How advanced analytics is impacting the banking sector
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How advanced analytics is impacting the banking sector. October 2015
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How advanced analytics is impacting the banking sector
- 1. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-75551 A Hitachi Data Systems Company How advanced analytics is impacting the banking sector Pentaho - Architected for the Future of Analytics MICHAEL HADDAD
- 2. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-75552 What I can (hopefully) tell you about: Analytics in banking Hitachi Data Systems and Pentaho Blending data Data scientists Technology for analytics
- 3. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-75553 Six to 10 mega trends • investing in compliance Regulatory changes and complexity • new challengers to disrupt • changing the way customers interact Digitalisation and technological advances Changes in investment and capital sources and returns • different expectations and ways of interacting with financial institutions Demographic and behavioural changes • who wants to be a banker? Global talent and skills race Business operating model pressures Reference: Jagdev Kenth, http://www.resilience.willis.com/;
- 4. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-75554 Using Customer Analytics to Drive Contextual Experiences Expedited Deployment of Digital Delivery Mobile-First Design Increasing Digital and Social Selling Mass Market Acceptance of Mobile Payments Focus on Security and Authentication Industry Consolidation Enhanced Customer Incentivisation Investment in Innovation, Incubation and Uncommon Alliances Increased Impact of Digital Disruptors Six to 10 mega trends Reference: Jim Marous, http://thefinancialbrand.com/
- 5. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-75555 Data analytics and these trends
- 6. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-75556 Use Data Analytics to Reduce risk React to events and circumstances Deliver personalised services Drive new technologies Retain skills
- 7. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-75557 Use cases Customer analytics, segmentation, attrition analysis, profitability analysis Marketing analytics, success of campaigns Fraud analytics, detection analysis Risk analytics, credit risk analysis
- 8. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-75558 BI then and BA now Ref: https://rapidminer.com/
- 9. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-75559 A Hitachi Data Systems Company Hitachi Data Systems and Pentaho
- 10. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-755510 Hitachi and Pentaho
- 11. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-755511 Hitachi and Pentaho
- 12. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-755512 Modern, unified data integration and business analytics platform • Broadest and deepest big data integration • Embeddable, cloud-ready analytics • Big data blending at the source Fast and Broad Innovation • Open source development model • 100% java, pluggable and extensible Critical mass achieved • Over 1,200 commercial customers • Over 10,000 production deployments Pentaho Mission Enabling the future of analytics
- 13. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-755513 Pentaho Key Points Meeting the demands of the big data-driven enterprise Blended Big Data at the source for more accurate insights Enterprise-ready data integration and simplified embedding for any environment Simplified analytics experience with a modern interface Analytics Blended Big Data Enterprise Big Data Integration
- 14. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-755514 Simplified Analytics Experience Simplified Administration, Management & Deployment Different Audiences, Different Needs Higher User Expectations Landslide of Data Blended Big Data for Comprehensive Analytics Enabling the Modern Big Data Driven Enterprise Key pressure points disrupting the traditional analytics market
- 15. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-755515 15 ANY Analytics • Reports • Dashboards • Visualizations • Discovery • Predictive Analytics ANY Environment • Data warehouses • Data marts • Stack vendors • Cloud • Embedded Existing & New Data Infrastructure & Processes ANY Data • Relational • Operational • Big Data • Data sources not yet anticipated… Billing Location Social Media Customer Web Network The New Reality Simplified Analysis for all Users
- 16. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-755516 A Hitachi Data Systems Company Blended Big Data
- 17. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-755517 Improve operational effectiveness • Machines/sensors: predict failures, network attacks • Financial risk management: reduce fraud, increase security Reduce data warehouse cost • Integrate new data sources without increased database cost • Provide online access to ‘dark data’ Drive incremental revenue • Predict customer behaviour across all channels • Understand and monetize customer behaviour • Begin to monetize data as a service Customer Value from Big Data Monetizing big data-driven use cases driving need to blend data
- 18. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-755518 Analytics Analyze quality of service: • Network outages • Dropped calls • Poor quality • Calls to support center For profiles of customers: • Up for renewal • Profitable • Multiple agreements/services • In competitive area Determine best action to take: • Billing Credit • Customer Coupon • No Action EDW Existing ETL Tool or PDI Customer Billing Provisioning Call Detail Records from: • Billing • Payment • Usage NoSQLNetwork Location PDI Call Detail Records from Network: • Outages • Drops • Service Quality PDI Blend revenue-related and quality-of-service data together to find customers at risk Why Blending at the Source Matters Customer Experience Analytics for loyalty and revenue
- 19. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-755519 • Just in time blending of data from multiple sources for a complete picture • Connect, combine and transform data from multiple sources • Query data directly from any transformation • Access architected blends with the full spectrum of Pentaho Analytics • Manage governance and security of data for on-going accuracy Accurate, Blended Big Data Analytics Optimally stored data, blended when needed EDW Existing ETL Tool or PDI Custom er Billing Provisioning NoSQL Network Location PDI PDI Analytics Just in time blending
- 20. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-755520 A Hitachi Data Systems Company Data Science
- 21. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-755521 The data analyst / data scientist
- 22. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-755522 Data scientist
- 23. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-755523 Why do we need them?
