Domo vs. JMP

I believe that companies that record large amounts of data about their product, service employers KPIs, etc, can make the best out of Domo for reporting and data analysis, but if a company handles little data and is not recording this consistently, it will not be of much use.

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

• Exceptional Transform area of the platform - any/all raw data can be manipulated and combined to create datasources that are very useful for our departments.
• Visualization layer is clean and is very business presentable. Its simple but includes depth. Visuals can be created/used by all roles throughout our firm, its not limited to analysts.
• Ability to communicate and talk about insights in the data - the communication tool is exceptional. I can @ mention specific users to bring attention to a discovery. I can start a private chat. I can annotate and communicate what I am seeing. I love it.
• Mobile experience is excellent. When I am on the road i can actively monitor and engage with my team due to my Domo dashboard on my mobile device. Its amazing to run the business and not worry while away from the office.

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• 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.

• Copying Reports - In Excel or Google Sheets, I like to make copies of similar reports and modify them as necessary for users. In Domo, makes copies of reports (called cards) but all copies are connected. So if you adjust the copy, it changes the original. This means a user has to recreate the report from scratch and then adjust it.
• Unforgiving SQL - Domo does allow users to write their own SQL codes, which is great. However, Domo's SQL code is pickier than the other SQL database I've used (Metabase).
• Text Alerts - Domo's alerts aren't always the smartest. Some of my dashboards are about the sales teams monthly performance and I get notified when there is a change of more than 20%. I always get notified at the start of the month that reports have changed from 'x' number to zero. This is expected because it's a monthly report and I hate getting texts about it. The only reason I don't term them off so I can be aware if something breaks in the middle of the month.

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• 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.)

Domo is a great up-and-coming product. There are many fantastic features that are extremely compelling to our end users, which makes Domo a great fit for our organization. As with many BI tools, there are items on the wish list that could make implementation, administration and usage easier, but I believe these will be addressed over time as the product matures and evolves. The overall concept and approach of this solution has definitely raised the bar in this area of expertise and I would like to see things advance in giving the enterprise tools that will make decision making easier and more robust in the future.

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.

The built-in user support and intuitive design of Domo makes it simple to learn and use. I find I can spend hours drilling deep, or else quickly gain an overview in just a few minutes. This is a great advantage since the flexibility fits well with the demands of my role. I enjoy using Domo since I know it will give me comparative data across almost every variable I might want to explore - I look forward to it!

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The GUI interface makes it easier to generate plots and find statistics without having to write code. The JSL scripting is a bit of a steep learning curve but does give you more ability to customize your analysis. Overall, I would recommend JMP as a good product for overall usability.

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Pretty responsive. It took a while to get a response in selecting data points for our particular cards. Might have taken about a month? I am not sure if this was something on our end or Domo's end. But haven't had any other issues yet that required additional support from Domo.

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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.

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

It is a true web-based application so implementation is much easier and smoother compared to other non-web based BI solutions. Also, they offered consulting services during the implementation process, which was helpful. Also, they provided lots of on-demand training courses and videos, which I liked.

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At the end of the day, end-user adoption and taking the burden of IT to build reports was my goal. Demoing and testing many BI tools, DOMO is the one that allowed both to happen.

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MS Excel with AnalysisToolPak provides a home-grown solution, but requires a high degree of upkeep and is difficult to hand off. Minitab is the closes competitor, but JMP is better suited to the production environment, roughly equivalent in price, and has superior support.

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• I mentioned a "previously un-analyzable" dataset that we were able to visualize in Domo - the result was business re-alignment for increased productivity, cost savings and ROI.
• It's tough to quantify the ability to provide insights that have been desired for years but not possible - we continue to amaze our executives and product managers with the analysis we can provide.

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• 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).

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