DRIVING THE CHANGE ENABLING TECHNOLOGY FOR FINANCE 15 TH FINANCE TECH FORUM SOFIA, BULGARIA APRIL

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1 DRIVING THE CHANGE ENABLING TECHNOLOGY FOR FINANCE 15 TH FINANCE TECH FORUM SOFIA, BULGARIA APRIL BRAD HATHAWAY REGIONAL LEADER FOR INFORMATION MANAGEMENT

2 AGENDA Major Technology Trends Focus on Big Data SAS for Big Data Focus on Data Visualization & Exploration SAS Visual Analytics Conclusions

3 MAJOR TECHNOLOGY TRENDS

4 MAJOR TECHNOLOGY TRENDS Data Exploration & Visualization In-Memory Infrastructure HW Costs DWH & Analytics Appliances Data Governance Data Management Data Quality Big Data Event Streaming & Real Time Mobile data Generation & Information Consumption Cloud

5 MAJOR TECHNOLOGY TRENDS INTER-RELATED Data Exploration & Visualization In-Memory Infrastructure HW Costs DWH & Analytics Appliances Data Governance Data Management Data Quality Big Data Event Streaming & Real Time Mobile data Generation & Information Consumption Cloud

6 MAJOR TECHNOLOGY TRENDS FOCUS AREAS Data Exploration & Visualization In-Memory Infrastructure HW Costs DWH & Analytics Appliances Data Governance Data Management Data Quality Big Data Event Streaming & Real Time Mobile data Generation & Information Consumption Cloud

7 FOCUS ON: BIG DATA

8 WHAT IS BIG DATA? DIFFERING OPINIONS Ask 10 people their view on what big data is and you will often get 10 different answers and a lot of buzzwords about technology!

9 WHAT IS BIG DATA? SAS OPINION Data that exceeds an organization s conventional database storage or processing capacity Relative not absolute

10 WHAT IS BIG DATA? OPINION: BIG DATA IS ABOUT MORE THAN JUST DATA! Data Analytics Business Process Integration

11 WHAT IS BIG DATA? HOW IS THE DATA ECOSYSTEM IMPACTED? Data Analytics Business Process Integration More data arriving more quickly in more varied forms than ever before More analytical models needed to be developed on more and varied data in a shorter amount of time Wider variety new applications More decisions, covering a wider variety of business processes, needing to be made at a variety of speeds (hours, ms)

12 WHAT IS BIG DATA? THE IMPACT OF THE 3 V S ON THE DATA ECOSYSTEM Volume - increasing amounts of data Velocity - increasing speed of data arrival Variety - new types of data Larger data stores needed More data stored outside the warehouse Backup and other IT costs going out of control Move to less history and more aggregation Loading of new data significantly slower ELT requires a massive HW increase at high cost Recreation of Marts a time consuming task Data Mart costs spiralling out of control Slow response times to queries from users or systems Governance of data takes on a new dimension in complexity Timely updates to data difficult Batch processing unable to keep up Impossible to collect and store all data before doing something to it On the fly transformation becomes a requirement Not all data can be stored will need to decide what to keep and what to let fly by Data streaming into organizations will drive the need for new technologies and skills Unstructured Data now a large proportion of all data Data such as CDRs and Network data are a normality and Sensor data increasingly a major part of the workload Skills and technologies largely not in place to handle in a robust way meaning lots of bespoke custom coded solutions No enterprise location to store most new data sources leading to a an unmanaged additional storage landscape Bespoke solutions needed to bring together structured and unstructured data sources. Incredibly hard or impossible to get a true single view of customer (or other entities) that requires combining of all these new varieties of data Being able to access all the data and guarantee acceptable processing times is important But what is truly important is being able to USE that data.

