Big Data In Healthcare

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Our vision on Big Data in the Healthcare sector

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Big Data In Healthcare

  1. 1. Big Data in theHealthcare SectorVision on HealthcareJune, 2012
  2. 2. Contents Page Background Information ■ Context 3 ■ Opportunities 4 ■ Scope 5 ■ Assessment 7 Approach ■ Overview 9 ■ Plan of Approach (based on Value Delivery Framework) 10 ■ Big Data Agile development 11 Appendices A. Curricula vitae 13 B. Technologies 17 C. Detailed description of methods 21 D. Case studies 28© 2012 KPMG Advisory N.V., registered with the trade register in the Netherlands under number 33263682, is a subsidiary of KPMG Europe LLP and a member firm of the KPMG 1network of independent member firms affiliated with KPMG International Cooperative (‗KPMG International‘), a Swiss entity. All rights reserved. Printed in the Netherlands. TheKPMG name, logo and ‗cutting through complexity‘ are registered trademarks or trademarks of KPMG International.
  3. 3. BackgroundInformationBig Data in the Healthcare Sector
  4. 4. Big Data in the Healthcare SectorContextHistorical productivity growth in the United States, 2000 – 2008 Opportunities for Big Data in Healthcare% Big Data has a large potential to contribute in many area‘s of24.0 the Healthcare industry (see figure on the left). At the moment23.5 there are some good initiatives, but this is not enough to keep23.022.5 up with the demand of healthcare service and the rising costs. Computer and electronic products Information Relevant improvements can be; 9.0 Administration, support and Wholesale trade ■ Electronic health records (EMR / EHR, or EPD in dutch) 3.5 waste management 3.0 Manufacturing which serve the customer/patient (availability of data). Transportation and warehousing 2.5 Real estate Finance and ■ Structuring data and information for service optimisation. 2.0 Professional services Insurance 1.5 and rental ■ Accurate information about patients can reduce mistakes. Utilities 1.0 ■ Cost optimisation through efficiency of new e-health 0.5 Retail trade Health care services. 0 Accommodation and food-0.5 Natural resources Government ■ Increased customer satisfaction (better e-health services). Arts and entertainment-1.0-1.5 Management of companies ■ Analysis of big datasets for R&D purposes.-2.0 Educational services The real question Other services-2.5-3.0 Construction Using Big Data is both a technological and strategic issue.-3.5 Besides cost-effectiveness, the health care sector needs to Low High achieve improvements in combining data from many sources, Big Data value potential index getting insight from their data and change the way they interact with customers, competitors and the market through data- driven decision making.Key:  Bubble sizes denote relative sizes of GDP.Source: US Bureau of Labor Statistics; McKinsey Global Institute Analysis. However, the health care industry will not achieve the significant value available from big data without radical changes in regulations and systemwide incentives. Achieving those changes will be difficult – but the potential prize is so great that health care executives and policy makers should not ignore these opportunities. Significant contributions can be expected from Data Mining and Analytics.© 2012 KPMG Advisory N.V., registered with the trade register in the Netherlands under number 33263682, is a subsidiary of KPMG Europe LLP and a member firm of the KPMG 3network of independent member firms affiliated with KPMG International Cooperative (‗KPMG International‘), a Swiss entity. All rights reserved. Printed in the Netherlands. TheKPMG name, logo and ‗cutting through complexity‘ are registered trademarks or trademarks of KPMG International.
