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Sander Klous - Op de 1e editie van Data Donderdag
Sander Klous - Op de 1e editie van Data Donderdag
Sander Klous - Op de 1e editie van Data Donderdag
Sander Klous - Op de 1e editie van Data Donderdag
Sander Klous - Op de 1e editie van Data Donderdag
Sander Klous - Op de 1e editie van Data Donderdag
Sander Klous - Op de 1e editie van Data Donderdag
Sander Klous - Op de 1e editie van Data Donderdag
Sander Klous - Op de 1e editie van Data Donderdag
Sander Klous - Op de 1e editie van Data Donderdag
Sander Klous - Op de 1e editie van Data Donderdag
Sander Klous - Op de 1e editie van Data Donderdag
Sander Klous - Op de 1e editie van Data Donderdag
Sander Klous - Op de 1e editie van Data Donderdag
Sander Klous - Op de 1e editie van Data Donderdag
Sander Klous - Op de 1e editie van Data Donderdag
Sander Klous - Op de 1e editie van Data Donderdag
Sander Klous - Op de 1e editie van Data Donderdag
Sander Klous - Op de 1e editie van Data Donderdag
Sander Klous - Op de 1e editie van Data Donderdag
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Sander Klous - Op de 1e editie van Data Donderdag

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Tijdens de eerste editie van Data Donderdag sprak Sander Klous, hoofd van het Big Data en Analytics team bij KPMG over Big Data ecosystemen in bedrijfsleven en maatschappij.

Tijdens de eerste editie van Data Donderdag sprak Sander Klous, hoofd van het Big Data en Analytics team bij KPMG over Big Data ecosystemen in bedrijfsleven en maatschappij.

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  • 1. Data analytics Sander Klous Big Data Analytics Lead klous.sander@kpmg.nl
  • 2. Contents Process ■ Approach ■ Big Data awareness event ■ Agile development ■ Evaluation Examples ■ Life changing events ■ Financial health ■ Hospital care path optimization ■ Data driven decision making ■ Dynamic influencer modeling ■ Crowd monitoring and event security ■ In store customer buying patterns © 2013 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 network of independent member firms affiliated with KPMG International Cooperative (‘KPMG International’), a Swiss entity. All rights reserved. Printed in The Netherlands. The KPMG name, logo and ‘cutting through complexity’ are registered trademarks of KPMG International. 1
  • 3. Process
  • 4. Our approach for a value added result The process of achieving a proof of concept for the selected cases is aligned with the possibility to develop business cases for the other opportunities Ambition Customer perspective Breaking the rules Vision Insight into digitalization Discovering the digital possibilities Related worlds Generating an appetite Ideas for digitalization Powertrack planning Business value Client cases Ambition Preparation for and creating client cases using a Big Data opportunity event Proof of Concept for selected cases and hand over Transformation of the solution and business model Continuous interaction to orchestrate Big Data strategy process and if necessary adjust business case directions. 2 weeks 10 weeks to be decided Coordinating the project using a phased and agile approach Envisioning… Preparing… Developing… Reviewing… uncovering client cases using a Big Data opportunities selected client cases for Proof of Concepts the Proof of Concepts of the selected client cases evaluating the results and determining the way forward © 2013 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 network of independent member firms affiliated with KPMG International Cooperative (‘KPMG International’), a Swiss entity. All rights reserved. Printed in The Netherlands. The KPMG name, logo and ‘cutting through complexity’ are registered trademarks of KPMG International. Part of this proposal Additional activities 3
  • 5. Preparation for and creating client cases using a Big Data awareness event Stakeholder commitment and involvement are key in the activities that proceed to the Big data awareness and opportunity event 2: Forward Thinking Conducting an extensive analysis that aims to identify relevant trends in the financial services industry and big data initiatives. 3: Related Worlds Identifying relevant business models in related industries will provide valuable learning experience as well as identifying future opportunities. 4: Customer Perspective A customer analysis identifies future needs and priorities that contribute in relevant customer profiles. 1: Shared vision 8: Big Data opportunity event To realize a shared vision a strategy analysis is required and includes an understanding of corporate and estrategy, a competitive benchmark, and portfolio analysis. 