Spotfire® is a data visualization platform that utilizes predictive analytics. In addition to data viz, it includes data wrangling capabilities, predictive analytics, location analytics, and real-time streaming analytics. Spotfire® is a business unit of Cloud Software Group, formerly known as TIBCO Spotfire.
$0.99
Per Hour (Starting)
Spotfire Data Science
Score 8.7 out of 10
N/A
Spotfire Data Science (formerly TIBCO Data Science) is a comprehensive platform for operationalizing data science, allowing users to scale data science across an organization to solve complex challenges faster and speed innovation. It is designed to enable data scientists to create innovative solutions using the latest machine learning techniques and open source developments. Create ML pipelines using a point-and-click UI or code. Orchestrate analytics using the tools, languages, and any…
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Pricing
Spotfire
Spotfire Data Science
Editions & Modules
Spotfire for Amazon Web Services
$0.99
Per Hour (Starting)
Spotfire Cloud - Consumer
$250/yr
per seat
Spotfire Cloud - Business Author
$650/yr
per seat
Spotfire Cloud - Analyst
$1250/yr
per seat
Spotfire Platform
Please contact Spotfire sales
Spotfire Cloud Enterprise
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Spotfire
Spotfire Data Science
Free Trial
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Yes
Free/Freemium Version
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No
Premium Consulting/Integration Services
Yes
No
Entry-level Setup Fee
No setup fee
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Additional Details
For Enterprise engagements, please contact TIBCO directly for a custom price quote.
Spotfire's key strength les in extent of customization possible and it's inherent Data Analytics capabilities. With in-memory and in-database analysis capabilities, it comes out as a high performance and high efficiency BI solution. Adding to it, Spotfire integrates the …
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A high level of data integration is available here it supports various data sources and so on. Collaborating features allow users to give access to the dashboard and merge data analytics with other team members. It can meet the demands of both small and large size business enterprises. A customized dashboard and reports are provided to meet the specific needs and get support of extensibility through APIs and customized scripts.
If you have an analytics department, Data Science is perfect for making analyses quicker. Data Science works well for web querying, automating analyses, sharing advanced analyses with others, and performing lots of other advanced analytical processes. Data Science is not a good fit if the analytics you do is stuff that Excel can do. The software is powerful, with lots of features, and unless you actually plan on using those features, it's not worth paying for.
It has a great user interface, easy to navigate and learn on the fly.
There are lots of great options for data organization and analysis! Makes it a handy tool for presentations as well.
A collaborative ability is highly valued for my company where we often work from home or on site. Being able to share the data with those in the office so multiple people can look at it is a great tool!
The donut chart is I guess a powerful illustrations but I hope it should be done quite simple in Spotfire. But in Spotfire there are lots of steps involve just to build a simple donut chart.
Table calculation (like Row or Column Differences) should be made simple or there should be drag and drop function for Table Calculation. No need for scripting.
Information Link should be changed. If new columns are added to the table just refreshing the data should be able to capture the new column. No need extra step to add column
Unfortunately, some functionality is hidden per upgrade to other versions. Feel data mining functionality would be useful, but not budget for software. At the current price point, would have expected more (such as Mathematica breadth of functionality for one price).
It is light on optimization capability.
Slow when considering very large datasets, performing things such as distribution identification
-Easy to distribute information throughout the enterprise using the webplayer. -Ad hoc analysis is possible throughout the enterprise using business author in the webplayer or the thick client. -Low level of support needed by IT team. Access interfaces with LDAP and numerous other authentication methods. -Possible to continually extend the platform with JavaScript, R scripts, HTML, and custom extensions. -Ability to standardize data logic through pre-built queries in the Information Designer. Everyone in the enterprise is using the same logic -Tagging and bookmarking data allows for quick sharing of insights. -Integration with numerous data sources... flat files, data bases, big data, images, etc. -Much improved mapping capability. Also includes the ability to apply data points over any image.
The company is hesitant to spend this much on software. They are primarily an engineering firm, and they don't understand the use of analytical software for environmental professionals.