  5. 5. Big Data in the Healthcare SectorOpportunities Market developments Improve competitive position by managing Big Data ■ Optimised customer service / treatment (e-healthcare) ■ 2009: 434 Petabytes stored in the United States (multi-media increasing). – By using all the available data from different policlinics and/or ■ North America and Europa now hold about 70% hospitals, the customer can benefit from a better and faster planning Data for a certain treatment. of healthcare data. Emerging markets will follow soon. ■ Save money through accurate data (efficiency) ■ 2015: global health data, 20 Exabytes. – Inaccurate data can cause delays, mistakes, miscalculations, etc. Big Data analytics can provide solutions to improve the quality of data. ■ Big Data can enable more than $200 billion a ■ Better treatments based on analysis of data (research) year in US by reducing national health care expenditure (around 8%). focusing on several – Big data can be used to facilitate research into specific topics when types of enablers. large datasets are available (treatment records, medical photo‘s/scans Market etc.) ■ 2010: Health contribution, $3.9 trillion which was 7% of estimated global GDP. ■ Improve position by proving quality of services ■ Improving the quality of treatments can reduce – The market position can be shown by offering insight into eg. success costs significantly. rates of treatments, speed of treatments, ground breaking research etc. ■ Analysis of extreme large datasets from many ■ Insurance optimisation through treatment analysis database types (medical institutions, insurance companies etc.). – The large amount of client information of insurances can be used to ■ Security / privacy issues. analyse treatments of medical institutions, determine which one is Challenges most (cost) effective, and use this data to save money. ■ Strategic and politic decisions slow down the optimization process (too many stakeholders) ■ Many different types of databases© 2012 KPMG Advisory N.V., registered with the trade register in the Netherlands under number 33263682, is a subsidiary of KPMG Europe LLP and a member firm of the KPMG 4network of independent member firms affiliated with KPMG International Cooperative (‗KPMG International‘), a Swiss entity. All rights reserved. Printed in the Netherlands. TheKPMG name, logo and ‗cutting through complexity‘ are registered trademarks or trademarks of KPMG International.
  6. 6. Big Data in the Healthcare SectorScope (1 of 2) The Information Pyramid Maslow pyramid: The benefits of Big Data Analytics More and more, IT is starting to play an important role in supporting the improvement and efficiency of healthcare. The amount of data is growing exponentially like storing electronic health records of patients (eg. MRI scans, charts, Stage 4: or telehealth video‘s), or all the organisational data of Wisdom healthcare institutions and other medical data (eg. manuals, guidelines, clinical research). Decisions Big Data Analytics is the missing link in organising all this data and creating valuable knowledge for all the involved parties. Cost reduction can for instance be achieved by Stage 3: Knowledge generation improving the quality of healthcare by using the analysis of Predictions Visualization Reporting medical records to get insight into ineffective treatments or by elimination (human) errors. Another important focus is to use all the existing data from Stage 2: Information retrieval specific treatments to find new leads for ground breaking Statistics it Analysis Query research. This type of analysis is now available because more and more data is stored digitally and accessible for different parties. Data of different types of sources like Stage 1: Data collection & Storage national or global demographics, Open Data and other ETL Data Fusion Data Integration relevant data can be combined with medical data to find clues for correlation and insights into a specific area. Organisa- EMR Treatment Research E-health Demo- Health Quality The figure on the right shows the Maslow information EHR information tional information services graphics insurance of service information pyramid. Effective Big Data analysis is the key component in providing Unique Customer Benefits (UCBs) Customer Medical data Peer to peer ‗Open Data‘ Competition information (institutions) networks Private data Public data© 2012 KPMG Advisory N.V., registered with the trade register in the Netherlands under number 33263682, is a subsidiary of KPMG Europe LLP and a member firm of the KPMG 5network of independent member firms affiliated with KPMG International Cooperative (‗KPMG International‘), a Swiss entity. All rights reserved. Printed in the Netherlands. TheKPMG name, logo and ‗cutting through complexity‘ are registered trademarks or trademarks of KPMG International.
