Spotfire and Tableau Positioning. Summary

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1 Licensed for distribution Summary So how do the products compare? In a nutshell Spotfire is the more sophisticated and better performing visual analytics platform, and this would be true of comparisons made with almost all other visual analytics platforms. For quite some time Tableau offered the more attractive interface, and was possibly easier to use. However, Spotfire 7 addressed these issues, and a great deal of effort has been made to meet the challenge set by Tableau in this respect. For more advanced analytics Spotfire has once again set the benchmark, offering advanced statistical, machine learning and prescriptive analytics, in addition to the familiar visual analytics interface. Of course, users do not have to employ these more advanced tools, but some are actually very easy to use as well as being powerful. One of the most significant differentiators is the TIBCO Enterprise Runtime for R (TERR) - a high performance R execution platform that runs tens of times faster than the default R executable. Once again this emphasizes the difference between the products - Spotfire was born for high volume, complex analytics, and Tableau is currently in the process of putting the pieces in place to try and emulate this. In terms of features such as mobile support, construction of visual artifacts, collaboration, ease of use, connection to popular data sources and web services and so on, then it just becomes a matter of preference. However, Spotfire has the more sophisticated offering with performance characteristics that are very hard to beat. Spotfire was born into the world to handle complex, high volume data with ease, and provide analysis through an attractive visual interface - in other words it was an early visual analytics platform. Oddly enough, even today many contemporary visual analytics platforms struggle to accommodate the data complexity and volumes that Spotfire was handling over a decade ago, and so its pedigree has always given it an advantage over much of the competition. TIBCO has a long history of providing technologies which address process and data integration at the enterprise level. In fact everything they do is concerned with the big and the complex, and so they acquired Spotfire in 2007 once it became clear that the technology had broad appeal in financial services, life sciences and other industries such as petrochemicals. Tableau came to life as a product that commercialized research carried out at Stanford University, and was entirely focused on providing a completely visual interface for query formation and visual representation of data. Tableau however is evolving to meet the big and the complex, and some recent developments move it further in this direction - particularly the new Hyper data engine, and better integration with advanced analytics tools such as R and Python. Even so Spotfire still sets the benchmark for sophistication and performance. Both products are easy-to-use, although users still need some level of training to do anything meaningful. Copyright butleranalytics.com 1

2 Spotfire and Tableau Ratings Spotfire Tableau Average Real-time Analysis Advanced Analytics Performance Scalability Data Preparation Governance Process Integration Extensibility Developer Support Collaboration AI Support Visualization Data Connectivity Mapping Ease-of-Use Mobile Spotfire Spotfire was born into the world to handle complex, high volume data with ease, and provide analysis through an attractive visual interface - in other words it was an early visual analytics platform. Oddly enough, even today many contemporary visual analytics platforms struggle to accommodate the data complexity and volumes that Spotfire was handling over a decade ago, and so its pedigree has always given it an advantage over much of the competition. TIBCO has a long history of providing technologies which address process and data integration at the enterprise level. In fact everything they do is concerned with the big and the complex, and so they acquired Spotfire in 2007 once it became clear that the technology had broad appeal in financial services, life sciences and other industries such as petrochemicals. Copyright butleranalytics.com 2

