SAS Visual Analytics Hands-On
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Presentation Objectives: Introduce you to a the new approach for BI Get hands on with Visual Analytics Understand how Visual Statistics extends the capabilities Learn how to get started with Visual Analytics Answer your questions
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SAS Visual Analytics Hands-On
- 1. Copyright © 2015, SAS Institute Inc. All rights reserv ed. SAS VISUAL ANALYTICS HANDS-ON INSTANT GRATIFICATION ON BIG DATA
- 2. Copyright © 2015, SAS Institute Inc. All rights reserv ed. AGENDA FOR THE DAY TIME 8:30 a.m. Registration & Breakfast 9:00 a.m. Visual Analytics Introduction and Overview 9:30 a.m. Visual Analytics Walk through 9:45 a.m. Break 10:00 a.m. Put SAS Visual Analytics through its Paces Using real data and real scenarios Explore all relevant data quickly and easily 10:45 a.m. Sharing Insights Through Visual Analytics Reporting Using sample reports to show dissemination of insight 11:00 a.m. Break 11:15 a.m. Visual Statistics Moving from analytics to statistics 11:50 am Wrap up and questions
- 3. Copyright © 2015, SAS Institute Inc. All rights reserv ed. SAS VISUAL ANALYTICS LAB OBJECTIVES • Introduce you to a the new approach for BI • Get hands on with Visual Analytics • Understand how Visual Statistics extends the capabilities • Learn how to get started with Visual Analytics • Answer your questions
- 4. Copyright © 2015, SAS Institute Inc. All rights reserv ed. Exploration Data Visualization ANALYTICS WHAT DOES ANALYTICS MEAN TO YOU? Traditional Reporting Creation and sharing of known information resulting in the deliver of reports Data Discovery Exploration of data to discovery new questions and opportunities Advanced Analytics Leverage Data to make the best decision Forecasting Optimization Prediction Standard Reports Dashboards Ad Hoc Query
- 5. Copyright © 2015, SAS Institute Inc. All rights reserv ed. BI CIRCA 1995 THE WAY BI TYPICALLY WORKS DATA SOURCES operational, transactional INFORMATION ORGANIZED summaries, samples, subsets INTELLIGENCE ADDED calculations, hierarchies, groups ANALYTICS PERFORMED descriptive, predictive, prescriptive APATHY Attributes: • Data rules the process • IT gathers and organizes the data • Multi step process • Analytics are an after thought • Outcome is a report IT DRIVEN REPORTS DELIVERED USER DRIVEN
- 6. Copyright © 2015, SAS Institute Inc. All rights reserv ed. BI TODAY BUSINESS INTELLIGENCE PROCESS REVISED DATA SOURCES operational, transactional ORGANIZE INFORMATION ADD INTELLIGENCE PERFORM ANALYTICS DESIGN REPORT ENTHUSIASM Attributes: • Data is still required • End Users organize and enrich data • Reports are visualized • Self serve and agility is a reality • Outcome is amazing IT DRIVEN USER DRIVEN
- 7. Copyright © 2015, SAS Institute Inc. All rights reserv ed. BI TODAY BUSINESS INTELLIGENCE PROCESS REIMAGINED ENTHUSIASM Give Business Users access to all the data • No more samples, subsets, summaries • Granular, detailed transactions: what the business does. Empower business users with tools to: • Organize the information • Add intelligence – data autonomy • Perform (and understand) advanced analytics • Design and customize their own reports SELF SERVICE ENABLEMENT EMPOWERMENT ENTHUSIASM
- 8. Copyright © 2015, SAS Institute Inc. All rights reserv ed. Data Autonomy VISUAL ANALYTICS WHEN TO CONSIDER IT Enterprise Data Sandbox Governed Visualization Analytical Insights Data Prep to Visualization ScalabilityLeverage SAS Investment Unguided Analysis
- 9. Copyright © 2015, SAS Institute Inc. All rights reserv ed. VISUALIZATION WHY IT MATTERS… Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Domestic $1,983 $2,343 $2,593 $2,283 $2,574 $2,838 $2,382 $2,634 $2,938 $2,739 $2,983 $3,493 International $574 $636 $673 $593 $644 $679 $593 $139 $599 $583 $602 $690 versus