13 WHAT IS BIG DATA? THE IMPACT OF THE 3 V S ON THE DATA ECOSYSTEM Volume - increasing amounts of data Velocity - increasing speed of data arrival Variety - new types of data Larger data stores needed More data More data stored outside outside the warehouse the warehouse Backup and other IT costs going out of control Move to less history and more aggregation Loading of new data significantly slower ELT requires a massive HW increase at high cost Governance of data takes on a new dimension in complexity Timely updates to data difficult Batch processing unable to keep up Impossible to collect and store all data before doing something to it On the fly transformation becomes a requirement Not all data can be stored will need to decide what to keep and what to let fly by Not all data can be stored Recreation of Marts a time consuming task Data streaming into organizations will drive the need Data Mart costs spiralling out of control will need to decide what to Slow response times to queries from users or systems for new technologies and skills keep and what to let fly by Unstructured Data now a large proportion of all data Unstructured Data now a large proportion of all data Data such as CDRs and Network data are a normality and Sensor data increasingly a major part of the workload Skills and technologies largely not in place to handle in a robust way meaning lots of bespoke custom coded solutions No enterprise location to store most new data sources leading to a an unmanaged additional storage landscape Bespoke solutions needed to bring together structured and unstructured data sources. Incredibly hard or impossible to get a true single view of customer (or other entities) that requires combining of all these new varieties of data Being able to access all the data and guarantee acceptable processing times is important But what is truly important is being able to USE that data.

14 WHAT IS BIG DATA? 3 V S IMPACT ON THE ANALYTICS ECOSYSTEM Data Analytics Business Processes Integration More data arriving more quickly in more varied forms than ever before More analytical models needed to be developed on more and varied data in a shorter amount of time Wider variety new applications More decisions, covering a wider variety of business processes, needing to be made at a variety of speeds (hours, ms)

15 WHAT IS BIG DATA? THE ANALYTICS LIFECYCLE: THE DRIVER OF THE ANALYTICS ECOSYSTEM Repeatable steps for obtaining the most value from all styles of analytics Leading companies recognise the EVALUATE / MONITOR RESULTS IDENTIFY / FORMULATE PROBLEM DATA PREPARATION need for a process, standardize it and then continuously focus on DEPLOY MODEL DATA EXPLORATION speeding up the cycle Big Data is already having an impact on the whole of the analytics lifecycle VALIDATE MODEL BUILD MODEL TRANSFORM & SELECT

16 SAS FRAUD FRAMEWORK FRAUD ANALYTICS FLOW Operational Data Sources Exploratory Data Analysis & Transformation Alert Generation Process Business Rules Alert Administration SAS Social Network Analysis Fraud Data Staging Network Rules Transactions Analytics Anomaly Detection Network Analytics Entities Predictive Modeling Internal Data Alert Management & Bi / Reporting External Data Intelligent Fraud Repository Learn and Improve Cycle Case Management

17 WHAT IS BIG DATA? THE IMPACT OF THE 3 V S ON THE ANALYTICS LIFECYCLE Volume - increasing amounts of data Velocity - increasing speed of data arrival Variety - new types of data Identify / Formulate Problem Problems expressed in terms of what is possible today not what needs doing to provide biggest competitive advantage Data Preparation More Data More Complexity More Time Required to Prepare Data Data Storage Costs Exploding and Historical Data generally compromised Data Exploration SQL and data size bound Slow response times More batch orientated as opposed to interactive Transform & Select Sample sizes becoming a smaller overall percentage of original data Data needs to be increasingly aggregated to control size Less time to acquire, transform and land data Batch cycles cannot respond fast enough No time to transform data into an IT approved schema before it should be used for modelling Analysts end up preparing data No way to look at data not yet in the warehouse No way to refresh data to show newly arrived data as part of exploration Transformation and selection cannot rely on the data that has been explored or prepared as the data might have changed by the time you are at this phase of the lifecycle Lack of skills and infrastructure Unable to utilize new sources of competitive advantage No way to combine structured and unstructured data to support Analytics or BI No way to explore the contents of semi-structured or unstructured data meaning it is often left out of exploration Variety blows up the amount of data meaning exploration if often not possible on all the data or too slow Additional sources complicate selection and transformation New types of data transformation issues The data transformation step of text parsing is the most important part to distinguish important from non-important information in unstructured data. Build Model Run-times for models increase Fewer model iterations possible reducing confidence the best model has been created Model and variable choice driven by run-time in order to meet SLAs Models often run on highly aggregated data and increasingly smaller samples Modelling generally always happening on out of date data Chance that with rapid volumes of new data models are already degraded before deployment Run time for models increase as new structured and semi-structured data sources are added causing all the issues outlined in volume. Unstructured data grows in importance but often handled as a silo away from structured data as well as being a slow cumbersome task Validate Model Model validation against smaller time horizons Less confidence in models than before Models often statistically valid but too slow for purpose forcing a new approach to be adopted Model validation becomes an even more critical part of the lifecycle. Model validation becomes a test bed against the latest data Model retraining might need to happen before deployment to pick up latest changes in data New models might need to be created before a model ever enters service Increased time for model validation due to more dimensions in the models Deploy Model Slower model execution times Less complex analytics employed to deliver results in acceptable timeframes at the expense of quality Evaluate / Monitor Results Faster degrading models Model monitoring too slow Models degrade much more before replacement / retraining Less complex analytics employed to deliver results in acceptable timeframes at the expense of quality. Models tend to be deployed as services embedded into business processes using a combination of new and existing data Model monitoring too slow to keep up with speed of degradation Models degrade much more before replacement / retraining Model scoring on unstructured data in production requires sophisticated pre-processing not currently practiced by most companies

