Heap vs. KNIME Analytics Platform

Scenarios when Heap was well suited: It is when a user claims that he encountered a bug without giving us the details of the error message. Scenarios where it is less appropriate: Its when we try to capture user interaction in our mobile app

Incentivized

KNIME Analytics Platform is excellent for people who are finding Excel frustrating, this can be due to errors creeping in due to manual changes or simply that there are too many calculations which causes the system to slow down and crash. This is especially true for regular reporting where a KNIME Analytics Platform workflow can pull in the most recent data, process it and provide the necessary output in one click. I find KNIME Analytics Platform especially useful when talking with audiences who are intimidated by code. KNIME Analytics Platform allows us to discuss exactly how data is processed and an analysis takes place at an abstracted level where non-technical users are happy to think and communicate which is often essential when they are subject matter experts whom you need for guidance. For experienced programmers KNIME Analytics Platform is a double-edged sword. Often programmers wish to write their own code because they are more efficient working that way and are constrained by having to think and implement work in nodes. However, those constraints forcing development in a "KNIME way" are useful when working in teams and for maintenance compared to some programmers' idiosyncratic styles.

Incentivized

• Realtime interactions which are captured automatically is a unique feature which no other product offers.
• Low code implementation is a big plus for marketing teams, as there is less dependency on engineering
• Detailed customer behaviours can be studied with captured replay sessions
• Due to all the above points - it helped to improve customer experience

Incentivized

• Summarize instrument level financial data with relevant statistics
• Map transactions from core extracts to groups of like transactions using rule engines
• Machine learning using random forests and other techniques to analyze data and identify correlations for use in predictive models
• Fill out sampling data from averages.

• I think Heap can improve its onboarding; I would love to see some info that is helpful for someone who isn't in Heap every single day.
• Sharing dashboards can be tricky, it feels like a huge learning curve that I already have forgotten how to do.

Incentivized

• It does not have proper visualization.
• Some other BI tools (QlikView) have much easier functions for data interaction.
• Some other BI tools (Tableau) can be set up much faster.
• It is not an easy tool to use for non-tech savvy staff.

Incentivized

It's a great platform. I'm glad that one of our product managers introduced it because it has allowed us to create all kinds of new functionality. We're not only able to create a better product experience from our communications because of Heap, but we're also able to generate all kinds of helpful analysis.

Incentivized

We are happy with Knime product and their support. Knime AP is versatile product and even can execute Python scripts if needed. It also supports R execution as well; however, it is not being used at our end

On a scale from 1-10, I find Heap to be incredibly user-friendly and easy to use. I enjoyed the training videos available and was quickly able to pick up how to create events and reports to track user interactions on our product. I would recommend Heap for its usability first and foremost.

Incentivized

KNIME Analytics Platform offers a great tradeoff between intuitiveness and simplicity of the user interface and almost limitless flexibility. There are tools that are even easier to adopt by someone new to analytics, but none that would provide the scalability of KNIME when the user skills and application complexity grows

Incentivized

I've never run into any issues with Heap's availability, Heap is always there when I need it. I haven't run into any issues like application errors or unplanned outages during my 2+ years of using Heap. Each and every time I log in to Heap I have a completely functional experience

Incentivized

Heap doesn't affect page load times considerably nor has a large impact [on] our overall score, as far as page loading times inside of the tool its pretty reliable to retrieve data as much as "instant" that it can be the delay seems to be on data getting tracked into the servers to be read but it's not significant.

Incentivized

Heap support has allowed us to troubleshoot and test a lot of different items. Their support team is always helpful and friendly, even when we come to them with the most complicated questions. I think this greatly improves the value proposition of the product because their support team is knowledgable and friendly.

Incentivized

KNIME's HQ is in Europe, which makes it hard for US companies to get customer service in time and on time. Their customer service also takes on average 1 to 2 weeks to follow up with your request. KNIME's documentation is also helpful but it does not provide you all the answers you need some of the time.

Incentivized

The implementation was smooth and easy. The Heap team helped us with implementation and it went great! Within a few weeks, we were fully up and running and utilizing the platform to its full capability. This is an additional thing that has made this platform so great and we couldn't recommend it enough.

Incentivized

KNIME Analytics Platform is easy to install on any Windows, Mac or Linux machine. The KNIME Server product that is currently being replaced by the KNIME Business Hub comes as multiple layers of software and it took us some time to set up the system right for stability. This was made harder by KNIME staff's deeper expertise in setting up the Server in Linux rather than Windows environment. The KNIME Business Hub promises to have a simpler architecture, although currently there is no visibility of a Windows version of the product.

Incentivized

Heap offers a ton of functionality on a single platform.It also has an smart data science layer to offers suggestions for next steps in the analysis, allowing us to explore alternative paths we may not think to take. The low-code option for updating data is appealing, and there is a lot of automation with minimal engineering effort.

Incentivized

Having used both the Alteryx and [KNIME Analytics] I can definitely feel the ease of using the software of Alteryx. The [KNIME Analytics] on the other hand isn't that great but is 90% of what Alteryx can do along with how much ease it can do. Having said that, the 90% functionality and UI at no cost would be enough for me to quit using Alteryx and move towards [KNIME Analytics].

Incentivized

The most challenging part of using Heap in a growing organization is the naming and structure in which reports and dashboards are organized. I work within the marketing department and our Heap leader internally works within the IT/Product department, which makes it challenging because we often don't speak the same language, so the learning curve has been steep without any specific use-case examples to leverage online.

Incentivized

• It has helped us make decisions on what products to scope for our MVP based on the usage data.
• It's allowed us to measure success with our beta product.
• It's helped us view our conversion rates in an easy and usable manner.

Incentivized

• It is suited for data mining or machine learning work but If we're looking for advanced stat methods such as mixed effects linear/logistics models, that needs to be run through an R node.
• Thinking of our peers with an advanced visualization techniques requirement, it is a lagging product.

Incentivized