- 10. Copyright © 2015, SAS Institute Inc. All rights reserv ed. SAS VISUAL ANALYTICS & SAS VISUAL STATISTICS DELIVERS A SINGLE SOLUTION FOR FASTER, SMARTER DECISIONS MOBILEDATA PREPARATION EXPLORATION REPORTING • Native iOS and Android applications that delivers interactive reports • Join data from multiple sources • Create calculated and derived columns • Load data • Perform ad-hoc analysis and data discovery • Apply exploratory analytics • Create dashboard style reports for web or mobile SAS LASR ANALYTIC SERVER ADMINISTRATION • Monitor SAS LASR Analytic server • Load/unload data • Manage security MODELING • Perform exploratory data analysis • Conduct rapid model prototyping Central Entry Point Integration Role-based Views
- 11. Copyright © 2015, SAS Institute Inc. All rights reserv ed. SAS VISUAL ANALYTICS SAS IN-MEMORY ARCHITECTURE Disk RAM CPU CPU SAS LASR Analytic Server • Data in-memory Ultra-fast processing • Extreme Parallelism Leveraging all processor cores • Distributed Environment Start small and grow
- 12. Copyright © 2015, SAS Institute Inc. All rights reserv ed. SAS VISUAL ANALYTICS PREPARING THE DATA… ANALYTICAL TABLE: • Simple flat table: rows and columns • Denormalized • Each row is an autonomous transaction • No summaries or total • No hierarchical structure
- 13. Copyright © 2015, SAS Institute Inc. All rights reserv ed. HANDS ON LAB
- 14. Copyright © 2015, SAS Institute Inc. All rights reserv ed. SAS® VISUAL ANALYTICS DELIVERS A SINGLE SOLUTION FOR FASTER, SMARTER DECISIONS Central Entry Point Integration Role-based Views MOBILE BIDATA BUILDER EXPLORER DESIGNER • Native iOS and Android applications that deliver interactive reports • Join data from multiple sources • Create calculated and derived columns • Load data • Perform ad-hoc analysis and data discovery • Apply advanced analytics • Create dashboard style reports for web or mobile SAS® LASR™ ANALYTIC SERVER ADMINISTRATOR • Monitor SAS® LASR™ Analytic server • Load/unload data • Manage security
- 15. Copyright © 2015, SAS Institute Inc. All rights reserv ed. SAS® VISUAL ANALYTICS DESIGNER Interface for designing reports and dashboards Leverage existing reports and data to create reports Self-service data import from most popular data sources, including Twitter and Salesforce Use multiple data sources in a single report Large number of visualization options with ability to create custom graphs WYSIWYG designer with integrated report viewer Precision and tiled layout Report object interactions, linking to sections, reports, stored processes, or external content Prompts at the report, section, and report object levels Comments in the report and object (charts and tables) levels Self-service Hierarchies: Drill down, up, and across siblings Quickly derive relative time calculations and create new calculated columns via expression builder Parameterize calculations, filters, display rules, and ranks Print report, section, object, share report via email, schedule reports to be distributed, setup and receive alerts via email or sms Export data to Excel and CSV/TSV document formats “Adaptive presentation” - build once, access on computer browser, mobile device, MS Office suite and SharePoint
- 16. Copyright © 2015, SAS Institute Inc. All rights reserv ed. SAS® VISUAL STATISTICS
- 17. Copyright © 2015, SAS Institute Inc. All rights reserv ed. SAS® VISUAL STATISTICS FUELING THE ANALYTICS LIFECYCLE SAS® Visual Statistics TEXT COMPETITIVE ADVANTAGE MANAGE DATA EXPLORE DATA EXPLORE & DEVELOP MODELS DEPLOY& MONITOR SAS® Visual Analytics
- 18. Copyright © 2015, SAS Institute Inc. All rights reserv ed. SAS® VISUAL STATISTICS RAPID MODEL BUILDING AND REFINEMENT
- 19. Copyright © 2015, SAS Institute Inc. All rights reserv ed. SAS® VISUAL STATISTICS ANALYTIC CAPABILITIES HOW IT CAN BE USED? Classification Predict outcomes using causal factors -, churn, campaign response, part failure Regression Estimate outcomes using causal factors - spend, policy premium, credit limit, etc. Clustering Segment data based on self-similarity – risk, profitability, demographic, etc. Group-By Models by segments/groups (e.g. location, store, owner, device, etc.).
