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SAP HANA Use Cases for Pharma Research & Development

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The deck introduces SAP HANA as next generation platform to enable a variety of use cases for pharma research and development.

The deck introduces SAP HANA as next generation platform to enable a variety of use cases for pharma research and development.

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  • 3 BILLION SCANS PER SECOND/PER COREScanning 3MB/msec/coreInserting 1.5M Records/secAggregating 12.5M Records/sec/core
  • Key iskeepingdata in memorytorevolutionaryreducethe time ittakestoreadandprocessdata. Anotherfactoristheabandonmentofrow-baseddatastorageandusingcolumnbasedstorageinstead.
  • Level 1: T-Mobile replicates data from Teradata into SAP HANA as a data mart for analyticsT-Mobile had significant data analysis issues. They had to aggregate data and even then could not assess the success/failure of their client marketing efficiently due to data latency. The most basic reporting took 10-15 minutes per report on aggregated, old information.SAP HANA Use Case(s) – Real-Time Reporting via BOBJ toolset providing Detailed Analysis of Marketing and PromotionsClient Benefit - Dramatic increase in proper merchandizing of programs to potential clients which has T-Mobile quoting that “increased revenue could be in the BILLIONS” as well as insight into demand and supply chain forecasting/reporting.Reporting was accelerated by 75-100X and expanded from basic static reporting to full ad-hoc exploration of data using SAP Explorer on granular data.Level 2: Medtronic started with Global Complaint Handling as a data mart, but had an eye on consolidating multiple data marts into a single EDW to support reporting and complaint handling on free text fields in both a searchable format and ad-hoc reporting. The current EDW could not support more than 60-char field lengths, disk I/O was a significant bottleneck and the overall cost of supporting the legacy database infrastructure was too high.SAP HANA Use Case(s) for real-time analytics platformGlobal Sales Reporting Global Complaint HandlingUnstructured Data Reporting and AnalysisMedtronic BenefitsProject went live on-time and the target data in HANA reports that took “tens of minutes” now take seconds. Medtronic is also able to provide structured and ad-hoc reporting on unstructured data lengths of up to 15,000 characters which was described as a “game changer”. The HANA platform was also leveraged create sales dashboards and enable mobile analytics. The data modeling was planned to take 3 months and only took one month due to the simplicity of the HANA tools and processes.Level 3: At this level you are building in-memory applications with SAP HANA as your single persistent databaseProduct: Real-time Big data (R+Hadoop+HANA)Business ChallengesLonger wait time (days) for patient results for hospitals that conduct cancer detection from base on DNA sequence matching Delay in new drug discovery and higher associated costs due to lack of insights in patient dataTechnical ChallengeBig data  Lack of speed, accuracy and visibility into data analysis results in huge costs and longer turnaround time for drug discovery and the identification of disease factorsBenefitsFor hospitals: Real-time DNA sequence data analysis makes it faster and easier to identify the root cause. Patient care based on genome analysis results can actually happen in one doctor visit Vs. waiting for several days or multiple follow-up visitsFor Pharmaceutical companies: provide required drugs in time and help identify “driver mutation” for new drug targetCompetition408,000 faster than traditional disk-based systemMKIand SAP HANA could alter the course of cancer research in human history It currently takes 2-3 days for a person to find differences in genome data between cancer patients and healthy people. MKI anticipates the time reduction with HANA to be 20 minutes  216x fasterHANA is about 408,000 times faster than traditional disk-based system (60 million recs) while performing independent data analysisHANA is about 5-10 times faster than another competitor. (190milion recs)R+ Hadoop + SAP HANA  HANA provides us powerful real-time computation capability, and R offers us easy ways to model and analyze the data. Hadoop is the platform with distributed pre-data processing and storage capabilities. Combining all three, we can store, pre-process, compute, and analyze huge amount of data
  • Key iskeepingdata in memorytorevolutionaryreducethe time ittakestoreadandprocessdata. Anotherfactoristheabandonmentofrow-baseddatastorageandusingcolumnbasedstorageinstead.
  • 3 BILLION SCANS PER SECOND/PER COREScanning 3MB/msec/coreInserting 1.5M Records/secAggregating 12.5M Records/sec/core
  • 3 BILLION SCANS PER SECOND/PER COREScanning 3MB/msec/coreInserting 1.5M Records/secAggregating 12.5M Records/sec/core
  • 3 BILLION SCANS PER SECOND/PER COREScanning 3MB/msec/coreInserting 1.5M Records/secAggregating 12.5M Records/sec/core
  • Key iskeepingdata in memorytorevolutionaryreducethe time ittakestoreadandprocessdata. Anotherfactoristheabandonmentofrow-baseddatastorageandusingcolumnbasedstorageinstead.
