Big data analytics evolving toward analytics 3.0 - guido oswald
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Terabytes and petabytes of data pour into the organization from operational and transactional systems, from scanning and facilities management systems, from inbound and outbound customer contact points, from social/mobile media and the web. The hopeful vision is to be able to harvest and harness every byte of relevant data and use it to make supremely informed decisions. By applying analytics to the seemingly unlimited flow of available data, data scientists can understand and address complex business issues in ways never before imagined – basing decisions on data-driven insight rather than intuition. Being data-driven pays off. Study after study confirms the obvious: Companies that invest in big data analytics develop deeper customer insights, discover new business leads, report higher win rates and are more profitable than their not-so-data-driven counterparts. You would think boards of directors and executives would be quick to embrace this winning culture, but it isn’t happening to the extent you might imagine. At least not yet. Big data analytics is currently entering the operational areas where predictive analytics, such as data mining, were already well rooted and data-driven decisions were the norm – such as campaign management, churn and credit risk. However, the strongest performing companies will take a more holistic view of big data. They are not just investing in analytics technologies within discrete operational areas; they are creating a pervasive analytics culture – and that is still rare. A first step into this direction can be the democratization of analytics or „Agile Business Intelligence“, where business users get much more freedom to gain insights out of (small and big) data. Technically this requires an In-Memory architecture that allows highly distributed computing, including analytical and statistical functionality.
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Big data analytics evolving toward analytics 3.0 - guido oswald
- 1. Copyr ight © 2015, SAS Institute Inc. All rights reser ved. BIG DATA ANALYTICS EVOLVING TOWARD ANALYTICS 3.0 @guidooswald
- 2. Copyr ight © 2015, SAS Institute Inc. All rights reser ved.
- 3. Copyr ight © 2015, SAS Institute Inc. All rights reser ved.
- 4. Copyr ight © 2015, SAS Institute Inc. All rights reser ved.
- 5. Copyr ight © 2015, SAS Institute Inc. All rights reser ved. TOM DAVENPORT Keeping Up with the Quants: Your Guide to Understanding and Using Analytics
- 6. Copyr ight © 2015, SAS Institute Inc. All rights reser ved.
- 7. Copyr ight © 2015, SAS Institute Inc. All rights reser ved.
- 8. Copyr ight © 2015, SAS Institute Inc. All rights reser ved. ANALYTICS 3.0 TYPES OF ANALYTICS Descriptive: report on the past. BI Reporting Predictive: use models based on past data to predict the future Prescriptive: use models to specify optimal behaviors and actions.
- 9. Copyr ight © 2015, SAS Institute Inc. All rights reser ved. Competitive Advantage What happened? Standard reports How many, how often, where? Ad hoc reports Where exactly is the problem? Query drill down What actions are needed? Alerts Raw data Clean data Degree of Intelligence ANALYTICAL DECISION MAKING Why is this happening? Statistical Analysis What if these trends continue? Forecast What will happen next? Predict What is the best that can happen? Optimize ANALYTICS 3.0
- 10. Copyr ight © 2015, SAS Institute Inc. All rights reser ved. ANALYTICS 3.0 ANALYTICS CONTINUUM
- 11. Copyr ight © 2015, SAS Institute Inc. All rights reser ved.