- 20. Copyright © 2015, SAS Institute Inc. All rights reserv ed. SAS® VISUAL STATISTICS MODELING TECHNIQUES • Predictive Techniques • Linear Regression • Logistic Regression • Generalized Linear Model • Classification Trees • Descriptive Techniques • Clustering • Group-By Processing • Auto-update
- 21. Copyright © 2015, SAS Institute Inc. All rights reserv ed. HANDS ON LAB
- 22. Copyright © 2015, SAS Institute Inc. All rights reserv ed. CLOSING COMMENTS
- 23. Copyright © 2015, SAS Institute Inc. All rights reserv ed. Exploration Data Visualization ANALYTICS WHAT DOES ANALYTICS MEAN TO YOU? Traditional Reporting Creation and sharing of known information resulting in the deliver of reports Data Discovery Exploration of data to discovery new questions and opportunities Advanced Analytics Leverage Data to make the best decision Forecasting Optimization Prediction Standard Reports Dashboards Ad Hoc Query
- 24. Copyright © 2015, SAS Institute Inc. All rights reserv ed. Data Autonomy VISUAL ANALYTICS WHEN TO CONSIDER IT Enterprise Data Sandbox Governed Visualization Analytical Insights Data Prep to Visualization ScalabilityLeverage SAS Investment Unguided Analysis
- 25. Copyright © 2015, SAS Institute Inc. All rights reserv ed. SAS® VISUAL ANALYTICS TRY BEFORE YOU BUY http://www.sas.com/en_us/software/business-intelligence/visual-analytics.html http://www.sas.com/en_us/software/analytics/visual-statistics.html
- 26. Copyright © 2015, SAS Institute Inc. All rights reserv ed. www.SAS.com QUESTIONS?
Notes de l'éditeur
- Hello everyone and Welcome to SAS’s Visual Analytics Hands-On Workshop, we are very happy to have you here. My name is ______________ and I am (position and title). I would also like to introduce you to (other SAS people in the room, i.e. Shawn and Ron/ Mark ) (Names, Titles, etc.). We are all thrilled that you will be joining us this morning (afternoon) for this interactive workshop.
- This is already started with Python programmers, etc. They did not wait for it… they went and created this streamlined process on their own. So how to address that crowd. The new data scientists have been doing this parallel process, with their own tools already. The problem is that it is often not organized, enterprise-wide, governed. SAS can help move it from ‘experimental status’ to productionized environment.
- Governed visualization – not desktop, able to secure and you have control Data Sandbox – Eliominate the proliferation of data to desktops this is a place where new/not yet corporate data can comingle with enterprise data Unguided analysis – Can look at any data and find your answers in a free form way Data autonomy – users can create variables, hierarchies and complex calculations Leverage SAS – ability to expose SAS information to users in a more consumable way, less time spent on mundane data presentation Analytic insight – ability to do simple statistical methods without the need to know stats. Forecasting, decision trees movement to VS Data Prep – We allow users to do the full data lifecycle in a secure, managed and shared way. DI metadata sharing etc. Scalability – Ability to scale and deliver value to the entrprise
- SAS Visual Analytics is comprised of these key components… Visual Data Builder Visual Analytics Administrator Visual Analytics Explorer Visual Analytics Designer Mobile BI The SAS LASR Analytic Server And a streamlined, action-oriented central entry point for the key capabilities is provided by the “Hub”. Let’s take a closer look at each of these components…
- SAS Visual Analytics Explorer - Organizations have realized the importance of analysing every possible aspect of their data, the need of analytically exploring any size of data and understand the patterns, trends more effectively going beyond tabular reports. SAS Visual Analytics brings a highly visual data exploration interface allowing users to take advantage of SAS predictive analytics power to gain insights from their data, with simple to use user actions, and surface consumable analytical results in a visual format, helping customers find relationships and discrepancies in their data. As part of the integrated infrastructure, users can share their findings to web and mobile users. One must note that visual exploration is about using advanced analytics to *visually explore* any size of data and it is not a reporting tool. Here is a sample screenshot of the exploration environment. The interface provides options for selected graphics as well as auto-charting capabilities, drag-n-drop environment for generating visualizations, interactive and dynamic filtering, ability to create dynamic hierarchies by the end-users without the need for pre-defined dimensional structures.
- SAS Visual Analytics Designer – a component that brings the capabilities of classic reporting and highly visual dashboarding as part of single report. The designer creates reports out of various visuals such as graphs, tables, gauges, prompts, geo maps, texts, and images with the ability to use multiple data sources as part of the single report. Users can set various types of interactions across the report objects in a WYSIWYG design format, derive new data items, create hierarchies, add comments, export data. Reports created in this interface (or exported from the Explorer interface) are readily available for users to access via browser or supported mobile devices. Here is a sample screenshot of the designer with various reporting elements, in a precision layout. Each visual element could have come from different data sources.
