Smarter Analytics. Barbara Cain. Driving Value from Big Data

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1 Smarter Analytics Driving Value from Big Data Barbara Cain Vice President Product Management - Business Intelligence and Advanced Analytics Business Analytics IBM Software Group 1

2 Agenda for today 1 Big data and analytics 2 Two big data use cases 3 Taking advantage of big data in your business 2

3 The pressures on organizations are at a point where analytics has evolved from a business initiative to a BUSINESS IMPERATIVE More organization are using analytics to create a competitive advantage And leaders are outperforming their competitors in key financial measures Respondents who believe analytics creates a competitive advantage 1.6x Revenue Growth % 70% increase % 2.0x EBITDA Growth % 2.5x Stock Price Appreciation Source: 2010 and 2011 datasets Massachusetts Institute of Technology. Analytics: The real-world use of big data Study conducted by IBM Institute for Business Value, in collaboration with Säid Business School at the University of Oxford. Source: Outperforming in a data-rich, hyper-connected world, IBM Center for Applied Insights study conducted in cooperation with the Economist Intelligence Unit and the IBM Institute of Business Value

4 ANALYTIC-DRIVEN ORGANIZATIONS are distinguished by their ability to leverage All information All information Transaction data Application data Machine data Social data Enterprise content At the point of impact All perspectives Past (historical, aggregated) Present (real-time) Future (predictive) All people All departments Experts and non-experts Executives and employees Partners and customers All decisions Major and minor Strategic and tactical Routine and exceptions Manual and automated 4

5 What do people say about big data? Big data is primarily about large datasets We will have to replace all older systems in the new world of big data Big data is only Hadoop Older transactional data does not matter anymore Data warehouses are a thing of the past Big data is for the internet savvy companies. Traditional businesses are immune We do not have the need or budget or skills, so we do not need to worry 5

6 What is this? Big data circa 3800 B.C. Let s not forget what we ve learned 6

7 Why is big data important now? The power of Data coming together to deliver Improved Outcomes Volume Velocity 1. Improve customer interaction with Enhanced 360º View of the Customer Variety Veracity 2. Optimize operations with Operations Analysis 3. Threat & Fraud with Security and Intelligence Extension with the power of Technology 4. Enrich your information base with Big Data Exploration 5. Gain IT efficiency and scale with Data Warehouse Augmentation 7

8 Big Data Example Why you don't get taxis in Singapore when it rains? Zafar Anjum Oct. 3, It is common experience that when it rains, it is difficult to get a cab in Singapore Most people would think that this unavailability of taxis during rain is because of high demand for cab services. Big Data has a very surprising answer for you 8

9 IBM provides a holistic and integrated approach to big data and analytics Big Data Analytics Smarter Analytics Predictive Analytics Content Analytics Decision Social Media Management Analytics Analytics Integration and Governance Performance Management Risk Analytics ANALYTICS Decision Management Content Analytics Business Intelligence and Predictive Analytics Big Data Platform Systems Management Application Development Discovery Accelerators Hadoop System StreamData Warehouse Data Computing Warehouse Content Management BIG DATA PLATFORM Hadoop System Stream Computing Data Warehouse Information Integration and Governance Information Integration and Governance SYSTEMS, STORAGE AND CLOUD 9

10 Big Data Analytics Solutions for the dimensions of Big Data Volume Velocity Variety Veracity Data at Rest Terabytes to exabytes of existing data to process Data in Motion Streaming data, milliseconds to seconds to respond Data in Many Forms Structured, unstructured, text, multimedia Data in Doubt Uncertainty due to data inconsistency & incompleteness, ambiguities,deception, model approximations 50x growth in volume of data to 25 ZB by Billion RFID sensors and counting 80% of world s data is unstructured 1 in 3 business leaders don t trust their data 10

12 1. Enhanced 360º View of the Customer: Needs Optimize every customer interaction by knowing everything about them Requirements Create a connected picture of the customer Mine all existing and new sources of information Analyze social media to uncover sentiment about products Add value by optimizing every client interaction Industry Examples Smart meter analysis Telco data location monetization Retail marketing optimization Travel and Transport customer analytics and loyalty marketing Financial Services Next Best Action and customer retention Automotive warranty claims 12

13 1. Enhanced 360º View of the Customer: Diagram SOURCE SYSTEMS CRM Name: J Robertson Address: 35 West 15 th Address: Pittsburgh, PA Cognos BI Cognos Consumer Insight ERP Name: Janet Robertson Address: 35 West 15 th St. Address: Pittsburgh, PA Legacy Name: Address: Jan Robertson 36 West 15 th St. Address: Pittsburgh, PA InfoSphere Master Data Management InfoSphere Data Explorer 360 View of Party Identity First: Last: Address: City: State/Zip: Gender: Age: DOB: Janet Robertson 35 West 15 th St Pittsburgh PA / F 48 1/4/64 BigInsights Streams Warehouse Unified View of Party s Information 13

