework that allows data analysts and data scientists to quickly process vast data sets. It was first developed in 2012 before being donated to the non-profit Apache Software Foundation. Designed to analyze unstructured big data, Spark distributes computationally heavy analytics tasks across many computers. While other similar frameworks exist (for example, Apache Hadoop) Spark is exceptionally fast. By using RAM rather than local memory, it is around 100x faster than Hadoop. That’s why it’s often used for the development of data-heavy machine learning models. It even has a library of machine learning algorithms, MLlib, including classification, regression, and clustering algorithms, to name a few. On the downside, consuming so much memory means Spark is computationally expensive. It also lacks a file management system, so it usually needs integration with other software, i.e. Hadoop.
10. Talend
Talend offers a suite of cloud apps for data integration. It’s designed to help businesses collect all their data in a single platform so that teams can access the right data when they need it.
The platform has a series of in-built machine learning components, which allow users to analyze data without the need to code. It uses classification, clustering, recommendation, and regression algorithms.
Talend offers a free open-source version and various commercial alternatives.
11. Excel
Microsoft Excel can be used to filter, organize, and visualize quantitative data, making it the perfect tool for performing simple data analysis. You can use a wide range of formulas, filters, and create pivot tables, charts and graphs, to synthesize the data you’ve gathered. But there’s a limit to the amount of data that Excel can handle, so you may need more powerful tools if you’d like to analyze data at scale.
Explore common functions and formulas for data analysis in Excel.
12. Airtable
Airtable is a user-friendly cloud collaboration tool defined as “part spreadsheet, part database”. It provides data analysis and data visualization functions (like other traditional spreadsheet tools) but with a powerful database on the backend. By using “views”, you can easily interact with the database to manage, track, and find data. Plus, developers can connect Airtable with other apps through an API.
Conclusion
Qualitative data analysis is very popular in statistics because it has its own methods which can be used to describe characteristics or changes of something. A qualitative analysis should be used to understand people’s desires thus creating better services. Qualitative research is the fastest-growing type of market research because it allows companies to gain insight into their consumers’ mindsets.