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Operationalizing the Analytics Enterprise


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Mark Zozulia, Principal, Deloitte Consulting LLP presented the keynote address on "Operationalizing the Analytics Enterprise" on April 4, 2014 at the Kelley Forum on Business Analytics 2014.

Mark Zozulia, Principal, Deloitte Consulting LLP presented the keynote address on "Operationalizing the Analytics Enterprise" on April 4, 2014 at the Kelley Forum on Business Analytics 2014.

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  • 1. Mark Zozulia Deloitte US Business Intelligence & Data Warehousing Practice Leader Operationalizing the Analytics Enterprise Kelley Forum on Business Analytics
  • 2. Agenda 1. Trends – Data as the “end” 2. Enablement – What is enterprise? 3. Operational Insights – Keeping pace… 4. Closing Remarks 5. Q&A
  • 3. Trends – Data as the “end” empowering the business insights as a service
  • 4. 1. Analytics Applied • Explosion in other countries • Global glue 2. CxO Viewpoints • Empowerment of the business with data • What the CIO needs to do to keep up 3. Vendor Perspectives • Pre-built analytic solutions / applications • Scalability, Enterprise-ready, Modernization Deloitte Global Analytics Summit – Munich Germany
  • 5. Analytics Aware 2009-2013 Analytics Applied 2013-2016 Insight Economy 2020+ “Big Data” “Internet of Things” “Analytics Enterprise” Cloud Computing Machine Learning / AI Data Scientists Crowd- sourcing Analytics as a Disruptor 2014-2018+ Analytics as R&D silo 1995 - 2009 Actuarial Models Smart phones Social Media Evolution of Analytics Data as a means to an end Data is the end Data as a service
  • 6. Internet Of Things Big Data Data Science / Machine Learning Converging Trends: Innovation: New Data New Processes New Insights • Integrated ecosystem – customers, employees, shareholders, suppliers • Zero Latency information flow • Secure data exchange Insight Economy • Culture of data-driven decision making • Integration of operational and behavioral data • Machine-learning detection of patterns and trends Road to the “Insights Economy”
  • 7. “We are moving to a world where the machines we work with are not just intelligent; they are brilliant. They are self-aware, they are predictive, reactive and social. It's a world where information… comes to us automatically when we need it without having to look for it… allowing us to remotely and automatically monitor, manage and upgrade industrial assets.” Marco Annunziata, Chief Economist, General Electric Internet of Things – “Industrial Internet”
  • 8. Challenges of Big Data Velocity Volume Variety Value + + = Sources: 1 2 3 Velocity Frequency of data generation 100 hours Of video uploaded to YouTube every minute1 2,000,000 queries On Google every minute2 47,000 App download per minute at the Apple Store3 Volume The growth of world data 1 terabyte hold the equivalent of roughly 210 single sided DVDs Variety Structured and unstructured data – types of Big Data Web and social media Data includes clickstream and interaction data from social media such as Facebook, Twitter, LinkedIn and blogs. Machine to Machine Data includes readings from sensors, meters, and other devices as part of the so- called “internet of things”. Big transaction data Includes healthcare claims, telecommunications call data records (CDRs), and utility billing records that are increasingly available in semi-structured and unstructured formats. Biometric Data includes fingerprints, genetics, handwriting, retinal scans, and similar types of data. Human-generated Data includes vast quantities of unstructured and semi-structured data such as call centre agents’ notes, voice recordings, email, paper documents, surveys, and electronic medical records.
  • 9. The Big Data Value Equation Velocity Volume Variety Value+ + =Veracity Viability+ + Veracity Establishing trust in data 1 in 3 business leaders don’t trust the information1 Uncertainty due to inconsistency, ambiguity, latency and approximation Value Return on investment Costs Risk of simply creating Big Costs without creating the value Insight Sophisticated queries, counter- intuitive insights and unique learning Viability Relevance and feasibility Hypothesis validation to determine if the data will have a meaningful impact Long-term rewards and better outcomes from hidden relationships in data “Does weather affect sales?” Sources: 1
  • 10. Enablement – What is Enterprise? new use cases new opportunities
  • 11. Industry Analytics Use Cases Heat Map: Warm Hot Boiling Industry/Domain Customer Supply Chain Workforce Finance Risk Consumer Business and Transportation Energy and Resources Financial Services Life Sciences and Health Care Manufacturing Public Sector Technology, Media and Telecommunications Source: Deloitte analysis, 2013
