16 Ratings
<a href='https://www.trustradius.com/static/about-trustradius-scoring' target='_blank' rel='nofollow noopener'>trScore algorithm: Learn more.</a>
Score 9.2 out of 100
87 Ratings
<a href='https://www.trustradius.com/static/about-trustradius-scoring' target='_blank' rel='nofollow noopener'>trScore algorithm: Learn more.</a>
Score 7.9 out of 100

Feature Set Ratings

    Platform Connectivity

    9.1

    Dataiku DSS

    91%

    JMP Statistical Discovery Software from SAS

    Feature Set Not Supported
    N/A
    Dataiku DSS ranks higher in 4/4 features

    Connect to Multiple Data Sources

    10.0
    100%
    4 Ratings
    N/A
    0 Ratings

    Extend Existing Data Sources

    9.9
    99%
    4 Ratings
    N/A
    0 Ratings

    Automatic Data Format Detection

    9.9
    99%
    4 Ratings
    N/A
    0 Ratings

    MDM Integration

    6.5
    65%
    2 Ratings
    N/A
    0 Ratings

    Data Exploration

    9.7

    Dataiku DSS

    97%

    JMP Statistical Discovery Software from SAS

    Feature Set Not Supported
    N/A
    Dataiku DSS ranks higher in 2/2 features

    Visualization

    9.7
    97%
    4 Ratings
    N/A
    0 Ratings

    Interactive Data Analysis

    9.8
    98%
    4 Ratings
    N/A
    0 Ratings

    Data Preparation

    9.8

    Dataiku DSS

    98%

    JMP Statistical Discovery Software from SAS

    Feature Set Not Supported
    N/A
    Dataiku DSS ranks higher in 4/4 features

    Interactive Data Cleaning and Enrichment

    9.9
    99%
    4 Ratings
    N/A
    0 Ratings

    Data Transformations

    9.9
    99%
    4 Ratings
    N/A
    0 Ratings

    Data Encryption

    9.7
    97%
    4 Ratings
    N/A
    0 Ratings

    Built-in Processors

    9.8
    98%
    4 Ratings
    N/A
    0 Ratings

    Platform Data Modeling

    8.7

    Dataiku DSS

    87%

    JMP Statistical Discovery Software from SAS

    Feature Set Not Supported
    N/A
    Dataiku DSS ranks higher in 4/4 features

    Multiple Model Development Languages and Tools

    5.4
    54%
    4 Ratings
    N/A
    0 Ratings

    Automated Machine Learning

    9.8
    98%
    4 Ratings
    N/A
    0 Ratings

    Single platform for multiple model development

    9.8
    98%
    4 Ratings
    N/A
    0 Ratings

    Self-Service Model Delivery

    9.8
    98%
    4 Ratings
    N/A
    0 Ratings

    Model Deployment

    8.9

    Dataiku DSS

    89%

    JMP Statistical Discovery Software from SAS

    Feature Set Not Supported
    N/A
    Dataiku DSS ranks higher in 2/2 features

    Flexible Model Publishing Options

    8.9
    89%
    4 Ratings
    N/A
    0 Ratings

    Security, Governance, and Cost Controls

    8.9
    89%
    4 Ratings
    N/A
    0 Ratings

    BI Standard Reporting

    Dataiku DSS

    Feature Set Not Supported
    N/A
    9.5

    JMP Statistical Discovery Software from SAS

    95%
    JMP Statistical Discovery Software from SAS ranks higher in 2/2 features

    Pixel Perfect reports

    N/A
    0 Ratings
    10.0
    100%
    1 Rating

    Customizable dashboards

    N/A
    0 Ratings
    9.0
    90%
    9 Ratings

    Ad-hoc Reporting

    Dataiku DSS

    Feature Set Not Supported
    N/A
    7.5

    JMP Statistical Discovery Software from SAS

    75%
    JMP Statistical Discovery Software from SAS ranks higher in 4/4 features

    Drill-down analysis

    N/A