- SAS Mobile BI - SAS Visual Analytics provides SAS’ native mobile application allowing users to view their reports and dashboards on their selected devices. Currently supported devices are iPad and Android-based tablets. SAS is continuously monitoring the market trend and the demand from customers for other operating systems like Windows and Blackberry and will be extending support for new devices in future releases as required. SAS Mobile BI provides adaptive presentation so that users will not have to create separate reports for each device type. With this offering, SAS Mobile BI provides all the required capabilities via a highly visual and interactive mobile application backed by centralised metadata security. While viewing the SAS Reports, the content will be downloaded to the device and thus fully support an interactive offline analysis. Administrators can force tethering, where mobile app will work only when it is connected to the server. When the report is closed, the data is wiped out.
- SAS Visual Statistics 7.1 Product Release Date/Month: Oct. 2014 Contact: Tapan Patel Last Updated: October, 2014
- Irrespective of big data or large data, every analytics project should go through the iterative analytics (data to decision) lifecycle. Typically four steps involved are: manage/prepare data, explore/visualize, model and deploy & monitor. The role of SAS Visual Statistics is to (primarily) address the data exploration and interactive model development stages of the analytics lifecycle. It allows customers to understand on why certain events, outcomes happen and what are the key relationships. Users ask for more interactions from the data, demand drill-down, etc. to identify the root cause and use the information to build predictive models. It allows customers to build and refine predictive models to assess a future outcome and explain what will happen? For example, is the transaction fraudulent or not or to assess future risk of repayment or how risky is the portfolio given certain conditions in future? Users can dynamically see the impact of changing model properties/parameters and fine tune the model to arrive at the desired results.
- Superior interactivity and Rapid Model Building and Refinement with software. What does it mean and how does that help? In-memory analytic computations dramatically shortens the time required to build and adjust models until you are confident, including the “prototype-review-refine” iterative cycle. It supports the “what you think is what you get” paradigm. In-memory processing…..Users can instantly see the impact of changing model settings – like adding new variable or removing outliers or changing parameters…. Interactive analytics…Designed for multiple passes through data for multiple analytical jobs. Or a change in a predictive model, does not requires them to repeat the entire process. Build numerous models by segments/groups instantly from a decision tree or clustering analysis (i.e. stratified modeling) to find new opportunities and take well-qualified decisions..
- Predictive analytics can be applied to variety of business issues across all industries and solutions areas….some of the sample use cases listed here could be assigned into common areas like: Clustering is the task of segmenting a heterogeneous population into a number of more homogenous subgroups. Segmentation does not rely on predefined classes or examples. The records are grouped together on the basis of self-similarity. Clustering is often done as a prelude to some other form of data mining. For example, market segmentation – cluster of customers with similar buying habits and find out which promotion would work best. Classification deals with prediction of discrete outcomes and arrive at Yes/No decisions with confidence. In business world, many decisions are binary in nature - Churn/No churn, Fraud/No Fraud, Credit Extension or Not, Will you respond to marketing campaign or not. Multi-level classification is similar except that that are more than two level - credit applicants as low, medium or high risk. Mobile vendor is interested in voluntary churn, involuntary churn, an active customer. Estimation or interval prediction is another form of prediction wherein target level is continuous (i.e. individual records are rank ordered). Examples – insurance firm predicting claim frequency, claim severity and pure premium based on your specific information, predicting box office receipts by segments, etc. Groups can be specified in source data or be the result of an analytical model (i.e. a cluster) for generating multiple models by segments/groups. The goal is to exploit relationships to the group.
- Setup regression analysis using both categorical and continuous business drivers Work with classification modeling using logistic regression and decision trees Data driven segmentation using clustering. SAS Visual Statistics lets you visually explore and evaluate segments for further analysis using k-means clustering, scatter plots and detailed summary statistics. Concurrently build numerous models and process results for each group or segment without having to sort or index data each time. The grouping variables, or their properties, can change from one action to the next and groups are processed without reordering the data. This means more results can be generated for each group on the fly without additional processing overhead.
- Governed visualization – not desktop, able to secure and you have control Data Sandbox – Eliominate the proliferation of data to desktops this is a place where new/not yet corporate data can comingle with enterprise data Unguided analysis – Can look at any data and find your answers in a free form way Data autonomy – users can create variables, hierarchies and complex calculations Leverage SAS – ability to expose SAS information to users in a more consumable way, less time spent on mundane data presentation Analytic insight – ability to do simple statistical methods without the need to know stats. Forecasting, decision trees movement to VS Data Prep – We allow users to do the full data lifecycle in a secure, managed and shared way. DI metadata sharing etc. Scalability – Ability to scale and deliver value to the entrprise