Fulfilling your data obsession

Agenda Why obsess about data? Data equipping your processes State of the nation 3

Why obsess about data? Customer experience Operational excellence Business innovation 4

Transforming the customer experience 5

Improving operational performance 6

Business model innovation The base interest rate on its current account is 0.25%. But for every 2,000 likes on Facebook the bank gets, the bank will add 0.05% to the interest. 4,000 likes will take it to 0.35% interest, rising to a maximum of 0.5%. 7

In the digital age, data is critical everywhere Customer Online Production Digital Transformation Strategy Logistics Finance Maintenance Workforce 8

Who is eating your lunch? 95% of respondents in a recent IBM Business Value Institute survey said that big data and analytics are required to match or outstrip competitors A majority of organizations report recouping their analytics investments within 7 to 18 months of implementation 9

Making marginal gains 11

A data-equipped example Large Spanish bank with over 13 million customers Have built an extensive infrastructure for analytics and big data Moved all existing data silos the new platform Now have over 80 analytics projects running across all areas of the business 80% of their processes are data-equipped and automated Have a single information repository and a universal corporate data model 12

Data analysis centre at Caixa Bank 13

Your View? What proportion of processes in your organization are data equipped? A. Most of our processes B. Some of our processes C. Few of our processes Vote now! 14

Strategic thinking is required Which are the critical areas of your business where new sources of data are available? How is this data changing your approach to these areas of your business? How are you currently managing that information through its lifecycle? What do you need to do to improve the data - > insight value chain? Where are the priority areas for investing to enhance this capability? 15

Data equipped processes need broad capabilities Discovering and developing data assets Understanding of information management principles Having a common data dictionary and master data view Ability to easily integrate data from different sources Ease of data discovery and visualization Culture of data driven decision making Understanding the analytical needs of the business Improving data quality and consistency A first-class platform for information management 16

Key trends in BI and analytics Exploratory analytics is the beginning of the evolutionary endpoint for BI. Smart governance and data management will shape exploratory analytics' success. Cloud will become the default home for new data. Embedded analytics will provide incremental benefits in a way that is invisible to most users. 18

Your View? What proportion of Dutch companies are planning to invest in new and upgraded Big Data solutions in 2016 A. < 20% B. 20 40% C. 40 60% D. 60 80% E. 80 100% Vote now! 19

BI & Analytics Investment Plans in the Netherlands for 2016 Real-time streaming analytics Analyzing unstructured data Big data Self-service visualization tools Enterprise performance management Data quality/data profiling Master data management Predictive analytics Embedded analytics Integrating unstructured data sources Query and reporting tools 63% 60% 57% 57% 57% 55% 52% 50% 47% 42% 37% N=60 0% 10% 20% 30% 40% 50% 60% 70% 20

Investment plans for Big Data in Western Europe 2016 Belgium France Germany Italy Netherlands Norway Spain Sweden UK Install new or replace Transform Enhance Maintain N=982 Do not have 21

A rapidly changing industry 22

Energy company plans for deploying analytics Work scheduling Customer insight Asset optimization Grid management Predictive maintenance Revenue protection Call center optimization Product development Workforce optimization Fully deployed Trialling Planning Considering N=371 No plans 23

A data-equipped example Energy distribution company in the Netherlands, headquartered in Arnhem Smart grid sensors generate over 3 billion records p.a. Combines transactional, geospatial and real-time grid data In-memory technology used for more accurate demand forecasting, done 96% faster Have created 85 models to analyze their operations Applications in load forecasting, asset condition monitoring, energy insights for consumers and many more 24

A data-equipped example Integrated energy company in the UK with over 5 million customer accounts Analytics applications include customer segmentation, churn assessment, probability modelling and product placement modelling Combines demographic data and lifestyle data with inhouse data to model customer propensity to churn Top 25% of customers are four times more likely to take dual fuel - and are therefore far less likely to churn For parent company EDF, the rollout of 35 million smart meters will generate 1.8 trillion data records annually 25

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