    0 Ratings
    7.7
    77%
    13 Ratings

    Formatting capabilities

    N/A
    0 Ratings
    6.5
    65%
    12 Ratings

    Integration with R or other statistical packages

    N/A
    0 Ratings
    7.7
    77%
    10 Ratings

    Report sharing and collaboration

    N/A
    0 Ratings
    8.0
    80%
    13 Ratings

    Report Output and Scheduling

    Dataiku DSS

    Feature Set Not Supported
    N/A
    8.6

    JMP Statistical Discovery Software from SAS

    86%
    JMP Statistical Discovery Software from SAS ranks higher in 4/4 features

    Publish to Web

    N/A
    0 Ratings
    9.0
    90%
    9 Ratings

    Publish to PDF

    N/A
    0 Ratings
    8.5
    85%
    12 Ratings

    Report Versioning

    N/A
    0 Ratings
    7.0
    70%
    1 Rating

    Report Delivery Scheduling

    N/A
    0 Ratings
    10.0
    100%
    1 Rating

    Data Discovery and Visualization

    Dataiku DSS

    Feature Set Not Supported
    N/A
    8.3

    JMP Statistical Discovery Software from SAS

    83%
    JMP Statistical Discovery Software from SAS ranks higher in 3/3 features

    Pre-built visualization formats (heatmaps, scatter plots etc.)

    N/A
    0 Ratings
    8.0
    80%
    16 Ratings

    Location Analytics / Geographic Visualization

    N/A
    0 Ratings
    9.0
    90%
    13 Ratings

    Predictive Analytics

    N/A
    0 Ratings
    8.0
    80%
    13 Ratings

    Attribute Ratings

    • Dataiku DSS is rated higher in 2 areas: Likelihood to Recommend, Support Rating
    • Dataiku DSS and JMP Statistical Discovery Software from SAS are tied in 1 area: Usability

    Likelihood to Recommend

    9.8

    Dataiku DSS

    98%
    4 Ratings
    7.3

    JMP Statistical Discovery Software from SAS

    73%
    28 Ratings

    Likelihood to Renew

    Dataiku DSS

    N/A
    0 Ratings
    10.0

    JMP Statistical Discovery Software from SAS

    100%
    16 Ratings

    Usability

    10.0

    Dataiku DSS

    100%
    1 Rating
    10.0

    JMP Statistical Discovery Software from SAS

    100%
    5 Ratings

    Availability

    Dataiku DSS

    N/A
    0 Ratings
    10.0

    JMP Statistical Discovery Software from SAS

    100%
    2 Ratings

    Performance

    Dataiku DSS

    N/A
    0 Ratings
    10.0

    JMP Statistical Discovery Software from SAS

    100%
    2 Ratings

    Support Rating

    9.3

    Dataiku DSS

    93%
    3 Ratings
    9.2

    JMP Statistical Discovery Software from SAS

    92%
    14 Ratings

    Online Training

    Dataiku DSS

    N/A
    0 Ratings
    7.9

    JMP Statistical Discovery Software from SAS

    79%
    3 Ratings

    Implementation Rating

    Dataiku DSS

    N/A
    0 Ratings
    9.6

    JMP Statistical Discovery Software from SAS

    96%
    4 Ratings

    Product Scalability

    Dataiku DSS

    N/A
    0 Ratings
    10.0

    JMP Statistical Discovery Software from SAS

    100%
    1 Rating

    Likelihood to Recommend

    Dataiku

    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.
    Read full review

    SAS

    It is perfectly suited for statistical analyses, but I would not recommend JMP for users who do not have a statistical background. As previously stated, the learning curve is exceptionally steep, and I think that it would prove to be too steep for those without statistical background/knowledge
    Read full review

