Transforming unstructured texts into structured datasets
MyRA Quant transforms your reports, notes, interview transcripts into structured spreadsheets, allowing you to define key variables and gain quantitative insights from your qualitative data with ease.
LET US CREATE YOUR DATASET
How MyRA turns your unstructured data into quantitative insights
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1. Upload interview transcripts, notes or other documents (rows)
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2. Define key variables to be extracted (columns)
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3. Receive a structured spreadsheet with quantitative insights
Key Features
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Bespoke data extraction service
Experience the convenience of a tailored consultancy service. Simply upload your reports or transcripts, and our expert team will handle the rest, delivering a structured spreadsheet with your defined variables.
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Customizable variables
Tailor the data extraction process to your research needs. Define variables such as binary options (e.g. Yes/No), descriptive narratives, or any other categories relevant to your study.
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Professional expertise
Our team of experienced analysts ensures the highest level of accuracy and consistency in your data extraction. We will have a one-on-one consultation with you (via email or video call) to ensure you get the most out of your data.
Case studies
Discover how some of our users utilised MyRA Quant.
How will you use MyRA to streamline your workflow?
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User need: A research team wanted to analyze a large dataset of tweets to identify common themes related to public sentiment on climate change.
Our solution: We took a dataset of tweets provided by the user, which contained tweets in one column. Using MyRA Quant, we used our advanced applied AI to categorize these tweets according to themes such as "policy support," "climate skepticism," "personal actions," and "corporate responsibility."
Example:
Dataset: Tweets about climate change
Themes identified: Policy support, climate skepticism, personal actions, corporate responsibility
Outcome: The user received a structured spreadsheet with tweets organized into the identified themes, providing clear insights into public sentiment on climate change. This enabled the research team to better understand- with quantitative insights - the distribution of opinions and conduct data analysis.
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User need: A think tank focusing on security issues had hundreds of disorganized interview transcripts and needed to draw relevant insights to establish trends over time.
Our solution: We analysed the interview transcripts, identifying key themes in the interviews. Our applied AI organized the transcripts, extracted significant themes and categorized the data, which were then compiled into a spreadsheet for data analysis.
Example:
Dataset: Interview transcripts related to perceived security threats.
Themes identified: What were the main perceived security threats (transforming narratives into categories), how did offenders conduct their activities, etc.
Outcome: The client received an organized spreadsheet summarizing the key insights and trends over time, giving them precise and clear quantitative insights. This helped them to efficiently process and interpret vast amounts of text, providing clear and actionable insights.