  7. 7. Big Data in the Healthcare SectorScope (2 of 2) How to create What wisdom is required to What is the impact on existing Prioritize, Assess and Focus Area competitive advantage? make the right decisions? architecture and processes Select 1 Medical treatment optimisation Multi Channel approach Cross correlations Data model not supported 2 E-Health customer service 3 Cost management 4 Complexity reduction 5 6 Investments Innovation Iterative approach 7 Business Models Stage 4: Wisdom Decisions How to realize? Prepare Explore Demonstrate Integrate Stage 3: Knowledge generation Timeline to be discussed Objectives Business Case development Sprint 1 Transition to support Predictions Visualization Reporting ■ What is expected f rom Big Data? ■ Context behind expectations ■ Existing Business Intelligence ■ Extract, Transf orm, Load design ■ Training ■ Introduction seminars Big Data ■ Design Visualization/Dashboards ■ Capability roadmap (a) ■ Big Data trends in Healthcare ■ Workshops with business & IT(a) ■ Ref ine Business Cases ■ Process design (continuous improvement) Approach ■ Develop Big Data scenarios (a) Sprint 2 ■ Integration architecture ■ How to approach Big Data projects Technical Analysis ■ Implement ETL and test impact Stage 2: Information retrieval ■ Example cases (in Healthcare)(a) ■ Existing landscape and initiatives ■ Algorithm development Transformation delivery ■ Available methods and Services ■ Project Portf olio (a) ■ Identif y data stores ■ Target Architecture development Statistics it Analysis Query Insight generation ■ IT inf rastructure analysis Sprint 3 ■ To be discussed ■ Focus areas ■ Applied and planned technologies (a) Mentoring and Advisory ■ Implement Big Data solution ■ Relate f ocus areas to Big Data ■ To be discussed Follow-up ■ Deploy algorithms ■ Big Data Quartets (Card Game) ■ Assess and Prioritize (value map)(a) ■ Implement Dashboards ■ Select and plan Proof of Concepts Stage 1: Data collection & Storage Follow-up ■ Who are the main stakeholders? ■ Establish PMO Follow-up ■ Target Architecture development ■ Expected timeline/planning ■ Planning f or transition to support ETL Data Fusion Data Integration ■ Workshop leading to project plan Project Management Office Note: (a) Detailed descriptions in appendices.© 2012 KPMG Advisory N.V., registered with the trade register in the Netherlands under number 33263682, is a subsidiary of KPMG Europe LLP and a member firm of the KPMG 6network of independent member firms affiliated with KPMG International Cooperative (‗KPMG International‘), a Swiss entity. All rights reserved. Printed in the Netherlands. TheKPMG name, logo and ‗cutting through complexity‘ are registered trademarks or trademarks of KPMG International.
  8. 8. Big Data in the Healthcare SectorAssessment Big Data Business case components Big Data architecture Classic ■ Small scale personal health records Structured Data External Data Internal Data ■ Insurance data High Volume ■ Treatment data Unstructured Data ■ Research data ■ Procedures High Velocity Extract Transform Load Big Data ■ Medical research data Sensor Data ■ Telehealth High Variability Big Data Analytics ■ National Electronic Health Records New Data Types ■ Scans/images, video‘s© 2012 KPMG Advisory N.V., registered with the trade register in the Netherlands under number 33263682, is a subsidiary of KPMG Europe LLP and a member firm of the KPMG 7network of independent member firms affiliated with KPMG International Cooperative (‗KPMG International‘), a Swiss entity. All rights reserved. Printed in the Netherlands. TheKPMG name, logo and ‗cutting through complexity‘ are registered trademarks or trademarks of KPMG International.
  9. 9. ApproachBig Data Services and Methods
  10. 10. Big Data Services and MethodsOverview Methods Deliverables Explore ■ Business Cases ■ Seminars and Workshops ■ Data Store identification ■ Technical analysis ■ Brainstorm sessions ■ Stakeholder management ■ Best practices ■ Assess, Prioritize, Select Demonstrate ■ Proof of Concepts ■ Project Management Office ■ Target Architecture ■ Scrum implementation teams ■ Extract, Transform, Load ■ Algorithm development ■ Dashboards/Visualization ■ Iterative delivery (agile) Integrate ■ Capability roadmap ■ Transition to Support ■ Project Portfolio ■ Technical training ■ Integration architecture ■ Process design ■ Transformation delivery ■ Mentoring and advisory© 2012 KPMG Advisory N.V., registered with the trade register in the Netherlands under number 33263682, is a subsidiary of KPMG Europe LLP and a member firm of the KPMG 9network of independent member firms affiliated with KPMG International Cooperative (‗KPMG International‘), a Swiss entity. All rights reserved. Printed in the Netherlands. TheKPMG name, logo and ‗cutting through complexity‘ are registered trademarks or trademarks of KPMG International.
