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Application Modernization

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Sulaiman64

This presentation provides an objective approach to make your legacy and custom-built applications agile and infused with intelligence. This allows your apps to utilize new and more substantial data sets as well as apply artificial intelligence and machine learning to take in-the-moment actions.

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1
2Company Confidential WE NOW LIVE IN THE AGE OF INTELLIGENCE
3 Insights Driving Business Outcomes Volume of Data Sources of Data System of Record Analytics Systems of Signals (Big Data) Before AI Systems of Intelligence ML Experiments AI Disruption THE TRANSFORMATIVE POWER OF DATA
4Company Confidential DIGITAL UPSTARTS BEGIN WITH DATA AND AI Are you competitive or falling behind? Systems of Record (70’s)à Systems of Signals (90’s) à Systems of Intelligence (10’s)
5Company Confidential IN THE LAST 15 YEARS, 52% OF FORTUNE 500 COMPANIES HAVE DISAPPEARED Westinghouse, Sears, MCI WorldCom, Toys "R" Us, Tower Records, Motorola, Sun …
6 Oct 30, 2017 The Amazing Ways Spotify Uses Big Data, AI And Machine Learning To Drive Business Success Bernard Marr Enterprise & Cloud “One example is the Discover Weekly feature on Spotify that reached 40 million people in its first year. Every user gets a personalized playlist every week from Spotify of music that they have not heard before on the service, but that will be something the listener is expected to enjoy—a modern- day version of a best friend creating a personalized mix tape.” Upstart
7 Aug 29, 2017 How Walmart Is Using Machine Learning AI, IoT And Big Data To Boost Retail Performance Bernard Marr Enterprise & Cloud Rejuvenated Incumbent, making big changes
8 WHAT IS THE COMMON THREAD?
9 THEY RUN INTELLIGENT APPLICATIONS Sense and take advantage of signals from many sources DATA-DRIVEN Decision-making in the moment vs. Looking in the rear-view mirror IN-THE-MOMENT Learns to predict, act, and improve AI / MACHINE LEARNING Change resources dynamically On any cloud, on-premise or hybrid ELASTIC AND AGILE They extend your competitive advantage and provide meaningful impact to the business
10 Technological Shift MOBILE PAYMENTS Market Demands SHIFTS IN DEMAND Increased Real time Data Sources WEARABLES INEVITABLE SEISMIC SHIFTS
11 Your custom applications are built on an architecture that could not have anticipated the seismic shifts YOU EMBARKED ON DIGITAL TRANSFORMATION 1.0 But what is holding you back?
12 LEGACY APPLICATIONS HAVE BEEN OVERLOOKED Rip and Replace Rewrites Are Overkill "Gartner predicts that every dollar invested in digital business innovation through to the end of 2020 will require enterprises to spend at least three times that to continuously modernize the legacy application portfolio." Gartner, 7 Options to Modernize Legacy Systems, June 1 2018 Cloud Migration Does Not Deliver Intelligence
13 RIP & REPLACE YOUR DATA INFRASTRUCTURE IS TOO DIFFICULT OLTPOLAP ML
14 SPLICE MACHINE DISTRIBUTED SQL PLATFORM Operational Database • Scale-out SQL • OLTP • Fast Enterprise Data Warehouse • In-Memory • OLAP • Massively Parallel Machine Learning • Notebook • Algorithms • Model Workflow • Deployment INTELLIGENT APPLICATIONS ARTIFICIAL INTELLIGENCE BUSINESS INTELLIGENCE OPERATIONAL INTELLIGENCE Converged Architecture On Premises
15Company Confidential Every credit card payment in the world streams onto Splice Machine in real-time, empowering groundbreaking customer service. • Lower fraud loss • Vastly improved customer service • New system can scale out elastically, future-proofing the business IMPACT • Giant footprint with blazing speed • 7PB of data • 2B records/day • 2M queries/day < 1sec SOLUTIONPROBLEM • Company set out to shorten dispute resolution from 46- 100 days to 31 days. • Old system did not have enough history and was too slow to enable new call center and API PAYMENT LEGACY MODERNIZATION Global Payment Company
