Breaking Bad Data: The Journey to Data-fuelled Digital Transformation

1. Breaking Bad Data The journey to data fueled digital transformation Jorgen Heizenberg CTO I&D NL Capgemini 25th May 2016

2. #INFA16 Walter White (Bryan Cranston) (c) AMC

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5. #INFA16Source: www.zdnet.com ©

6. #INFA16 Business Cases | Automotive Copyright © Productivity „Change the fan belt in 6 days to prevent A/C from failing.“ Environment. „Improve your fuel efficiency by shifting before 2.200 RPMs.“ Performance. „Extend the life of your vehicle and get more power by using higher octane fuel.“ Performance. „Deactivate ECO Mode to get more power for passing.“ Safety. „Avoid another accident by maintaining a 5 meter distance.“ Service. „Stop ahead. Roadside assistance is behind you.“ Sales. „Upgrade to a 5 series to get the performance you need.“ Economy. „Save money by down- shifting instead of breaking.“ Sales. „Change your tires in 2 weeks to get improve performance and ensure safety.“ Service. „Add coolant to prevent over-heating.“

7. #INFA16 Performance. „Improve output 2% by opening air vent „A.“ Sales. „You need a new A27 fuse in 96 hours.“ Safety. „Avoid accidents by closing the lid before activating the machine.“ Environment & Sales „Decrease emissions by using our new synthetic lubircant.“ Productivity. „This module will fail in 7 hours. A service technician is already on the way.“ Productivity & Sales. „Your hopper will be empty in 3 hours.“ Efficency. „“Combine parts in trays to reduce tray inventory and reduce conveyor usage.“ Service & Sales. „Order parts or schedule service. This module has not been turned on in 2 days.“ Safety & Productivity. „The unit will over-heat in 3 hours. Add coolant or turn-off.“ Safety. „Schedule training. This unit is not being operated properly.“

8. #INFA16 Big, Agile and Diverse data GB TB PB GB/s MB/s KB/s Day Hour Min Sec Sub-sec BIG FAST Data Warehouses NoSQL Event Processing Tools Hadoop In-memory databases Historical Data StreamingData (Events) OLTP Databases *Source: Capgemini’s TechnoVision 2015

9. #INFA16 Makes businesses thrive on insights in many different ways … FOUR WAYS in which data-driven insights are changes businesses Efficiency and cost focus Use of insights to identify potential operational efficiencies in the business and so reduce costs. But also: IT cost reduction through modernization of the data landscape, leveraging next-generation Big Data technology. Growth of existing business streams Insights are used to enhance existing market offers through better understanding of customers/ consumers and of the effectiveness of marketing & sales. Growth through market disruption from new revenue streams Big Data is changing traditional business boundaries. Enterprises explore business areas that were unknown or unthinkable before. Monetization of data itself, with the creation of new lines of business. In some industries – such as in financial services, media & entertainment and telecommunications - it is already apparent that the data organizations hold is becoming their major product. Source: Big & Fast Data: The Rise Of Insight-driven Business

10. #INFA16 … creating direct business value. High rail usage, complex assets, increasing data volume (track sensor data) Reduce Maintenance Cost; Improve Asset Availability & Service Delivery Reduced Maintenance effort & Cost; Higher Asset availability; Improved service & performance Saved 112 MIO CAPEX Saved 13 MIO OPEX & Less delay Linear Asset Decision Support solution, helps Network Rail get access to enhanced insight at the point of action, ensuring reduced maintenance cost, higher asset availability and improved service delivery Linear Asset Decision Support solution; Consolidated data, consistently available, Visual, easy to interpret format; in the hands of the track engineers Our track engineers across the country can now access critical asset-related data (with LADS solution) where and when they need it the most, enabling them to better target the most appropriate type of work to the right place. Getting our asset interventions right the first time, saves cost and helps us run an even safer, better performing railway. – Patrick Bossert, Director of Asset Information #INFA16 INNOVATION AWARD WINNER

11. #INFA16 What do companies use digital initiatives for? Previous research: companies were neglecting operations in their digital transformation Source: Capgemini Consulting – MIT Sloan Management Review, “Embracing Digital Technology: A Strategic Imperative”, 2013 43% 40% 40% 30% 26% Enhance existing products and service Improve customer experience Expand reach Launch new products and services Automate operational processes

12. #INFA16 70% 18% 12% Things are changing - 70% of organizations now prioritize operational analytics over front office Source: Capgemini Consul2ng and Capgemini Insights & Data, Opera2ons Analy2cs Survey, December 2015 Percentage of companies which now focus more on opera2onal analy2cs than on customer/ front oﬃce analy2cs 49% 50% 68% 68% 75% 75% 75% Neutral Focus more on operational analytics than on customer/ front office analytics Focus more customer analytics than on operational analytics

13. #INFA16 Areas where Manufacturing Companies can use Data to Gain Benefits The size of the prize explains the strategic shift toward operations from customer-facing initiatives Source: Technet, “The $371 Billion Opportunity for “Data Smart” Manufacturers”, May 2014 $162B $117B $55B $38B Employee productivity Operational improvement Product Innovation Customer facing