  11. 11. Big Data Services and MethodsPlan of Approach (based on Value Delivery Framework) Prepare Explore Demonstrate Integrate Timeline to be discussed Objectives Business Case development Sprint 1 Transition to support ■ What is expected from Big Data? ■ Existing Business Intelligence ■ Extract, Transform, Load design ■ Training ■ Context behind expectations ■ Introduction seminars Big Data ■ Design Visualization/Dashboards ■ Capability roadmap(a) ■ Big Data trends in Healthcare ■ Workshops with business & IT(a) ■ Refine Business Cases ■ Process design (continuous improvement) Approach ■ Develop Big Data scenarios(a) Sprint 2 ■ Integration architecture ■ How to approach Big Data projects Technical Analysis ■ Implement ETL and test impact ■ Example cases (in Healthcare)(a) Transformation delivery ■ Existing landscape and initiatives ■ Algorithm development ■ Available methods and Services ■ Project Portfolio(a) ■ Identify data stores ■ Target Architecture development ■ To be discussed Insight generation ■ IT infrastructure analysis Sprint 3 ■ Focus areas ■ Applied and planned technologies(a) ■ Implement Big Data solution Mentoring and Advisory ■ Relate focus areas to Big Data ■ To be discussed Follow-up ■ Deploy algorithms ■ Big Data Quartets (Card Game) ■ Assess and Prioritize (value map)(a) ■ Implement Dashboards Follow-up ■ Select and plan Proof of Concepts Follow-up ■ Who are the main stakeholders? ■ Establish PMO ■ Target Architecture development ■ Expected timeline/planning ■ Planning for transition to support ■ Workshop leading to project plan Project Management OfficeNote: (a) Detailed descriptions in appendices.© 2012 KPMG Advisory N.V., registered with the trade register in the Netherlands under number 33263682, is a subsidiary of KPMG Europe LLP and a member firm of the KPMG 10network of independent member firms affiliated with KPMG International Cooperative (‗KPMG International‘), a Swiss entity. All rights reserved. Printed in the Netherlands. TheKPMG name, logo and ‗cutting through complexity‘ are registered trademarks or trademarks of KPMG International.
  12. 12. Big Data Services and MethodsBig Data Agile development Challenges Big Data poses new challenges for organizations: ■ Fast changing data sets means solutions need to be flexible Service design ■ Speed in information delivery is critical projects ■ Business requirements are unpredictable Concept Requirements ■ Volumes of data are growing development workshops Agile brings value to Big Data projects Agile methods are well suited to support this environment of high complexity and high uncertainty by supporting: Implementing Throwaway ■ Interaction between business users, developers and data experts ensures Agile Agile Design understanding between participants prototypes & Development ■ Frequent delivery of a working solution help with realistic planning and managing stakeholder expectations ■ Collaboration between stakeholders creates a common goal and clear Agile User development requirements for all involved experience prototypes ■ Responding to change supports business agility and greatly improves end user satisfaction Agile Evolutionary assessments prototypes© 2012 KPMG Advisory N.V., registered with the trade register in the Netherlands under number 33263682, is a subsidiary of KPMG Europe LLP and a member firm of the KPMG 11network of independent member firms affiliated with KPMG International Cooperative (‗KPMG International‘), a Swiss entity. All rights reserved. Printed in the Netherlands. TheKPMG name, logo and ‗cutting through complexity‘ are registered trademarks or trademarks of KPMG International.
  13. 13. Appendix ACurricula Vitae
  14. 14. Big Data TeamMini Bio‟s (1 of 4) Sander Klous Sipho van der Putten Fabian Jansen KPMG Management Consulting KPMG Management Consulting KPMG Management Consulting Laan van Langerhuize 1 Laan van Langerhuize 1 Laan van Langerhuize 1 1186 DS, Amstelveen 1186 DS, Amstelveen 1186 DS, Amstelveen Tel +31 20 656 7186 Tel +31 20 656 7831 Tel +31 20 656 7257 klous.sander@kpmg.nl vanderputten.sipho@kpmg.nl jansen.fabian@kpmg.nl Function and Sander has over 15 years of experience in large scale Sipho obtained his PhD in Particle- and Astroparticle Fabian has a background in applied physics and obtained a Specialization distributed computing, real-time systems and data Physics at the VU university in Amsterdam. For his PhD he PhD in High Energy Physics with one of the major CERN processing technologies. He is responsible for the developed a new data analysis framework which takes full experiments. As such Fabian has a large skill set when it distributed computing services at KPMG, which includes Big advantage of the extreme scalability and flexibility of state- comes to handling and analyzing large quantities of data. Data, Smart Grid and cloud computing. of-the-art computing facilities. He has presented his work at As a physicist by nature Fabian is an expert in modelling Sander holds a PhD in High Energy Physics (HEP) and various international conferences, has published extensively and analysis of complex data, in extracting the desired worked at CERN, generally accepted as the cradle of Big on the matter and has become an expert in the field of high information and in employing state-of-the-art technology to Data processing in the world. He received a number of performance computing. let the data work for you. grants and awards related to high performance distributed Furthermore Sipho is an expert in model building and computing and is a recognized international expert with developing algorithms and analyses. Techniques employed numerous publications in this area. during his PhD find direct application in the business world in the form of Big Data Analytics. Professional Sander‘s clients are mostly middle size and large Sipho has worked for clients to help them develop