- 12. Copyr ight © 2015, SAS Institute Inc. All rights reser ved. The Forrester Wave™: Agile Business Intelligence Platforms, Q3 2014, Forrester Research, Inc., July 3, 2014. The Forrester Wave is copyrighted by Forrester Research, Inc. Forrester and Forrester Wave are trademarks of Forrester Research, Inc. The Forrester Wave is a graphical representation of Forrester's call on a market and is plotted using a detailed spreadsheet with exposed scores, weightings, and comments. Forrester does not endorse any vendor, product, or service depicted in the Forrester Wave. Information is based on best available resources. Opinions reflect judgment at the time and are subject to change. IN THE FORRESTER WAVE FOR AGILE BUSINESS INTELLIGENCE PLATFORMS
- 13. Copyr ight © 2015, SAS Institute Inc. All rights reser ved. 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
- 14. Copyr ight © 2015, SAS Institute Inc. All rights reser ved. SAS® VISUAL ANALYTICS HUB Home page for all users with guest access Role based views Navigation to varied end user actions Ad-Hoc Data Exploration Report Creation Report Viewing Administration tasks Data Preparation System Administration and Monitoring Theme Design User content management Recent, favorites, collections Search Collaboration via commenting
- 15. Copyr ight © 2015, SAS Institute Inc. All rights reser ved. SAS® VISUAL ANALYTICS DATA BUILDER Search for tables Join and append tables Import Information Maps Create computed columns (full expression builder) Filter columns and rows (aggregated data or not) Load table, spreadsheet, or star schema in memory Quick access to Twitter feeds and Oracle tables Allow pre and post-processing steps Schedule query execution
- 16. Copyr ight © 2015, SAS Institute Inc. All rights reser ved. SAS® VISUAL ANALYTICS ADMINISTRATOR Monitor Environment Resources (CPU, I/O, Memory) Processes (user session focused) Mobile device logging history Manage Server and Data Start/stop in-memory server Load/unload tables to/from memory and local data storage Register tables in metadata Manage Security Table and row level security Mobile device blacklisting and whitelisting Manage Alerts
- 17. Copyr ight © 2015, SAS Institute Inc. All rights reser ved. SAS® VISUAL ANALYTICS EXPLORER Visual data exploration and discovery to find relationships, trends, discrepancies etc. Auto-charting, visual filtering, etc. Various Visuals: Cross tabs, Bar-Charts, Line Charts, Scatter Plots, Bubble Plots, Geo Maps, Box Plots, Heat Maps, Network Diagrams, Word Cloud, and others Consumable Analytics: Correlation (relationships), Regression (linear, quadratic, cubic, p-spline, best fit), Forecasting (multiple algorithms), Scenario Analysis, Decision Tree, Text Analytics, Summary statistics Comments in the exploration and visualization levels Self-service Hierarchies: Drill down, up, and across siblings Selection and brushing modes for discovering relationships Quickly derive relative time calculations and create new calculated columns via expression builder Save and share as exploration, report, PDF, email Export data to CSV document format
- 18. Copyr ight © 2015, SAS Institute Inc. All rights reser ved. SAS® VISUAL ANALYTICS DESIGNER Interface for designing reports and dashboards Leverage existing reports and data to create reports Use multiple data sources Large number of visualization options with ability to create custom graphs WYSIWYG designer with integrated report viewer Precision and tiled layout Report object interactions and prompts 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 Print, share report via email, setup alerts Export data to Excel and CSV/TSV document formats “Adaptive presentation” - build once, access on computer browser, mobile device, MS Office suite and SharePoint
- 19. Copyr ight © 2015, SAS Institute Inc. All rights reser ved. SAS® VISUAL ANALYTICS VIEWER Visualize reports previously created Multiple data sources Interactions and prompts Hierarchy navigation Collaboration via commenting Print, subscribe to alerts Export data to Excel and CSV/TSV document formats Leverage existing SAS security and allow guest access
- 20. Copyr ight © 2015, SAS Institute Inc. All rights reser ved. SAS® VISUAL ANALYTICS MOBILE Native apps for iPad and Android tablets Highly visual and interactive: filtering, highlighting, drill-down, zooming, etc. Collaboration via commenting Subscribe to alerts Share via email with annotation Offline capabilities Leverage existing SAS security and allow guest access Additional security focused on support for device encryption, remote wipe, blacklisting, whitelisting, SSL connections, mobile tethering, application passcode, etc.