- 24. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-755524 Why do we need them? Hitachi Data Systems and Pentaho
- 25. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-755525 Analytics allows us to complement the machine Intelligence Augmentation An increasing number of humans work for the machine Analytics improves human’s insights Weak human plus computer is better than a grandmaster
- 26. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-755526 A Hitachi Data Systems Company Analytics technology
- 27. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-755527 Pentaho and MongoDB
- 28. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-755528 Pentaho and NoSQL Databases
- 29. Vendor-neutral: Integration of disparate technologies curated and supported via ecosystem of partners Flexible: Flexibility built into architecture at multiple levels from infrastructure to Data services to analytics libraries Modular: Pluggable light- weight architecture to allow appropriate tool for data management Solution focused: Focus on the full stack from the physical infrastructure to analytics modules to help with solution building OPAL (Open Platform for Analytics)
- 30. Hitachi Big Data Analytics Ecosystem Data Connectors (HSDP For Streaming Data) Data Services (Pentaho Tools for Integration, Orchestration, ETL, Indexing) Analytic Libraries (R, Weka, …) System Integration & Customization Services Visualization (Extracts, Dashboards, Reports) Platform Services (Hadoop, Storm, Spark, Cassandra, MongoDB) Infrastructure (Hyper-Converged Scale-Out Platform) Cloud-Enabled Public Private Hybrid Telecom HealthCare Public SafetyData Center Finance OTHER ONE HITACHI SOLUTIONS Common Analytics Framework (Developed jointly with Hitachi R&D Labs) BIG DATA LAKE Video, Image & Audio Email & Documents TX Transactional Data TX IT, Sensor & Machine Logs Social Media Custom Solutions
- 31. Big Data Solution Development Challenges A big part of solution development for analytics involves orchestration of various moving parts in the infrastructure: ‒ Domain experts need access to appropriate data sets backed by technology stack to gather insights based on intuitions. ‒ Time to value for a solution can be accelerated by providing an agile mechanism to deploy from prototype to production application quickly. Infrastructure Installation Component Deployment Application Logic Implementation Testing Configure Cluster & Monitoring
- 32. Easier to Provision Infrastructure Easier to Deploy Services Easier to Monitor, Troubleshoot, and Validate Easier to Develop & Integrate New Services Opal – Big Data Analytics Made Easy & Robust Infrastructure Installation Component Deployment Application Logic Implementation Testing Configure Cluster & Monitoring Before With Opal
- 33. © 2015, Pentaho. All Rights Reserved. pentaho.com. Worldwide +1 (866) 660-755533 A Hitachi Data Systems Company Thank You blog.pentaho.com @Pentaho Facebook.com/Pentaho Pentaho Business Analytics JOIN THE CONVERSATION. YOU CAN FIND US ON:
Hinweis der Redaktion
- Analysts appear to be fairly unanimous on many of the mega trends in banking 2. FinTech companies 3. Diito as 1 takes it’s toll 4. Different expectations of millennials. 5. From boring to do I want to manage all those regs. 6. Specialist providers, margins
- More of a technology focus
- Analysts appear to be fairly unanimous on many of the mega trends in banking 2. FinTech companies 3. Different expectations of millennials. 4. From boring to do I want to manage all those regs. 5. Specialist providers, margins Visa study: advice Parents 78% friends 45%, internet 40%
- I’m going to introduce you to some of the features and technologies of data analytics.
- To do this you cant look at only your data.
- Let’s take a look at why you need to blend big data, and how Pentaho provides the best approach to doing so.
- How Hitachi announced it
- How analysts saw it
- Pentaho 5.0 reinforces Pentaho’s mission of delivering the future of analytics. Pentaho had continued to invest in BI and DI together with over 100 new features in PDI and over 250 in the platform overall. Continued investments in big data—new integrations—specifically with Mongo and Cassandra—and continues to shield customers from changes in the market. Open core and pluggable platform allows us to innovate quickly. Pentaho is battle tested with over 1200 commercial customers.
