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Business process based analytics
 

Business process based analytics

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    Business process based analytics Business process based analytics Presentation Transcript

    • Business AnalyticsBusiness Process and AnalyticsKAIST박준성 교수 2012. 12. 13 Copyright © 2012. Dr. June Sung Park. All rights reserved.
    • Big DataWhat is the most important partof the term big data? big data both neither 2
    • Big DataWhat is the most important partof the term big data? big data both neither What organizations do with big data is what is most important. The analysis your organization does against big data combined with the actions that are taken to improve your business are what matters. Analytics only produces business value if it is incorporated into business processes, enabling business managers and users to act upon the findings to improve organizational performance. Bill Franks, Taming the Big Data Tidal Wave, Wiley, 2012. 3
    • Enterprise Analytics Capability Using analytics in mainstream business activities is one of the effective habits of a successful organization. 4
    • Case Study: Amcor Company Global packing company in Australia with 20+K employees and $7B revenue in 2008. Challenge CEO initiated “Vale Plus” approach requiring measurement of “pocket margin” of 1M products down to the invoice level. 5
    • Case Study: AmcorProject Phase I: • Built a data warehouse over a year consolidating data from 32 apps. • Biggest challenge was standardizing and cleansing data. Phase II: • Built analytics and visualization over a month using an easy-to-use tool. • Business people with knowledge of processes helped fit the BI app into Amcor’s processes. • Rolled out the BI app in 4 stages, conducting usability tests, and training users by their business managers. 6
    • Case Study: AmcorResult Lessons Learned 500 users adopted it for daily use Link BI projects with strategic initiatives. in 3 months. Align BI output with corporate KPIs. Incentives introduced that are Implement BI within business processes. based on pocket margins. Have business people, not IT people, Corporate gross margin determine BI use cases, i.e., where and improved. how they would use the BI app in process execution. Take enough time to consolidate silo data into a single version of standardized and cleansed data. Pick a tool easy for rapid deployment and easy for users. Have business managers train users. 7
    • Pattern-Based Business Strategy Technology Market Your Company Supplier Competition Enterprises should listen to signals and understand when the signals are patterns that require adaptation. Listening requires enterprises to combine traditional data sources with new sources of data. Gartner, Pattern-based strategy: compete in the new economy using Gartner’s business pattern framework, Sept. 14, 2005. 8
    • Pattern-Based Business Strategy Collective Defined Creative Case Management Business Process Social Media Database / Data Mart / Warehouse Enterprise Mobility Service-Oriented Architecture Big Data Analytics Process Orchestration Complex Event Processing Event-Driven Architecture Anticipated Exceptions Anticipated Exceptions Enterprises should focus their investments on a balanced diversity of business activities in the defined, creative, collective and exceptions categories that enable them to innovate and respond to change of patterns. 9
    • Case Study: Investments in Big Data Analytics TXU Energy installed smart electric meters in customer homes and read the meter every 15 minutes. Based on an analysis of the metering data, it applies dynamic pricing to shape demand curve during peak hours. This eliminates the need for adding power generating capacity, saving millions of dollars for the company and saving customer expenditures as well. T-Mobile USA has integrated data across multiple IT systems to combine customer transaction and interactions data in order to better predict customer defections. By leveraging social media data along with transaction data from CRM and billing systems, T-Mobile USA has been able to “cut customer defections in half in a single quarter”. US Xpress collects about a thousand data elements ranging from fuel usage to tire condition to truck engine operations to GPS information, and uses this data for optimal fleet management and to drive productivity saving millions of dollars in operating costs. 10
    • Pace-Layered IT Strategy Case Management Explorative Apps Social Media Enterprise Mobility Big Data Analytics Exploitative Apps Complex Event Processing Event-Driven ArchitectureStable Digital Foundation Business Process Database / Data Mart / Warehouse Service-Oriented Architecture Process Orchestration Applications move across layers as they mature, or as the business process shifts from experimental to well-established to industry standard. You, however, cannot innovate on an unstable foundation. Many apps for both disruptive and sustaining innovations should be based on processes and data in the stable foundation Gartner, Accelerating innovation by adopting a pace-layered application strategy, Jan. 9. 2012. 11