- 21. Copyr ight © 2015, SAS Institute Inc. All rights reser ved. MORE INFO …SOME LINKS http://www.sas.com/de_de/insights/big-data/data-scientist-report.html http://www.sas.com/de_de/events/14/sas-data- scientist/ausbildungsprogramm.html
- 22. Copyr ight © 2015, SAS Institute Inc. All rights reser ved. BIG DATA ANALYTICS EVOLVING TOWARD ANALYTICS 3.0 @guidooswald
Editor's Notes
- Tom Davenport, Director of Research for the International Institute for Analytics (IIA) ; Distinguished Professor in Management and Information Technology at Babson College; He has also taught at the Harvard Business School, the University of Chicago, Dartmouth’s Tuck School of Business, and the University of Texas at Austin and has directed research centers at Accenture, McKinsey & Company, Ernst & Young, and CSC. He is also a Senior Advisor to Deloitte Analytics. Tom earned a Ph.D. from Harvard University in social science.
- Web 3.0 is about data (the quote is actually NOT from the book) Jeff Bezos (Amazon): “We never throw away data” http://www.amazon.com/Keeping-Up-Quants-Understanding-Analytics/dp/142218725X
- What defines Analytics 1.0? Analytics 1.0 represents an era in which enterprises start assembling business intelligence systems and expertise to drive reporting and descriptive analytics. During this era, very few enterprises view their systems as capable of generating predictive or prescriptive analytics. Enterprises focus on the internal, structured data that they generate without giving much thought to other types or sources of data. In this era, most organizations do not view their data as a valuable asset, like equipment or inventory. What defines Analytics 2.0? The primary difference from Analytics 1.0 is the emergence of big data: fast moving, external, large and unstructured data coming from various new and interesting sources. As such, it has to be stored and processed rapidly, often with parallel servers running technologies like Hadoop. The overall speed of analytics increases, and visual analytics (a form of descriptive analytics) gains prominence; however, predictive and prescriptive techniques are still not the main use of analytics. The users are primarily online firms. In this stage, a new community of data scientists emerges that fosters experimentation, hacking and data mashups. Regardless of industry, most enterprises are discussing new data product business opportunities that may lie ahead of them. Big data is still very popular and, for many organizations, remains a challenge they are struggling to overcome.
- synthesis of traditional analytics (1.0) and big data (2.0), organizations are combining large and small volumes of data, internal and external sources, and structured and unstructured formats to yield new insights in predictive and prescriptive models. New products (e.g. location based services)
- There are known knowns. There are things we know that we know. There are known unknowns. That is to say, there are things that we now know we don't know.
- This diagram shows two axes, degree of intelligence and the level of competitive advantage that can be achieved. I am going to propose that using data and applying analytics to data, can accelerate the loop of Intelligence and Experience that links strategy and operations. Most would agree that in the area of collections and recoveries, the historic intelligence is not a great predictor of the present, yet alone the future. Organisations start with data and may build data marts to allow them to access the data locked away in operational systems. Some bring in most data, others pick the data sources that are based upon past experience, so often complaints data and call centre file notes are omitted and yet both could be really useful in segmentation and predictive modelling. The data needs to be cleaned up as it is consolidated – garbage in garbage out! Then there is a whole set of reports queries and alerts that tell you where you have been or may also tell you where you are today, providing the information is available fast enough. But it is when you start to apply analytics to the data that business intelligence and competitive starts to grow Exploring data is all about understanding more about the data and relationships between data sources than you knew from experience or intuition. Yes we may know that impairment on zero rate balance transfers on the credit card are a I risk group, but what other factors are key in determining the different segments that we may wish to apply different collections strategies to? Forecasting is not about continuing the line on the graph, but about applying a range of forecasting analytical techniques to sets of data to work out what is most likely to occur in the future. Prediction involves building models based upon past experience. These models may be very complex and predict a binary outcome or a probability outcome. So for example, we might explore the customer base to identify the factors that are most likely to lead to default, purchase or churn. We could then build models based upon that data and predict which customers may impair and if they did, which would respond best to pre-delinquency contact? Finally, the pinnacle of the use of analytics is the use of optimisation analytics to deploy resources appropriately to achieve the greatest collection of debt with business constraints. So by way of example, if we wanted to put all impaired customers through at least one collection strategy, pull the over 90 days debt down by 30%, tackle a problem with the silver card customers, make only one call to each customer a week, have enough call centre staff to avoid any caller waiting longer the 5 seconds for an answer, what would be the right level of outbound mailing to generate the optimum level of collections whilst giving all objectives an appropriate level of attention. I will explore this in more detail later. That’s the power of SAS Analytics. According to Gartner (in a report issued February 2008): “SAS dominates in advanced analytic solutions. No other vendor in the Magic Quadrant has its range of capabilities or can point to the same number of advanced analytic deployments.” Forrester Research (in a report issued July 2008) says that “SAS remains the best game in town for fully integrated high-end analytics from a single vendor.”