models Fabian has performed and published numerous data and Industry (international) organizations with complex computing and analysis algorithms to asses their current IT analyses, both by himself and in collaboration. For these he Experience infrastructures, often Service Oriented Architectures infrastructure and was responsible for communicating the has designed, developed and implemented the necessary (implemented or under development). He has been project results of the models and analyses to non-experts. computational tools. lead for a number of multi-disciplinary IT assessments and In service of KPMG Fabian has performed several quality IT readiness reviews. Currently he is the lead architect of a reviews of IT systems and processes, assessing the quality Shell team responsible for a large scale program to migrate of source code, infrastructure and architecture. a set of application landscapes to a new secure environment. Education, ■ PhD High Energy Physics, VU, Amsterdam ■ MSc. Experimental Physics, University of Amsterdam ■ PhD High Energy Physics, VU, Amsterdam Licenses & ■ MSc Experimental Physics, VU, Amsterdam ■ Minor in Computer Science ■ MSc Applied Physics, Twente University Certifications ■ MSc Control Systems, U. of Hertfordshire ■ Intern High Energy Physics, Cambridge University ■ BSc Mechanical Engineering, HTS Alkmaar© 2012 KPMG Advisory N.V., registered with the trade register in the Netherlands under number 33263682, is a subsidiary of KPMG Europe LLP and a member firm of the KPMG 13network of independent member firms affiliated with KPMG International Cooperative (‗KPMG International‘), a Swiss entity. All rights reserved. Printed in the Netherlands. TheKPMG name, logo and ‗cutting through complexity‘ are registered trademarks or trademarks of KPMG International.
  15. 15. Big Data TeamMini Bio‟s (2 of 4) Maarten Hoekstra Jan Amoraal Tünde Bálint KPMG Management Consulting KPMG Management Consulting KPMG Management Consulting Laan van Langerhuize 1 Laan van Langerhuize 1 Laan van Langerhuize 1 1186 DS, Amstelveen 1186 DS, Amstelveen 1186 DS, Amstelveen Tel +31 20 6564063 Tel +31 (0)65 207 8945 Tel +31 (0)65 198 8517 Hoekstra.maarten@kpmg.nl Amoraal.jan@kpmg.nl balint.tunde@kpmg.nl Function and Maarten graduated in Software Engineering at the Jan‘s background is in experimental High Energy Physics Tünde is specialized in design and development of Specialization University of Amsterdam. He is specialized in both static and he has over 8 years of experience in developing distributed systems. She has extensive experience in analysis and cloud computing, which enables him to design software frameworks and algorithms for data acquisition, system planning, architecture, design and development life- high quality algorithms that are ideally suited for distributed data processing, and physics analyses. cycle. Her field of expertise includes cloud computing and environments. Jan loves to experiment and play with new technologies. security in distributed systems and the challenges pertaining Maarten takes a theoretical approach to software The physicist inside him believes that there is a solution to to distributed data management and design/implementation engineering and likes to know the details of underlying every problem no matter how big or small and he loves to of parallel algorithms. techniques before implementing them. This is a key point work out and implement complex and abstract ideas. when working with Big Data where simply redoing a calculation is not always an option. Professional Maarten has five years of experience as a core developer in Jan has performed and published numerous data analyses, Tünde designed and developed a grid middleware and Industry a small but innovative company, where he put a lot of theory both by himself and in collaboration. For these he has independent job submission system, which provides an Experience into practice. He migrated a large application stack to the designed, developed and implemented the necessary abstract layer that solves the problems encountered in a Amazon cloud and designed and build several applications computational tools. Amongst these tools is a framework for jobs life cycle in several existing grid middleware systems. using relevant NoSQL and batch processing techniques. the CERN LHCb experiment, that uses the immense amount The system furthermore addresses issues regarding of experimental data for calibration purposes. authentication, authorization, monitoring and data At KPMG Jan is involved in quality and security reviews of management. IT systems and processes, assessing the quality of source At KPMG she is involved in migration and IT assessment code, infrastructure and architecture. Jan is furthermore projects. She is currently working as an architect involved in the KPMG Smart Grid activities and is responsible for the migration of a set of application researching ICT solutions for Smart Girds. landscapes to a new secure environment. Tünde is active member of the KPMG Cloud Center of Excellence. Education, ■ MSc. Software Engineering, University of Amsterdam ■ PhD High Energy Physics, VU, Amsterdam ■ MSc Grid Computing, Computer Science track, Licenses & ■ BSc. Technische Informatica, HvA Amsterdam ■ MSc in particle physics, University of Amsterdam University of Amsterdam Certifications (Cum Laude) ■ BSc Computer Engineering, TU Cluj-Napoca, Romania© 2012 KPMG Advisory N.V., registered with the trade register in the Netherlands under number 33263682, is a subsidiary of KPMG Europe LLP and a member firm of the KPMG 14network of independent member firms affiliated with KPMG International Cooperative (‗KPMG International‘), a Swiss entity. All rights reserved. Printed in the Netherlands. TheKPMG name, logo and ‗cutting through complexity‘ are registered trademarks or trademarks of KPMG International.