Basic tasks like generating meaningful information from large sets of raw data are very easy. The next step of linking to multiple live data sources and linking those tables and performing on the fly analysis of the imported data is understandably more difficult.
Even though, it's a rather stable and predictable tool that's also fast, it does have some bugs and inconsistencies that shut down the system. Depending on the details, it could happen as often as 2-3 times a week, especially during the development period.
Generally, the Spotfire client runs with very good performance. There are factors that could affect performance, but normally has to do with loading large analysis files from the library if the database is located some distance away and your global network is not optimal. Once you have your data table(s) loaded in the client application, usually the application is quite good performance-wise.
Support has been helpful with issues. Support seems to know their product and its capabilities. It would also seem that they have a good sense of the context of the problem; where we are going with this issue and what we want the end outcome to be.
The instructor was very in depth and provided relevant training to business users on how to create visualizations. They showed us how to alter settings and filter views, and provided resources for future questions. However, the instructor failed to cover data sources, connecting to data, etc. While it was helpful to see how users can use the data to create reports, they failed to properly instruct us on how to get the dataset in to begin with. We are still trying to figure out connections to certain databases (we have multiple different types).
The online training is good, provides a good base of knowledge. The video demonstrations were well-done and easy to follow along. Provided exercises are good as well, but I think there could be more challenging exercises. The training has also gone up in price significantly in the last 3 years (in USD, which hurts us even more in Canada), and I'm not sure it is worth the money it now costs (it is worth how much it cost 3 years ago, but not double that.)
The original architecture I created for our implementation had only a particular set of internal business units in mind. Over the years, Spotfire gained in popularity in our company and was being utilized across many more business units. Soon, its usage went beyond what the original architectural implementation could provide. We've since learned about how the product is used by the different teams and are currently in the middle of rolling out a new architecture. I suggest:
Have clearly defined service level agreements with all the teams that will use Spotfire. Your business intelligence group might only need availability during normal working hours, but your production support group might need 24/7 availability. If these groups share one Spotfire server, maintenance of that server might be a problem.
Know the different types of data you will be working with. One group might be working with "public" data while another group might work with sensitive data. Design your Library accordingly and with the proper permissions.
Know the roles of the users of Spotfire. Will there only be a small set of report writers or does everyone have write access to the Library?
ALWAYS add a timestamp prompt to your reports. You don't want multiple users opening a report that will try and pull down millions of rows of data to their local workstations. Another option, of course, is to just hard code a time range in the backing database view (i.e. where activity_date >= sysdate - 90, etc.), but I'd rather educate/train the user base if possible.
This probably goes without saying, but if possible, point to a separate reporting database or a logical standby database. You don't want the company pounding on your primaries and take down your order system.
Spotfire is significantly ahead of both products from an ETL and data ingestion capability. Spotfire also has substantially better visualizations than Power BI, and although the native visualizations aren't as flexible in Tableau, Spotfire enables users to create completely custom javascript visaualizations, which neither Tableau or Power BI has. Tableau and Power BI are likely only superior to Spotfire with respect to embedded analysis on a website.
I prefer Spotfire Data Science's approach. It is more natural and fits the way I think. I prefer to use Spotfire Data Science's VB for writing macros. It is real code, meaning that I do not need to trick the software to do what I need and there are no implied loops over solving simple problems. The graphs are publication quality and can be edited by hand or using a macro if I am building hundreds of them. Spotfire Data Science had a user-friendly approach to building lengthy data processing streams (in its workspaces). It is just so fast for analyzing a dataset that you have never seen before and efficient for ongoing work on the same data.
In an enterprise architecture, if Spotfire Advanced Data services(Composite Studio),data marts can be managed optimally and scalability in a data perspective is great. As the web player/consumer is directly proportional to RAM, if the enterprise can handle RAM requirement accomodating fail over mechanisms appropraitely, it is definitely scalable,
Our company has had the program for less than 1 year. We don't expected a positive return this year. The goal is for Data Science to led to defined projects by the end of the end of the year and implementation in the following two. Overall, we are planning on 4 years to fully recoup the cost of the software and the cost of implementing identified projects.
