Operationalizing the Analytics Enterprise

Mark ZozuliaDeloitte US Business Intelligence & Data Warehousing Practice Leader

Operationalizing the Analytics EnterpriseKelley Forum on Business Analytics

Agenda1. Trends – Data as the “end”2. Enablement – What is enterprise?3. Operational Insights – Keeping pace…4. Closing Remarks5. Q&A

Trends – Data as the “end”empowering the businessinsights as a service

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

AnalyticsAware

2009-2013

AnalyticsApplied

2013-2016

InsightEconomy

2020+“Big Data”

“Internet of Things”

“Analytics Enterprise”

CloudComputing

Machine Learning / AI

DataScientists

Crowd-sourcing

Analytics asa Disruptor

2014-2018+

Analyticsas R&D silo

1995 - 2009

ActuarialModels

Smart phones

SocialMedia

Evolution of Analytics

Data as a means to an end Data is the end Data as a service

InternetOf

ThingsBig Data

Data Science / Machine Learning

ConvergingTrends:

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”

“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”

Challenges of Big Data

Velocity

Volume

Variety

Value

+

+

=

Sources:1 http://www.theverge.com/2013/5/19/4345514/youtube-users-upload-100-hours-video-every-minute2 http://mashable.com/2012/06/22/data-created-every-minute/3 http://gartnerevent.com/SYMfactoids/

Velocity Frequency of data generation

100 hoursOf 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 mediaData includes clickstream and interaction data from social media such as Facebook, Twitter, LinkedIn and blogs.

Machine to MachineData includes readings from sensors, meters, and other devices as part of the so-called “internet of things”.

Big transaction dataIncludes healthcare claims, telecommunications call data records (CDRs), and utility billing records that are increasingly available in semi-structured and unstructured formats.

BiometricData includes fingerprints, genetics, handwriting, retinal scans, and similar types of data.

Human-generatedData 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.

The Big Data Value Equation

Velocity Volume Variety Value+ + =Veracity Viability+ +

Veracity Establishing trust in data

1 in 3business leaders don’t trust the information1

Uncertaintydue to inconsistency,

ambiguity, latency and approximation

Value Return on investment

CostsRisk of simply creating Big Costs without creating the value

InsightSophisticated queries, counter-intuitive insights and unique learning

Viability Relevance and feasibility

Hypothesisvalidation to determine if

the data will have a meaningful impact

Long-termrewards and better

outcomes from hidden relationships in data

“Does weather affect sales?”

Sources:1 http://businessoverbroadway.com/in-data-we-trust

Enablement –What is Enterprise?new use casesnew opportunities

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

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

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

demandBuild capabilityInnovation (and cost

take-out) through architecture

Operational Insights– Keeping pace…what to do how to start

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 DeliverySprinkle 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 FeedbackEnable informed decision-making and collect feedback for process improvement

Analytics as the “Insight Restaurant”

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?

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

IdeaProof

of Concept

Requirements

Pre-Design

Design

Deploy

Next Generation Analytics Ecosystems

Closing remarksanalytics appliedanalytics enterprise

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

Questions?

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