  16. 16. Big Data TeamMini Bio‟s (3 of 4) Erik van den Brom Gerald Hemmers Daniel Wolfs KPMG Management Consulting KPMG Management Consulting KPMG Management Consulting Prinses Catharina Amaliastraat 5 Prinses Catharina Amaliastraat 5 Laan van Langerhuize 1 2496 XD, Den Haag 2496 XD, Den Haag 1186 DS, Amstelveen Tel +31 70 338 2305 Tel +31 (0)70 382426 Tel +31 (0)65 198 8517 Vandenbrom.erik@kpmg.nl hemmers.gerald@kpmg.nl wolfs.daniel@kpmg.nl Function and Erik‘s background as economist and information manager, Gerald has over the 15 years of experience in (web) Daniël is an advisor in the area of strategic change and its Specialization combined with his practical experience in software development, design/ usability and prototyping. He is impact on individuals, with a knack for providing unexpected development allow him to translate business strategy into IT responsible for the proposition prototyping (part of Agile perspectives that makes you notice your challenges in a strategy and make him the linking pin between business and design and development) within KPMG, which includes whole different way. IT. graphic design for mobile and desktop application, data analyses and usability. Gerald has a great understanding of CMS and Portal systems and the current web standards. With his visual background in Arts and his deep knowledge of usability, Gerald not only create stunning images but also has the ability design for the need of the user. Professional Erik has experience as a project manager and workshop Gerald has design a large number of 3d and 3d animation, In the past four years Daniel has supported several clients and Industry facilitator for numerous software development projects . graphic user interfaces and dashboards for companies in a in formulating and establishing an appropriate (IT) strategy. Experience Recently Erik was responsible for the realization of the large number of services lines. Gerald worked on several With a focus on the process to achieve this strategy, not complete IT landscape needed to support a new Law on projects as a project manager. His combined knowledge of purely the act of writing it. Childcare in the Netherlands (budget €15 million). graphic design and user interactions brings the best of both Daniel is increasingly focusing on the impact of (strategic) Erik has specialized in agile software development and worlds. change on individuals and groups, by performing research developed and teaches KPMG‘s agile awareness training. on his "change process that goes to Hollywood" (Dutch title: "een veranderproces zoals je dat normaal alleen in films ziet"). Education, ■ MSc. Information Management, TiasNimbas Business ■ Master of Arts, Multimedia Studies, ■ MSc, Management Consultancy, Licenses & School, Tilburg University of interaction design Hilversum Erasmus University, Rotterdam (Cum Laude) Certifications ■ MSc. Economics, Erasmus Universiteit Rotterdam ■ Prince 2 Practitioner ■ MSc. Master of Business Informatics, University of Utrecht ■ Certified Information Systems Auditor (CISA) ■ Scrum Master ■ Certified Scrum Professional (CSP) ■ BSc. Information Science, University of Utrecht© 2012 KPMG Advisory N.V., registered with the trade register in the Netherlands under number 33263682, is a subsidiary of KPMG Europe LLP and a member firm of the KPMG 15network of independent member firms affiliated with KPMG International Cooperative (‗KPMG International‘), a Swiss entity. All rights reserved. Printed in the Netherlands. TheKPMG name, logo and ‗cutting through complexity‘ are registered trademarks or trademarks of KPMG International.