    Pros

    Dataiku

    • 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.
    Read full review

    SAS

    • JMP is designed from the ground-up to be a tool for analysts who do not have PhDs in Statistics without in anyway "dumbing down" the level of statistical analysis applied. In fact, JMP operationalizes the most advanced statistical methods. JMP's design is centred on the JMP data table and dialog boxes. It is data focused not jargon-focussed. So, unlike other software where you must choose the correct statistical method (eg. contingency, ANOVA, linear regression, etc.), with JMP you simply assign the columns in a dialog into roles in the analysis and it chooses the correct statistical method. It's a small thing but it reflects the thinking of the developers: analysts know their data and should only have to think about their data. Analyses should flow from there.
    • JMP makes most things interactive and visual. This makes analyses dynamic and engaging and obviates the complete dependence on understanding p-values and other statistical concepts(though they are all there) that are often found to be foreign or intimidating.
    • One of the best examples of this is JMP's profiler. Rather than looking at static figures in a spreadsheet, or a series of formulas, JMP profiles the formulas interactively. You can monitor the effect of changing factors (Xs) and see how they interact with other factors and the responses. You can also specify desirability (maximize, maximize, match-target) and their relative importances to find factor settings that are optimal. I have spent many lengthy meetings working with the profiler to review design and process options with never a dull moment.
    • The design of experiments (DOE) platform is simply outstanding and, in fact, the principal developers of it have won several awards. Over the last 15 years, using methods broadly known as an "exchange algorithm," JMP can create designs that are far more flexible than conventional designs. This means, for example, that you can create a design with just the interactions that are of interest; you can selectively choose those interactions that are not of interest and drop collecting their associated combinations.
    • Classical designs are rigid. For example, a Box-Benhken or other response surface design can have only continuous factors. What if you want to investigate these continuous factors along with other categorical factors such as different categorical variables such as materials or different furnace designs and look at the interaction among all factors? This common scenario cannot be handled with conventional designs but are easily accommodated with JMP's Custom DOE platform.
    • The whole point of DOE is to be able to look at multiple effects comprehensively but determine each one's influence in near or complete isolation. The custom design platform, because it produces uniques designs, provides the means to evaluate just how isolated the effects are. This can be done before collecting data because this important property of the DOE is a function of the design, not the data. By evaluating these graphical reports of the quality of the design, the analyst can make adjustments, adding or reducing runs, to optimize cost, effort and expected learnings.
    • Over the last number of releases of JMP, which appear about every 18 months now, they have skipped the dialog boxes to direct, drag-and-drop analyses for building graphs and tables as well as Statistical Process Control Charts. Interactivity such as this allows analysts to "be in the moment." As with all aspects of JMP, they are thinking of their subject matter without the cumbersomeness associated with having to think about statistical methods. It's rather like a CEO thinking about growing the business without having to think about every nuance and intricacy of accounting. The statistical thinking is burned into the design of JMP.
    • Without data analysis is not possible. Getting data into a situation where it can be analyzed can be a major hassle. JMP can pull data from a variety of sources including Excel spreadsheets, CSV, direct data feeds and databases via ODBC. Once the data is in JMP it has all the expected data manipulation capabilities to form it for analysis.
    • Back in 2000 JMP added a scripting language (JMP Scripting Language or JSL for short) to JMP. With JSL you can automate routine analyses without any coding, you can add specific analyses that JMP does not do out of the box and you can create entire analytical systems and workflows. We have done all three. For example, one consumer products company we are working with now has a need for a variant of a popular non-parametric analysis that they have employed for years. This method will be found in one of the menus and appear as if it were part of JMP to begin with. As for large systems, we have written some that are tens of thousands of lines that take the form of virtual labs and process control systems among others.
    • JSL applications can be bundled and distributed as JMP Add-ins which make it really easy for users to add to their JMP installation. All they need to do is double-click on the add-in file and it's installed. Pharmaceutical companies and others who are regulated or simply want to control the JMP environment can lock-down JMP's installation and prevent users from adding or changing functionality. Here, add-ins can be distributed from a central location that is authorized and protected to users world-wide.
    • JMP's technical support is second to none. They take questions by phone and email. I usually send email knowing that I'll get an informed response within 24 hours and if they cannot resolve a problem they proactively keep you informed about what is being done to resolve the issue or answer your question.
    Read full review