16Company Confidential Migrate Global Claim, Client, & Policy Application To The Cloud and make intelligent. • Record Time-To-Market • No cloud vendor-lock-in • In-the-moment fraud, AML, litigation IMPACT • Migrate business application from legacy on-premise DB2 architecture to Distributed SQL on multiple clouds • Drive real-time insights with hybrid OLAP/OLTP • Incorporate AI/ML in real-time app SOLUTIONPROBLEM • Takes too long to open up a new operating entity due to data center implementations • AI/ML models for fraud, AML, litigation suffer staleness due to data latency • Analytical insights not fast INSURANCE LEGACY MODERNIZATION Leading European Insurance
17Company Confidential Enhance patient outcome, maximize reimbursable revenue and improve clinical trials for its member network with AI-based tools that predict best neurological therapies and patient conditions • Implement patient-centric precision medicine using data driven approach • Maximize reimbursable revenues • Select optimal clinical trial participants IMPACT • Objective multidimensional quantitative analysis based on expansive population data across clinics • Effectively evaluate neurological condition, monitor disease progression and identify therapy efficacy (add AI/ML) SOLUTIONPROBLEM • Diagnosis of neurological condition is highly complex • Traditional tests do not capture critical thresholds of disease progression • Clinical trials can fail even if patient outcomes improve HEALTHCARE ADVISOR
18 SPLICE MACHINE CORE DIFFERENTIATORS Scale Out without Sacrificing SQL Run Existing Legacy Applications Make In-the- Moment Decisions Deploy on Any Cloud or On- Premise In-Database ML Manager Unify Analytics Into Your Apps
19 Migrate to Distributed SQL MODERNIZATION 2.0 A Less Expensive and Risky Approach Unify Analytics Inject AI/ ML 1 2 3 RDBMS DW ML Workbench Distributed SQL INTELLIGENT APPLICATIONS ARTIFICIAL INTELLIGENCE BUSINESS INTELLIGENCE OPERATIONAL INTELLIGENCE
20 RE-WRITES ARE UNTENABLE Migrate SQL Migrate Reports Create New ML Models Deploy Models Rewrite Application in NoSQL Program Reports in App Create New ML Models Deploy Models Months or Years • No Changes to ANSI SQL • Vendor SQL rewritten • Stored Procedures convert with tools • BI Reports leveraged • Tuning required • In-DB ML makes ML avoid ETL • ML Manager makes it easy to experiment and deploy in application • All business logic touched causing QA nightmare • Join algorithms have to be written in application – never as good as 40 years of RDBMS research • ACID Transactions need to be handled in application – Rollback hard to do • BI Reports rewritten in application layer or additional ETL to separated BI SQL – same as it ever was • ETL Latency • ML Engines need to be integrated • Heavy ETL procedures need to be written • ETL latency Splice NoSQL Small Team Small Army
21Company Confidential SPLICE MACHINE ARCHITECTURE Node 1 Low Latency (In-Memory, SSD) High Latency, Low Cost (S3, HDFS,Local) Message Queue (Kafka) Applications Business Intelligence External Machine Learning Compute Nodes Service Layer Storage Authentication / Authorization / Encryption Native Notebooks Bulk Import / Export JDBC / ODBC Machine Learning API Rest API / MicroService / Containers Node 1 Parser Planner Cost-based Optimizer Executor On Premises Node 2 Parser Planner Cost-based Optimizer Executor … Node 3 Parser Planner Cost-based Optimizer Executor
22 SUPPORTED BY INDUSTRY ICONS Roger Bamford Mike Franklin Jonathan Goldick Abinhav Gupta Marie-Anne Neimat Andy Pavlo Ken Rudin Bruce Cleveland Bill Ericson Jay Fulcher Drew Harman Ray Lane Advisory Board Board of Directors
23 Identify Applications That: • Are slow • Cannot scale or suffer giant DB costs • Need new data sources • Cannot take actions on predictions • Need hybrid cloud strategies • Suffer from latency of data movement • Are batch and should be in the moment WHERE TO START?