  17. 17. Big Data TeamMini Bio‟s (4 of 4) Ruben Logjes KPMG Management Consulting Laan van Langerhuize 1 1186 DS, Amstelveen Tel +31 (0)6-1162 5721 logjes.ruben@kpmg.nl Function and Ruben is a passionate consultant with 4 years of extensive Specialization knowledge in the field of usability research and multimedia. Combining technical knowledge, the architectural overview and the understanding of company processes make him a valuable intermediate between users and ICT professionals. His social skills and professional attitude contribute to a pleasant way of communicating with all team members. Professional Ruben is experienced in making (and reviewing) usable and Industry software designs. He is familiar with obtaining and Experience maintaining user support while designing a makeable solution. On the other hand Ruben will strive for maximum business value and not take a simple ―No‖ for an answer. Education, ■ Msc. Industrial Design Engineering, Delft University of Licenses & Technology. Certifications ■ PoC / Prototyping skills© 2012 KPMG Advisory N.V., registered with the trade register in the Netherlands under number 33263682, is a subsidiary of KPMG Europe LLP and a member firm of the KPMG 16network of independent member firms affiliated with KPMG International Cooperative (‗KPMG International‘), a Swiss entity. All rights reserved. Printed in the Netherlands. TheKPMG name, logo and ‗cutting through complexity‘ are registered trademarks or trademarks of KPMG International.
  18. 18. Appendix BTechnologies
  19. 19. TechnologiesExisting Technologies Reporting Connecting to a Big Data processing facility A Big Data processing facility is connected to existing data stores via an ETL ERP process, similar to a typical Data Warehouse solution. The Big Data store is unstructured, offering horizontal scalability advantages compared to classic Data warehouse Data Warehouse solutions. Nevertheless, a thorough impact analysis should CRM be made to determine the effects of the offloading to a Big Data processing facility on existing systems. The following aspects should be taken into ETL Ad-hoc Process reporting consideration. Datamarts Data ■ Structure of existing Data Stores base ■ IT Infrastructure layout ■ Applied Technologies Files OLAP Analysis Operation systems platforms Computer Software hardware applications platforms IT infrastructure Networking and Internet platforms communications Data Consultants management and integrators and storage© 2012 KPMG Advisory N.V., registered with the trade register in the Netherlands under number 33263682, is a subsidiary of KPMG Europe LLP and a member firm of the KPMG 18network of independent member firms affiliated with KPMG International Cooperative (‗KPMG International‘), a Swiss entity. All rights reserved. Printed in the Netherlands. TheKPMG name, logo and ‗cutting through complexity‘ are registered trademarks or trademarks of KPMG International.
  20. 20. TechnologiesMapReduce k1 v1 k2 v2 k3 v3 k4 v4 k5 v5 k6 v6 The Five Steps of Every Big Data Analysis ■ Iterate over a large number of records ■ Extract something of interest from each (Map) ■ Shuffle and sort intermediate results ■ Aggregate intermediate results (Reduce) map map map map ■ Generate final output a 1 b 2 c 3 c 6 a 5 c 2 b 7 c 8 Shuffle and Sort: aggregate values by keys Four Paradigms for Big Data Analysis a 1 5 b 2 7 c 2 3 6 8 ■ Scale out, not up – Avoid large SMP and Shared Memory machines ■ Move processing to the data – Work within the cluster bandwidth reduce reduce reduce ■ Process data sequentially, no random access – Seeks are expensive, disk throughput is reasonable ■ Seamless scalability (e.g. public cloud) a 6 b 9 c 19 – Optimal benefits from the tradable machine hour Source: Credits to Jimmy Lin (University of Maryland).© 2012 KPMG Advisory N.V., registered with the trade register in the Netherlands under number 33263682, is a subsidiary of KPMG Europe LLP and a member firm of the KPMG 19network of independent member firms affiliated with KPMG International Cooperative (‗KPMG International‘), a Swiss entity. All rights reserved. Printed in the Netherlands. TheKPMG name, logo and ‗cutting through complexity‘ are registered trademarks or trademarks of KPMG International.
  21. 21. TechnologiesBigTable (NOSQL) BigTable: A Distributed Storage System for Structured Data BigTable is a distributed storage system for managing structured data that is designed to scale to Petabytes of data across thousands of commodity servers. Many projects at Google store data in BigTable (e.g. web indexing, Google Earth, and Google Finance). BigTable maps three values (row key, column key and timestamp) into an associated arbitrary byte array. The scalability is achieved by splitting the table along a row chosen such that the tablet will be ~200 megabytes in size. BigTable Supports lookups, inserts, deletes and allows for single row transactions only .Each Tablet is stored on a different node with a tunable replication factor. HBase is the open source implementation of BigTable from Hadoop. Other implementations are: ■ Apache Cassandra ■ MongoDB NOSQL ■ CouchDB ■ Etc.© 2012 KPMG Advisory N.V., registered with the trade register in the Netherlands under number 33263682, is a subsidiary of KPMG Europe LLP and a member firm of the KPMG 20network of independent member firms affiliated with KPMG International Cooperative (‗KPMG International‘), a Swiss entity. All rights reserved. Printed in the Netherlands. TheKPMG name, logo and ‗cutting through complexity‘ are registered trademarks or trademarks of KPMG International.
