What do Analytics, Tom Cruise and Bob Dylan have in Common? Presentation for Assimil8 September 2015 1 Tony Boobier WW Executive IBM Analytics
Objectives Analytics and Data in Context Trends Implementation 2
3
Changes across Business require new Approaches Distribution Economy Customers Competition Regulation Growth Risk
A World of Increasing Complexity I think it s time we stopped relying on instinct
The Mega Technology Trends Cloud Mobile Social Analytics
The Era of Big Data
Big Data: Fueling the Enterprise The characteristics of big data: the 4V s Volume Velocity Variety Veracity Data at Rest Data in Motion Data in Many Forms Data in Doubt
Analytics Extracts Value from Data
Business Value We are Already in the Fourth Age of Analytics Cognitive Reporting Last quarter s results Optimization Highest returning portfolio based on risk appetite Alerts Unusual activity Simulation Impact of rising rates Understands natural language, hypothesizes, adapts & learns Prescriptive Predictive Descriptive Analysis Product profitability Stochastic Optimization Managed exposure to Cat Risks Forecasting Trending analysis Modeling Predicting elasticity of insurance rates Discovery # security breaches this month vs. last
Cognitive Computing? käg-nə-tiv (adjective): of, relating to, or involving conscious mental activities (such as thinking, understanding, learning, and remembering) Cognitive computing and cognitive based systems accelerate, enhance and scale human expertise by: Learning and building knowledge, Understanding natural language and Interacting more naturally with humans than traditional programmable systems What will Your World in the era of Cognitive Computing look like?
2014 - Four Transformative Shifts 1 A solid majority of organizations are now realizing a return on investments within a year 2 Customer centricity still dominates analytics, but organizations are increasingly targeting operational challenges. 3 Integrating digital capabilities into business processes is beginning to transforming organizations 4 The value driver for big data has shifted from volume to velocity 12
Shift 1: Most organizations get a return on their analytics investments within the first year Return on investment period n = 913 63% of surveyed organizations realize a positive return on their analytic investments within one year 13
Shift 2: Customer centricity still dominates analytics but operational improvement is closing Organizational objectives for use of data and analytics 31% are using data and analytics to improve customer acquisition 22% are using data and analytics to improve customer experience 14
Shift 3: Integrating digital into business processes is transforming organizations 15 n = 1036
Shift 4: A shift from volume and variety, to velocity and veracity 2012 2014 4 Vs of big data Scalable / extensible infrastructure Scalable storage infrastructures enable larger workloads High-capacity warehouses support the variety of data Data integration topped the data priorities of most organizations Volume Variety Data at scale Data in many forms Data at speed Data as trustworthy Velocity Veracity Agile and flexible infrastructure Big data landing platform expands the structured and unstructured data available for usage Real-time analysis processing enables in the moment actions Trustworthiness is now the top data priority across majority of organizations Source "Analytics: The real-world use of big data. How innovative organizations are extracting value from uncertain data." IBM Institute for Business Value in collaboration with the Saïd Business School, University of Oxford. October 2012. 16
Implementing Analytics is like Being in a Race 17
The are 4 Types of Organisations Front Runners Joggers The Pack Spectators 10% 14% 45% 31% Using analytics to drive business processes within most business functions Using advanced analytics technologies to manage the volume, velocity and variety of data available with agility and speed. Using analytics to automate and optimize operations Using real-time analysis, and integrated and shared operational data, while piloting a wide variety of advanced components; Using analytics to drive or inform business processes within multiple business functions Still building an integrated enterprise foundation for analytics, Descriptions of the speed-driven clusters Using only the bare minimum of analytics within business processes, yet have aspirations Few have the technical capabilities to support analytic capabilities beyond basic reporting and compliance levels Source: Analytics: The speed advantage, IBV 2014IBM
Front Runners Outpace the Rest of the Field 69% of Front Runners created a significant positive impact on business outcomes using data and analytics in the past 3 years 60% of Front Runners created a significant positive impact on revenues using data and analytics in the past three years 53% of Front Runners created a significant competitive advantage using data and analytics 19
Successful Organizations have 3 key capabilities Like Front Runners, they : Acquire a diverse dataset and manage it for speed Analyze a robust and unique dataset and rapidly create meaningful insights Act on data insights quickly to achieve targeted business outcomes Note: Chart only shows percentage of respondents who indicated (4) Well or (5) Very Well 20
Acquire Source and manage data in ways that create flexibility and agility in how and when the data is used Key capability characteristics to acquire and manage data with speed 1 Blend traditional data infrastructure components with newer big data sources 2 Use real-time data processing and analysis to act in the moment 3 Implement information governance to accelerate trust, integration and standardization within their data environments 21
Analyse Create meaningful insights and quickly analyze robust datasets Key capability characteristics to accelerate the analysis of data 1 Analyze diverse datasets to create more meaningful insights 2 Use advanced analysis tools 3 Develop talent which combines business knowledge with analytics 22
Act Acting on data-driven insights to positively impact business outcomes Key capability characteristics in the ability act on data insights quickly 1 Integrate digital and process transformations to quickly give insight that drives rapid business outcomes 2 Embed analytics within business processes to enable precise, quick actions 3 Use comprehensive visualisation to quickly understand and act on large or dynamic datasets 23
IBM Watson Analytics The Democratization of Analytics Single Interface Explore > Predict > Assemble Quick start intuitive interface Guided discovery & visualization Key business driver insights Easy data upload and search capabilities Natural language dialogue Dashboard and storytelling authoring
WatsonAnalytics.com get started today!
Are there other ways of Winning the Race? 26
Can a business focus on finding differentiation and just rent the rest? IBM Bluemix Digital Innovation Platform
Come gather 'round people Wherever you roam And admit that the waters Around you have grown And accept it that soon You'll be drenched to the bone If your time to you is worth savin' Then you better start swimmin' Or you'll sink like a stone For the times they are a-changin'. Ref Bob Dylan
Boobier@uk.ibm.com @tboobier