  • 3 BILLION SCANS PER SECOND/PER COREScanning 3MB/msec/coreInserting 1.5M Records/secAggregating 12.5M Records/sec/core
  • 3 BILLION SCANS PER SECOND/PER COREScanning 3MB/msec/coreInserting 1.5M Records/secAggregating 12.5M Records/sec/core
  • 3 BILLION SCANS PER SECOND/PER COREScanning 3MB/msec/coreInserting 1.5M Records/secAggregating 12.5M Records/sec/core
  • Key iskeepingdata in memorytorevolutionaryreducethe time ittakestoreadandprocessdata. Anotherfactoristheabandonmentofrow-baseddatastorageandusingcolumnbasedstorageinstead.
  • 3 BILLION SCANS PER SECOND/PER COREScanning 3MB/msec/coreInserting 1.5M Records/secAggregating 12.5M Records/sec/core
  • Transcript

    • 1. Why it could be beneficial for pharma R&D to engage into a discussion about SAP HANA Marc Maurer / September 9th 2013, v4
    • 2. Intention of this slide deck  In the past 40 years, SAP has been known as the world’s leader for ERP applications.  Over the last few years, SAP did undergo a major transformation to dramatically broaden its portfolio and to come up with a breakthrough technology named SAP HANA.  This technology represents an in-memory based real-time data/analytics platform that is especially suited to adress the data management challenges of big pharma R&D.  The Hasso Plattner Institute (HPI), SAP, and a number of academic and big pharma firms are currently collaborating to plan and implement a number of different HANA use cases.  We believe that it would be beneficial for pharma R&D to start a discussion with SAP/HPI to learn about use cases and to explore how to adress existing problems or future challenges.  This slide deck adresses on a high-level the technology, some proof points, pharma R&D use cases, and a number of ways how to continue the conversation. © 2013 SAP AG. All rights reserved. Confidential 2
    • 3. 1. Technology
    • 4. SAP HANA Platform – More than just a database Next generation platform Any Apps SAP Business Suite Any App Server Supports any Device and BW ABAP App Server SQL MDX R JSON Open Connectivity SAP HANA Platform SQL, SQLScript, JavaScript Search Data Virtualization Text Mining Stored Procedure & Data Models Application & UI Services Business Function Library Predictive Analysis Library Database Services Planning Engine Rules Engine Replication, Streaming and ETL Integration Services Transaction Unstructured Machine HADOOP Real-time Locations Other Apps SAP HANA Platform Converges Database, Data Processing and Application Platform Capabilities & Provides Libraries for Predictive, Planning, Text, Spatial, and Business Analytics to enable business to operate in real-time. © 2013 SAP AG. All rights reserved. Confidential 4
    • 5. SAP HANA Platform – More than just a database SAP HANA Innovations Innovation Massively parallel execution High throughput sequencing and analysis 12 TB DRAM servers in 2014 Large Data Sets in-memory Genomics, proteomics and patient data Compression (5-20x) Large data sets in-memory Genomics, proteomics and patient data Combined Column and Row Store Column = Fast Queries Adhoc queries using clinical data Partitioning: In-Database computing Analyze large data sets Complex computations Genome alignment Proteomics and Imaging data No aggregate tables T Application Multi-core architecture + Benefit Flexible modeling No data duplication Data Model for combined clinical and omics data Text Analytics Use of unstructured data Physician’s letters Scientific Literature © 2013 SAP AG. All rights reserved. Confidential 5
    • 6. SAP HANA adoption model A platform that scales Globalized Pervasive Analytics SAP HANA as a platform for all your analytics Localized SAP HANA as a data mart  Public available proteomics database with 50 TB storage and 160 processing units  11,000 datasets from human cancer cell lines, tissues and body fluids  Covers 92% of the Human proteome © 2013 SAP AG. All rights reserved.  Multiple scenarios  Consolidation database that scales with multiple nodes  Unstructured data analysis (e.g. Text analytics)  Predictive analytics  Analytics for mobile users SAP HANA as a platform for analytics and applications  Genomic DNA analysis in real-time to transform cancer patient care  Increased speed, accuracy and visibility for drug discovery  Real-time Big data (R+Hadoop+HANA)  408,000 faster than traditional disk-based system Confidential 6
    • 7. SAP HANA Platform – More than just a database SAP HANA Innovations 2. Proof points Innovation Massively parallel execution High throughput sequencing and analysis 12 TB DRAM servers in 2014 Large Data Sets in-memory Genomics, proteomics and patient data Compression (5-20x) Large data sets in-memory Genomics, proteomics and patient data Combined Column and Row Store Column = Fast Queries Adhoc queries using clinical data Partitioning: In-Database computing Analyze large data sets Complex computations Genome alignment Proteomics and Imaging data No aggregate tables T Application Multi-core architecture + Benefit Flexible modeling No data duplication Data Model for combined clinical and omics data Text Analytics Use of unstructured data Physician’s letters Scientific Literature © 2013 SAP AG. All rights reserved. Confidential 7