  22. 22. Appendix CDetailed description of methods
  23. 23. Detailed description of methodsWorkshop on Business Cases (U-Collaborate) Hands-on and results driven Our approach – work towards a joined result Your benefits Core of our approach is its joined effort with the 1. Straight to the best solution; stakeholders of the organization. We aim to 2. Thinking based on solution directions; establish manageable and workable Big Data solutions, based on creative working methods 3. Focus on hands-on sessions; that challenge participants to deliver executable 4. Safeguard ability to execute solutions. From the very first start of the workshop we will be working together to properly 5. Contributions of everybody involved prioritize the goals and ambitions of the organization with respect to Big Data deliverables. ■ Determine boundary ■ Brainstorm about conditions and Risks desired outcome. 4 Anchor Create 2 ■ Discuss Accelerators ■ Establish joined and and Decelerators of daring vision of the Big Data scenario future. ■ Manage adoption ■ Clear vision: agreement on the added value each Big Data scenario should generate. ■ Start meeting ■ Planning milestones ■ Supported ideas: experience each scenario by ■ Identify connection and activities a joined effort to determine and design it. between participants ■ Expand planning and desired results ■ Assign ■ Involvement: clarity on the contribution of ■ Determine existing responsibilities. everybody involved to the scenario execution situation based on individual and 1 Explore Plan 3 and their participation in its implementation. collective memory.© 2012 KPMG Advisory N.V., registered with the trade register in the Netherlands under number 33263682, is a subsidiary of KPMG Europe LLP and a member firm of the KPMG 22network of independent member firms affiliated with KPMG International Cooperative (‗KPMG International‘), a Swiss entity. All rights reserved. Printed in the Netherlands. TheKPMG name, logo and ‗cutting through complexity‘ are registered trademarks or trademarks of KPMG International.
  24. 24. Detailed description of methodsWhat does a U-collaborate event contain? Introduction In pictures History Cartoonist To achieve a common goal, an elaborate Live-drawn cartoons not only capture the The use of pictures allows one to create and A common vision about historic decisions and introduction and outlining the journey is an essence, it also facilitates interaction and visualize the future state of an organization. events fosters a better understanding. important element in any introduction. creates understanding. Walking with milestones Articulating on both concerns… …and benefits Formulating milestones and actions make it Defining and articulating on both concerns and Articulating on each individual‘s contribution tangible and fosters the creation of a mutually actions ensure progress and continuity. ensures that knowledge will be retained. accepted and understood plan.© 2012 KPMG Advisory N.V., registered with the trade register in the Netherlands under number 33263682, is a subsidiary of KPMG Europe LLP and a member firm of the KPMG 23network of independent member firms affiliated with KPMG International Cooperative (‗KPMG International‘), a Swiss entity. All rights reserved. Printed in the Netherlands. TheKPMG name, logo and ‗cutting through complexity‘ are registered trademarks or trademarks of KPMG International.
  25. 25. Detailed description of methodsDevelop Big Data Scenarios Articulate Costs Intermediate verification to stay on track Benefits and Challenges Validate Big Identify Big Data scenarios Data scenarios with corporate Validate strategic and economic results of the identified Big strategy 1 Data scenarios, are they feasible?― Fill in the business case Measurable change by filling in business cases incrementally ― based on the ICT-strategy 2 What needs to be registered, analyzed and monitored to obtain the desired project deliverables? Identify progress and performance indicators Define program Integrate in daily structure and operations organization 2 3 How to safeguard Big Data related strategy and activities when every day operations are dominating?© 2012 KPMG Advisory N.V., registered with the trade register in the Netherlands under number 33263682, is a subsidiary of KPMG Europe LLP and a member firm of the KPMG 24network of independent member firms affiliated with KPMG International Cooperative (‗KPMG International‘), a Swiss entity. All rights reserved. Printed in the Netherlands. TheKPMG name, logo and ‗cutting through complexity‘ are registered trademarks or trademarks of KPMG International.

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