    • Evolution of Enterprise IT Matured enterprise architecture is today based on standardized and integrated processes and data, and service-oriented Mobile Cloud architecture of apps. Computing (2010-2015) Technical Debt Payoff (2005-2010) E-Business Process-Orchestrated (1995-2005) Mobile + Social + Cloud Cloud Services Client/Server + Big Data Computing Process Management (1990-1995) IT Dark Age IT Modernization SOA-Based Online (1980-90) Process Integration Computing Standardization EA-Based (1970-80) Batch Computing IT (1950-1970) Reengineering Process Stable Digital Foundation J. W. Ross, P. Weill and D. C. Robertson, Enterprise Architecture as Strategy, HBS Press, 2006. 12
    • Stable Digital Foundation Business process management (BPM) is an ideal technology for agile development of explorative, exploitative and core apps for an enterprise. SOA embodies the middle-out architecture where business processes can be reengineered in flight to quickly implement new business use cases reusing core business services. Business service repository and data federation layers virtualize and synchronize physical apps and data to provide an integrated and standardized foundation. Composite Apps Business Process Composition Business Service Repository Metadata-Based Data Federation Physical Apps Physical Data Sources Gartner, EIM reference architecture: an essential building block for enterprise information management, Sept. 14, 2005. 13
    • Enterprise Architecture Business Architecture BPM  ACMBusiness-IT Alignment Application Architecture SOA  EDA Data Architecture MDM  Big Data Technical Architecture TRM  Virtualization EA is a strategy planning process ensuring business-IT alignment across the enterprise using the architectural approach. Matured EA employs BPM, SOA and MDM disciplines to enable quick alignment between business use cases and app delivery by reusing common master data and core services. 14
    • Enterprise Architecture Strategy Plan Demand Plan As Is To Be BA AA IT Asset DA Investment Mgmt Plan TA Transformation Project Portfolio Mgmt 15
    • Business Process Management BPMN 2.0 Design Graphical modeling, process simulation, business rules BPEL4WS, BPEL4P Implement Code generation Execute BPMS Automation, workflow and integration Monitor Business activity monitoring, automated process discovery and dashboards Optimize Analyze and dynamically adjust business processes and rules 16
    • Business Process Modeling Enterprise Content Mgmt Data Analytics Data modeling is designing the intended use of data. Process and data modeling cannot be done separately. 17
    • Business Process Reengineering Adaptive Case Management Social Collaboration Process innovation is often enabled by redesigning the flow of information. 18
    • Adaptive, Intelligent and Social BPM Analytics, social network and adaptive case management are integrated into BPM for performance monitoring and reporting, forecasting, scenario modeling, complex decisions, planning, real-time situation recognition, immediate next action recommendation, etc. Enterprises need business process and performance management maturity that enables cross-functional accountability and top-down/bottom-up information flows. Enterprise Content Mgmt Data Analytics Adaptive Case Social Management Collaboration Forrester, Forrester wave: dynamic case management, Jan. 31, 2011. 19
    • Adaptive, Intelligent and Social BPM Integration of analytics into operational processes—which contrasts with past approaches that separated analytical work from transactional work— empowers the workforce to make better and faster contextualized decisions in order to guide work toward optimal outcome, and its impact is immediately apparent to business people because it changes the way they do their jobs. http://bps.opentext.com/resources/ot_bps_OT-Process360_ds.pdf 20
    • BPM Maturity Model Enterprises usually cannot skip maturity levels. Enterprises should develop a long-term roadmap to improve their maturity level, based on the current state assessment and the readiness check for the next immediate actions. SOA EA BPR iBPMS BSC Gartner, ITScore overview for business process management, Sept. 17, 2010. 21
    • Advanced BPM Initiatives Tomorrows business operations require integration of real-time intelligence. Process is the unifying construct for intelligent operations. Integration of BPM and automated analytics into SOA-based iBPM is an important business evolution underway. Gartner, Business process management key initiative overview, July 22, 2011. 22