3 Spotfire provides both analytical depth and breadth, and caters for casual, relatively superficial analysis, through to in-depth complex analysis - all through the same interface and toolset. It can be used on a stand-alone desktop, or be part of an enterprise analytics environment with live streaming data, integrated statistical analysis and predictive analytics. The analytical capability of Spotfire is tiered. Novice users, such as new or casual business users, or those with modest requirements need go no further than the first tier a drag and drop visual interface for the creation of virtually any chart, graph, map, etc. The next level is called Out-of-the-Box Advanced Analytics, and is well suited to business analysts and skilled business users. It includes the ability to incorporate statistical features into visuals, look for correlations, apply regression models and sophisticated forecasts, and cluster data to find similarities between different instances within a database. Much of this is available from a sidebar menu and simply requires users to enter the relevant parameters. The scope of Spotfire is enhanced considerably by its tight integration with the R statistical and analytical language. It also features a very fast runtime environment for R (TERR the TIBCO Enterprise Runtime for R), which overcomes the relatively poor performance of the open source platform and makes it suitable for enterprise deployment. Spotfire supports other languages such as SAS, MATLAB, KNIME, S+, and data platforms such as Spark and MapReduce databases. These capabilities mean developers can create sophisticated analytical functions that are made available to users in much the same way as any other function, and are called Data Functions within Spotfire. For very advanced functionality Spotfire can work with TIBCO StreamBase so that analytical models can be applied to real-time streaming data. This has obvious applications in the Internet of Things, and broad support is provided for Apache Spark. With its advanced analytics capabilities Spotfire is often used in businesses that must deal with technical or financial data. Energy companies, pharmaceuticals, and firms in financial services are all examples of where Spotfire finds wide usage. The platform is also surprisingly open and extendible, with various APIs and a SDK. DevOps will find the exposed APIs particularly useful. Spotfire comes in three variants: Spotfire desktop provides a stand-alone environment for users to explore their data and create visualizations and dashboards. An in-memory engine means the analysis is very fast, and for larger data sets an in-database engine can be used via various connectors. Spotfire Platform enables sharing and collaboration in an enterprise setting. It also embraces other forms of analytics including predictive, prescriptive, content (e.g. text) analytics, location analytics and real-time analytics. Only a few suppliers challenge this breadth of capability and at much higher cost. Spotfire Cloud provides full Spotfire functionality via a cloud based service. A web browser interface is used and generous cloud storage is provided for data. The speed of Spotfire is greatly enhanced by its clever memory and data management. For more modest data sets the in-memory processing ensures very high performance. Copyright butleranalytics.com 3

4 Larger data sets can be processed in-database, and a hybrid approach called On-Demand optimizes the data held in local memory and that held in the database. This is unique to Spotfire. The new Recommendations feature presents the most suitable representation of data (the actual data and not a static prototype) with just a few mouse clicks, and a redesigned data panel makes data selection and filtering straightforward. Grouping is also a new feature where similar items (a collection of products or regions for example) can be grouped together on a chart simply by dragging the mouse across them. More recent developments include an extensive data wrangling environment, a very smart chart recommendations engine, inline predictions, more scalability of the back-end architecture, KPI charts, smart inferencing of relationships between data, good interfaces to Attivio (the cognitive search platform) and new advanced analytics (automatic detection of correlations, line similarity, K-means clustering on-the-fly). Finally, the mapping capabilities of Spotfire are among the best in the industry with complex functionality such as routing coming straight out the box. Tableau Tableau has become the most widely used visual analytics platform thanks to its relentless focus on the visual paradigm, excellent sales and marketing, and significant ongoing investment in product development. The focus on visual analytics means there is no scripting language in Tableau, and every effort has been made to ensure that functionality is available via menu systems and a drag-and-drop interface. However, the world of business analytics is moving fast and converging. If Tableau had insisted on a visual only product, with no support for other forms of analytics, it would have found the market drifting away from it. More recently it has provided excellent integrations with R and python, and is in the process of rolling out a beefier analytics database engine under the name of Hyper. It was quite well known that for analysis involving several large data sets, Tableau could experience scaling problems. Hyper will alleviate this issue in many, but not all, instances. The visual environment provided by Tableau is relatively easy to use, although meaningful analysis will require some level of training. It also automates many common tasks, and while this is often useful it can sometimes assume too much - by treating a measure as a measure for example, when the user wants to treat it as a dimension. Even so users generally like the attractive interface and visuals, and Tableau has recently put great emphasis on the mapping functionality within the product. Data preparation has often been cited as a weak point in Tableau, but here again the product will soon see the addition of extra capabilities. Maestro is the name given to new functionality, and true to style Tableau is placing great emphasis on the visual interface so that users can see a real-time sample of data that reflects the transformations they are making. Tableau Desktop can connect to a wide variety of data sources and with the new Hyper data engine offers considerably increased speed and an ability to handle much larger data volumes. Resulting dashboards can be shared using Tableau Server or Tableau Online. It Copyright butleranalytics.com 4