14. #INFA16 However, only 18% of organizations are achieving the desired benefits across their operations Source: Capgemini Consulting and Capgemini Insights & Data Low High 41% 21% 18%20% Strugglers Laggards Game Changers Optimizers Success in Realizing Benefits LevelofImplementation Analytics initiatives are extensively integrated into business operations Analytics initiatives are still at Proof of concept stage Level of Implementation: Low Medium indicates analytics initiatives are still at Proof of concept stage or are integrated into some of business operations. High indicates Analytics initiatives are extensively integrated into business operations Success in Realising Benefits: Low Medium indicates firms are not able to realize desired benefits or moderately successful in realising desired benefits. High indicates firms are highly successful in realising desired benefits from analytics initiatives

15. #INFA16 Data What are Game Changers doing differently? Characteristics of Game Changers Governance Source: Capgemini Consulting and Capgemini Insights & Data 11% 27% 23% 45%43% 59% 48% 68% Integration of Data to Achieve Single View of Operations Data Routinely Collect Unstructured Data to Improve the Quality of Data Use External Data to Enhance Insight High Utilizaton of Operations Data Laggards Game Changers 28% 52% Analytics is an Essential Component of Decision making Process Laggards Game Changers

16. #INFA16 Operational Analytics Transformation Path to Value Source: Capgemini Consulting and Capgemini Insights & Data Low High Strugglers Laggards Game Changers Optimizers Success in Realising Benefits LevelofImplementation High !  Develop a structured view of Analytics Initiatives across the organization !  Build B-case and assess operations-wide impact of analytics initiatives !  Identify availability and level of integration of data within organization !  Ensure continuous executive sponsorship for analytics initiatives !  Build centralized teams to coordinate efforts !  Appoint analytics champions to steward analytics initiatives !  Align initiatives to organisation’s strategic objectives !  Institute governance mechanism to implement insights across levels !  Set-up a feedback loop with stakeholders to review performance

17. #INFA16 Business Value impacted by Business & IT alignment

18. #INFA16 Manage !  Data governance and security !  Collaboration !  Value generation !  Program delivery !  Data-driven culture !  Information strategy !  Skill development !  Master data mgmt !  Metadata mgmt !  Data quality mgmt !  Operations, SLA’s !  Orchestration Supported by (Business) Architecture ValueAct Insight AnalyzeInformationProvideSource data Customer profitability Operational cost cutting Risk prevention Market share increase Business Applications !  Customer campaign !  Trigger activity Business Processes !  Trigger event !  Adjust process Decision makers !  Approve/reject business opportunities !  Develop new business models and products Customer Experience !  Next best offer !  Customer lifecycle !  Customer value Operational Process Optimization !  Supply chain optimization !  Asset maintenance !  Quality management !  Process optimization Risk, Fraud !  Financial risk !  Operational risk !  Fraud !  Cyber crime Disruptive Business Model !  New products !  New business models Search What is relevant? Explorative How does it work? Descriptive What happened? Diagnostic Why did it happen? Predictive What will happen? Prescriptive How to act next? Data asset descriptions Processed data !  Measures, KPI’s !  Dimensions, Master data Granular data !  Events !  Context information Internal data !  IT managed applications (ERP, SCM, CRM) !  Business owned informal data !  Documents, mail, images, voice, video !  Web and mobile apps !  B2B !  Internet, Social, Internet of Things (machine, sensor) !  Third party data: market, weather, climate, geolocation !  Open data !  … External Data Business performance Performance improvement

19. #INFA16 Manage Provide Analyze Act Information Source data Insight Value Explorative Data Exploration Descriptive Reporting Diagnostic Ad-hoc Querying Predictive Data Mining, Machine Learning Prescriptive Next Best Action Search Search, Retrieval !  Data governance and security !  Collaboration !  Value generation !  Program delivery !  Data-driven culture !  Information strategy !  Skill development !  Master data mgmt !  Metadata mgmt !  Data quality mgmt !  Operations, SLA’s !  OrchestrationStream Describe, classify Ingest Store Prepare Refine, blend Manage lifecycle Structured data !  IT managed applications (ERP, SCM, CRM) !  Business owned informal data !  Third party data Unstructured Data !  Social !  Documents, mail, images, voice, video Semistructured data !  Internet !  Internet of Things (machine, sensor) !  Server logs !  B2B Business ApplicationsBusiness ProcessesDecision makers That allows for Big, Agile and Diverse data Data at rest Data in motion Data WarehouseData Asset Catalog !  Index !  Tags !  Metadata Aggregated data Dimensional & master data Measures, KPI’s Load Extract Transform Manage Quality Aggregate Historize Data Lake Business rules Predictive modelsBusiness results Alerts Signals Granular data EventsContextual information Analytical Sandbox

21. #INFA16 While placing a premium on data quality, governance and Security Data Improvement Areas: 1.  Data Quality (77%) 2.  Data Security (75%) 3.  Standardization (71%) Source: Informatica & Capgemini research May 2016

22. #INFA16 The journey to data fueled digital transformation Defining digital business objectives and the design of a data management roadmap to harness new data sources Digital objectives & data management roadmap Ensure executive sponsorship and leadership of big data initiatives. Anything below boardroom level will not be enough to drive lasting change. Executive Sponsorship Create a robust, collaborative data governance framework that enables organizational agility, while incorporating data security, and data quality. Data Governance Framework Extend existing information architecture by modernizing data warehousing systems while integrating new big data technologies. Extend data landscape Work towards a dynamic, data-driven culture that involves both executives and employees at the earliest stages in developing, using and improving big data solutions. Data driven culture and… Source: Informatica & Capgemini research May 2016

23. #INFA16 Jorgen Heizenberg – Capgemini @jheizenb Breaking Bad Data