5: Business Value Based on the strategy, trends, and customer profiles analyses, the main value drivers are determined and will form the basis for potential business or revenue models. 6: Fact wall Adding facts to the fact wall during the preparation will ensure that all insights are used during the event that contributes to client’s future business model. Creating client cases by stakeholders using an outside-in view on the possibilities of Big Data for the client business model and strategy. 7: Opportunity event preparation To achieve the mutually defined results during the Big data awareness session, preparation is key. This phase consists of multiple activities to prepare the necessary input for the awareness session. In addition to these activities, client stakeholder commitment and involvement are key success factors during this phase. © 2013 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 network of independent member firms affiliated with KPMG International Cooperative (‘KPMG International’), a Swiss entity. All rights reserved. Printed in The Netherlands. The KPMG name, logo and ‘cutting through complexity’ are registered trademarks of KPMG International. 4
  • 6. Pilot for selected cases and hand over Based on the selected client cases, working pilots are delivered in an agile approach 2: Getting ‘Ready’ The Scrum Team has to get ready and the Product Backlog has to be groomed. Refined client cases serve as the starting point for development. 1: Deploying the Big Data environment 3: Delivery using Scrum The Scrum Team and Product Owner develop working prototypes in 4 Sprints. Each Sprint starts with a Sprint Planning Meeting and end with a Retrospective. Our agile approach allows for changing requirements throughout the development cycle and ensures getting maximum value from the Big Data environment Based on the selected client cases, the required Big Data environment is deployed. There are, for example, three scenario’s: 1. Using the existing Teradata EDW 2. Using a Hadoop cluster 3. Using a Teradata Aster cluster Scenario I. Using the Teradata EDW Scenario II. Using a Hadoop cluster Scenario III. Using a Teradata Aster cluster © 2013 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 network of independent member firms affiliated with KPMG International Cooperative (‘KPMG International’), a Swiss entity. All rights reserved. Printed in The Netherlands. The KPMG name, logo and ‘cutting through complexity’ are registered trademarks of KPMG International. 5
  • 7. Transformation of the solution and business model Evaluation of the pilot and defining the future path... Pilot Retrospective When the prototypes have been used for a couple of weeks/months, a pilot Retrospective will be held: 1. The value of the pilot will be evaluated. What did it contribute? What have we learned? This concerns both technical as well as organizational findings. 1: Pilot review Using our proven assessment methodology, we will evaluate the pilot findings together. 2: Future path 2. What is the future path for the client concerning Big Data? What are the next steps? How can we leverage data as a strategic asset? What is needed to make us a data-driven company? Transformation of the solution and business model The pilot Retrospective is used as input for development of the Big Data Strategy and allows for uncovering Big Data opportunities that maximize business value in support of the client's proposition or uncover possibilities for business model innovation. The possibilities of Big Data for the client will be re-assessed. How do we leverage the value of our data? What actions are needed? © 2013 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 network of independent member firms affiliated with KPMG International Cooperative (‘KPMG International’), a Swiss entity. All rights reserved. Printed in The Netherlands. The KPMG name, logo and ‘cutting through complexity’ are registered trademarks of KPMG International. 6
  • 8. Timeline Approach is characterized by clear steps and focuses on delivery in 12 weeks Weeks (dates to be discussed) 1/2 3/4 5/6 7/8 9/10 11/12 13 Client Big Data strategy development Activities Envision Go / No Go Prepare Go / No Go Go / No Go Develop ■ Kick-off meeting ■ Workshop ■ Workshops ■ Awareness session ■ Product Backlog ■ Sprint Planning meetings, Demo’s, Retrospectives Review ■ Retrospective ■ End product ■ Documentation ■ Roadmap Project management Results ■ ■ ■ Selected client cases Organizational awareness Basic Big Data environment ■ Refined client cases ■ Ready Product Backlog and Ready Team 2 Data scientists (KPMG) 2 Facilitators (KPMG) 1 Visualisation expert (KPMG) Working product increments (two week cycle) 1 Product Owner (Client) 1 SME (KPMG) People 8-12 Key players (Client) ■ 2 Data scientists (KPMG) 1 Project Manager (KPMG) 4 Key players (Client) 1 Project Manager (KPMG) 1 Product Owner (Client) 1 SME (KPMG) 2 Data scientists (KPMG) 1 Facilitator (KPMG) 1 Visualisation expert (KPMG) 1 Data scientists(KPMG) 1 Project Manager/Scrum Master (KPMG) 1 Teradata expert 1 Project Manager (KPMG) 1 Teradata expert 1 Teradata expert © 2013 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 network of independent member firms affiliated with KPMG International Cooperative (‘KPMG International’), a Swiss entity. All rights reserved. Printed in The Netherlands. The KPMG name, logo and ‘cutting through complexity’ are registered trademarks of KPMG International. 