    • iBPMS Talend provides open source solutions for data integration, data profiling, data cleansing, master data management, enterprise service bus, Hadoop connection, cloud enablement, and BPM. Using Talend solutions, you can load data from multiple sources into a master data hub as a SoR, apply the data quality tool to resolve data conflicts, and provide clean data services for automated decisions in business processes or for business workers whose workflow is orchestrated by BPM. 23
    • iBPMS iBPMS has 10 core components: Orchestration engine for processes and cases Model-driven composition Human-driven workflow Content-driven workflow Connectivity of process to resources Active analytics On-demand analytics Business rule management Process repository BPMS administration 24
    • Service-Oriented Enterprise Architecture Portal Business Process Business Service SaaS Component Metadata Service Data Mart / Warehouse Database Big Data 25
    • SOA Implementation using BPM Suite BPEL ProcessProcess Redesign using BPMN Process KPI Definition Process Simulation Implementation Service BPM UI and Monitoring Service Integration Test Specification Implementation Realization and Execution 26
    • Linking BPM to Analytics based on SOA: SAP Netweaver BPM-specific BI content in InfoCube (star schema) OLAP data Query on InfoCube Result in WSDL Dashboard rendering data from BPM 27
    • Enterprise Information Management Information governance and metadata management is critical to any initiative that uses data to drive improvements to business outcome. 28
    • Enterprise Information Management 29
    • Enterprise Information Management Initiative Through 2015, 85% of Fortune 500 organizations will be unable to exploit big data for competitive advantage. 1 Explore fundamental technology trends, such as big data, mobile, social media, cloud computing, and how they reinforce each other to offer opportunities and risks. 2 Plan based on business strategy and enterprise architecture. 3 Model business requirements and detail specification for solution delivery. 4 Choose technologies and vendor/service providers. 5 Implement, test and release the solution iteratively, seeking user feedback. 6 Operate the solution, measure performance, revise the solution and refine governance processes. Gartner, Information innovation: innovation key Initiative overview, Apr. 27, 2012. 30
    • Analytics Framework Analytic apps can work with any kind of data, including transactions, events, unstructured contents, website data, social networks, and Internet of things (machines, sensors). Increase Analytics, however, should resolve management challenges first. analytical skills Embed analytics into the Establish corporate of centralized business process and workflow. performance metrics. analytic team as well as self- service analysts within business Attract units.corporate execs to participate. Ensure data quality andFind use cases consistency. and justifybusiness cases. Build requirement Create engineering organization competency. culture of valuing fact- Consumerize Balance between standardization and diversification,based decisions. through mobile custom-design and packaged apps, on-premise and delivery. cloud, SQL and NoSQL, storage and in-memory Gartner, Analytics key Initiative overview, July 22, 2011. 31
    • Analytics Maturity Model Enterprises usually cannot skip maturity levels. Enterprises should develop a long-term roadmap to improve their maturity level, based on the current state assessment and the readiness check for the next immediate actions. Gartner, ITScore overview for business intelligence and performance management, Sept. 17, 2010. 32
    • Analytics Roadmap Planning Enterprises should assess the current level of maturity using a analytics framework, find areas of weakness and opportunities for improvement, set up a long-term roadmap to raise the maturity level, follow the EA process to determine and execute short-term improvement initiatives, and put in a continuous improvement program. Data Consistency and Quality Culture of Analytic Fact-Based Decision Competencies Requirement Process Engineering and Metrics Methodology Exec Commitment and Governance 33
    • Analytics Lifecycle Acquire data Organize data ETL or ELT Data platform Data source (DB, DW, Hadoop) Set requirements Select and build and hypotheses models Analyze Take BPM Analytics data action for insight Embed into Extract rules operation BRM Make decision 34
    • Analytics Methodology: IBM and Capgemini 35
    • Analytics Methodology: IBM 36
    • Analytics Requirement Metamodel Big data needs big process. (Forrester Research) Big data without a process context and a compelling use case for a specific user class is like a Maserati without an engine. Big data with proven values will become structured.Process Model Use Case Model UX Model Business Process Actor Use Case Persona Rule Actor I/O Info Process Event Communication Activity Association Use Case User Task Information Model Service Data Use Case User Task Scenario Scenario Dictionary Analytics User Concept Glossary Data Model Map 37