- -A simplified way to utilize analytics so that anyone within and organization can meet their personal needs and specifications -Utilization of ANY type of data from ANY source to deliver powerful insight. This also means a company can easily scale their data integration and pull in new resources, as well as blend data from existing data infrastructure. -A simplified deployment and management system to easily embed pentaho
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- -You need to be able to handle ANY DATA. No one used to think about Twitter or FB data, so we are positioning to handle that data -You need to be able to handle ANY ENVIRONMENT. You can utilize existing architecture, but be ready for new tools and growth -You need to be able to handle ANY TYPE OF ANYLTIC REPORTING. Whether a data scientist, senior manager or anyone else, you can deliver powerful analytical insight
- Let’s take a look at why you need to blend big data, and how Pentaho provides the best approach to doing so.
- Let’s look at an example of blending at the source to better understand these points. Here we are looking at an example of Telco customer experience analytics. Customer Experience Analytics have the same goal in every industry – preventing customer churn and creating better loyalty in order to protect and grow revenue – after all, in this age of commodotisation, service and fast response to product requests become the new differentiators driving loyalty in most industries. Telco customer allegiance comes mostly from satisfaction with calling plans and the quality and availability of service. Call detail records have long been created and derived from the operational systems for access to BI and reporting systems via warehousing, but they only make up part of the picture. (Build click 2) Quality of service changes in real time dependent on the network – was the customer able to connect, to hear, to remain connected without being dropped, etc? This network-based data is usually captured in a Big Data source that is capable of handling the volume and unstructured nature of the data, and must be blended with the Call Detail Record information to give the complete picture of a customer’s experience. (Build click 3) With Pentaho, you can easily create architected, blended views across both the traditional Call Detail Records in the warehouse, and the network data streaming into the Big Data/NoSQLstore (MongoDB in this example) without sacrificing the governance or performance you expect. These blended views allow your analysts and customer call centers to get this accurate, of-the-minute information in real time to determine the best action to take for each customer to maximize their satisfaction and retain them as loyal customers even when outages or other service quality issues occur.
- Other solutions in the market talk about blending - but it’s not apples to apples. Blending “at the glass”, i.e. blending done by end users or analysts away from the source with no knowledge of the underlying semantics, often delivers inaccurate or even completely incorrect results, as there is no way to ensure that the chosen fields being matched truly do match. For instance, think what happens when someone matches two fields both named “revenue” in records that match on “customer”, but one is a monthly sum total and the other is a daily total – this won’t be apparent to that analyst since they are blending based on similar names. The analyst then runs a summation that adds the two together as the day’s total revenue from that customer. He/she will have unwittingly added the monthly figure into each day’s total, distorting the actual revenue generated from that customer dramatically. Your business then targets that customer as highly profitable and offers significant discounts to maintain their interest. Not only have you targeted the wrong customer and potentially ignored the real profitable customers in favor of him, but you’ve also now given him undeserved discounts. The net result lowers your revenue from this customer, and potentially loses you profitable others who were more deserving but left you in favor of competitors offering them discounts. You’ve made the wrong decision because the analytics themselves were inaccurate and incorrect. Your only choice to avoid this with tools that blend like this is to train every user and analyst on the semantics of the data to ensure reliable results – a solution that’s largely infeasible for most organizations as it would take far too much time and expense while impacting productivity. Even if you can take on this level of investment in training, you still face issues with the timeliness of the data, since these tools do not pull from the source systems. How do you know the data pulled is indeed the latest and therefore the most accurate on that level as well?
- This “just in time”, architected blending delivers accurate big data analytics based on the blended data. You can connect to, combine, and even transform data from any of the multiple data stores in your hybrid data ecosystem into these blended views, then query the data directly via that view using the full spectrum of analytics in the Pentaho Analytics platform, including predictive analytics. Most importantly, since these blends are architected on the source data, you maintain all the rules of governance and security over the data while providing the ease of use and real time access needed for today’s agile analytics requirements. Sensitive data is kept from those who are not allowed to use or view it. You maintain full lifecycle and change management and control, so you can assure the blends being used meet changing requirements. You preserve auditability. Your blends are designed with full knowledge of the underlying data volumes and source system capabilities and constraints, preserving throughput and performance during analytic access and preventing the “query from hell”/”runaway query” problems prevalent in many federation tools. Combining the power of design via drag-and-drop across all data sources, including schemas generated on read from big data sources, with knowledge of the full data semantics - the real meaning, cardinality, and match-ability of fields and values in the data - means your business gets accurate results in its analytics, leading to optimized decisions and actions that can really impact your business positively and improve your results.
- Let’s take a look at why you need to blend big data, and how Pentaho provides the best approach to doing so.
- No longer about the DBA.
- Let’s take a look at why you need to blend big data, and how Pentaho provides the best approach to doing so.
- I’m just giving some examples.