Big Data and Analytics 21 A Technical Perspective Abhishek Bhattacharya, Aditya Gandhi and Pankaj Jain November 2012

Transcription

1 Big Data and Analytics 21 A Technical Perspective Abhishek Bhattacharya, Aditya Gandhi and Pankaj Jain November 2012

2 Between the dawn of civilization and 2003, the human race created 5 exabytes of data Now we generate that every 2 days Total amount of global data is expected to grow to 2700 exabytes during 2012, up 48% from Exabyte = 1,000,000 Tb 2

3 Big Data Defined Techniques and technologies that make handling data at extreme scale affordable. VARIETY Structured -> Semi-structured -> Unstructured VOLUME Terabytes -> Exabytes VELOCITY Batch -> Streaming Data Source: Forrester Research, ctoforum.org 3

4 Evolution of Analytics Descriptive Predictive Prescriptive 1990s 2000s Late 2000s 2010s What happened? Standard Reporting Why did it happen? Query / Drill down What could Happen? Simulation What should I be doing? Optimization 4

5 How is Big Data Analytics Different? BIG DATA ANALYTICS Complex Algorithms / Linear Programming Experimental, Ad Hoc Mostly Semi- Structured External + Operational 10s of TB to 100's of PB's Mathematics Workload Variety Sources Volumes TRADITIONAL BI Addition (Aggregation) Repetitive Structured Operational GBs to 10s of TBs 5

6 The Big Data Lifecycle Manage Insight Enrich Source: hadoop.apache.org; Microsoft.com; ibm.com 6

7 Manage Data ANY DATA, ANYWHERE, ANY SIZE Relational Non-Relational Streaming Data Movement Source: hadoop.apache.org; Microsoft.com; ibm.com 7

8 ENRICH by Combining and Refining! Refine Discover Combine Source: Microsoft.com, oracle.com, ibm.com 8

9 Insight Anywhere, Any Device, Any User ANY DATA, ANYWHERE (DEVICES), ALL USERS Source: Microsoft.com, oracle.com, ibm.com 9

10 BIG DATA REQUIRES AN END-TO-END APPROACH INSIGHT Self-Service Collaboration Corporate Apps Devices F(x) ENRICH Discover Combine Refine MANAGE Relational Non-relational Analytical Streaming Source: Microsoft.com, ibm.com 10

11 Product Proliferation We are spoilt for choice in the marketplace 11

12 12 Source: Product Logos of Big Data Companies

13 Aggregate Oriented DB Enterprise Data Warehouse In-Memory Stores Hadoop Source: Product Logos of Big Data Companies 13

14 ENTERPRISE DATA WAREHOUSES Requires referential integrity and structured data - lack of flexibility and agility Analytics and aggregation using OLAP Shared-nothing MPP Architecture enable massive scale out architecture Volume Variety Ingestion Velocity Processing Velocity Analytics Complexity Best suited for Analytics using structured data Key considerations include Data Quality/Governance, structuring data, segmenting analytics workloads 14

15 HADOOP Volume Variety Ingestion Velocity Processing Velocity Analytics Complexity Java-based open-source framework Hadoop Core MapReduce and HDFS Structuring delayed until analytics performed Flexibility as business grows/evolves Flexibility to build complex algorithms/models for analytics purposes Only option for Petabyte Range Best suited for batch-oriented analytics Works best when it s possible to design analytics algorithms as scatter-gather Key considerations: HDFS- file size, map-reduce algorithm., sequential file processing, data distribution 15

16 IN-MEMORY STORES Volume Variety Ingestion Velocity Processing Velocity Analytics Complexity Maintains data in-memory and SSD Leverages shared-nothing architecture to provide scalability In memory Databases (IMDB) row or column oriented schema In-memory Data grids (IMDG) keyvalue and de-normalized IMDB: Best suited for real-time analytics on structured data. Used for specialized data marts as well as for OLTP needs Key considerations: Data organization, parallel query IMDG: Suited for fast key-based data access patterns or processing. Key considerations: data distribution, keydefinition, data-process co-location 16

