Why do I need a Chief Data Officer

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Why do I need a Chief Data Officer? (The Case for Appointing a CDO)

Why do I need a Chief Data Officer? (The Case for Appointing a CDO) Texas Government Data Forum 2018 Austin, TX – June 21, 2018 Derek Strauss, Gavroshe CEO (former CDO at TD Ameritrade, 2012 -2016) derek@gavroshe. com 614. 581. 0113 @derek_strauss Gavroshe Proprietary & Confidential 1

OUTLINE Outline • Building a Data & Analytics Capability – Why? And Why Now?

OUTLINE Outline • Building a Data & Analytics Capability – Why? And Why Now? • The Role of the Chief Data Officer • Measuring Success The CDO is responsible for accelerating enterprise innovation and transformation through strategic management and use of data and analytics. Proprietary and Confidential 2

Building a Data & Analytics Capability – Why? And Why Now? Gavroshe Proprietary &

Building a Data & Analytics Capability – Why? And Why Now? Gavroshe Proprietary & Confidential 3

Why and Why Now? e. g. DOD Data Not Shared Across the Enterprise Who

Why and Why Now? e. g. DOD Data Not Shared Across the Enterprise Who Owns Data? The only person in the Army who owns data is the Secretary of the Army. Everyone else is a caretaker or maintainer of data. -- The Honorable Thomas E. Kelly III Deputy Undersecretary of the U. S. Army NIMH Army STARRS Coordination Meeting Army Health Promotion Risk Reduction Suicide Prevention Report 2010 n n As part of its on-going effort to improve Army data, the Secretary of the U. S. Army appointed the first CDO in 2010. There is a fierce urgency in the Army to treat data as a strategic asset and exploit data for actions: u u Responses to events in theaters and to adaptive adversaries that use technology to their advantage Wikileaks Net-Centric Information Sharing ERP implementation Proprietary and Confidential 4

Why and Why Now? (contd. ) Different initial use cases and value propositions: –

Why and Why Now? (contd. ) Different initial use cases and value propositions: – Reduced tax fraud – Improved healthcare – Informing public policy – Economic development – Operational efficiency – Data Ethics Proprietary and Confidential 5

Andrew Mc. Afee - October 2012 Harvard Business Review article: “Big Data: The Management

Andrew Mc. Afee - October 2012 Harvard Business Review article: “Big Data: The Management Revolution” Proprietary and Confidential 6

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The Role of the Chief Data Officer Gavroshe Proprietary & Confidential 8

The Role of the Chief Data Officer Gavroshe Proprietary & Confidential 8

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MIT sponsorship of is. CDO – see www. iscdo. org Proprietary and Confidential 10

MIT sponsorship of is. CDO – see www. iscdo. org Proprietary and Confidential 10

MIT sponsorship of ICDO@UALR Proprietary and Confidential 11

MIT sponsorship of ICDO@UALR Proprietary and Confidential 11

ICDO@UALR course offerings Foundational Knowledge for CDOs Technical and Managerial Skills for the CDO:

ICDO@UALR course offerings Foundational Knowledge for CDOs Technical and Managerial Skills for the CDO: n n n n Data Strategy Data/Enterprise Architecture Data Governance Data Quality Data Technologies Data Visualization & presentation Change Management, People Skill, Leadership Skill Proprietary and Confidential 12

Measuring Success Gavroshe Proprietary & Confidential 13

Measuring Success Gavroshe Proprietary & Confidential 13

OUTLINE Measuring Success The CDO must gain organizational buy-in from the start as to

OUTLINE Measuring Success The CDO must gain organizational buy-in from the start as to how the Office of the Chief Data Officer will be measured. Some approaches are: q Business Value realization q Relief from Technology Pain Points q The Change-enabled Enterprise – making it easier to introduce change Proprietary and Confidential 14

E EX PL AM Tracking and Projecting Progress Proprietary and Confidential 15

E EX PL AM Tracking and Projecting Progress Proprietary and Confidential 15

High Level Success Metrics Examples Metric Possible Measures Score Current Target Past Trend Overall

High Level Success Metrics Examples Metric Possible Measures Score Current Target Past Trend Overall Score Adoption of Data Dictionary Process Effective Governance Have all the Data Owners been identified Completeness and Approvals by Data Stewards of Dictionary terms Savings demonstrated through the elimination of redundant tools and platforms Cost Reduction/ Avoidance of duplication of data and databases through Data Governance Review How many projects and dollars where saved through oversight Data quality score based on completeness and accuracy as governance defined the data item Quality Improvements What is the accuracy of data in the sources of record? Are we meeting all our SLA requirements? Is the data owner displaying accountability through optimizing the process What is the overall Dollar value attributed to Cost reductions and Quality Improvements Business Value How many data providers and vendors been eliminated through process of looking at redundancies Are we compliant with all the regulatory requirements? Regulatory Compliance Can we provide required data to all regulatory organizations? Proprietary and Confidential 16

About Gavroshe Ø In our 31 st consecutive year of business Ø Co-Authors with

About Gavroshe Ø In our 31 st consecutive year of business Ø Co-Authors with Bill Inmon of the book “DW 2. 0 – The Architecture for the Next Generation of Data Warehousing” Ø Creators of the Gavroshe 7 Streams Play Book for Chief Data Officers™ q q A recognized Industry Leader in the area of Office of Chief Data Officer Enablement A recognized Industry Leader in Full-Lifecycle Data Management Enablement Encompassing ALL Data Assets Sponsors of the DW 2. 0 Certification Training program for next generation data warehousing Comprehensive Data Management Consultants – From Strategy to Performance Tuning and Ongoing Operations, e. g. : v State Dept of Job and Family Services v Metropolitan City Data Program Proprietary and Confidential 17

A perspective from DW 2. 0 Ø History repeats itself – 3 waves: q

A perspective from DW 2. 0 Ø History repeats itself – 3 waves: q Metadata Repository q Data Warehouse q Big Data Lake Ø Each of these waves has displayed similar characteristics: q Initiated by innovative conceptual thinking, holding out great promise of business value q Euphoric demand q “Silver bullet” products, falling out of the sky q Frantic adoption, without big-picture-thinking, leading to inevitable failures q Disillusionment Proprietary and Confidential 18

It’s raining silver bullets! ata Metad ry ito Repos Da l cia i f

It’s raining silver bullets! ata Metad ry ito Repos Da l cia i f rti ce A igen / L l M tel In ta La Data se Warehou ke Bus in elli ess gen ce Int Look – a fresh batch of silver bullets!!! Yahoooooo! Can’t wait to get mine! Let’s get it right this time…. . Proprietary and Confidential 19

Questions ? Derek Strauss, Gavroshe CEO (former CDO at TD Ameritrade, 2012 -2016) derek@gavroshe.

Questions ? Derek Strauss, Gavroshe CEO (former CDO at TD Ameritrade, 2012 -2016) derek@gavroshe. com 614. 581. 0113 @derek_strauss Gavroshe Proprietary & Confidential 20