big data strategies – organizational structure and technology

The First Step in Information Management

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Big Data Update!Or, to be grammatically correct:

An Update on Big Data

Monthly CDO Webinar Series

Brought to you in partnership with:

#CDOVisionAugust 4, 2016

pg 2© 2016 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential

Big Data Success Areas Challenging Areas

Why We’re Here

Purpose: Take a practical look at what really is going on around advanced analytics

and Big Data

Outcome: Practical advice for value-added, sustainable application of

Big Data and Analytics

© 2016 First San Francisco Partners www.firstsanfranciscopartners.com pg 3

Why: A lot of money is being spent with little (overall) return

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What could go wrong?

…I dont believe that result

Larger quantities of data dampen the

errors

Deeper insights …no results yet


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…I cant find anything

… what does it mean?

…we can’t hire anyone

to do this


Current State of Big Data Efforts

Root Cause Analysis Applied

Operating Framework

Leadership and Management Justification

Enabling Systems and

Practices

Business Alignment

Lack of Awareness


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No Governance, inadequateleadership

Targeted problems, one

off, wrong answer

Cultural and political

ignorance, “Data driven?”

Miracle hunting vs.

solid direction

DQ, Don’t understand. Can’t find, “Toys first”


Organization, Management, Alignment

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Root Cause Analysis in the Organization

Operating Framework − No Governance, inadequate leadership

Data lakes become data swamps Need to “walk the data talk”

Leadership and Management Justification− Targeted problems, one off, wrong answer

Lack of Awareness− Culture not ready

Cultural and political ignorance, − When management does not like the answer

Data driven maturity acceleration Data driven not defined


CDO Challenges

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Root Cause Analysis in the Organization

Business Alignment − Miracle hunting vs. solid direction

Frame the questions incorrectly

− Nervous about cost and results, end up doing reports

Enabling Systems and Practices− Data unsuitable− No meaning (meta data) − No ability to find it − Buy infrastructure


CDO Challenges

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Organization Scenario for Analytics

Sponsor - Data Governance

Business Steward Leads

Data Governance OfficeDGO Chair

IT LeadDG Coordinator

Data Management / IT Support Group

Data Governance Working Group

Data Stewards

Marketing Fin.Accting

Fin. Treasury Risk HR

Fin.FP&A

Fin. Ext.Reporting

Legal/ Compliance

IT Advisor

Enterprise Infrastructure Committee

Executive Office(CEO)

Strategy & Risk(CRSO)

IT(CIO)

Finance(CAO/CFO)

Global Services (COO)

PMO

Executive Sponsor Analytics

Head of Business Analytics

Analytics Working Group

Analytic DirectorsCredit

AnalyticsClient

AnalyticsMarket

Analytics

LOB … LOB … LOB …

CDO This is a role / relationship chart and NOT an organization chart

Analysts, Scientists

© 2016 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidentialpg 10

Data Management (Big Data) / IT Support Group

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Organization Scenario for Analytics

Sponsor - Data Governance

Business Steward Leads

Data Governance OfficeDGO Chair

IT LeadDG Coordinator

Data Management / IT Support Group

Data Governance Working Group

Data Stewards

Marketing Fin.Accting

Fin. Treasury Risk HR

Fin.FP&A

Fin. Ext.Reporting

Legal/ Compliance

IT Advisor

Enterprise Infrastructure Committee

Executive Office(CEO)

Strategy & Risk(CRSO)

IT(CIO)

Finance(CAO/CFO)

Global Services (COO)

PMO

Executive Sponsor Analytics

Head of Business Analytics

Analytics Working Group

Analytic DirectorsCredit

AnalyticsClient

AnalyticsMarket

Analytics

LOB … LOB … LOB …

CDO This is a role / relationship chart and NOT an organization chart

Analysts, Scientists

© 2016 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidentialpg 11

Data Management (Big Data) / IT Support Group

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The State of Technology

pg 12

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pg 13

Technology

© 2016 First San Francisco Partners www.firstsanfranciscopartners.com

Define your use cases before selecting your tools!#CDOVision

Technology scenarios – evolving

Technical vs business conversation− Simpler terms and explanation − Return to known access methods

SQL, Connectors to common tools− More mission critical, managerial applications

“Old” DW technology –− Not going away – being integrated − Find right spot for right data


Not old and new, rather it is all

about defining the correct

architecture

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Technology scenarios – evolving

“Old” Big Data technology − Re-architecture of original Hadoop

installations − Integration into rest of enterprise architecture

New Big Data technology− SPARK, SQL, NoSQL − Manage the cost of ownership − Data persistency and erosion are main stream

considerations


Not old and new, rather it is all

about defining the correct

architecture

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pg 16

Reference Architecture: How Big Data Fits In

© 2016 First San Francisco Partners www.firstsanfranciscopartners.com Proprietary and Confidential

Data Quality Business Rules EngineMeta Data ManagementSecurity/Privacy

DATA GOVERNANCEAcquisition Management Aggregation/

PersistenceAccess/Delivery

Staging (structured

data)

• Cleanse• Enrich• Transform• Create

golden record (MDM)

Sources

ODS

Data Mart(s)

Analytics

Data Services

Other Data Retrieval Systems

Archival services

EDWStructured:• Internal• External

Unstructured:• Sentiment• Clickstream• PDF

Semi-structured:• XML, JSON

Staging(Semi-structured & unstructured data) Hadoop

Vari-structured:• IoT

Streaming • IoT Spark SQL, NoSQL

Data Lakes

TCO

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Wrap Up and Take-Aways

pg 17

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Take-Aways

• Organizations still struggle for skilled resources

• Interaction with rest of EIM keyResources

• Descriptive BI isn’t going away• Still need to explicitly look for value Alignment

• Optimization needs to occur across Descriptive and Predictive BI

• Enterprise architecture incorporation Outcome


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Thank you!John Ladley

[email protected] O’Neal

[email protected]

Next in the CDO Vision series:September 1, 2 PM ET

CDO Interview