17 AGGREGATE ORIENTED DB Volume Variety Ingestion Velocity Processing Velocity Analytics Complexity Highly scalable and available distributed data-stores De-normalized data structures, data organised as Aggregates. Data saved as key-value, documents or columns Enable faster read/writes on aggregates Best suited for analytics on semistructured data where access patterns that can be bound in a key Key considerations: data distribution, aggregate structure, key-definition, data-process co-location 17

18 Product Category Comparison Volume Variety Ingestion Velocity Processing Velocity Analytics Complexity Enterprise Data Warehouse Hadoop In-Memory Stores Aggregate- Oriented DB Specific product selection will depend on an assessment of data and analytics requirements 18

19 ADVANCED PHYSICAL PORTFOLIO OPTIMIZATION in te gra tion cov er age pre vis i bil i ty Aditya Gandhi COPYRIGHT 2011 SAPIENT CORPORATION CONFIDENTIAL 19

20 CHALLENGE Making the next buck is harder Constantly changing environment Decisions are narrow or historical COPYRIGHT 2011 SAPIENT CORPORATION CONFIDENTIAL 20

21 CHALLENGE Vast but un-captured information Increasing volume / complexity Coarse-grained operations COPYRIGHT 2011 SAPIENT CORPORATION CONFIDENTIAL 21

22 CONCEPT Toolset like a chess simulator Takes in current state of the board Provides best actions to take COPYRIGHT 2011 SAPIENT CORPORATION CONFIDENTIAL 22

23 Framework Markets Price forecasts Forward Curves Volatilities Costs and Tariffs Asset Characteristics Commodity In Commodity Out Transport Storage Processing Plants Beginning positions Storage Inv In transit Inventory Exch Imbalance Optimization User Actions TARGET TRANSACTIONS: Mkt Optimization formulates the optimal shape of transactions based on target portfolio and beg positions EXECUTED TRANSACTIONS: Exogenous and endogenous constraints and factors cause deviation from plan COPYRIGHT 2011 SAPIENT CORPORATION CONFIDENTIAL 23

24 Retail Analytics Pankaj Jain COPYRIGHT 2011 SAPIENT CORPORATION CONFIDENTIAL 24

25 Aspects of Retail Analytics Market Basket Analytics Credit and Loyalty Card Analytics Shopper Insight Store Location Data Geo Demographics Category Segmentation Product Affinity Brand Knowledge Lifestyle and Life Stage Segmentation Brand Awareness Impulse Shopping Sociology Income/Education Infrastructure Customer Segmentation Loyalty Store Location Store Size Store Format Competitive Analysis COPYRIGHT 2011 SAPIENT CORPORATION CONFIDENTIAL 25

26 Retail Analytics Business Problems When will customer visit the store next? How much money will customer spend during the next visit? How many customers are price sensitive? How can I find gaps in the product range? How do I balance my product range across store formats? Do my shoppers buy across range? What should be delisted to introduce new product COPYRIGHT 2011 SAPIENT CORPORATION CONFIDENTIAL 26

27 Analytics Lifecycle Insight Summarized Data Template Reports Rapid Analysis POS & Other Data Poor Structure Volume Inconsistent Organized Data Volume Segmented Continuously Improved Processed Data Segmentation Complex Algorithm Attributes Enrich COPYRIGHT 2011 SAPIENT CORPORATION CONFIDENTIAL 27

28 Business Outcome Big Data Small Insights Effective Promotions and Communication o Over 8% increase in Steadfast customer and 5% more sales o Over 80% acceptance of offers o Over a million $ growth in the category Over 60% growth in the range with higher repeat sales and new customers due to Range analysis. Addition of three new aerated drinks increased the sales of that category by 12%. Overall higher consistent business growth. COPYRIGHT 2011 SAPIENT CORPORATION CONFIDENTIAL 28

29 Conclusion Big data has more dimensions than just "Big" Lifecycle is critical Choose your product and platform wisely Big data analytics is lot more insightful than just analytics o Big Data Small Insights o Ask the right question Ramp up your college statistics and mathematics! COPYRIGHT 2011 SAPIENT CORPORATION CONFIDENTIAL 29

30 Thank You! 30