Data analysis automation: How AI is changing research processes?

‍Interest in AI processes that improve the efficiency and quality of research is growing daily, and data analysis is an integral part of this process in many research fields. As data grows exponentially, the need for advanced tools and techniques to process it is growing. Today, AI-based automated data analysis plays a significant role in managing vast amounts of information, and qualitative data analysis is becoming a tool tailored to the needs.

Artificial intelligence (AI) has not just changed but transformed how research is conducted, offering modern data analysis techniques and revolutionising many fields. Innovative AI-based methods are used to organise massive amounts of quantitative data and provide insights from qualitative data. Data scientists can now combine both types of data to obtain comprehensive results, opening up new possibilities and avenues for research.

Quantitative and Qualitative Analysis in Research Processes

For decades, analysing data was time-consuming and complex, requiring much manual effort. Traditionally, manual researchers transcribed recordings, developed coding frameworks, and analysed the results using methodologies such as grounded theory. This manual approach is slow, prone to human error, and difficult to scale with large data sets.

The process has relied primarily on quantitative analysis and less often on qualitative analysis.

• Quantitative analysis is a research method that collects, analyses, and interprets numerical data – enabling objective conclusions and predicting future trends.
• Qualitative analysis is organising, analysing, and interpreting qualitative research data – non-numerical information – to extract insights, develop hypotheses, and discover patterns. Qualitative data is unstructured compared to quantitative data.

Unstructured data includes interview transcripts, audio and video files, open-ended survey responses, feedback, reviews, and observation notes. Qualitative analysis examines the human experience, trying to answer the “why” and “how” behind people’s words and actions. Until recently, truly understanding motivations and experiences was considered an exclusively human skill.

Using AI for Qualitative Analysis

At the same time, with the growing adoption of AI in qualitative research, questions arise about the future role of this technology in the field of data analysis, such as:

• Will AI become a partner for qualitative researchers?
• Does AI have the potential to replace humans completely?

There are various ways to use AI for qualitative research. AI can be used for informal coding, summarising large chunks of data, brainstorming with data, as a second pair of eyes, or as a starting point for creating a codebook. It can also potentially help formulate research questions or interview guidelines.

AI-powered automated qualitative data analysis

Unlike traditional qualitative analysis, where researchers manually read, code, and organise data, AI-powered qualitative data analysis automates many of these tasks using natural language processing (NLP) and machine learning (ML) algorithms. And while ML tools have been around for a long time, analysing interviews and surveys using them used to be a substantial technical challenge.

AI tools streamline the qualitative data analysis process by automating data analysis. Data analysis automation is the process of collecting, preparing, and analysing unstructured data and then visualising, grouping, identifying patterns, detecting hidden themes, analysing sentiment, and generating predictive models using AI tools.

Automated data analysis involves:

1. Collecting data by connecting to data sources such as databases, APIs, cloud applications, etc., and ingesting it into an automated system.
2. Data processing involves preparing and transforming the collected data for analysis.
3. Analysis automation involves using algorithms and statistical models to discover patterns, make predictions, detect anomalies, etc.
4. Visualisation and reporting – displaying analytical insights in dashboards, charts, graphs, and reports that update automatically as data is refreshed.

Challenges to automated data processing

While automated AI data processing offers many benefits, it also brings challenges, including security and operational efficiency of systems. For example:

• Protecting personal and confidential data from unauthorised access and misuse requires encryption, access control, and compliance with privacy regulations.
• Handling complex, unstructured data requires advanced tools, expertise, and skilled personnel.
• Integrating data from multiple sources and formats requires a strategy and compatible systems to ensure smooth data flow.

Fully automated research results in qualitative data analysis using AI are not yet possible. Despite its advances, AI still can’t “read between the lines” of human interactions (a hallmark of inductive analysis).

Automated data analysis can reduce workload by 80% compared to manual analysis. However, it’s important to remember that AI can't replace humans in certain tasks. It can’t interpret patterns in a way that reflects human judgment and cultural contexts. These tasks require understanding and adaptation, where AI has no reference point. Unique human insight is still key to interpreting data, and AI is designed to support and enhance these skills, not replace them.

According to a recent report, 56% of researchers already use AI for qualitative analysis, up from 20% in 2023

Choosing a tool for qualitative research analysis

Choosing the right solution can not only meet research goals but also streamline workflows, which will translate into more reliable insights and impact the success of qualitative research. It’s worth considering your research needs and the data types you’ll analyse, such as text, audio, or video. Each option can offer unique capabilities tailored to specific industries. These could include:

1. Scientific research, where numerical data can be integrated with thematic analytics – for example, in educational research, survey results are combined with interview excerpts to deepen understanding of outcomes and student engagement.
2. Market research, where consumer insights analysis combines quantitative metrics, such as sales data, with qualitative metrics, such as customer review data.
3. Medical research, where clinical trial statistics combine patient feedback to statistically validate treatment efficacy while understanding patient experiences to improve healthcare outcomes.

By combining the precision of quantitative data with the depth of qualitative data, researchers can now discover nuances and gain a deep understanding of complex phenomena.

Using proNote in data analysis in qualitative research

One tool that uses automatic data analysis in qualitative research is proNote Research. This highly innovative research tool uses AI to support the data analysis process in qualitative research. In this case, automation using AI significantly increases efficiency, reduces the costs associated with manual data processing and report generation, and eliminates human errors.

The proNote tool is perfect for, among others, transcription, diarisation, sentiment analysis, analysis of data from interviews, workshops or focus groups and creating reports. It brings invaluable benefits in the form of:

• shortening the analysis time;
• increasing precision;
• cost savings.

By using proNote in qualitative data analysis, you can:

• identify key parts of the conversation;
• automatically mark answers to questions;
• summarise notes in a question-and-answer format;
• generate topic-based insights;
• transform audio/video recordings from qualitative research into detailed research reports

The future of automated data analysis

1. Cloud-based aolutions

The shift away from traditional on-premises infrastructure and toward cloud computing is revolutionising data analysis. It allows companies to efficiently organise large volumes of data while maintaining agility and scalability. On-demand access to scalable resources reduces costs and increases operational flexibility.

2. LLM and GenAI in data analysis

Large Language Models (LLMs) powered by advanced generative AI (GenAI) have emerged as transformative tools capable of automating and improving qualitative analysis. For example, increasingly well-designed prompts in LLM tools have increased the clarity of responses, thereby increasing researcher confidence. The potential for integrating LLM with human expertise, improving model robustness, and improving evaluation methodologies predict future innovations in applying LLM in qualitative analysis.

3. Increased data privacy

Data analytics is closely linked to data privacy and ethical data processing, and more stringent regulations such as GDPR are driving the need for technologies that protect confidential data. Organisations will increasingly adopt differential privacy, data anonymisation, and secure computing techniques to safeguard confidential information.

Summary

Integrating AI with data analytics is revolutionising many fields that rely on research for development and efficiency. It enables the automation of vast amounts of information, its analysis, and the discovery of patterns and insights. Data analytics tools are changing how qualitative data is processed, and business decisions based on it are made.

You may be wondering how to integrate AI into your qualitative research analysis process or see the need to streamline your work. Choosing the right tool can significantly impact ease of implementation and quick results. proNote Research is the first tool on the market that automates qualitative researchers' work to such a high degree.

‍Discover all proNote capabilities and sign up for a free 7-day trial.