Notably

Notably

学术研究

St. Petersburg ,Florida 1,559 位关注者

The research platform your qualitative data deserves. Everything you love about spreadsheets & sticky notes.

关于我们

The intelligence platform your qualitative data deserves.

网站
https://notably.ai
所属行业
学术研究
规模
2-10 人
总部
St. Petersburg ,Florida
类型
私人持股
创立
2021
领域
ux、uxresearch、designthinking、saas、uxdesign、qualitativeresearch和research

地点

Notably员工

动态

  • Notably转发了

    查看Mike Stevens的档案,图片

    Founder / Editor at Insight Platforms

    Here’s a handy visual guide to Qualitative Data Analysis software tools. I'm sure we missed some. We usually do. You can find out more about all of these solutions on Insight Platforms; you can even watch on-demand webinars and demos of many of them. Cynthia Portugal Norbert Sari Karen Albert Jack Bowen Amel Mechalikh Alok Jain Jiten Madia Marco Rovagnati Jim Longo Tom Higgins Kristin Dorsey Nina G. Justin Perkins Dave Kaye Paul Chesterman Christy Weeks Debi Hart Nihal Advani #marketresearch?#innovation?#ai?#insights?#technology #uxresearch

    • Market Landscape - Qualitative Data Analysis Solutions - Insight Platforms
  • Notably转发了

    A company recently came to us with a huge challenge: over 200,000 open-ended survey responses collected over 5 years, all needing to be analyzed and synthesized into insights within weeks. ?? This problem isn’t unique to big co. Government agencies and orgs conducting large scale medical or consumer research face similar overwhelming data challenges. The manual methods we use for small studies won't work. But AI alone isn’t enough. While AI can process data quickly, it often misses the nuance and depth needed for meaningful insights. Some specific limitations with an AI-only approach: ? AI focuses on high-frequency terms, often missing emotional undertones, contradictions, or deeper themes. ? It struggles with tone, sarcasm, and complex emotional feedback. ? AI might group irrelevant data together because of its reliance on statistical frequency. ? It can reflect the biases of the datasets it’s trained on, often missing underrepresented voices. ? And the key limitation: handling extremely large datasets. Most consumer platforms require data to be processed in smaller batches due to memory and processing limits, leading to fragmented, disjointed insights. That’s why we launched a new service. ??♀? It's a perfect blend of our backgrounds in consulting, research expertise, and the technology we’ve built with Notably. I truly think our product is the best qualitative analysis tool on the market and you won't find anyone more passionate about synthesis and big data challenges than our team. ?? I put together this blog post to elaborate on the challenges and break down how we approach this challenge with our blend of tech & services. If you have a mountain of data you need help analyzing quickly, our team is here to help! https://lnkd.in/dQ3-z6D8 #uxresearch #qualitativedata #consultingservices #AI #datanalysis #AIandTech #HumaninTheLoop

    How to Analyze Large Qualitative Datasets with AI: Challenges, Solutions, and Best Practices

    How to Analyze Large Qualitative Datasets with AI: Challenges, Solutions, and Best Practices

    notably.ai

  • 查看Notably的公司主页,图片

    1,559 位关注者

    You can use Notably's custom AI templates to find opportunities for more research. Explore the nooks and crannies of your data and look for threads to pull on with questions like: ? Is there indication of an even larger theme at play? ? What different angles could be explored for a more holistic understanding of the topic? ? What outliers exist in the data that should be further researched? ? What gaps or context is missing that should be filled? Here's a boilerplate prompt to steal, tweak, build on, and use on your past research in Notably: ---- You’re a design researcher conducting secondary research to look for more opportunities to conduct primary research. Your goal is to identify from the data gaps in knowledge that could be filled with further research such as 1:1 interviews, focus groups, surveys, diary studies, field research, observations, intercepts, or desk research. Analyze the data and answer the following questions: What patterns emerged in the data that may indicate an even larger theme worth exploring with more research? What different angles could be explored that contribute to this larger theme for a more holistic understanding of the topic researched? What outliers exist in the data that should be further researched to see how they might play into the bigger picture? What knowledge gaps or context is missing that could be filled with additional research? [Add additional criteria for more research] End with 3 focused, specific recommendations for follow-on research including ‘why’ and ‘how.’ #uxresearch #qualitativeresearch #ux #uxdesign #airesearch #ai

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