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Succeeding with Analytics

Explore the outside-in view of how organizations strive to succeed with analytics and learn about the impact of recent technology innovations. Gain insights from real-world experiences and understand the key considerations for achieving success with analytics.

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Succeeding with Analytics

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  1. Succeeding with Analytics Svein Tore Bø

  2. Introduction • Outside-in view of how organization strive to suceed with analytics and considerations to achieving success • SAS Institute - Platform study (132 Interviews and 477 qualified survey participants) • Impact of recent technology innovations • Experience from the field • ”Analytics” • For the purpose of this presentation ”analytics” serves as a broad term. From the tasks of data exploration/insigth through traditional statistics to advanced Analytics and AI and Macine Learning

  3. DATA VALUE ANALYTICS “Data without analytics is value not yet realized” - Oliver Schabenberger, SAS Institute

  4. Mapping out enterprise-wide analytics Things in common for organization that has fully embedded analytics. • Enterprise-wide analytics requires flexibility of thinking • Outcome focused • Recognize importance of advanced analytics – Analytics is a key tool for the future NSIs: How to innovate? Finding new patterns? In new data? From new sources? NSIs: What services should NSIs provide in the future. What benefits society? NSIs: How to leverage new techniques in traditional work? Study: Still a long, hard road to travel for many organizations, for Analytics to become a part of ‘what they do’.

  5. Analytics Platform vs. Analytic Tools BIG DATA LANDSCAPE 2017 IT Benefits Data Governance Cost efficiency Security and GDPR Agility and speed Time to market Business Benefits Faster Insights Less time preparing Foster Collaboration Trust in data Analytics Culture

  6. The need for a framework (Process)

  7. Understanding the True Value(and cost) of Analytics Value of analytics • Communication • Understanding vs Decision power • Cross function Collaboration • Data Science vs Business under standing vs Operationalization Cost of analytics • Data needs • Data Quality • Failing (fast) • Model Governance E.g. «The NetFlix Prize»

  8. People, Process, Technology The ‘Analytics’ folks However: Technology ∆ Process ∆ People The ‘IT’ folks !!!???!!! I just built 850 new models. When can you put them into production?

  9. PROGRAMMING LANGUAGES DATA & MODEL GOVERNANCE SCALABILITY CHOICE CONTROL DATA SOURCES SECURITY& PRIVACY ANALYTIC TECHNIQUES DEPLOYMENT TALENT

  10. PROGRAMMING LANGUAGES DATA & MODEL GOVERNANCE SCALABILITY CHOICE CONTROL DATA SOURCES SECURITY& PRIVACY ANALYTIC TECHNIQUES DEPLOYMENT TALENT

  11. PROGRAMMING LANGUAGES DATA & MODEL GOVERNANCE SCALABILITY CHOICE CONTROL DATA SOURCES SECURITY& PRIVACY ANALYTIC TECHNIQUES DEPLOYMENT TALENT

  12. Perceptions on the future Analytics for Everyone Analytics Everywhere Artificial Intelligence

  13. Thank you! Svein Tore Bø Mobile: +47 92 23 45 35 sveintore.boe@sas.com

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