Big Data & Analytics. The. Deal. About. Jacob Büchler Cand. Polit. IBM Denmark, Solution Exec IBM Corporation

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1 The Big Data & Analytics Deal About Jacob Büchler Cand. Polit. IBM Denmark, Solution Exec. 1

2 Big Data is All Data from Everywhere Big Data Is Becoming The Next Natural Resource We have for the first time an economy based on a key resource [information] that is not only renewable, but self-generating. Running out of it is not a problem, but drowning in it is. -- John Naisbitt Volume Use greater amounts of data Velocity Use data more quickly Use more types data Use uncertain data Variety Veracity 2

3 How is Big Data transforming the way organizations analyze information and generate actionable insights? 3

4 Paradigm Shifts Enabled by Big Data Traditional Approach Big Data Approach Analyzed Information Available Information Analyze Small Subsets of Information Analyze ALL Available Information Analyze All Information Leverage more of the data being captured 4

5 Paradigm Shifts Enabled by Big Data Traditional Approach Big Data Approach Analyzed Information Analyzed Information A Small Amount of Carefully Cleansed Information A Very Large Amount of Messy Information Carefully Cleanse Information Before Analysis Analyze All Information As Is Reduce effort required to leverage data 5

6 Paradigm Shifts Enabled by Big Data Traditional Approach Big Data Approach Analyze data AFTER it has been processed and landed in a Warehouse or Mart Analyze data IN MOTION as it is generated, in real-time Leverage data as it is captured 6

7 Paradigm Shifts Enabled by Big Data Traditional Approach Big Data Approach Hypothesis Question Data Exploration? All Information Analyzed Information Answer Data Actionable Insight Correlation Start with Hypothesis, Test Against Selected Data Explore ALL Data, Identify Correlations Data leads the way and sometimes correlations are good enough 7

8 Complementary Analytics Traditional Approach Structured, analytical, logical New Approach Creative, holistic thought, intuition Internal App Data Mainframe Data Transaction Data OLTP System Data Data Warehouse Structured Repeatable Linear Hadoop and Streams Unstructured Exploratory Dynamic Multimedia Web Logs Social Data Text Data: s Sensor data: images ERP Data RFID Traditional Sources New Sources 8 8

9 The 5 Key Use Cases Big Data Exploration Find, visualize, understand all big data to improve decision making Enhanced 360 o View of the Customer Extend existing customer views (MDM, CRM, etc) by incorporating additional internal and external information sources Security/Intelligence Extension Lower risk, detect fraud and monitor cyber security in real-time Operations Analysis Analyze a variety of machine data for improved business results Data Warehouse Augmentation Integrate big data and data warehouse capabilities to increase operational efficiency 9

10 Studies show that two thirds of banks have big data activities underway; which enable significant competitive advantage Big data activities Realizing a competitive advantage % % 58% 70% increase Respondents were asked to describe the state of big data activities within their organization. Respondents were asked To what extent does the use of information (including big data) and analytics create a competitive advantage for your organization in your industry or market. Respondent percentages shown are for those who rated the extent a [4 ] or [5 Significant extent]. The same question has been asked each year. Total respondents n = 1144 Source: The real world use of Big Data, IBM & University of Oxford

11 Customer-centric analytics followed by Risk are the primary domains for financial services firms to leverage Big Data capabilities Financial Services 16% 2% Customercentric outcomes Operational optimization 21% 50% Risk / financial management New business model Employee collaboration 11% Source: The real world use of Big Data, IBM & University of Oxford

12 Selected Big Data Customer Case Studies 12

13 BNL (Gruppo BNP Paribas) leverages unstructured client data to improve customer retention Need Link to the case study om/common/ssi/ecm/e n/imc14573usen/imc1 4573USEN.PDF Drive customer retention activities based on behaviors instead of only transactions Leverage branch teller notes, call center notes and client s to identify changing client behaviors Track social media sentiment analysis to measure impact of targeted campaigns Benefits Reduce attrition from 6% to 3% Optimize offers and cross sell to increase average products per customer from 1.4 to Improve client advocacy

14 ING rapidly analyzes client behavior to personalize and optimize offers and treatments Need: Rapidly analyze client behaviors to offer personalized marketing offers across all channels Solution: IBM PureData for Analytics, Unica Interact Benefits: Improved offer acceptance for incomplete product requests by 50% A 98 percent increase in key client interaction performance metrics such as click stream data, analytical reporting and queries The marketing division is currently exploring adding additional applications on the existing platform given the success of the solution

15 Teikoku Databank, Ltd. shortened the time to process billions of textual data items from several days to 30 minutes Need Teikoku Databank, Ltd. owns data on millions of companies. To stay competitive, they wanted to analyze this proprietary information in combination with big data gathered from the Internet. Benefits Able to process billions of items of textual data in 30 minutes Analyzing 4.75-fold more data for customers, which has improved their competitive advantage 15 Home

16 Big Data is Enabling a Paradigm Shift in Analytic Approaches Traditional Approach Structured & Repeatable Analysis Big Data Approach Iterative & Exploratory Analysis Business Users Determine what question to ask IT Delivers a platform to enable creative discovery IT Structures the data to answer that question Business Explores what questions could be asked Monthly sales reports Profitability analysis Customer surveys Customer insight Fraud scheme discovery Asset optimization 16

17 How have most companies made information available for decision making across the enterprise? 17

18 Handling structured information - exiting database management technologies Predictive analytics Legacy source systems Data Warehouse Deep BI Operational analytics Large scale structured data management Business analytics & Reports Traditional structured data 18

19 Un-structured/Streaming data Structured data Big Data & Analytics Capabilities behind Big Data? structured/un-structured Information Users of Information + Real time streaming (in-motion) Predictive Analytics Video/audio Network Geospatial Predictive Data warehouse Business analytics & Big Data Storage & Analyze (at-rest) Ad hoc Text/entity analytics Data mining Machine learning Deep BI Operational analytics Large scale structured data management Reports 19

20 You can t control the evolution 20

21 THINK 21

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