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Big Data for Bigger Decisions & Better Business Soumendra Mohanty Nasscom India Leadership Feb 16 th , 2012 Mumbai

Nilf2012_ Big Data For Bigger Decisions & Better Business_ Soumendra Mohanty

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  • 1. Big Data for Bigger Decisions & Better Business Soumendra Mohanty Nasscom India LeadershipFeb 16th, 2012Mumbai

2. The Value of Information 3. There is an Explosion in Data and Real World Events4 Billion Internetusers by 20121.3 Billion RFID tags in200530 Billion RFID today4.6 Billon Mobile Phones World WideCapital marketdata volumes grew Twitter process1,750%, 2003-06 7 terabytes ofdata every dayWorld Data Centre forFacebookClimateprocess 220 Terabytes of Web data10 terabytes of 9 Petabytes of additionaldata every day data 4. Data is becoming part of every industry and business functionBig Data is top of mind for virtually every industry, impacting core businessprocesses. Resources Upstream Oil & Gas companiesHealth monitor 40K sensors per asset Electronic health records, home (combined with 4d seismic imagery)health monitoring, telehealth, and to drive real-time production new medical imaging devices drive operations and maintenance &data deluge in a connected health reliability programs. world. Public Sector Retail USPS applies unique barcodes so itEmerging location based data, group can seamlessly induct and account purchasing and online leads allow for postage. This results in ~1BRetailers to continuously listen, pieces per day, scanned multipleengage and act on customer intent times throughout the supply chain.across the purchasing cycle.Financial ServicesCommunications Pioneers in Big Data, Capital Mobile usage data for Service Markets firms continue to innovateProviders unlock new business around low latency systems to models and revenue streams from unlock trading arbitrageOutdoor Ad placement to medical opportunities.adherence. 5. The Big Data OpportunityExtracting insight from an immense volume, variety, variability and velocity of data,in context, beyond what was previously possible. Variety: Integrate multiple relational and non-relational data types and schemas Velocity: Streaming data and large volume data movement Variability : Point in time data applying business context and criticality of time Volume: Scale from terabytes to zettabytes5 6. Business Expectations: Maximize Return on DataValue of DataReturn on Data = Cost of DataDefinitive Value of Data Data within the firewalls, in the EDWs, Data Marts, Reports,Dashboards, this data is currently used today to help/run businesses; proven, tried, tested!Perceived Value of Data Data outside the firewalls, we have a view that this data is valuable,but it is not proven yet! 7. Accentures view - how Big Data will evolve Current viewFuture view (i.e. 6-7 years out)Two disconnected worlds and ad-hocTarget: hybrid architectures are emerging to supplement RDBMS-basedarchitecture for unstructured datadata warehouses with tools for unstructured data End-to-End Data Governance, MDM, DQ Actionable Insights Data Management Insight Generation Ingestion We believe both the worlds need to co-exist for some time 1. Data can be voluminous and poly-structured prime for big data 2. Data can be less on volume but poly-structured prime for big data 3. Data can be less on volume and structured only not prime for big data 4. Data can be less on volume and semi-structured likely to become a big data candidate in the near future 8. 5 Interventions that are necessary to address the emerging data platformIntervention 1 Next generation Data Warehouse (structured data + In memory databases + columnar dataarchitectures)Intervention 2 Hadoop and the likes of cassandra etc (unstructured data)Intervention 3 Making data available from the Source (upload, refining, publishing)Intervention 4 BI Tooling, Advanced Data Visualization, Real Time Insight GenerationIntervention 5 Robust Data Management principles covering end to end data governance spanning across distributed systems, this would enable the feedback loop from ingestion to insights to actions 5 Actionable 4 Insights End-to-End Data Governance, MDM, DQ Data Management 12 Insight Generation 33 Ingestion 9. The Big Data Approach Treat data as a strategic asset, seek to maximize its value to the organization Invest in common services, data platforms and tools Rapidly prototype, deliver, and measure value-added data services, evolve over time Data-driven decision making Sharing of platform, tools and code Experimentation and continuousimprovement with academic rigor End-to-end ownership of servicesCulture