    • Analytics Requirement Engineering for SOA Enterprise Business Architecture Strategy Conceptual Process Model UX Conceptual Model Data Model Business Use Case Conceptual Req’ts Model Service Model Executable Process Model Software Logical Data Req’ts Schema UI Use Case Design Scenario Analytics Test Service Case Specification 38
    • Analytics Requirement Engineering for SOA Design Model Portal UX UI Business Case Test Process Scenario Business Case Use Service SaaS ComponentProcess Process Model Exec Metadata Service Data Mart / Service Service Warehouse Model Spec Database Schema Big Data Model Data Data Service-Oriented Architecture 39
    • Analytics Requirement Engineering for SOA: IBM Service Service Process Use Case Model Process Model Specification Implementation Orchestration IndustryReference Model Data Model IBM, Building service-oriented solutions with IBM industry models and Rational software development platform, 2007. 40
    • Analytics Requirement Engineering for SOA: Capgemini 41
    • Case Study: PayPalCompanyGlobal e-commerce business allowing payments and money transfers to bemade through the Internet.Role of Global Business Analytics Team Managing Down: Ensure connection between the analysis they do and the actions the company takes. Work closely together with business people for right questions and right interpretation of findings. Managing Up: Establish themselves as thought partners, not data providers, to the executive, and translate analytical insights into actionable recommendations. Veronika Belokhvostova, HeadAnalytics Team Members of Global BusinessBusiness analysts with a mix of technical and business skills. Most having Analytics at PayPalMBAs in addition to data analysis skills.Project ExamplesAnalysis of customer behaviors and interactions for improving products andmarketing, analysis of the impact of website redesign, analysis of the effectof promotional pricing, diagnosis of of revenue leakages, analysis of theimpact of risk management policies on customers, etc. Renee Ferguson, Mining data at PayPal to guide business strategy (Interview with Veronika Belokhvostova), MIT Sloan Management Review, Sept. 2012. 42
    • Process-Driven Big Data Analytics Initiative Big data analytics requires a data- savvy business strategy to achieve competitive advantage. Keep the process transparent; it is key to successful big data projects. Educate process owners about potential big data opportunities now readily available through start- small, cost-effective analytics tools and techniques. The value delivered from an investment in big data analytics must be visible and measureable. 43
    • Process-Driven Big Data Analytics Initiative Use low-cost, open-source tools in early pilots to demonstrate the feasibility of big data projects. Explore the increasing number of public datasets now available through open APIs. Produce a resource plan that identifies big data skill gaps. Look for business- savvy analysts (especially data scientists) and analytics-savvy business leaders who can work together to find what business should do based on analytic results and then do it. Assess resource needs for information infrastructure and identify technical gaps when supporting big data solutions. 44
    • Data Scientist Business Use Cases Analytics Apps Analytics Common Services RT-OLAP Analytic Algorithms Visualization e.g. BigQuery e.g. Greenplum e.g. Pentaho In-Memory Data Data Models ETL e.g. GridGain e.g. NoSQL, RDB e.g. Kettle Basic Data Transformation e.g. Map Reduce, Pig, Hive, Sqoop, Lucene File System NoSQL DB e.g. HDFS e.g. Hbase (In-Memory) Stream Processing e.g. Flume, Avro Distributed Agents Thomas Davenport and D. Patil, Data scientist: the sexiest job of the 21st century, Harvard Business Review Oct. 2012. 45
    • Case Study: SearsCompanyAmerican chain of department storesChallenge Decided to generate greater value from the huge amounts of customer, product and promotion data collected from its stores. Took 8 weeks, due to highly fragmented databases and data warehouses, to generate personalized promotions, at which point many of them were no longer optimal. Andrew McAfee and Erik Brynjolfsson, Big data: the management revolution, Harvard Business Review, Oct. 2012. 46
    • Case Study: SearsSolution Set up a Hadoop cluster in 2010, and used it to store incoming data from its stores and to hold data from existing data warehouses. Conducted analyses directly on the cluster, with the processing time reduced from 8 to 1 week, and still dropping. Got help from Cloudera initially, but over time internal IT and analysts became comfortable with the new tools and methods. 47
    • Q&A