Dataiku vs. Epicor Grow

Dataiku DSS is very well suited to handle large datasets and projects which requires a huge team to deliver results. This allows users to collaborate with each other while working on individual tasks. The workflow is easily streamlined and every action is backed up, allowing users to revert to specific tasks whenever required. While Dataiku DSS works seamlessly with all types of projects dealing with structured datasets, I haven't come across projects using Dataiku dealing with images/audio signals. But a workaround would be to store the images as vectors and perform the necessary tasks.

Incentivized

Grow.com is well suited if you have a lot of data or client data to manage. If you do not have tons of data, there are other options out there that are a bit more simple and easy to use and a better price.

Incentivized

• The intuitiveness of this tool is very good.
• Click or Code - If you are a coder, you can code. If you are a manager, you can wrangle with data with visuals
• The way you can control things, the set of APIs gives a lot of flexibility to a developer.

Incentivized

• I love the fact that you have a team of developers who can help me build metrics or fix metrics that I do not create properly. I had to learn SQL in order to really build the metrics that mattered, which was a lot of fun, but resulted in many broken metrics. The vendor's team was great in responding to me requests for help!
• Really, really appreciate the fact that Grow has SQL formulas to reference when building metrics. Again, as a novice, this accelerated my ability to learn and not have to leave your site and resources and stay within the Grow web properties.
• Great account managers who check in regularly and are willing to help out whenever needed.

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• End product deployment.

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• There are basic labeling issues with some of the graph types. Wish there was more customizability here, especially on complex graphs.
• Could help more with analysis of data and trends, but that's going into full-fledged BI territory.
• The functionality of being able to snapshot data isn't quite there.

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As I have described earlier, the intuitiveness of this tool makes it great as well as the variety of users that can use this tool. Also, the plugins available in their repository provide solutions to various data science problems.

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This tool is definitely a little bit difficult to be able to figure out at first, but after some time of learning you can definitely pick it up. I do wish there were more resources online that would be able to help speed up the learning process

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The support team is very helpful, and even when we discover the missing features, after providing enough rational reasons and requirements, they put into it their development pipeline for the future release.

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The support team is very helpful and in every situation we have needed help in, they have been able to assist. So I have had a good experience with them

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Strictly for Data Science operations, Anaconda can be considered as a subset of Dataiku DSS. While Anaconda supports Python and R programming languages, Dataiku also provides this facility, but also provides GUI to creates models with just a click of a button. This provides the flexibility to users who do not wish to alter the model hyperparameters in greater depths. Writing codes to extract meaningful data is time consuming compared to Dataiku's ability to perform feature engineering and data transformation through click of a button.

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Grow doesn't have as many advanced analytics features and other capabilities as some other 'dashboard' platforms on the market. Simply, if you want a tool for internal use only that has high-level analytics capabilities (regressions, completely custom visualizations, modeling), Grow probably isn't the platform for you. To put it another way, if you have a team of data engineers and scientists doing complex analysis, Grow might not make the most sense. However, if you understand the value of data/visualization/dashboards, but lack some of those skillsets in your company, Grow might be the perfect fit. Not only is it incredibly scalable and reasonably priced compared to other market solutions, but their transform capabilities really stand out. They have many native data transformations that mimic SQL coding of a dataset, without requiring any knowledge of SQL. They've converted these transformations to easy-to-use tools in the platform, which are perfect for the less-technical user.

Incentivized

• Given its open source status, only cost is the learning curve, which is minimal compared to time savings for data exploration.
• Platform also ease tracking of data processing workflow, unlike Excel.
• Build-in data visualizations covers many use cases with minimal customization; time saver.

Incentivized

• Made it so much easier and faster to access data across tools
• Lessened the time exporting and uploading data to excel/sheets for analysis
• Made data and metrics more easily accessible to others in our orgs

Incentivized