The Future of Big Data in Business Intelligence

1. Institute of Technology Management Projektskizze «Competence Center Automotive Intelligence» 23. September 2011 Robert Winter, Felix Wortmann Institut für Wirtschaftsinformatik Big Data – Hype or Radical Change?Zug, January 14th 2015 Felix Wortmann Assistant Professor for Technology Management, Scientific Director Bosch IoT Lab

2. Agenda 1 Big Data – Starting Point, Value Proposition and Technology 2 Rethinking Big Data – Towards Data-driven Innovation 3 Data-driven Innovation – Key Challenges 4 Conclusion

3. Rise of Big Data = Fall of Business Intelligence? Google Trends Analysis Big Data Business Intelligence Data Warehousing 2005 2007 2009 2011 2013

4. Business Intelligence is there to stay Business Intelligence is about technologies, applications, and processes to facilitate decision making. BI has evolved with a focus on internal, structured data. Data Warehouses as high quality and reliable information backbones. Source: Wahlster, 2013; Siemens, 2012; Gartner, 2013.

5. Unstructured DataGoogle: 1 Petabyte of Data per hour Source: Economist, 2010.

6. KLM geo-spatially tagged mobile transactions per day 600 billion Semi-structured DataUS mobile networks: 600 billion geo-spatially transactions per day Source: ParStream, 2011. Source: IBM, 2011.

7. Big Data in a nutshell Big data is a category of technologies, applications, and processes for gathering, storing, accessing, and analyzing data that goes beyond the capabilities of todays BI systems: Three Converging Domains Business intelligence Content intelligence Real time intelligence Volume: From gigabyte and terabyte to petabyte and exabyte. 2 3 1 Velocity: From hours and minutes to seconds and milliseconds. Variety: From structured data to semi- and unstructured data. Source: TDWI, 2011; Wixom and Watson, 2010.

8. Big Data technology Three major big data technology domains do exist. Vision: One infrastructure (“data operating system”) for unified data storage, processing and analysis. Technology Domains A. Business Intelligence C. Real Time Intelligence B. NoSQL& Content Intellig. Domain of relational databases Innovation on the basis of main memory approaches Innovation on the basis of Hadoop capabilities Relational databases are there to stay > Domain of Hadoop software stack HDFS and HBase for data storage MapReduce, Pig and Hive for data processing Lucene and Mahout for language processing > Current discussion dominated by Kafka, Storm Kafka as a distributed, persistent message broker Storm as a real time computation system Both proven technologies but early Apache projects > > > > > > > > > > Structured Data Unstructured /structuredData Data Streams

9. First wave of Big Data use cases Big data as a key driver for Social CRM use cases Marketing Sales Service &Support Innovation Collabo-ration Customer Experience Social Marketing Insights Social Sales Insights Innovation Insights Social Support Insights Collaboration Insights Seamless Customer Experience Rapid Social Marketing Response Rapid Social Sales Response Rapid Social Response Crowdsourced R&D Enterprise Collaboration VIP Experience Social Campaign Tracking Proactive Social Lead Generation P2P Unpaid Armies Extended Collaboration Social Event Management Source: Altimeter, 2011.

10. Agenda 1 Big Data – Starting Point, Value Proposition and Technology 2 Rethinking Big Data – Towards Data-driven Innovation 3 Data-driven Innovation – Key Challenges 4 Conclusion

11. Data-driven innovation Big Data is not mainstream yet. Vendors are pushing hard to bring their solutions to the Fortune 1’000. Companies realize: Big Data is just one avenue towards data-driven business innovation. Value proposition A. Example Ikea Data-driven products and services C. IoT Examples High Resolution Mgmt, data-driven products and services Locus of innovation B. Example Target Mass individualization Valuechain Fully exploiting existing data assets Leveraging new data assets Resource-based lever

12. A. Example Ikea: Data-driven products and services Leveraging existing data to create customer value.

13. B. Example Target: Mass individualization Often, it is not about more raw data but about moving from the average (customer segment analysis) to the individual A. Pregnancy-Prediction B. Foster Automatic Beh. C. Hide Activities • Collect shopping data • Identify pregnant customers and their due date • 25 products serve as the basis to calculate a pregnancy score and estimate the due date • Basis: Pregnant women load up on vitamins or buy unusually large quantities of unscented lotion, … • Compare shopping behavior of individual to known behaviors • Send ad booklet to customers specifically designed for them • Pregnancy: Coupon that’s useful a month before due date might be worthless • Timing matters • “As long as a pregnant women thinks she hasn’t been spied on, she’ll use the coupons.” • “Mix ads for things we knew pregnant women would never buy with baby ads” • Result: Baby coupons look random and are used Source: Duhigg, 2012.

14. C. IoTExamples The Internet of Things (IoT) is about the convergence of the digital and physical world, i.e. physical objects get connected to the internet. Thing-based Function Data-drivenService = THING + DATA + Bulb Presence Light Security Storage capacity AutomaticReplenishment Bin Stock Towing vehicle Predictive Maintenance, Optimization Tractor Diagnostics Innovation through High Resolution Management: We only can manage what we can measure. That’s the benefit of quantifying the physical world. Source: Fleisch, 2013.

15. Agenda 1 Big Data – Starting Point, Value Proposition and Technology 2 Rethinking Big Data – Towards Data-driven Innovation 3 Data-driven Innovation – Key Challenges 4 Conclusion

16. Privacy and ethics It is us who implement the “good” or “bad”. Trust is a necessary precondition for business.

17. Culture Big Data is all about innovation. However, companies show a passionate reliance on the past. 10 Barriers for Breakthrough Innovation 1. Cultural inertia and unwillingness to change a ‘winning formula’ 2. Contentment and complacency 3. Processes and structures that become rigid and unyielding 4. Strong and unquestioned beliefs and corporate sacred cows 5. Conservatism and fear of losing the current profit stream 6. Strong vested interests and politicking 7. Managerial overconfidence or even arrogance 8. Unyielding habits and company norms 9. Overreliance on what has worked in the past 10. Passive and uncritical thinking and quick dismissal of information that conflicts with current view Source: Markides, 2000; Gassmann, 2011.

18. Hybris: Example Google Flu Trends Big data are a supplement for, rather than a substitute to, traditional data collection and analysis. Source: Lazer et al. 2014.

19. Agenda 1 Big Data – Starting Point, Value Proposition and Technology 2 Rethinking Big Data – Towards Data-driven Innovation 3 Data-driven Innovation – Key Challenges 4 Conclusion

20. Big Data – Hype or Radical Change? Data-driven innovation will continue to disrupt whole industries. However, data-driven innovation goes beyond big data. 1 CFO as Head of IT • BI is there to stay • There is a new set of complementary Big Data tools that go beyond BI in respect to volume, velocity, variety • High Resolution Management as a mega trend especially in times of current economic conditions • Corporate Finance as a role-model and partner to assess data-related opportunities 2 CFO as Head of Corporate Finance • Beyond Big Data: Ultimately it is about data-driven business model innovation • Data-driven innovation needs a top-down and bottom-up approach • Innovation is about exploration and exploitation. Exploration is fundamentally different from exploitation. 3 CFO as Member of the Executive Board

21. Institute of Technology Management Projektskizze «Competence Center Automotive Intelligence» 23. September 2011 Robert Winter, Felix Wortmann Institut für Wirtschaftsinformatik Big Data – Hype or Radical Change?Zug, January 14th 2015 Felix Wortmann Assistant Professor for Technology Management, Scientific Director Bosch IoT Lab