JMP® is statistical analysis software with capabilities that span from data access to advanced statistical techniques, with click of a button sharing. The software is interactive and visual, and statistically deep enough to allow users to see and explore data.
$1,320
per year per user
Sisense for Cloud Data Teams
Score 6.5 out of 10
N/A
Sisense for Cloud Data Teams (formerly Periscope Data) is a data visualization tool that allows users to connect to their SQL databases to create sharable, interactive dashboards. In addition to SQL, its analytics integrate with R and Python, allowing users to prep datasets, perform analysis, and create their own visualizations. Sisense acquired Periscope Data in mid-2019.
N/A
Pricing
JMP
Sisense for Cloud Data Teams
Editions & Modules
JMP
$1320
per year per user
No answers on this topic
Offerings
Pricing Offerings
JMP
Sisense for Cloud Data Teams
Free Trial
Yes
No
Free/Freemium Version
No
No
Premium Consulting/Integration Services
No
No
Entry-level Setup Fee
No setup fee
No setup fee
Additional Details
Bulk discounts available.
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More Pricing Information
Community Pulse
JMP
Sisense for Cloud Data Teams
Considered Both Products
JMP
Verified User
Anonymous
Chose JMP
Much better than Excel for deep data dives, but also much steeper learning curve. And the cost is significantly higher - Excel is provided by default, but we have to request a JMP license each year.
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 …
JMP is superior to the MS Excel product in its graphical presentation and graphical exploration platforms. It has minor deficiencies in the lack of a 'goal seek' formula (although one can sort of get to this using the simulation platforms in some of the higher level ML …
Compared to other, similar programs, JMP is outstanding in ease of use and ability to be used by almost anyone across an organization. It is more fluid, user friendly, and, most importantly, requires no coding experience. The only two areas where it is not as good as …
JMP is more user-friendly, in my opinion, as it doesn't require any coding or searching for hours into cryptic folders for the analysis you want to perform. It is also very good for recording large data sets. Moreover, it is compatible with Microsoft Excel.
For me, JMP is the best and easy way to run regressions. I wouldn't use it for other more advanced models. I decided to use it because we got it for free since we are technically an academic institution.
I have only used STATA as a statistical package, and they are completely different tools. JMP has a much better layout and ease of use, but may not be as powerful as STATA for advanced processes. Overall speed and ease of use makes it like a combination of ms excel and stata …
We actually use both JMP and IBM SPSS, but I think JMP's complexity lends itself to more in-depth statistical analyses. SPSS is designed for that as well, but we tend to use it more for quicker analyses, and we have found that JMP is far more powerful.
Verified User
Anonymous
Chose JMP
Minitab, MODE. JMP is more user-friendly, interactive, and visual, with larger variety of analysis and tools. DOE platform itself is superior to any other software, instead of fitting the problem to classical design, the design is fitted to any problem and constraints.
MS Excel is good for manipulating data and providing flexible data arrays, but has serious deficiencies in its graphical displays and analytic capabilities. This is where JMP has its greatest advantages...see some of my previous comments, but I see these software applications …
Verified User
Anonymous
Chose JMP
Compared to: MSExcel - Useful from engineering data analysis perspective Matlab - cost/ expensive licensing
I much prefer the ability to code my programs which is the main method used in both SAS and R. These software choices allow for quicker, more efficient, and more advanced analysis techniques. The one area that JMP is above these is in graphics and visual displays of data. JMP …
Quality and Reliability Engineering Intern, Manufacturing, Intel
Chose JMP
Well, JMP is excellent for statistical analysis. So, this product it is well used for statistical analysis and data analytics.
Verified User
Anonymous
Chose JMP
As I stated before, you can use Excel to do many similar things to JMP; you can even use SAS to create graphs without having to do any sort of exporting. If you use SAS, however, you know these graphs are hideous, and sometimes using an Excel graphs makes you look like you are …
I heard good things from colleagues who have used JMP. We did not get too far down the SPSS route before we decided to go with JMP because of price and perceived benefit from my colleague's advice.
JMP simply excels against its competitors and the best way we know that is from our clients who have switched from other products. They recognize that their analytical capabilities are much higher with JMP then with whatever tools they used in the past. The ability to integrate …
Verified User
Anonymous
Chose JMP
JMP is more powerful in terms of data graphing, correlation analysis, profiler capability, and DOE functionality.
JMP is better with visual data representation, and as a general statistics exploration package. Technical needs like Design of Experiments are just easier to do in JMP
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.
RStudio requires custom code from programmers and the decision was made to move to software instead of software developers. Microsoft Power BI didn't have the right data subscription capabilities for our customers.
