AddMaple

Data analysis that feels more high-speed Shinkansen 🚄 ✅ smooth ✅ fast ✅ comfortable And well, less antiquated, mind-numbing and stop-starty 🚂 ❌ overwrites ❌ jargon ❌ un-fun

We can't wait for this! Bring a laptop/computer, with internet. We'll bring the data, the banter, the AI credits - and we'll get to explore how to code with an AI Co-Pilot Research Partner together.

What a time to be an insights professional? If you're looking to find the latest tools for research, Insight Platforms is a must visit. Research tech is exploding! You might ask, why is AddMaple not on this list? There will be an update soon, focusing on short form qualitative analysis, within the context of customer feedback forms, surveys, and other qual data within structured datasets. We can't wait to see what's out there!

We have a burning question ⁉️

So excited to be part of this tomorrow!

Join us tomorrow for a wholesome lunch and learning session hosted by the ICG, Insight Consultants Group. We'll be hearing how other insight professionals analyse tens of thousands of open ends and get to quant insights from raw data fast. The event is online and free. Ange will be showing you quick tips guided by time savers enjoyed by AddMaple users, Dan Young from Shed Research - multi-award winning customer insight consultancy and Barbara du Perron from bric. 🎈We sincerely hope you can join us 🎈 We'll be offering a few discounts on the call ranging from 20% off to 50% off to ICG members, so do register if you've been on the fence.

🦩 We're quite serious about enjoying the process of data analysis 🦩 We learn through play, and this is something at the heart of AddMaple. If we enjoy the process of finding insights, we'll keep our curiosity reserves topped up to explore more. We might not be the biggest team, and we make up for it by making time for fun. We applied to speak at IIEX and ended up with a new side product / playground 🌈🤣. We hope you have as much fun comparing text classification outcomes between sentiment models as much as do! We'll go live on Tuesday with details to watch for free in Ange's post.

While we were preparing for a talk, we built a playground! And you'll get to try it soon too! We pulled together different algorithms and models trained for sentiment analysis onto one webpage, so you can see how each one tags the same public reviews as either positive, negative, neutral, mixed. It's just a spot of fun. This is not an AddMaple product folks - it's a playground 🍭 for insights professionals and researchers to play with the raw approaches to solving the age old problem of quantifying qualitative text data. You'll meet the legends, the heavyweights in sentiment analysis, whether that be LLMs like the BERT suite, or semantic algorithms in python, namely VADER, TextBlob, SentiWordNet, or indeed the latest Gen AI tools like GPT4. We'll release the playground, or snip the ribbon at IIEX AI. And you can come along too, it's free to register and watch talks on the latest AI innovations in the insights industry. Registration details in the comment. Thanks Greenbook - it's gonna be a blast!

This is written by real humans who have done research. Who have experienced research findings cast aside because they go against an already-made decision. We get it. Hence our post to you dear researcher. Dear researcher. We've heard the mantra, 'Fail Faster'. Our work as researchers is at its core about finding facts and sharing them as fast and effectively as we can. What management and other team members do with the facts is up to them. We cannot be held responsible if others don't want to hear/see what we uncovered. And we need to stop taking responsibility for that. The data speaks for itself - let's just amplify it transparently, intelligently and creatively. By facts we mean, the objective and verifiable data points that reveal what we can rely on to be true, within given constructs of what what we know, what we know we don't know and with sincere awareness that we don't know what we don't know. We don't need to know everything. We need to remain curious. If existing data can get us to facts faster - we use that. Our job is not to research for the sake of it, it is to share facts faster. If there is nuance, we surface it. We find and share the story the data is telling, and then simply let the data speak. If there is ambiguity, we let it spark further discussion. Our role is to dig and reveal what we find. Repeat. Our roles become fulfilling when our audience begins to think and consider what the data is saying. People have so much on their minds, with so much to do. But let us not undervalue how profound it is to facilitate thinking - when what we are thinking of is important. We need our audience to question whether information is trustworthy and credible so that they are empowered to act accordingly. It is not the job of the researcher to convince or persuade. It is the job of the researcher to convey findings in a way that is clear, honest and that sparks thinking, questioning, reasoning, discussion and ultimately more EXPLORATION. Presenting back our findings isn't the end of our journey but the beginning of our next exploratory journey after thoughtful deliberation. We're simply here to share information. Letting the data speak for itself fully to tell its story without masking or manipulation. We at AddMaple are passionate about this. We're here to help you 'Share Facts Faster', so that you can pivot and move on faster. With less pain, data cleaning, headaches, formatting and so on. And we're passionate about transparency and repeatability. So much so that we have an (i) icon on every shared chart, which shows the reader which filters and parameters were used to create the chart. It brings transparency, clarity and repeatability. This means that your reader knows how to create the same chart from the data for themselves. It also indirectly shows them how to create the chart slightly differently for different findings. After all, our superpower is curiosity. And we succeed when we spread curiosity to others. 

What do you do if you have an open dataset you'd like to drill into? Like this Glassdoor dataset from Kaggle with over 830k reviews from employees? One way is to open the file and let AddMaple transform the raw data into an Explorable dashboard that you can pivot, cross tabulate, segment and explore further - all on your own system.