GoodData is an analytics platform used by organizations to deliver real-time, governed insights, embedded into products, customized for users, and integrated into any data environment.
N/A
JMP
Score9.8 out of 10
N/A
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.
I think it works nicely for shops that want the analytical power and are ok to host their own infrastructure for the data and etl. For smaller operations with limited budgets but still high demand for analytical features the math may not work out.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
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
The source datasets are often complex, semi-structured and un-linked to key entities. With GoodData, all of these datasets are unified to serve as a central semantic data model layer, building into a galaxy schema with dimensions, bridge, and facts, which then forms the backbone that powers the [...] data intelligence cloud. Building insights and dashboards become a much easier task once the underlying data model is designed. GoodData enforces certain best practices as a BI tool, which must be adhered to get the true value of the raw data. For e.g. the source FDA dashboard may just show inspection data but the Site Profile dashboard built on GoodData goes beyond the standard information and shows more insight into site risk scores and can be drilled into details. There is blog written on this topic: [...].
GoodData provides a rich collection of visualization options that help us create compelling story-telling via dashboards. Being well-prepared for FDA inspections is essential for maintaining product quality, regulatory compliance, and avoiding serious business setbacks. FDA inspections are critical events that can shape a company’s market access and reputation. The FDA itself offers the FDA Data Dashboard, but it doesn’t make every document available. There is a blog written on this topic: [...].
Medical devices and technologies do not stop evolving after they receive regulatory approval. Once a product hits the market, it faces real-world usage, compliance challenges, and an array of regulatory scrutiny. Managing these postmarket dynamics is critical to a product’s long-term success and patient safety. However, many companies struggle to keep track of relevant events across a product’s markets, from adverse event reports to changing regulations. Postmarket Intelligence developed on GoodData platform enables us to solve that problem. It empowers MedTech companies to efficiently monitor, assess, and act on postmarket data—saving time, improving decision-making, and ensuring compliance with industry standards. Anyone who is used to trying to get the data they need from the various FDA, and other regulatory agency websites, knows that collecting, cleaning, and structuring that data takes hours. And that’s before any analysis can get done. We enable customers to free up time to focus only on the high-value analysis and subsequent recommendations to leadership, rather than wrangling the data.
The data pipeline refresh that is provided by GoodData Platform is also quite useful from data engineering perspective. The Automated Data Distribution v2 or commonly called as ADD refresh follows a set pattern of identifying the analytical data model through output stage which helps abstract the complex table definition to simpler views that can help with quick rebuild at the data warehouse level while loading the data into GoodData's ADS storage layer. The import first way of loading data into GoodData's cloud storage, followed by querying for any aggregations or metrics on the GoodData analyzer, makes this simple and fast.
GoodData's latest product i.e. Cloud also offers several good features like Analytics as Code which helps software engineering teams follow a code-first approach to analytics, where building insights, dashboards or even datasets can be done in YAML templates or serviced by REST APIs. This is particularly forward thinking in the modern technology stack and evolving industry requirements. These provide seamless integration options to front-end and backend code, embedded analytics with multiple choices from HTML to React based workloads. At [...], we are currently exploring most of these features while planning for a future migration from Platform to Cloud.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
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.
Good Data is already have certain customizable options. However, having more flexibility in customizing reports and dashboards & control over the visual aspects would enhance the overall user experience.
To make Good Data even more powerful tool, improving the speed and responsiveness of the tool, especially during data-intensive tasks, would be a significantly helpful.
For new users, the interface can be made more user friendly which would promote easy navigation through features of tool.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info
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.)
Because gooddata really helps us in processing data to make reports or dashboards. So we are very satisfied when we use it. What we like is the flexible use of charts. We change at will the use of charts to display in reports or dashboards. Thank you Gooddata for helping companies like us who need flexibility in usage
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
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.
From a customer perspective it is incredibly usable. We have more users building their own reports that would normally need custom work from our support team. The back end can be daunting when trying to configure things like new data elements or push changes to a report to all existing customers.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
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.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info
Support team has been highly responsive and helpful from our first initial deployment to present day. They engage and work with us. know when to escalate for more challenging problems. They also follow up. Overall have had a very good experience with support
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info
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.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
Implementations are hard and we had limited technical resources. We relied too heavily on GD care team. When we found technical gaps, they weren't simple to overcome
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info
GoodData comparing to other platform is very easy to use, customer support and on-boarding support. Set of features, speed of integration in our platform. Also great benefit for us was very competetive pricing.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
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.
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. TR verified that a representative sample of customers was invited. More Info
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).
A de minimis incentive was given to thank the reviewer for their time. The incentive was not used to bias or drive a particular response, nor was the incentive contingent on a positive endorsement. More Info