5 comes in two editions - the Professional Edition supports more data connections and the sharing options mentioned previously. The Personal Edition has fewer data connections and results can be shared by packaging up the data and visualizations in a file. Tableau Server comes with additional facilities for managing a distributed analytics capability. It supports scheduling of data refreshes, authorization and authentication, and broadcasting of visualizations to the community of users. Mobile support includes native ipad and Android apps. Tableau Online is a cloud hosted version of Tableau Server with access through a web browser. Tableau also offers Tableau Public, which is a web based facility for creating visualizations which can then be incorporated into a web site, and Tableau Reader for viewing Tableau visualizations. All business intelligence platforms need to support more advanced forms of analytics, and Tableau has responded to this with the addition of clustering analysis within the product, and good support for R and Python. Tableau cluster analysis, a method of segmenting data based on the values of various attributes, allows users to identify how data might fit into naturally occurring clusters. The support for R is quite extensive, and for business users it can be made almost transparent. Data are passed to R via a simple script, and once R has completed processing the results are passed back to Tableau for display. Late in 2016 Tableau announced TabPy, an API that enables evaluation of Python code from within a Tableau workbook. The ability to generate a meaningful text narrative from data is also available in Tableau using platforms such as Narrative Science and WordSmith. These natural language generation tools simply take data from a Tableau chart and analyze it for significance. Analysis of sales data for example might result in a narrative showing how sales compare with previous periods, and which product groups have underperformed and those that have exceeded expectation. The Hyper database, acquired by Tableau in 2016, is the gateway to better performance, and the accommodation of streaming data. Hyper is just 7 years old and was developed at the Technical University of Munich. It possesses the rare quality of being able to handle data updates and insertions at the same time as queries. For now, Tableau is focusing on query execution and is coy when pressed on the simultaneous update capability. However, it is clear that this would support real-time streaming of data, although Tableau would need modifications to be able to display such data in real-time. Hyper will replace the Tableau Data Engine and it seems that for many queries users will see something around a ten-fold increase in query performance. These performance increases come from the nature of the Hyper data structures, but also from clever use of contemporary hardware, and particularly nvram memory. Additional cores display a linear increment in performance, and distributed query processing is likely to be a future. Copyright butleranalytics.com 5

6 Conclusion Spotfire and Tableau are wholly different products, and the choice of one over the other will depend on need. Tableau does allow users to create attractive visuals, but to a large extent it ends there. More advanced analytics can be achieved using R and Python, but Tableau is largely a stand-alone environment offering relatively poor integration with the broader business process, data and analytics environment. Spotfire on the other hand will simply go as deep as users want to go, providing full integration with business processes, and specifically the emerging Internet of Things. There is much more to business analytics than visual analysis. This is simply the user interface, or presentation layer of the analytics stack. Tableau does venture deeper into the layers to some extent with its R and Python integrations, but for now this is where it stops, and Tableau does not offer true real-time analytics. Spotfire and TIBCO on the other hand offer the full stack - visual analytics, statistical analysis, predictive analytics, machine learning, prescriptive analytics, real-time analytics, data and process integration, and TIBCO is one of only a handful of suppliers offering this level of capability. And so, the comparison with Tableau is really a comparison of apples and oranges. All but small businesses should consider a broad analytical environment, simply because there should never be any possibility of running up against the buffers. Spotfire and more broadly TIBCO offers this kind of future proof architecture. Tableau on the other hand can form part of a broad analytics capability, but other products and infrastructure will be needed. Copyright butleranalytics.com 6