    • 8. SAP HANA and R&D: Proof points The White House honors SAP in Nov 2013 © 2013 SAP AG. All rights reserved. Confidential 8
    • 9. SAP HANA and R&D: Proof points The White House honors SAP in Nov 2013 © 2013 SAP AG. All rights reserved. Confidential 9
    • 10. SAP HANA and R&D: Proof points Strategic partnership between SAS and SAP Overview: Deliver a joint technology, product and GTM roadmap that will leverage SAP HANA in-memory platform and SAS advanced analytics. Bring 5 SAS industry applications on SAP HANA and validate with pilot customers while delivering on the strategic roadmap by 1H 2014. Proposal: A phased approached where SAP and SAS can immediately deliver immense customer value with the following:  Embedding SAS predictive model scoring and selected algorithms for direct use in SAP HANA to reduce the “data to compute” distance  Deliver 5 industry SAS solutions on SAP HANA and “powered by HANA”  Mid to long term, bring additional SAS algorithms to SAP HANA, optimize selected SAS solutions for SAP HANA and deliver on a larger GTM for an expanded set of customers © 2013 SAP AG. All rights reserved. Confidential 10
    • 11. SAP HANA and R&D: Proof points SAP is a leader in big data analytics Gartner Magic Quadrant for Data Warehouse Database Management Systems, Feb 2013 © 2013 SAP AG. All rights reserved. Forrester Wave: Big Data Predictive Analytics Solutions, Q1/2013 Confidential 11
    • 12. SAP HANA Platform – More than just a database SAP HANA Innovations 3. Use cases Innovation Massively parallel execution High throughput sequencing and analysis 12 TB DRAM servers in 2014 Large Data Sets in-memory Genomics, proteomics and patient data Compression (5-20x) Large data sets in-memory Genomics, proteomics and patient data Combined Column and Row Store Column = Fast Queries Adhoc queries using clinical data Partitioning: In-Database computing Analyze large data sets Complex computations Genome alignment Proteomics and Imaging data No aggregate tables T Application Multi-core architecture + Benefit Flexible modeling No data duplication Data Model for combined clinical and omics data Text Analytics Use of unstructured data Physician’s letters Scientific Literature © 2013 SAP AG. All rights reserved. Confidential 12
    • 13. R&D innovations in life sciences Challenges in pharma R&D and how HANA adresses them Challenges of data analysis and data management in big pharma Characteristics of HANA Thight integration of scientific data and analysis algorithms as relevant scientific data is usually distributed over many locations and stored in many different formats  User can implement domain-specific application logic (from high level SQLscript, full support of all "R" libraries to native function libraries)  All application logic is executed directly on data; no need of data transfer between different systems As the different activities for development (e.g. assays, disease models, etc.) need to be transparent, versioning of algorithms and data is important  Every calculation model (algorithm) in HANA is registered in a repository; easy to re-create previous analysis steps  Every data record is associated with a transaction identifier; records can be mapped to revisions of calculation models to allow versioning Support non-relational data structures and operations  HANA supports data structures such as graphs to avoid emulating them on top of relational data (which often results in poor performance) Support of big data initiatives  HANA is integrated with map reduce implementations such as Hadoop to allow parallel exploitation of big data sources Intuitive interface to design analysis pipelines, a system that is accessible to a wide range of users with a broad range of skill sets (scientists, analysts, developers)  Analysis pipelines are defined via a graphical user interface in HANA Studio  Researchers can compare results generated by different pipelines © 2013 SAP AG. All rights reserved. Confidential 13
    • 14. R&D innovations in life sciences Where HANA could be used in pharma R&D Target discovery Bioinformatics Lead discovery Preclinical dev. Genomics HT screening Translation. medicine Target identification Define disease Identify targets Collect & analyze data Select targets Target validation Design validation exper. Validate drug targets Collect & analyze data Select validate targets Sequencing Alignment Variant calling Annotation & analysis Proteomics Protein sequencing Analysis1 Assay development Design/test/adapt assay Transfer assay In silico data acquisition In silico design exper. Primary screening Secondary screening Tertiary screening Collect & analyze data Lead development Filter cluster compoun. compounds compounds Synthesize Test compounds Synthesize compounds T1 T2 T3 T4 Preclin. & P1 studies P2/P3 trials P4 & Outcomes Res. Population analysis Tox check/safety Pharmacodynamics Pharmacokinetics Animal testing Optimize leads Filter cluster leads Synthesize lead Test compunds Synthesize leads LT toxicity (2 species) In vitro pharmacology areas with potential use of HANA 1 For more information see www.proteomicsdb.org or https://www.youtube.com/v/ao4oStycKnw © 2013 SAP AG. All rights reserved. Confidential 14