  • 12. CxO Viewpoints 1. Analytics has landed on the agenda for most CXOs— it’s no longer the sole domain of a few select teams buried deep in the business 2. Analytics-focused collaboration between CXO stakeholders is rising rapidly in importance 3. CEOs need to engage more and serve as the orchestrator
  • 13. Creating the Analytics Enterprise Value, not science experiments Vision Mission Key Objectives Companies achieving competitive advantage with information require new organizational, transformational, and technology approaches for enabling the analytics enterprise • Operationalizing high value business use cases through data mining, discovery and visualization • Defining new organization models that redefine traditional roles between IT and the business • Integrating big data with traditional data in data warehouses • Optimizing core business intelligence and reporting environments • Architecting purpose-built, high-performance analytic technology ecosystems Analytic “factories” to keep pace with business demand Build capability Innovation (and cost take-out) through architecture
  • 14. Operational Insights – Keeping pace… what to do how to start
  • 15. MENU “I’m in the mood for fish tonight…” Order Listen to the customer first and the value sought Business Opportunity “We can substitute that. And may I recommend a wine?” Server and Sommelier Understand the issues in the context of a function and industry, we can begin to translate business needs into analytical requirements Visioning Plating and Delivery Sprinkle with chives and garnish Displaying the analysis in an intuitive and compelling way Visualization and Delivery Consumption and Reviews “Is your meal to your liking?” Insights and Feedback Enable informed decision- making and collect feedback for process improvement Analytics as the “Insight Restaurant”
  • 16. Top Questions Enabler Awareness Understanding the needed people, processes and technology enablers Analytics Momentum Generating excitement, buzz and demand in the organization for analytic solutions Leading from the Front Aligning the analytics organization behind corporate goals and priorities Capacity & Skills How do we make sure we have the right set of skilled resources available to deliver on business demand? Priority Insights How do we make sure our “Phd” type resources are answering difficult questions, not building proof of concepts? Data Platforms How do we work with our IT partners to stand up a platform that enables quick access to high quality data on a global scale? Efficient Delivery How do we stand up an efficient delivery model aligned to critical business segments and also a center of excellence? Where to Focus & What to Expect Processes How do we implement processes that promote collaboration across the business?
  • 17. Getting Started – The Program Journey “Agile Analytics” • Work through agile sprints to build dashboards and analytical models • Align with IT delivery models • Train end users and roll application out to the enterprise “Prioritize and Analyze” • Establish a business driven analytical conformity layer • “Harden” POCs with certified data • Iteratively define requirements using real data and tools “The Art of Possible” • Stakeholders determine use cases utilizing “sandboxes” • Demonstrate POCs for analytic applications through roadshows Idea Proof of Concept Requirements Pre-Design Design Deploy
  • 18. Next Generation Analytics Ecosystems
  • 19. Closing remarks analytics applied analytics enterprise
  • 20. Key Takeaways The Light at the End of the Tunnel is a Train New Data, New Processes, New Insights New Skills Required – Get or Grow Them Rethink Decision Making GET STARTED
  • 21. Questions?
  • 22. As used in this document, “Deloitte” means Deloitte LLP and its subsidiaries. Please see for a detailed description of the legal structure of Deloitte LLP and its subsidiaries. Certain services may not be available to attest clients under the rules and regulations of public accounting. “This presentation contains general information only and is based on the experiences and research of Deloitte practitioners. Deloitte is not, by means of this presentation, rendering business, financial, investment, or other professional advice or services. This presentation is not a substitute for such professional advice or services, nor should it be used as a basis for any decision or action that may affect your business. Before making any decision or taking any action that may affect your business, you should consult a qualified professional advisor. Deloitte, its affiliates, and related entities shall not be responsible for any loss sustained by any person who relies on this presentation. Copyright © 2012 Deloitte Development LLC. All rights reserved. Member of Deloitte Touche Tohmatsu Limited