H2O.ai vs. ThoughtSpot

Most suited if in little time you wanted to build and train a model. Then, H2O makes life very simple. It has support with R, Python and Java, so no programming dependency is required to use it. It's very simple to use. If you want to modify or tweak your ML algorithm then H2O is not suitable. You can't develop a model from scratch.

Incentivized

It is well suited when the same data is consumed by many different people with different analytics and visualization requirements because, if you have the data available in ThoughtSpot, every user can prepare different views. Also, it is a good reporting tool, you can get rid of slides if you have a good dashboard prepared, gaining flexibility and agility.

Incentivized

• Excellent analytical and prediction tool
• In the beginning, usage of H20 Flow in Web UI enables quick development and sharing of the analytical model
• Readily available algorithms, easy to use in your analytical projects
• Faster than Python scikit learn (in machine learning supervised learning area)
• It can be accessed (run) from Python, not only JAVA etc.
• Well documented and suitable for fast training or self studying
• In the beginning, one can use the clickable Flow interface (WEB UI) and later move to a Python console. There is then no need to click in H20 Flow
• It can be used as open source

Incentivized

• Beautiful visualizations. The visuals are distinct, clean, and easy to discern from one another.
• Intelligent querying functionality. When looking to manipulate the data, the search function makes it easy to manipulate the features in the data, along with aggregating them in the way you'd like.
• Embedding! It has been a smooth process thus far for our product & technical teams to work with ThoughtSpot and bring it into our product.

Incentivized

• Better documentation
• Improve the Visual presentations including charting etc

Incentivized

• It would be great if ThoughtSpot can add the feature to filter by clicking on visualizations. i.e if I click on a particular data point in the chart if the full dashboard can filter just for that particular data point.
• Color coding the heatmap with different colors like green to orange to red.

Incentivized

I give it just waiting because passport is brilliant and it has helped our organisation In advancing to the next stage in the age of AI. It has allowed or non-tech people to better service and clients in a cost-effective way. George port has allowed us to create new products for us and for our clients increasing our revenue streams and reducing clients churn

Incentivized

The rating is because of the ease of use of the interface as it has a no code interface that makes it easy to setup data pipelines without extensive programming. Cloud native integration: It integrates seamlessly with cloud based data warehouses. Automated data loading, Scalability, Cost Effective, Transformations, Data Governance and security.

Incentivized

it's available unless there is a server or system update etc. sometimes the timing of this is bad (for example during a month end close)

Incentivized

It does what it is supposed to. Would be nice to have a bit more insights

Incentivized

The overall experience I have with H2O is really awesome, even with its cost effectiveness.

Incentivized

I give it this meeting because the team is not only help able to help us in the current solutions but also amazing and taking feedback and feeding it back to their development team which includes more products and features into ThoughtSpot

Incentivized

inhouse in-person training. Took a bit to long to get the basics.

Incentivized

poor instructions

Incentivized

Understand use case and model and design accordingly

Incentivized

Both are open source (though H2O only up to some level). Both comprise of deep learning, but H2O is not focused directly on deep learning, while Tensor Flow has a "laser" focus on deep learning. H2O is also more focused on scalability. H2O should be looked at not as a competitor but rather a complementary tool. The use case is usually not only about the algorithms, but also about the data model and data logistics and accessibility. H2O is more accessible due to its UI. Also, both can be accessed from Python. The community around TensorFlow seems larger than that of H2O.

Incentivized

We also explored Tableau Ask Data. Tableau is our standard for BI in our organization. We want to use the smallest amount of tools in our company to have the best adaption. ThoughSpot will fill a few gaps that we have with our current set up and will also enhance out offering for our employees in the transition of being more data driven within in near future

Incentivized

Because it is very reliable, inside the situation, we need strong internet connection to access a lot of data but easily never had any downtime except during the upgrades

Incentivized

• Positive impact: saving in infrastructure expenses - compared to other bulky tools this costs a fraction
• Positive impact: ability to get quick fixes from H2O when problems arise - compared to waiting for several months/years for new releases from other vendors
• Positive impact: Access to H2O core team and able to get features that are needed for our business quickly added to the core H2O product

Incentivized

• Time to market ROI is massive vs hiring the full-time dedicated team to build and maintain a frontend multi-tenant SaaS data viz product.
• It will be interesting to see over time how the advanced features play out in terms of usability and end value, such as Natural Search, which we are very excited about, and the machine learning tools.

Incentivized