    • 15. R&D innovations in life sciences Proven benefits of HANA for genomics 408,000x faster than traditional disk-based systems in technical Proof of Concept 216x faster DNA analysis result – from 2-3 days to 20 minutes Supported By: Carlos Bustamante lab 1,000x faster tumor data analyzed in seconds instead of hours 2-10 sec for report execution © 2013 SAP AG. All rights reserved. Confidential 15
    • 16. R&D innovations in life sciences Selected use cases for pharma R&D Use cases for pharma research Use cases for pharma development  Secondary and tertiary analysis of genome data: Reduce time to analyse genome processing pipelines to minutes and hours. Automatic search in structured and unstructured data sources including entity extraction. For proteomics there is also a public available proteomics database powered by HANA (see www.proteomicsdb.org)  Clinical trial data cleansing: Automatic reformatting of clinical trial data from one format to another, automatic systematic quality monitoring to save outsourcing costs and clinical trial throughput speed.  Speeding up pathway analysis: Executing complex queries like «find a new molecule able to dock to kinase XYZ to inhibit enzymatic activity» much faster.  Clinical trial design: Analysis of patient cohorts in realtime; to make trial protocol adaptations ad hoc and saving time during trial design phase.  3D structures: Representing genomic/proteine structures in 3D e.g. to visually explore genetic pathways or comparing gene sections with a genome reference database (to identfy variants/mutations).  Patient recruiting optimization: Iincreasing forecast accuracy for recruiting patients into trials and addressing questions like how to select the right investigator, etc.  Virtual patient simulation: Combining molecular patient data with models of tumor cells to simulate the effects of different drugs.  Clinical trial optimization: Data platform to increase performance for trial simulations and integrating internal and external data sources.  Interorganizational data analysis: Several HANA instances in different research/healthcare organizations allow cross-analysis without moving confidential data between the organizations.  Fallen angels: Re-analysis of failed clinical trials where HANA could identify variants that responders and nonresponders have in common to propose companion diagnostic in order to recover investments into failed trials. Other use cases: Trial fraud management, risk-based trial monitoring, iRise clinical trial app, patient engagement apps (www.carecircles.com) © 2013 SAP AG. All rights reserved. Confidential 16
    • 17. SAP HANA Platform – More than just a database SAP HANA Innovations 4. Next steps Innovation Massively parallel execution High throughput sequencing and analysis 12 TB DRAM servers in 2014 Large Data Sets in-memory Genomics, proteomics and patient data Compression (5-20x) Large data sets in-memory Genomics, proteomics and patient data Combined Column and Row Store Column = Fast Queries Adhoc queries using clinical data Partitioning: In-Database computing Analyze large data sets Complex computations Genome alignment Proteomics and Imaging data No aggregate tables T Application Multi-core architecture + Benefit Flexible modeling No data duplication Data Model for combined clinical and omics data Text Analytics Use of unstructured data Physician’s letters Scientific Literature © 2013 SAP AG. All rights reserved. Confidential 17
    • 18. R&D innovations in life sciences How to start the conversation  Webconference with specialists from HPI/SAP to discuss other use cases available, answer questions, and find possibilities for on-site interactions  On-site workshop with one of the following three scenarios:  Focused approach based on concrete customer ideas and requirements  Use case approach leveraging experience of other intiatives with other partners  1-day design thinking workshop to discover new and radically different ways for solving a data-related research problem of customer  M310 course: 6 students from Stanford university work two days a week for 9 months on a specific customer problem including documentation and prototype © 2013 SAP AG. All rights reserved. Confidential 18
    • 19. Contact information: Dr. Marc Maurer Senior Global Account Executive Email: marc.maurer@sap.com Tel. +41 79 9642 42 90