24

More Related Content

Application Modernization

  • 1. 1
  • 2. 2Company Confidential WE NOW LIVE IN THE AGE OF INTELLIGENCE
  • 3. 3 Insights Driving Business Outcomes Volume of Data Sources of Data System of Record Analytics Systems of Signals (Big Data) Before AI Systems of Intelligence ML Experiments AI Disruption THE TRANSFORMATIVE POWER OF DATA
  • 4. 4Company Confidential DIGITAL UPSTARTS BEGIN WITH DATA AND AI Are you competitive or falling behind? Systems of Record (70’s)à Systems of Signals (90’s) à Systems of Intelligence (10’s)
  • 5. 5Company Confidential IN THE LAST 15 YEARS, 52% OF FORTUNE 500 COMPANIES HAVE DISAPPEARED Westinghouse, Sears, MCI WorldCom, Toys "R" Us, Tower Records, Motorola, Sun …
  • 6. 6 Oct 30, 2017 The Amazing Ways Spotify Uses Big Data, AI And Machine Learning To Drive Business Success Bernard Marr Enterprise & Cloud “One example is the Discover Weekly feature on Spotify that reached 40 million people in its first year. Every user gets a personalized playlist every week from Spotify of music that they have not heard before on the service, but that will be something the listener is expected to enjoy—a modern- day version of a best friend creating a personalized mix tape.” Upstart
  • 7. 7 Aug 29, 2017 How Walmart Is Using Machine Learning AI, IoT And Big Data To Boost Retail Performance Bernard Marr Enterprise & Cloud Rejuvenated Incumbent, making big changes
  • 8. 8 WHAT IS THE COMMON THREAD?
  • 9. 9 THEY RUN INTELLIGENT APPLICATIONS Sense and take advantage of signals from many sources DATA-DRIVEN Decision-making in the moment vs. Looking in the rear-view mirror IN-THE-MOMENT Learns to predict, act, and improve AI / MACHINE LEARNING Change resources dynamically On any cloud, on-premise or hybrid ELASTIC AND AGILE They extend your competitive advantage and provide meaningful impact to the business
  • 10. 10 Technological Shift MOBILE PAYMENTS Market Demands SHIFTS IN DEMAND Increased Real time Data Sources WEARABLES INEVITABLE SEISMIC SHIFTS
  • 11. 11 Your custom applications are built on an architecture that could not have anticipated the seismic shifts YOU EMBARKED ON DIGITAL TRANSFORMATION 1.0 But what is holding you back?
  • 12. 12 LEGACY APPLICATIONS HAVE BEEN OVERLOOKED Rip and Replace Rewrites Are Overkill "Gartner predicts that every dollar invested in digital business innovation through to the end of 2020 will require enterprises to spend at least three times that to continuously modernize the legacy application portfolio." Gartner, 7 Options to Modernize Legacy Systems, June 1 2018 Cloud Migration Does Not Deliver Intelligence
  • 13. 13 RIP & REPLACE YOUR DATA INFRASTRUCTURE IS TOO DIFFICULT OLTPOLAP ML
  • 14. 14 SPLICE MACHINE DISTRIBUTED SQL PLATFORM Operational Database • Scale-out SQL • OLTP • Fast Enterprise Data Warehouse • In-Memory • OLAP • Massively Parallel Machine Learning • Notebook • Algorithms • Model Workflow • Deployment INTELLIGENT APPLICATIONS ARTIFICIAL INTELLIGENCE BUSINESS INTELLIGENCE OPERATIONAL INTELLIGENCE Converged Architecture On Premises
  • 15. 15Company Confidential Every credit card payment in the world streams onto Splice Machine in real-time, empowering groundbreaking customer service. • Lower fraud loss • Vastly improved customer service • New system can scale out elastically, future-proofing the business IMPACT • Giant footprint with blazing speed • 7PB of data • 2B records/day • 2M queries/day < 1sec SOLUTIONPROBLEM • Company set out to shorten dispute resolution from 46- 100 days to 31 days. • Old system did not have enough history and was too slow to enable new call center and API PAYMENT LEGACY MODERNIZATION Global Payment Company