18 WHAT IS BIG DATA? THE IMPACT OF THE 3 V S ON THE ANALYTICS LIFECYCLE Identify / Formulate Problem Data Preparation More Data Volume - increasing amounts of data Velocity - increasing speed of data arrival Variety - new types of data Problems expressed in terms of what is possible today not what needs doing to provide biggest competitive advantage Data Storage Costs Less time to acquire, transform and land data More Complexity Batch cycles cannot respond fast enough More Time Required to Prepare Data No time to transform data into an IT approved schema before it should Exploding Data Storage Costs and be used for modelling Exploding Historical and Analysts end up preparing data Historical Data Data SQL and generally data size bound compromised Data Exploration Slow response times More batch orientated as opposed to interactive Transform & Select Sample sizes becoming a smaller overall percentage of original data Data needs to be increasingly aggregated to control size No way to look at data not yet in the warehouse No way to refresh data to show newly arrived data as part of exploration Transformation and selection cannot rely on the data that has been explored or prepared as the data might have changed by the time you are at this phase of the lifecycle Lack of skills and infrastructure Unable to utilize new sources of competitive advantage No way to combine structured and unstructured data to support Analytics or BI Variety blows up the amount of data meaning exploration Variety blows up the amount of data if often not meaning possible exploration if often on not possible all on all the data or too slow the data or too slow No way to explore the contents of semi-structured or unstructured data meaning it is often left out of exploration Additional sources complicate selection and transformation New types of data transformation issues The data transformation step of text parsing is the most important part to distinguish important from non-important information in unstructured data. Build Model Run-times for models increase Fewer model iterations possible reducing confidence the best model has been created Model and variable choice driven by run-time in order to meet SLAs Models often run on highly aggregated data and increasingly smaller samples Modelling generally always happening on out of date data Chance that with rapid volumes of new data models are already degraded before deployment Run time for models increase as new structured and semi-structured data sources are added causing all the issues outlined in volume. Unstructured data grows in importance but often handled as a silo away from structured data as well as being a slow cumbersome task Validate Model Model validation against smaller time horizons Less confidence in models than before Models often statistically valid but too slow for purpose forcing a new approach to be adopted Deploy Model Slower model execution times Less complex analytics employed to deliver results in acceptable timeframes at the expense of quality Evaluate / Monitor Results Faster degrading models Model monitoring too slow Models degrade much more before replacement / retraining Model validation becomes an even more critical part of the lifecycle. Model validation becomes a test bed against the latest data Model retraining might need to happen before deployment to pick up latest changes in data New models might need to be created before a model ever enters service Less complex analytics employed Less complex to analytics deliver employed results to deliver results in acceptable timeframes at the expense of quality. in acceptable timeframes at Models tend to be deployed as services embedded into business processes using a combination of new and existing data the expense of quality Model monitoring too slow to keep up with speed of degradation Models degrade much more before replacement / retraining Increased time for model validation due to more dimensions in the models Model scoring on unstructured data in production requires sophisticated pre-processing not currently practiced by most companies

19 Big data affects your ability to effectively use Analytics in a timely manner for fact based decision making

20 WHAT IS BIG DATA? HOW ARE BUSINESS PROCESSES IMPACTED? This is when you truly see the value of Analytics Data Analytics Business Process Integration This is where The rubber meets the road More data arriving more quickly in more varied forms than ever before More analytical models needed to be developed on more and varied data in a shorter amount of time Wider variety new applications More decisions, covering a wider variety of business processes, needing to be made at a variety of speeds (hours, ms)

