Empowering the Masses with Analytics

Transcription

1 Empowering the Masses with Analytics THE GAP FOR BUSINESS USERS For a discussion of bridging the gap from the perspective of a business user, read Three Ways to Use Data Science. Ask the average business user what they know about Business Intelligence (BI) and data analytics, and most will claim to understand the concepts. Few, however, will profess to know how analytics works or to have the skills needed to put it into practice. Despite being knowledgeable about their industry and experienced in running their organizations, the majority of business users lack expertise in analytics and visualization techniques but that doesn t stop them from wanting to have a go. This situation has led to ease of use and accessibility becoming the main focus for recent updates from all the leading BI vendors but making tools easier and more widely accessible is only part of the answer. A better approach is to work both sides of the gap. To make tools that can empower business users to discover and unlock value in their data and that extend capabilities for experts, so they can share the analytics workload, improve efficiency, and focus on higher level work. Unfortunately, the products available from most data analytics vendors tend to fall short of this two-sided goal, concentrating purely on running analytic models and visualizing results. TIBCO Spotfire, by contrast, bridges the data science skills gap and enables organizations of any size to take full advantage of all their analytic resources. Here s how. RECOMMENDATIONS Recommendations, a built-in analytics intelligence wizard enables anyone with or without real expertise to create best practice visualizations, or even entire dashboards, in just a few clicks. We don t claim that Spotfire Recommendations can do everything. You still have to connect Spotfire applications to the data to be analyzed. It cannot build complex predictive models; However, by leveraging best practices about what charts to use for different types of data, when to use aggregations, how to use time series correctly, and so on, Recommendations helps even a novice user choose how best to visualize the results of a data analysis.

2 WHITEPAPER 2 Figure 1 Recommendations 1: After loading US Department of Housing and Urban Development data on the homeless population, clicking on the Recommended visualizations icon, and selecting homeless and state in the data panel (left side), Spotfire returns the visualizations above, any of which can be saved to a canvas. Selecting other options in the data panel provides additional visualizations. The Recommendations engine will automatically and continually profile the data and metadata held in analysis files. The distinct count of values in each column, data types, range of values, potentially hierarchical relationships among columns, along with numerous other characteristics are continuously surveyed and logged. Then, when a user clicks the associated Recommendations icon, that data profile, together with their chosen selections, is used to present a gallery of potentially useful visualizations that can be selected by the user. Although there are no guarantees of game-changing insights in the first plot or in any of the suggested visualizations, business users will see a gallery of best practice charts, among which is likely to be something they find of value.

3 WHITEPAPER 3 Because the visualizations fully render the actual data, users will be browsing for insights instead of clicking or dragging to configure plots before seeing what they produce. As the user chooses from the suggestions, Spotfire builds a complete dashboard of linked, configurable graphics with supporting filters and controls to discover and explore the data source in more detail. These capabilities can have significant and far-reaching implications. For novice business users, Spotfire Recommendations reduces the need to study the mechanical aspects of building a visualization. Instead it empowers them to take ownership of at least part of the data analytics workflow, which in turn frees up analytics specialists to concentrate on more complex tasks. Of course, Recommendations can also be used by analysts and data scientists to fill gaps in their knowledge and expertise, while dramatically accelerating the creation of more fully featured data dashboards and applications for users lacking the time or inclination to do it themselves. PREDICTIVE MODELING Another key strength and advantage over less capable BI products is the ability to use Spotfire to extrapolate from an insight and develop new strategies using predictive modeling. These techniques can improve decision-making using the organization s collective experience. For example: Identifying what customers are most likely to buy based on previous purchasing history Customizing train timetables to better suit passenger needs based on journey length, carriage capacity, time of day Predictive analytics can help business users: Increase confidence and effectiveness in decision-making through discovery of meaningful patterns and important data Anticipate and react to emerging trends Reduce or manage risk through scenario planning, forecasts, and fraud detection Forecast behavior and pre-emptively act upon it to, for example, increase upsell rates or decrease churn

