You really need both: solid, intuitive product design and good new user onboarding. Even with a great product, new uses will still need help discovering and understanding how to get value from your product.
Tooltips aren't always the answer though. Onboarding that's more seamlessly integrated into the experience are better, like Retool, Superhuman, or Intercom. You can see those experiences here: https://twitter.com/philvb/status/1617921908510699520?s=20&t...
Tours aren't always bad, but they're often just poorly implemented, with too many steps that don't mean anything to the user. Onboarding that's contextual, like embedded tips, or relevant, like targeted onboarding by persona, or action-oriented onboarding, like checklists tend to be better.
After leading onboarding and in-app education teams at Dropbox, I started Dopt [0], which is a react component library and SDKs to make it easier to build tours, but also more contextual and less distracting onboarding experiences like embedded tips and checklists. My hope with Dopt is that you can still build tours when necessary (like 2 step tours to introduce a new feature), but have a bigger and better toolkit for all types of onboarding and education.
why does your website need to gather cookies? first thing I'm hit with is a cookies splash screen, I'm wondering what I'm being tracked for.
I'm also curious about this:
>Hybrid work
On the face of it, I like the upfront callout, however how did you guys land on Hybrid being the solution? Especially in the light of working 100% remote leads to more productive employees[0]
For cookies: we're using Posthog for tracking and it's helpful for us to understand how people use our website and product.
Hybrid vs remote is sometimes polarizing, but hybrid has been really great for us to balance the heads down time and no commute of remote with being able to jam on stuff in person.
Interesting. When Hybrid was made as a decision, where there any data points that were considered in that decision, or was it just made from the start, or top down?
Do you think its not possible to have the same levels of collaboration remotely if the culture adjusts for remote only type expectations?
(I rarely get to ping decision makers about these things, I apologize for the intrusion, I'm trying to gather as much data on this as possible recently)
All 3 founders worked remotely at larger companies during the pandemic and when we started the company in late '21 we knew we wanted to be hybrid, so it was made top down from the start. For a small team, I personally think there are culture and productivity benefits for being in person. I think other companies have shown remote is absolutely viable for small and large companies, but to your point, there has to be a lot of intention around it. I don't buy into the dogmatic views of hybrid vs remote: I think it's more about understanding what's right for you and the company, and as a leader making the culture, policy, and practices work for what you want. For me and Dopt, hybrid has been a dream :)
thanks for the feedback on the website! we're going to be making some changes over the next month, so we'll make this more clear.
glad you think this is a great solution. it sounds very similar to the conversational design tool you made. we've taken some inspiration from tools like voiceflow. we think they have a nice flow builder.
Yes, completely agree that each dataset requires decisions to be made that can't be automated, but there are huge opportunities for tools to assist users in understanding what cleaning decisions they might want to make and how those decisions affect the data. Most data cleaning tools do a very poor job of helping the user visualize and understand the impact cleaning has on data - they're usually very low level (such as pandas).
As an example of a tool: Trifacta (disclaimer I work here) https://www.trifacta.com/products/wrangler/. We're trying to improve data cleaning with features such as suggesting transforms the user might want, integrating data profiling through all stages to discover and understand, and transform previews so the user can understand the impact.
I think there's a huge opportunity for better tools in the problem space.
It's hard to evaluate when the projects are so new. It was a similar problem with Crossfilter/Datavore/Data.js/Miso Dataset when they all came out.
If I had to pick, I'd go with Vega because I think it has the most potential to develop a community around it. Especially as a bridge between Python/R and D3.js through JSON. Think iPython notebooks, R Shiny, etc.
They are all libraries for managing datasets with fast or convenient filtering. Useful for linking visualizations together through a common data model.
None have become as popular as d3.js though, so it's still hard to compare them.
The interaction model is something that very few charting libraries have. Also we try to make Polychart.js very easy to get started in, so that creating a simple line or bar chart would take minimal amount of code. Vega is amazing as well for how flexible it is.
Tooltips aren't always the answer though. Onboarding that's more seamlessly integrated into the experience are better, like Retool, Superhuman, or Intercom. You can see those experiences here: https://twitter.com/philvb/status/1617921908510699520?s=20&t...