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BUSINESS INTELLIGENCE & ADVANCED ANALYTICS DISCOVER | PLAN | EXECUTE JANUARY 14, 2016 Who is... Data Governance Master Data Management Data Quality Metadata Management Security and Lifecycle Management Data Integration Big Data Hadoop NoSQL Spark Data Warehouse Real-Time Batch Business Intelligence Operational & Analytical w w w. i n t r i c i t y. c o m / v i d e o s Channel Planning & Forecasting Predictive Analytics Organizations Diverse Data Needs CIO Technology Priorities Business Intelligence Defined Business Intelligence (BI) provides historical, current and predictive views of business operations and supports business decisions ranging from operational to strategic. Common functions of business intelligence technologies are reporting, online analytical processing (OLAP), analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics and prescriptive analytics. Big Data Supports and Enables BI Big Data Defined (4 V’s) Velocity Volume Huge data size, terabytes - petabytes Velocity V V V Variety Veracity V Various data sources (social, mobile, M2M, structured and unstructured data) High speed of data flow, change and processing Veracity Various levels of data uncertainty and reliability Volume Volume Velocity Velocity Big Data Volume • increasing data amount • handling Terabytes – Petabytes • 1 TB = 1 000 000 000 000 bytes = 1012bytes = 1000 gigabytes Variety Veracity Veracity • 1 PB = 1 000 000 000 000 000 B = 1015bytes = 1000 terabytes Velocity Volume Velocity Velocity Velocity of data processing Need to process data in a fast way, considering their huge amount (TB, PB) and variety (distributed, mix of structured and unstructured data) E.g. • Searching Twi er posts • Fast analysis of video streams (Youtube), Variety Veracity Veracity • Image recogni on (real- me augmented reality, online photo faces recogni on), • Sound analysis (speech recogni on) • Text analysis (Internet search) Variety M2M Social media Mobile Huge variety of big data • Data from various data sources • Social media (Facebook, Twi er) • Mobile data (loca on, tex ng) • Machine to Machine systems (GPS, logs, RFID…) • Database systems Velocity Volume Velocity • Variety Veracity Veracity Handling various data types • Structured • records in databases (customers, billing, stock exchange data…), Machine logs • Unstructured • free text, video, sounds Veracity Volume Variety Velocity Velocity Veracity Veracity Veracity of big data Various data uncertainty and reliability • Imprecision of data (especially of unstructured data – e.g. text message can have double meaning) • Different level of data quality of structured data (data values are certain and precise) and unstructured data (fuzzy interpre ng of images, free text, speech wording …) • Technical data quality issues (various formats, source availability, updates) Right Information, Right People, Right Time The right information delivered to the right people at the right time creates real value by: •Improve efficiency of operations •Provide easy access to reports •Deliver right data at the right time •Comply with regulatory •Enable better and faster decisions Increase User Adoption & Productivity THE HIGH-PERFORMANCE Increase ROI ENTERPRISE Increase confidence in decision making & compliance Improve time to decision making information repository that enables the decision makers in IT & business users and executives Improve human capital management Drive accountability Accuracy of Information Big Data circa 1960 Why Big Data? Governed Information Value Optimization Why Big Data? IoT - Fueling the Push to Big Data Big Data - Lowers Cost Practical Examples Fraud Detection Compliance Algorithmic Trading Smart Drugs Resource Scheduling Early Detection Churn Prevention IoT Self Driving Cars Consumer Analytics Demand Planning Omni Channel One Size Does Not Fit All Examples Examples Small Data Inventory levels Characteristics Characteristics Hundreds – thousands of records Typical tools Typical tools Analytical methods Analytical methods Personal computer, Excel, SPSS, R, other basic statistics software Simple statistics Millions of records, mostly structured data Server workstation computer, Relational database systems, data warehouses Advanced statistics, business intelligence, data mining, Sales records, Customers database (small and medium companies) Cloud, data centers, Distributed databases, NoSQL, Hadoop MapReduce, Distributed File Systems, Machine Learning, Predictive Analytics (megabytes) Customer databases Large Data (gigabytes- terabytes) Big Data (terabytes – petabytes) Customer interactions (social media, mobile), multimedia (video, images, free text), location-based data, RFIM Business Intelligence Maturity & Value Business Intelligence Best Practices 01 Data Governance: Formation, Education & Communication 02 Information Value Optimization 03 Best Practices: Make Good Investments Great (TCO) 04 Organization: Shared Services – Shared Vision (ROI) 05 Support Diverse Business Needs & Systems Measure Quality to Manage/Enforce Quality – Master Data Management Move from Hindsight to Insight to Foresight Use existing technology investment more efficiently – Enterprise Data Warehouse 2.0 Repurpose planned investments to achieve organizational excellence Aligned People, Process & Technology – Right Skills www.intricity.com/videos Arkady Kleyner Executive VP & Co-Founder 244 Fifth Avenue, Suite 2026 New York, NY 10001 Office: 212-461-1100 x5650 Mobile: 917-434-4783 E-mail: [email protected]