What Does A Chief Data Officer Do?

WHAT DOES A CHIEF DATA OFFICER DO?

The Chief Data Officer is tasked with integrating historical customer data across all business lines and products, developing searchable metadata layers to enhance business insights, and creating robust data visualization tools. This role involves establishing a comprehensive data management framework, including a single source of truth and a customer interaction hub, to enhance customer experiences and support advanced analytical models. The officer also leads the development of new data-driven products, defines data strategies with executive stakeholders, and utilizes enterprise information to drive decision-making and improve performance.

A Review of Professional Skills and Functions for Chief Data Officer

1. Chief Data Officer Responsibilities

Data Product Creation and Monetization: Be responsible for the determination and creation of new data products and the identification of new ways in which to monetize/commercialize existing data within the business.
Data Architecture Leadership: Lead the architecture and technical strategies of connected data, advanced analytics, and data science, delivering transformed, data-driven platforms and secure and competitive capabilities.
Team Leadership: Lead a senior team to develop an enterprise-wide, business-linked, cohesive understanding of data assets and their relationships.
Data Asset Integration and Commercialization: Leverage the integration and commercial potential of these data assets.
Autonomous Data-Driven Opportunity Execution: Be responsible for identifying and executing new data-driven opportunities with a high level of autonomy and focus on outcomes (including building third-party partnerships to either acquire or share data).
Data Analytics Advocacy and Team Leadership: Advocate for data analytics value, and lead cross-disciplinary teams.
Multi-Dimensional Data Solutions Creation: Create solutions for business problems, typically involving multi-dimensional data sets to improve predictability and performance, drive efficiencies, and insight into services provided.
Architecture and Change Directive Development: Lead the development and communication of clearly defined implementable architecture and technology directives enabling change programs to deliver improved changes.
Technical Standards and Data Strategy Governance: Develop and govern the implementation of technical and architecture standards, guidelines, and practices that implement data strategies.
Data Transformation Leadership: Lead in the transformation from structured to unstructured data, evaluating new technologies and approaches leading to scalable, global data platforms.
Solution and Technology Evaluation Assurance: Assure that the innovative solutions and technologies recommended will technically deliver and meet the needs of the business, analytical teams, and data privacy needs.

2. Chief Data Officer Job Summary

Data Management Ownership: Own data management best practices, teams, and related processes to achieve standards of data quality, efficiency, and effectiveness aligned to Avaya business global growth.
Data-Driven Leadership: Lead data-driven insights that support exploitation of strategic and tactical business opportunities, and champion a data-driven, decision-making culture.
Regulatory and Compliance Leadership: Lead regulatory and compliance programs related to data and analytics assets.
Data and Analytics Governance Leadership: Organize and lead a data and analytics governance council to provide executive sponsorship and oversight for governance policy creation and compliance.
Audit Oversight: Ensure the performance of independent audits, as appropriate.
Budget Management: Develop, manage, and control the annual budget for the office of the CDO.
Center of Excellence Leadership: Organize and lead a data governance, management, and analytics center of excellence.
Team and Role Definition: Define member responsibilities and accountabilities, define job roles, recruit candidates, and manage directly or indirectly a team of data and analytics governance leaders and senior information management professionals in regions or business units across a complex, international group enterprise.
Advanced Analytics Development: Develop Avaya's capacity to develop insights with advanced analytics.
Data Science Recruitment and Development: Recruit and develop data science competencies and resources for the corporate exploitation of big data and sourced data as well as the liberation of dark data.
Data Analytics Techniques Utilization: Use a combination of open source, cloud, and social era tools and techniques for data analytics, machine learning, mining, and visualization.

3. Chief Data Officer Accountabilities

Leadership: Provide intellectual, managerial, and commercial leadership to define near-term operating model for the CDO function.
Talent Acquisition: Identify critical capabilities and hiring requirements for the operational elements of the function (e.g., data sourcing, mapping, integration, quality assurance, etc.).
Hands-on Execution: Roll up sleeves and drive hands-on execution to build and scale up the operational elements.
Technology Partnership: Partner with the DandA technology team in standing up a robust data-delivery pipeline.
Data Availability Analysis: Work with product managers to determine the availability of required data within DandA, BNY Mellon and the associated usage rights.
Commercial Negotiation: Collaborate with Procurement and Third-Party Governance (TPG), Legal, COO, and CFO functions to ensure appropriate commercial terms are agreed to with the suppliers and established via contracting.
Vendor Strategy: Engage the bank’s Digital Partnership and Procurement teams to ensure that discussions with larger vendors are strategic.
Operational Management: Manage operational elements of the function (e.g., dealing with client issues, internal operational outages, etc.).
Crisis Communication: Own communication on late, erroneous data that has a broad impact on clients.
Client Experience: Provide a seamless experience for clients using multiple DandA products (hosted on the public and private cloud).
Data Governance Strengthening: Strengthen the data governance capability of the DandA business while working with the bank-wide CDO function.
Data Operations Management: Built and managed a data operations or data aggregation function for a large data provider.