Google Analytics works well but it does not have all of the bells and whistles that Periscope Data offers. Google Analytics is best used in a Google environment but if you are using other tools and programs outside of the Google universe, then Periscope Data is a much better …
Periscope is far more robust than these two similar products. For a start up just getting going they are fine alternatives, but as your business scales Google Charts becomes a hassle to manage and Keap becomes too much of a generalized product. While your business increases it …
Periscope is by far the best we looked at - I was evaluating as a user, not the primary decision maker, and user interface and ease of use was the primary factor in my decision. It is very easy to navigate and manipulate, and has an overall very polished view.
Periscope's lightweight footprint and customizable SQL-based reports make it a better choice for us than Tableau or Microsoft BI. We deal with millions of rows of transactional data in a SQL Server data warehouse, so having seamless front-end integration makes reporting seamless.
LookML was able to simplify development of views involving window calculations, but slowed down the overall development cycle as minor SQL edits required heavy code reviews.
Tableau has great interactive options but has proved non-performant with our database mix (MySQL, …
This is currently our primary visualization tool. There is no real option to do any meaningful math on your data, and you only have access to a very, very limited subset of information you stream into the cache. Periscope far exceeds this tool with the ability to combine data …
Different in the sense that you need to be able to manipulate data with Periscope while you just need to understand how data is manipulated with Amplitude (Periscope = you need to master SQL vs Amplitude = you need to understand the logic of SQL). Periscope is a more powerful …
Periscope Data enables data wrangling and is more familiar to SQL-savvy people - which are mostly analysts. Writing query is not really tedious for them, so it is not a huge problem. Quick and nice response from Periscope support is really helpful for the users.
Many organizations have seen their analytical capabilities, and the results from them, plateau. Of these, we've observed, that most of them didn't appreciate that they could do (even) better. These companies should definitely consider JMP. Any company that is research-based can benefit from accelerating their research, learning more in less time, effort and cost, with JMP's tools. Basically, any organization that is hungry enough for improvement to seek out better ways is suitable for JMP. Those who are happy with their current performance are not likely to consider the changes, though they were not major impediments by our clients, required.
Sisense for Cloud Data Teams is suited so well for our project that works with lots of data and needs some ways to share data internally or externally with our clients. It's very easy to pull out the data from the sense in best and in a suitable format and moreover a huge number of options are available there to represent the data. All features of this Sisense for cloud data teams software can be taken advantage of if you have a team who are well versed in data analytics, data management, and programming.
Rapid deployment of polished T-SQL-based data visualization charts and dashboards. Periscope supports a variety of database technologies, and allows users to write custom queries to display data.
Included caching to reduce server load.
Outstanding customer service/support, with expert advice as needed.
Constant updates and new features.
Built-in SQL formatters take the pain out of manipulating date/time objects.
I've mentioned this earlier, but the licensing agreements are very prohibitive. I work with a company where my role has become less and less doing my own analytics and more and more trying to help other people in that role. As we are bringing more people "up to speed" it's hard to justify licenses for 2-3 people when they aren't full time, Six Sigma black belts just looking at stats all day. A floating license option would make this a no-brainer, since these people could continue their other work and add JMP usage as they grow their skills, but this is not something JMP/SAS has offered.
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.
My company has had Periscope for various use cases in the past and I think that this program opens up complex data reports to non-technical people in a really accessible way (even though the learning curve is a big one). We are now integrating Sisense for Cloud Data Teams at a larger level both for internal data exploration and for customer facing dashboards and reports.
The helpful tips are great for new users. I am always able to find solutions to a tool I am working with through the hep section. And my area has a users group that meets each quarter to share ideas and view upcoming JMP revisions.
We actually use both JMP and IBM SPSS, but I think JMP's complexity lends itself to more in-depth statistical analyses. SPSS is designed for that as well, but we tend to use it more for quicker analyses, and we have found that JMP is far more powerful.
Periscope is far more robust than these two similar products. For a start up just getting going they are fine alternatives, but as your business scales Google Charts becomes a hassle to manage and Keap becomes too much of a generalized product. While your business increases it is generally best to get multiple specialized pieces of technology to help you maintain integrity in your data, and Periscope Data allows. Worth the money.
JMP has resulted in literally millions of dollars in ROI due to identification of correctable errors.
Use of JMP control charts JMP has greatly simplified and improved interpretation of Lean, FMEA, and PDSA type analyses.
Use of JMP has enable the testing and subsequent selection of 'best practices' saving uncounted hours in false starts based on 'collective experience'.
The down side is that JMP is not a 'magic box', one still has to take care in applying the tools properly. Moreover, time-consuming approaches using JMP may still be the 'order of the day', because the service (even power user) is unaware of significant shortcuts available for free on the JMP community website.