Datawisp

Startups often “eat their own dog food.”?Datawisp?is no exception to that, especially as a data company. At Datawisp we’ve found that the more we base product and marketing decisions on actual data through Datawisp - instead of our gut and feedback from friends and family - the more our KPIs go up. Check out the full blog post on how we use Datawisp internally to improve our own product, reduce CAC, and better understand our users. Link in the comments below!

Recently, we published a case study comparing Datawisp against some of the big players in the data analytics + AI space, including offerings from Microsoft, Amazon, and more. We used our own internal benchmark for Wispy to evaluate the different platforms because there was no established benchmark for this application. We based it on real questions that were asked by some of our customers and on data sets that looked similar to those our customers brought in. Immediately, we received a flood of messages and comments. Overwhelmingly the response was "this was very needed." We've updated our case study (and our benchmark scores to reflect a few changes): 1) Databricks Genie was used instead of Databricks Assistant (it's much better) 2) Snowflake was included with all the competitors despite it not strictly meeting the requirements (it produces SQL, not charts/tables) 3) Datawisp was graded manually by our data scientist instead of using our automated internal benchmarking tool (the same methodology as for the other platforms) A link to the updated case study is included in the comments. Despite performing the best in our benchmarking, we know from real customer experiences that there's still many many things we can improve about Wispy - so we've decided to continue working on our benchmark (and on Wispy!) and updating it as we go along. We're also going to make it publicly available and fully transparent and include instructions on how to evaluate the responses. Link to the case study is in the comments. Special thanks to Chao Cai from Databricks for his feedback on the benchmark and for pushing us to include a better competitor :)

Thanks to everyone who came out to our #SFTechWeek event "Can AI replace your data scientist" and to our panelists Mengying Li and Leo Li for sharing their insights with the crowd! TL;DR in case you missed it - Generative AI is already starting to have an impact on the world of data science and helping teams work more efficiently by automating / speeding up certain tasks. As the technology evolves we'll see more use cases. However, data scientists are more than just SQL writing machines - they understand your business, your strategy, and know how to work cross functionally with other (human) teams to achieve company goals. AI won't be replacing them anytime soon :)

*sneak peak* We benchmarked all the top BI companies' AI products against Datawisp and did a full write-up of the results. It wasn't particularly close. Full analysis coming soon.

What we're building at Datawisp is a brand new take on data analytics - one that prioritizes the needs of business people without technical skills (99% of your company). I've shared some product videos before, but here's a much simpler way of illustrating how it works.

We're hosting another event at a16z SF Tech Week!

Episode #70 of the future founder podcast is live. In this episode, I speak with Mo Hallaba, CEO of Datawisp. We discuss the grit it takes to truly scale a startup and how he plans to create powerful business and societal impact by making data more accessible to all. Check out the full episode on YouTube: https://lnkd.in/gNXRyQfq

Do you work with data? Imagine the time you could save just asking Wispy instead of emailing back and forth with a data scientist. Imagine what your data scientist could do with the time saved answering basic business questions.

For anyone in eCommerce, you know how difficult it can be to know where to focus your time and money... Should I try to refine and promote a particular product? Which one(s)? Should I focus on a particular type of user / demographic / behavior? Who? Which campaigns are REALLY working? What is my customer LTV and how can I improve it? So you try to wrangle this data from 20 different internal and external sources only to realize spreadsheets can't do what you want. Then you try to recruit someone who knows SQL / Python / R to help you only to realize they are in high demand and you need to keep iterating on what you really need to review. If only you could speak to your data in plain English, have it build an analysis itself, and allow you to keep refining, THEN you could get the results you REALLY want. Sound familiar? ?? If this resonates with you, we created a quick guide to *Uncovering Your Most Profitable Products, Customers, and Channels* that you should read. Link in the comments below.

Hey guys, thanks for tuning into our webinar on Data Driven SaaS Growth. In case you missed it, feel free to watch the video here! If you have any questions on how to get started, feel free to post questions on this thread, at www.datawisp.io, or directly to Cam Priest. Have a great weekend!