4. Chief Data Officer Functions

Data Asset Cataloging and Assessment: Cataloging and assessing the current data assets for the business, with a key focus on what data we have and the value it could provide.
Data Utilization Understanding: Understanding who uses the current data and how, if at all, that is being harnessed for current decision-making.
Data Strategy Scope Definition: Understanding what questions can be answered as a business, which needs to form the scope of the data strategy.
Data Value Maximization: Ensuring leverage data to bring maximum value to continuously assess new ways of maximizing new sources of data.
Data-Centric Strategy Design: Designing and delivering a joined-up and innovative data-centric company strategy with key focus on eradicating data duplication and striving for a single source of truth.
Data Quality and Security Maximization: Maximizing data quality and security and data governance along the way.
Data Integration Management: Working with the Architecture function to ensure data integration approaches adhere to best practice, and that they are secure and maximize efficiency through best-of-breed data integration approaches.
Master Data Management Implementation: As part of the strategy, delivering a Master Data Management (MDM) approach.
Organizational Data Culture Promotion: Working with peers within the organization in support of the data strategy to deliver an organization-wide data culture and work to then deliver this effectively.
Business Performance Metrics Development Support: Working with stakeholders and peers to support the development of Business Performance Metrics and associated dashboards.

5. Chief Data Officer Job Description

Cost Savings and Revenue Enhancement: Ensure the commercial and can identify ways of achieving effective cost savings and increasing revenue based on insights derived from data.
Data Analytics Opportunity Identification: Searching and recommending data analytics opportunities for the business.
Data Compliance Strategy: Understanding data regulatory compliance in the industry and defining a strategy.
Data Security Compliance: Secure the data and ensure data/regulation compliance, GDPR, and data security (working with the Infosec team), are all understood and adhered to with continuous assessment and improvement.
Risk Management in Data: Contributing to and owning data-related risks in the organization risk register and driving suitable mitigating steps to eradicate this where possible.
Data Quality Assurance: Assessing data quality across data sources and data capture mechanisms and ensuring data quality to drive well-informed decision-making.
Customer Relationship Management: Building trustful and effective relationships with key strategic customers, listening to their needs and problems, and determining how the data strategy can ultimately support these to drive continuous improvement.
Resource Assessment for Data Strategy: Assessing and recommending what resource is required to deliver the data strategy.
Data Utilization for Product Development: Responsible for identifying opportunities to harness data and surface this to external customers through Sea/suite products to drive new functionality/services and potential additional revenue streams.
Stakeholder Support Management: Stakeholder management, ensures that they provide the right level of support.
Strategic Roadmap Assistance: Assisting stakeholders with road mapping and contributing to strategic information.
Strategic Data Integration Direction: Owning the strategic direction for how to integrate data with customers and 3rd party vendors.

6. Chief Data Officer Overview

Access Dynamic Computing: Develop access to dynamic computing (e.g. access to GPU farm to resolve more complex simulation and mathematical optimization problems).
Inexpensive Storage Access: Provide access to inexpensive storage to allow the business to rapidly scale.
Vault Concept Implementation: Implement the Vault Concept across all PII data while still giving the businesses the flexibility to conduct research and draw insights from the data.
Infrastructure as Code Leverage: Leverage infrastructure as a code to allow new environments to be rapidly set up and existing ones to rapidly scale.
Common/Prebuilt ETL Tools Leverage: Leverage of common/prebuilt ETL (extract – transfer – load) tools to gather first-party and third-party digital data.
Real-Time Insights Delivery: Deliver actionable insights to customers based on real-time triggers.
Real-Time Integrated View Provision: Provide real-time integrated view of all customer data and interactions from both first-party and third-party data sources.
Integrated Historical Data Development: Develop integrated historical customer data across all lines of business, products, and services (ability to support multi-economic cycle analysis).
Dynamic Data Integration Development: Develop dynamic integration of both first-party and third-party data (e.g. digital agencies, digital partners).
Metadata Dictionary Oversight: Oversee the completion of a metadata dictionary for all imported data sources.