1 Teradata expert 7
  • 9. KPMG Big Data Analytics Platform Optimize time to market Our DNA Service as a Data Institute in an Eco-System • Output via API/ custom visualization Input Data Entire (custom) solutions for (multiple) clients • 1 Real-time processing Backend Technologies employed Cloudera Hadoop Cluster Batch processing 2b 3 • Based on Open Source resources • MongoDB Cluster Flexible and scalable solution 4 Service Layer Security Rest Interface 5a User Request 1’ Widgets 5b Monitoring: Ganglia • 2a Configuration management: Puppet Storm Cluster Web Apps Load Balancer Varnish Cache Varnish Cache 6 II. Crowd & Mobility Management © 2013 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 network of independent member firms affiliated with KPMG International Cooperative (‘KPMG International’), a Swiss entity. All rights reserved. Printed in The Netherlands. The KPMG name, logo and ‘cutting through complexity’ are registered trademarks of KPMG International. 8
  • 10. Examples For internal use only
  • 11. Personalized Offerings and Pricing Life-changing events Combine the internal data of a bank with publicly available data from Funda.nl Company A Analytics Institute Data Hub Customer Company B & 1 encryption encryption 3 2 3 © 2013 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 network of independent member firms affiliated with KPMG International Cooperative (‘KPMG International’), a Swiss entity. All rights reserved. Printed in The Netherlands. The KPMG name, logo and ‘cutting through complexity’ are registered trademarks of KPMG International. 10
  • 12. Risk Management Financial Health Pattern recognition in income and expenses – Financial Health predictions Parameters a & b towards Financial Health Dept. Expenses within the range for sustainable behavior Parameters a & b towards NonFinancial Health Dept. Customers who were never considered by the Financial Health Dept. balanced their spending with their income. © 2013 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 network of independent member firms affiliated with KPMG International Cooperative (‘KPMG International’), a Swiss entity. All rights reserved. Printed in The Netherlands. The KPMG name, logo and ‘cutting through complexity’ are registered trademarks of KPMG International. 11
  • 13. Operational Excellence Hospital care path optimization Simulation Reality Monte Carlo simulation to analyze operational efficiency of Operation Room planning at a hospital. 2012 2014 © 2013 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 network of independent member firms affiliated with KPMG International Cooperative (‘KPMG International’), a Swiss entity. All rights reserved. Printed in The Netherlands. The KPMG name, logo and ‘cutting through complexity’ are registered trademarks of KPMG International. 12
  • 14. Customer Profiling and Operational Efficiency Decision-making based on generated revenue The most popular handset types used in the entire Netherlands versus Amsterdam. The information is based on the revenue per antenna from analysis of the call detail records. Note that Samsung is most popular in general, but in Amsterdam, the trendier Apple’s iPhone is mostly used instead. © 2013 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 network of independent member firms affiliated with KPMG International Cooperative (‘KPMG International’), a Swiss entity. All rights reserved. Printed in The Netherlands. The KPMG name, logo and ‘cutting through complexity’ are registered trademarks of KPMG International. 13
  • 15. Customer Profiling Dynamic Influencer Modeling in Social Media Networks Dynamic Influencer Modeling is used to mine social networks for their influential members. It calculates the influencers according to both the formal and informal connections. It computes the influencers dynamically, computing the influencers in real-time using the output of Tribes identification information. Each of the connections is weighted dynamically, to create ranking of members for diverse topics, such as “Toyota Prius”, “fuel economy” and “baseball draft”. Dynamic Influencer Model of Business on Twitter © 2013 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 network of independent member firms affiliated with KPMG International Cooperative (‘KPMG International’), a Swiss entity. All rights reserved. Printed in The Netherlands. The KPMG name, logo and ‘cutting through complexity’ are registered trademarks of KPMG International. 14