21 WHAT IS BIG DATA? IMPACT WILL BE FELT THROUGHOUT THE ORGANIZATION WHEREVER DECISIONS ARE BEING MADE USING DATA! Data that exceeds an organization s conventional database storage or processing capacity Data that impedes an organizations ability to make high quality fact based decisions in a timely manner

22 WHAT IS BIG DATA? QUESTION: IS YOUR BUSINESS IS SITTING ON A BIG DATA TIME BOMB? Most organizations stop here Data Analytics Business Process Integration Big Data Big Data Big Data

23 SAS FOR BIG DATA SOLUTIONS AND USE CASES

24 SAS HIGH- PERFORMANCE ANALYTICS SAS IN-MEMORY ANALYTICS SAS HPA SAS LASR ANALYTIC SERVER

25 SAS FRAUD FRAMEWORK FRAUD ANALYTICS FLOW Operational Data Sources Exploratory Data Analysis & Transformation Alert Generation Process Business Rules Alert Administration SAS Social Network Analysis Fraud Data Staging Network Rules Transactions Analytics Anomaly Detection Network Analytics Entities Predictive Modeling Internal Data Alert Management & Bi / Reporting External Data Intelligent Fraud Repository Learn and Improve Cycle Case Management

26 CUSTOMER CASE STUDY IN-MEMORY ANALYTICS PROCESS FOR PROBABILITY OF DEFAULT 167 Hours Result: many more runs through analytics lifecycle DATA EXPLORATION M O D E L D E V E L O P M E N T MODEL DEPLOYMENT Result: Net Charge Offs (NCOs) reduced by >1% Bottom-line Impact: Tens to Hundreds of Millions of Dollars 84 SECONDS

27 FOCUS ON: DATA VISUALIZATION & EXPLORATION

28 VISUALIZATION THE EYES HAVE IT

29 LIFE EXPECTANCY DATA VISUALIZATION WHAT IS DATA VISUALIZATION?

30 DATA VISUALIZATION NOW MORE THAN EVER!

31 DATA VISUALIZATION WHAT DATA VISUALIZATION DOES FOR BUSINESSES

32 DATA DISCOVERY WHAT ARE BUSINESS STARTING TO DO WITH IT? There will come a time when the traditional BI platform will not be needed anymore! Self-service, fast, interactive, visual, unconstrained data access will be the norm! Note Only the content within the box above is from Gartner Add new capabilities today and steadily replace old platforms!

33 SAS FOR DATA VISUALIZATION SOLUTIONS AND USE CASES

34 SAS VISUAL ANALYTICS BUSINESS VISUALIZATION DRIVEN BY ANALYTICS EXPLORATION AND VISUALISATION POWER OF ANALYTICS RAPID DELIVERY OF MOBILE INSIGHTS

35 SAS VISUAL ANALYTICS A SINGLE SOLUTION FOR FAST AND INTELLIGENT DECISIONS SAS VA Central Entry Point Integration Role-based Views DATA BUILDER ADMINISTRATOR EXPLORER DESIGNER Analyze data from different sources Create calculated columns Load data Monitor SAS LASR Analytic server Load/Unload data Manage security Free exploration and visualization of data from different sources Apply complex analytics Create intuitive representations of data to distribute insight discovered using the VA Explorer in visual dashboards MOBILE BI Distribute interactive reports on traditional channels like the Web or on mobile devices like Android or ipad SAS LASR ANALYTIC SERVER

36 SAS FRAUD FRAMEWORK FRAUD ANALYTICS FLOW Operational Data Sources Exploratory Data Analysis & Transformation Alert Generation Process Business Rules Alert Administration SAS Social Network Analysis Fraud Data Staging Network Rules Transactions Entities Analytics Anomaly Detection Predictive Modeling SAS VA Network Analytics Internal Data Alert Management & Bi / Reporting External Data Intelligent Fraud Repository Learn and Improve Cycle Case Management

37 CONCLUSIONS

38 Your data is a corporate asset. And it s trying to tell you things! With Data Visualization and exploration tools you can easily find out what that is and more besides. And with In-Memory Analytics that something becomes actionable and monitorable over time and in real time, no matter the size of the data.

39 SAS VISUAL ANALYTICS TRY IT NOW!! HEAD NODE DATA NODE Please come join us at our stand where you can try SAS Visual Analytics live! Copyr i g ht 2012, SAS Ins titut e Inc. All rights res er ve d.

40 THANK YOU! sas.com

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