7. Chief Data Officer Details and Accountabilities

Metadata Layer Development: Develop an easy to use searchable metadata layer to accelerate the development of business insights and analytical solutions.
Advanced Analytics and Model Research, Development and Management: Conduct advanced analytics and model research, development, and management.
Data Visualization Tool Building: Build flexible data visualization tools supporting both prebuilt views and new queries required by internal teams driving the democratization of data and insights across the organization.
Analytical Research Support: Ability to support a wide range of analytical research and model development activities across the enterprise.
Data Integration: Build a single source of truth for all data leveraged across many distinct analytical models.
Customer Interaction Hub Development and Management: Develop and manage a Customer Interaction Hub supported by an arbitration engine to systematically deliver the Next Best Experiences to customers.
Model Life Cycle Management Support: Support model life cycle management processes and supporting tools, including automatic data lineage and documentation to support more efficient MRM activities.
Simulation and Optimization for Finance: Apply true simulation and optimization platform capabilities to support Finance needs.
CI/CD Pipeline Definition for Model Deployment: Define CI/CD pipeline to bring new models to production and to continuously provide postproduction support Governance.
Metadata Structure and Data Lineage Utilization: Define metadata structure and systematically use data lineage tools.
Data Stewardship Organization: Create data stewards organized by data domain responsible for data quality and remediation efforts.

8. Chief Data Officer Tasks

Data Management: Responsible for holistic data management across the entire value chain of ideals.
Strategic Development: Technical understanding of their specific importance, responsible for the strategic development of software and tech stack for data processing.
Business Acumen: Strong business sense and a close eye on the needs of internal and external customers, enable the entire organization to use data to their individual needs.
Analytical Solutions: Find data-driven solutions that improve analytical skills, recommendation services, and price predictions.
Partner Support: Strengthen the support for shop partners for individual success.
Customer Insights: Get better customer insights.
Information Provision: Provide customers with only the information they need.
Organizational Culture: Create a prosperous working environment, based on the ideal’s core values including freedom, trust, and responsibility.
Executive Reporting: Report directly to the ideal CEO and are part of the ideal management board.
Budget Management: Develop, manage, allocate, and govern the annual budget for the office of the CDO.

9. Chief Data Officer Roles

Data Strategy Development: Provide overall data strategy for Affinity Solutions products, working closely with the company’s Product, Sales, and Data Partner Integration teams.
Cross-functional Collaboration: In conjunction with Affinity’s Sales, Business Development, Data Partner Integration, and Client Success teams.
Business Development Driving: Drive business development to bring on partners who will provide core data assets required to power Affinity’s products.
Partner Role Identification: Those partners may perform other roles, including acting as a channel partner/reseller, an OEM customer, a technology partner, and/or joint product development partner.
Strategic Leadership: Provide BD/strategy leadership with key partners designated by the company.
Cross-functional Team Leadership: Provide cross-functional leadership of core data partnership team whose mission is data strategy, partnerships, and implementation, with representation from Product, Sales, and Data Partner Integration teams.
MarTech Stack Integration: Put Affinity on agencies’/advertisers’ MarTech stack (including clean rooms, measurement, programmatic advertising/marketing, e.g., Omnicom’s Omni platform, Horizon Media’s blu).
Strategic Guidance: Provide strategic guidance to other functions at Affinity on data and media strategy.
Brand Evangelism: Evangelize Affinity’s brand, offering, and unique IP to agencies, advertisers, and consultancies.
Leadership on Data Applications: Provide perspective and leadership on creating categories around data and media applications.
IP Strategy and Protection: Guide isolating and protecting Affinity’s IP in partnership negotiations.

10. Chief Data Officer Additional Details

Multi-Economic Cycle Data Integration: Develop integrated historical customer data across all lines of business, products, and services.
Searchable Metadata Development: Develop easy-to-use searchable metadata layers to accelerate the development of business insights and analytical solutions.
Data Visualization Tool Building: Build flexible data visualization tools supporting both prebuilt views and associated filters.
Unified Data Source Building: Build a single source of truth for all data leveraging across many distinct analytical models.
Customer Interaction Hub Management: Develop and manage a customer Interaction Hub to improve customer experiences.
Simulation and Optimization Application: Apply accurate simulation and optimization platform capabilities to support business needs.
CI/CD Pipeline Definition: Define CI/CD pipeline to bring new models to production and to continuously provide postproduction support.
New Product and Digital Services Support: Support the commercial development of new products and services and support Digital Services.
Data Strategy and Vision Establishment: Work with executive leaders and other key stakeholders to establish a vision and strategy for all data activities that both create value and reduce organizational risk.
Enterprise Data Utilization and Analysis: Explore the value of enterprise information assets and utilize analytics for decision-making and augmentation of human performance.
New Data Product Leadership: Lead the research, strategy creation, and development of new data products or services to expand markets, monetize data, and grow company revenue.