  • 16. Location Aware services Crowd monitoring and event security during the coronation KPMG Managed Service Big Data Analytics platform Analytics as a Service (AaaS) © 2013 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 network of independent member firms affiliated with KPMG International Cooperative (‘KPMG International’), a Swiss entity. All rights reserved. Printed in The Netherlands. The KPMG name, logo and ‘cutting through complexity’ are registered trademarks of KPMG International. 15
  • 17. Location Aware services In store monitoring: understanding customer buying patterns Background Tracking customer behaviour is important information which can be gathered using several types of sources. WiFi signals and old-school security cameras are for instance used more and more to store shopping patterns from customers. Benefits ■ The goal is to define which variables affect a purchase and make changes accordingly. For example, a store might deploy more salespeople, alter displays or put out red blouses instead of blue, according to a story on Bloomberg Challenges ■ Storing WiFi signals for analysis. ■ Analyzing the customer behavior and plotting patterns, graphs and trend lines. ■ Storing sales data, or motion sensors for extra information about certain products. ■ Storing other relevant (social Media) information. Solution ■ The analysis of any of these separate data streams will provide vital information about the shopping behaviour of customers. Combining all the data creates even richer information. © 2013 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 network of independent member firms affiliated with KPMG International Cooperative (‘KPMG International’), a Swiss entity. All rights reserved. Printed in The Netherlands. The KPMG name, logo and ‘cutting through complexity’ are registered trademarks of KPMG International. 16
  • 18. Location Aware services Live dashboards Service Catalog Customer Benefits Capture rate ■ How many people that walk by actually enter Location based loyalty ■ Reward Programs (bonus miles) ■ Customer Engagement levels (bronze, silver, gold) Dwell time ■ How long does an average visitor stay inside Conversion ■ How many customers that enter buy a product Visits ■ How many people visit and how does this vary over stores Returning visits ■ How often do customers visit a store Heatmaps ■ What is the most visited area in a store Benchmarking ■ A/B testing between different locations and dates Store Layout Optimization ■ What is the best store layout Trends ■ Above indicators over time Location aware discounts ■ Based on customer loyalty In store navigation ■ Help customers find their preferred store path Queue time optimization ■ Minimize waiting time at cash registers or counters Hottest item identification ■ Help customers identify best selling products Collaborative filtering ■ Suggest new products related to customer interest Personalized offerings ■ Personalized offerings based on better customer profiles Customer journey ■ Help customers with their purchase decision in stores and at home © 2013 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 network of independent member firms affiliated with KPMG International Cooperative (‘KPMG International’), a Swiss entity. All rights reserved. Printed in The Netherlands. The KPMG name, logo and ‘cutting through complexity’ are registered trademarks of KPMG International. 17
  • 19. Location Aware services Heatmap of CHEP 2013 (Conference in Beurs van Berlage) © 2013 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 network of independent member firms affiliated with KPMG International Cooperative (‘KPMG International’), a Swiss entity. All rights reserved. Printed in The Netherlands. The KPMG name, logo and ‘cutting through complexity’ are registered trademarks of KPMG International. 18
  • 20. © 2013 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 network of independent member firms affiliated with KPMG International Cooperative (‘KPMG International’), a Swiss entity. All rights reserved. Printed in the Netherlands. The KPMG name, logo and ‘cutting through complexity’ are registered trademarks or trademarks of KPMG International.

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