- Enterprise Decision Management Event Stream processing
- democratization of analytics or "Agile Business Intelligence", where business users get much more freedom to gain insights out of (small and big) data. Technically this requires an In-Memory architecture that allows highly distributed computing, including analytical and statistical functionality.
- democratization of analytics or "Agile Business Intelligence", where business users get much more freedom to gain insights out of (small and big) data. Technically this requires an In-Memory architecture that allows highly distributed computing, including analytical and statistical functionality.
- You can…through a game-changing solution from SAS. SAS Visual Analytics brings together in-memory technology, visual exploration of Big Data, and the ability to publish insights to mobile devices…in a way that’s unlike any other solution before. All of your data. Analyzed all at one. All in seconds. And results delivered wherever you are. ------------------------------------------------ SAS Visual Analytics can help you in situations where… You have BIG data and a VARIETY of data You need analytics to get to an answer You need to do variable exploration to build a better, more accurate model SAS Visual Analytics… Offers visual data exploration and intelligent auto-charting for quick understanding of important data Leverages in-memory analytic server for rapid calculations on big data Drives information sharing on big data analysis through web-based reports and mobile Enables IT to provide access to all of the data, eliminating the need for sub-setting data or creating multiple views of data
- 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 Hub - Organization have users performing various roles and performing various different functions using the BI environment. This could vary from simple access to existing reports and dashboards to doing analytical analysis and data exploration to data management activities. SAS Visual Analytics provides landing Home interface, a personalised and secured central location for all users as a entry point for their daily activities using SAS Visual Analytics environment. Here is a sample image of the possible layout of a user’s Home page visible after login providing a user specific access to various capabilities. Users can have their own favourite set of reports, dashboards, explorations, and stored processes as a starting point, or can access the recently accessed ones, or search for and open existing content, or create new ones, or manage collections. The interface would provide search capability across the enterprise and provide collaboration and communication channels with other users.
- SAS Visual Data Builder - Data administrators can join tables, create new calculated columns, apply filters, select a subset of columns, import information maps, etc. Query results can be stored as sas datasets, database tables, co-located data storage tables, or they can be loaded in memory. The query can be saved in the SAS Metadata Repository, shared, and retrieved for further modification. Queries can also be scheduled.
- SAS Visual Analytics Administrator - Monitoring functions are provided for the SAS LASR Analytic Server. It can monitor hardware resources and users sessions, and for mobile users it can keep track of logging history and maintain a whitelist or blacklist for security purposes (example: lost or stolen devices). Through that interface, administrators can stop and start LASR servers, and load or unload tables to/from memory. Administrators can also secure tables and apply row level security. Offers a centralized view of all alerts defined in reports for easy of maintenance.
- 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.
- Data sources are accessed through SAS/Access engines as usual and staged in the Blade Environment for exploration and reporting. The Blade Environment comprehends a set of dedicated blades (commodity hardware) that has 4 main functions: mid-tier, server tier, compute tier, and data tier In the server tier, SAS Workspace server and SAS Metadata server work together to make data available to the clients in a two stage process: First the data is loaded into Hadoop, as a permanent disk store, and then in a second stage the data is loaded into memory, from where SAS LASR Analytic Server (compute tier) is able to process clients requests for fast response times. SAS Visual Analytics Server (SAS Metadata Server, Workspace Server and mid-tier) takes one of the blade nodes. SAS Metadata server can be external and shared with other SAS Solutions. Web-based and Mobile clients get access to the clustered SAS LASR Analytic Server through the mid-tier, while SAS Management Console (SMC) has direct access to SAS Metadata Server.