  • 16. 16Company Confidential Migrate Global Claim, Client, & Policy Application To The Cloud and make intelligent. • Record Time-To-Market • No cloud vendor-lock-in • In-the-moment fraud, AML, litigation IMPACT • Migrate business application from legacy on-premise DB2 architecture to Distributed SQL on multiple clouds • Drive real-time insights with hybrid OLAP/OLTP • Incorporate AI/ML in real-time app SOLUTIONPROBLEM • Takes too long to open up a new operating entity due to data center implementations • AI/ML models for fraud, AML, litigation suffer staleness due to data latency • Analytical insights not fast INSURANCE LEGACY MODERNIZATION Leading European Insurance
  • 17. 17Company Confidential Enhance patient outcome, maximize reimbursable revenue and improve clinical trials for its member network with AI-based tools that predict best neurological therapies and patient conditions • Implement patient-centric precision medicine using data driven approach • Maximize reimbursable revenues • Select optimal clinical trial participants IMPACT • Objective multidimensional quantitative analysis based on expansive population data across clinics • Effectively evaluate neurological condition, monitor disease progression and identify therapy efficacy (add AI/ML) SOLUTIONPROBLEM • Diagnosis of neurological condition is highly complex • Traditional tests do not capture critical thresholds of disease progression • Clinical trials can fail even if patient outcomes improve HEALTHCARE ADVISOR
  • 18. 18 SPLICE MACHINE CORE DIFFERENTIATORS Scale Out without Sacrificing SQL Run Existing Legacy Applications Make In-the- Moment Decisions Deploy on Any Cloud or On- Premise In-Database ML Manager Unify Analytics Into Your Apps
  • 19. 19 Migrate to Distributed SQL MODERNIZATION 2.0 A Less Expensive and Risky Approach Unify Analytics Inject AI/ ML 1 2 3 RDBMS DW ML Workbench Distributed SQL INTELLIGENT APPLICATIONS ARTIFICIAL INTELLIGENCE BUSINESS INTELLIGENCE OPERATIONAL INTELLIGENCE
  • 20. 20 RE-WRITES ARE UNTENABLE Migrate SQL Migrate Reports Create New ML Models Deploy Models Rewrite Application in NoSQL Program Reports in App Create New ML Models Deploy Models Months or Years • No Changes to ANSI SQL • Vendor SQL rewritten • Stored Procedures convert with tools • BI Reports leveraged • Tuning required • In-DB ML makes ML avoid ETL • ML Manager makes it easy to experiment and deploy in application • All business logic touched causing QA nightmare • Join algorithms have to be written in application – never as good as 40 years of RDBMS research • ACID Transactions need to be handled in application – Rollback hard to do • BI Reports rewritten in application layer or additional ETL to separated BI SQL – same as it ever was • ETL Latency • ML Engines need to be integrated • Heavy ETL procedures need to be written • ETL latency Splice NoSQL Small Team Small Army
  • 21. 21Company Confidential SPLICE MACHINE ARCHITECTURE Node 1 Low Latency (In-Memory, SSD) High Latency, Low Cost (S3, HDFS,Local) Message Queue (Kafka) Applications Business Intelligence External Machine Learning Compute Nodes Service Layer Storage Authentication / Authorization / Encryption Native Notebooks Bulk Import / Export JDBC / ODBC Machine Learning API Rest API / MicroService / Containers Node 1 Parser Planner Cost-based Optimizer Executor On Premises Node 2 Parser Planner Cost-based Optimizer Executor … Node 3 Parser Planner Cost-based Optimizer Executor
  • 22. 22 SUPPORTED BY INDUSTRY ICONS Roger Bamford Mike Franklin Jonathan Goldick Abinhav Gupta Marie-Anne Neimat Andy Pavlo Ken Rudin Bruce Cleveland Bill Ericson Jay Fulcher Drew Harman Ray Lane Advisory Board Board of Directors
  • 23. 23 Identify Applications That: • Are slow • Cannot scale or suffer giant DB costs • Need new data sources • Cannot take actions on predictions • Need hybrid cloud strategies • Suffer from latency of data movement • Are batch and should be in the moment WHERE TO START?
  • 24. 24