14 Consumer products company improves information access across 30 different repositories Need Intuitive user interface for exploration and discovery across 30 different repositories Encompass all global offices and be deployed quickly for a lower total cost of ownership Provide secure search capabilities across sharepoint sites, intranet pages, wikis, blogs and databases Benefits Able to identify experts across all global offices and 125,000 users worldwide Eliminated duplicate work and effort being performed across all employees Improved discovery and findability across global organization Provided internal knowledge and information that has led to improved decision making 14

15 2. Operations Analysis: Needs Apply analytics to machine data for greater operational efficiency Requirements Analyze mass volumes of machine data with sub-second latency to identify events of interest as they occur 15 Apply predictive models and rules to identify potential anomalies or opportunities as they occur Understand service levels in real-time by combining operational and enterprise data Monitor systems to proactively increase operational efficiency and avoid service degradation or outages Industry Examples Automotive advanced condition monitoring Chemical and Petroleum condition-based Maintenance Energy and Utility condition-based maintenance Telco campaign management Travel and Transport real-time predictive maintenance

16 . Operations Analysis: Diagram Raw Logs and Machine Data Real-time Monitoring InfoSphere Streams SPSS Modeler Capture Data Stream Identify Anomaly Decision Management Historical Reporting and Analysis InfoSphere BigInsights Raw Data SPSS Modeler Predict and Classify Aggregate Results Cognos BI SPSS Modeler Predict and Score Data Warehouse Store Results Federated Navigation and Discovery 16

17 Ufone reduced churn and kept subscribers happy, helping ensure that campaigns are highly effective and timely Need To ensure that its marketing campaigns targeted the right customers, before they left the network To keep its higher usage customers happy with campaigns offering services and plans that were right for them Benefits Predictive analytics is expected to improve the campaign response rate from about 25% to at least 50% CDRs can be analyzed within 30 seconds, instead of requiring at least a day Expected to reduce churn by approximately 15-20% 17

19 IBM Institute for Business Value and the Saïd Business School partnered to benchmark global big data activities IBM Institute for Business Value IBM Global Business Services, through the IBM Institute for Business Value, develops fact-based strategies and insights for senior executives around critical public and private sector issues. Saïd Business School University of Oxford The Saïd Business School is one of the leading business schools in the UK. The School is establishing a new model for business education by being deeply embedded in the University of Oxford, a world-class university, and tackling some of the challenges the world is encountering. 19

20 The study showed four phases of adoption Big data adoption Educate Explore Engage Execute Focused on knowledge gathering and market observations Developing strategy and roadmap based on business needs and challenges Piloting big data initiatives to validate value and requirements Deployed two or more big data initiatives and continuing to applying advanced analytics Percentage of total respondents Percentage of total respondents Percentage of total respondents Percentage of total respondents 24% 47% 22% 6% When segmented into four groups based on current levels of big data activity, respondents showed significant consistency in organizational behaviors Total respondents n = 1061 Totals do not equal 100% due to rounding 20

21 The study highlights how organizations are moving forward with big data Customer analytics are driving big data initiatives Big data is dependent upon a scalable and extensible information foundation Initial big data efforts are focused on gaining insights from existing and new sources of internal data Big data requires strong analytics capabilities The emerging pattern of big data adoption is focused upon delivering measureable business value 21

22 Recommendations for getting started Assess which Use Case would you most benefit from? What part of the business would benefit from expanding the data set and analytics to provide more complete answers? What part of the business is not using analytics today, but would benefit from analytics for their user community or to fuel their processes using new information sources? What information do I collect today, or what analytics do I perform, that would be highly valuable as an information set to others? Assess existing skills. You may need to: Evolve your existing analytics and information capabilities Raise your corporate competency Get ready to address performance, scalability, simplicity and cost True value is gained from a hybrid of existing and new investments 22

23 How to learn more For additional information including whitepapers and demos, please visit: Big Data Hub Smarter Analytics Reference: Big Data for Smarter Decision Making by Colin White Big Data Analytics - TDWI ebook IBV Study - Education: Social Media Analytics, YouTube video Understanding Big Data ebook Free online education at bigdatauniversity.com Services: Develop your Big Data strategy with help from IBM Global Business Services 23

24 THINK 24 ibm.com/bigdata ibm.com/smarteranalytics

25 Please note IBM s statements regarding its plans, directions, and intent are subject to change or withdrawal without notice at IBM s sole discretion. Information regarding potential future products is intended to outline our general product direction and it should not be relied on in making a purchasing decision. The information mentioned regarding potential future products is not a commitment, promise, or legal obligation to deliver any material, code or functionality. Information about potential future products may not be incorporated into any contract. The development, release, and timing of any future features or functionality described for our products remains at our sole discretion. Performance is based on measurements and projections using standard IBM benchmarks in a controlled environment. The actual throughput or performance that any user will experience will vary depending upon many factors, including considerations such as the amount of multiprogramming in the user s job stream, the I/O configuration, the storage configuration, and the workload processed. Therefore, no assurance can be given that an individual user will achieve results similar to those stated here. 25