    Cons

    Dataiku

    • End product deployment.
    Read full review

    SAS

    • In general JMP is much better fit for a general "data mining" type application. If you want a specific statistics based toolbox, (meaning you just want to run some predetermined test, like testing for a different proportion) then JMP works, but is not the best. JMP is much more suited to taking a data set and starting from "square 1" and exploring it through a range of analytics.
    • The CPK (process capability) module output is shockingly poor in JMP. This sticks out because, while as a rule everything in JMP is very visual and presentable, the CPK graph is a single-line-on-grey-background drawing. It is not intuitive, and really doesn't tell the story. (This is in contrast with a capability graph in Minitab, which is intuitive and tells a story right off.) This is also the case with the "guage study" output, used for mulivary analysis in a Six Sigma project. It is not intuitive and you need to do a lot of tweaking to make the graph tell you the story right off. I have given this feedback to JMP, and it is possible that it will be addressed in future versions.
    • I've never heard of JMP allowing floating licenses in a company. This will ALWAYS be a huge sticking point for small to middle size companies, that don't have teams people dedicated to analytics all day. If every person that would do problem solving needs his/her own seat, the cost can be prohibitive. (It gets cheaper by the seat as you add licenses, but for a small company that might get no more than 5 users, it is still a hard sell.)
    Read full review

    Pricing Details

    Dataiku DSS

    Starting Price

    $0

    Editions & Modules

    Dataiku DSS editions and modules pricing
    EditionModules
    DiscoverContact sales team1
    BusinessContact sales team2
    EnterpriseContact sales team3

    Offerings

    Free Trial
    Free/Freemium Version
    Premium Consulting/Integration Services

    Entry-level set up fee?

    No setup fee

    Additional Details

    JMP Statistical Discovery Software from SAS

    Starting Price

    $125 per month

    Editions & Modules

    JMP Statistical Discovery Software from SAS editions and modules pricing
    EditionModules
    Personal License$125.001
    Corporate License$1,510.002

    Footnotes

    1. per month
    2. Per Month Per Unit

    Offerings

    Free Trial
    Free/Freemium Version
    Premium Consulting/Integration Services

    Entry-level set up fee?

    No setup fee

    Additional Details

    Likelihood to Renew

    Dataiku

    No answers on this topic

    SAS

    JMP has been good at releasing updates and adding new features and their support is good. Analytics is quick and you don't need scripting/programming experience. It has been used organization wide, and works well in that respect. Open source means that there are concerns regarding timely support. Cheap licensing and easy to maintain.
    Read full review

    Usability

    Dataiku

    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.
    Read full review

    SAS

    The overall usability of JMP is extremely good. What I really love about it is its ability to be useable for novices who have no coding experience, which is not the case with most other, similar, programs. It can output a fast and easy analysis without too much prior coding or statistical knowledge.
    Read full review

    Support Rating

    Dataiku

    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.
    Read full review

    SAS

    Support is great and give ease of contact, rapid response, and willingness to 'stick to the task' until resolution or acknowledgement that the problem would have to be resolved in a future build. Basically, one gets the very real sense that another human being is sensitive to your problems - great or small.
    Read full review

    Online Training

    Dataiku

    No answers on this topic

    SAS

    I have not used your online training. I use JMP manuals and SAS direct help.
    Read full review

    Alternatives Considered

    Dataiku

    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.
    Read full review

    SAS

    It is great because it has UI menus but it costs money whereas the other programs are free. That makes it ideal for beginners but I think that RStudio and Python are going to make someone a lot more marketable for future opportunities since most companies won't pay for the software when there is a great free option.
    Read full review

    Return on Investment

    Dataiku

    • 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.
    Read full review

    SAS

    • ROI: Even if the cost can be high, the insights you get out of the tool would definitely be much more valuable than the actual cost of the software. In my case, most of the results of your analysis were shown to the client, who was blown away, making the money spent well worth for us.
    • Potential negative: If you are not sure your team will use it, there's a chance you will just waste money. Sometimes the IT department (usually) tries to deploy a better tool for the entire organization but they keep using the old tool they are used too (most likely MS Excel).
    Read full review

    Screenshots

    Add comparison