4 WHITEPAPER 4 Spotfire predictive analytics tools are implemented in three ways: 1 TIBCO Enterprise Runtime for R (TERR), a production-class environment for running R scripts and packages, combining the agility of open source R with the speed and reliability of an enterprise platform 2 TIBCO Spotfire Statistics Services, a predictive analytics ecosystem for the seamless integration of legacy analytic investments with Spotfire applications 3 Visual modeling tools, functionality that delivers deep predictive insights without the need for statistical programming 1 THE R CONNECTION While R is used extensively to prototype and test analytical models, for production, developers will typically re-implement the models in another language or commercial analytics platform. This rework is necessary primarily because, with limitations in performance and scalability, R was never really built for business-critical environments. There are no tools to help with R coding in Spotfire, but it includes TERR, a robust and commercially-supported, enterprise-class environment for running analytic models written in R, enabling those models to move from prototyping to production without the need to rewrite or port code. TERR lets you: Exploit existing expertise in R Leverage existing R scripts and applications without the need for re-coding Eliminate time and resources spent re-implementing R code for production, or time spent prototyping on an unwieldy platform Rapidly cycle from prototyping to production to deliver faster time to insight and faster time to market Execute R scripts and applications and enable the results to surface in the visually rich Spotfire environment Apply consistent models across multiple applications and uses, eliminating uncertainty when analytic models on different platforms disagree Continually refine models and provide their consistent application so everyone is always using the best analytics Technical benefits include: Delivery of higher performing memory management, enabling linear scalability as larger data sets are analyzed A provision for licensing, embedding, and redistributing A platform suitable for ongoing investment to ensure analytic needs can be met both now and into the future TERR is widely integrated across TIBCO technologies, and it is the engine for Spotfire predictive modeling tools. It can be embedded in Spotfire applications and called upon locally or accessed remotely through TIBCO Spotfire Statistics Services, described next.

5 WHITEPAPER 5 2 STATISTICS AS A SERVICE TIBCO Spotfire Statistics Services can create a repository of reusable predictive analytic functions that users can apply without the need for in-depth expertise in statistics. The process starts with data scientists skilled in this area developing analytics using industry standard languages and platforms, including R, SAS, and MATLAB. They can then upload the code to Spotfire Statistics Services, making it more generally available to other Spotfire application developers and analysts. Then, without the need for any coding or deep understanding of the details of the functions involved, developers across the organization can quickly integrate the analytic into a Spotfire application and share it across a wide community of users. They can more easily visualize the results of their models and analyses and deploy those models from the managed centralized Spotfire Statistics Services. Sample out-of-the-box predictive analytics are also included to help you get started, along with templates to suit a variety of applications. Spotfire Statistics Services also enables users to make use of Teradata Aster to run in-database predictive analytics on big data from Spotfire applications or TERR scripts.

6 WHITEPAPER 6 3 VISUAL MODELING TOOLS With data scientists in short supply, there are plenty of companies who would like to benefit from predictive analytics, but are unable to do so. To address this need and bridge the skills gap, Spotfire offers visual predictive modeling tools that provide predictive analytics without the need for in-depth statistical or coding skills. Far from a second-class option, these tools make use of the native Spotfire TERR engine to execute predictive models within a production environment. The tools also support a full workflow for real predictive modeling, enabling users to create, evaluate, and iterate predictive models using powerful Spotfire visualization technologies. Equally important, users can test models on existing data, apply predictions to new data, and embed predictive models in applications all without any R coding or other analytics products. Spotfire predictive modeling tools linear and logistic regression, classification, and regression trees are standard in Spotfire. Using point-and-click, these tools are designed for use by business users and enable workflows that improve their productivity without the involvement of data scientists. TAKING ACTION One final consideration is the ability to capitalize on insights generated using data analytics to not just predict important trends and outcomes, but to socialize findings, collaborate, make decisions, and execute on them. As part of a portfolio of TIBCO Analytics products, Spotfire not only bridges the skills gap, it empowers organizations of any size to take action in real time. Learn more about Spotfire and the TIBCO Fast Data platform at Global Headquarters 3307 Hillview Avenue Palo Alto, CA TEL FAX TIBCO Software empowers executives, developers, and business users with Fast Data solutions that make the right data available in real time for faster answers, better decisions, and smarter action. Over the past 15 years, thousands of businesses across the globe have relied on TIBCO technology to integrate their applications and ecosystems, analyze their data, and create real-time solutions. Learn how TIBCO turns data big or small into differentiation at , TIBCO Software Inc. All rights reserved. TIBCO and the TIBCO logo, and Spotfire are trademarks or registered trademarks of TIBCO Software Inc. or its subsidiaries in the United States and/or other countries. All other product and company names and marks in this document are the property of their respective owners and mentioned for identification purposes only. 01/25/16