Chief Data Officer (CDO) Certification
(Deploying Data Technologies Program)
- The courses in this program focus on the technical considerations for implementing data initiatives.
- The courses address the different considerations for effectively starting/introducing a technology versus scaling up the use of the technology. It is intended for experienced IT professionals.
- Technical Data Concentrations Include Data Analytics, Textual Analytics, AI (Artificial Intelligence), Big Data, Blockchain, Business Intelligence, Cognitive Computing, IoT (Internet of Things), Knowledge Management, & Robotics Process Automation.
While the course descriptions are below, click here to learn more about GIIMs CDO Program
Prerequisite for this program: Introduction to Data
Completed the course Introduction to Data or have the equivalent experience prior to taking courses in this certificate.
Select at least 4 courses from the following:
- Courses 1 – 3 are required
- All courses are available face-to-face and synchronously online
- Courses marked with an * are also available asynchronously via the GIIM Cloud/Web
1. Building the Data/Analytics Organization *
This course addresses the organizational elements of the Data and Business Analytics (including cognitive computing and robotics process automation) functions by focusing on the management, structural/reporting, and human resource/skills considerations of data and business analytics. Topics such as determining where the group(s) should report, how they are assessed/measured, the necessary skills and how to source them, key data/analytics/cognitive computing processes, data governance, how to lead data-driven innovation in products and services, IT and non-IT roles, and customer and competitor alignment, all driven by the demand to improve the quality and speed of business decisions, minimize the risks/challenges for implementing them, and how to leverage data as a strategic asset. By concentrating on IT’s data, analytics, and cognitive computing responsibilities, in essence this course puts the candidate in the role of the CAO/CDO (Chief Analytics Officer/Chief Data Officer) as they define the vision, strategies, missions, and build the management processes and organization/skills necessary to deploy these data driven initiatives. The course focuses on the important organizational structure in terms of separate or combined organizations, and placement within the overall enterprise and IT organizational structures. This course is geared for managers and consultants engaged in building and growing this organization, including CIOs and non-IT executives to help prepare the enterprise to leverage their investment in Big Data/BA. It combines the optional Building & Managing the Analytics Organization and Building & Managing the Data Organization courses (E & F) below.
2. Managing the Data Transformation *
This course addresses the business digital transformation underway that are being driven/enabled by the changes in design and management of data for business intelligence/business analytics (BI/BA) and cognition systems as enterprises evolve to leveraging Big Data (and Internet of Things). It focuses on the emerging data sources (e.g., social, mobile, robotics process automation), data models, IT data management processes, and data integration considerations as they pertain to BI/BA and cognitive computing (from marketing to human resources).
The goal is to raise thought-provoking technical issues prompted by the rapid evolution of business and data technologies, as well as to provide practical information for immediate use. The course is organized around the following transformational themes:
- Data Sources
- Data Technologies
- Data Applications
- Infrastructure Considerations
The emphasis is on the industry considerations resulting from the integration of emerging data/knowledge, analytics, and cognitive technologies.
3. Analytics, Applications & Techniques *
This course will focus on providing candidates with a well-grounded understanding and appreciation of the contemporary methods, tools and techniques used to make analytics an integral part of managerial decision making. It will concentrate on the approaches for realizing the hidden knowledge in corporate databases and will help participants make near-real time intelligent business and operation decisions. The course will introduce various types of analytics including: reporting/visualization, predictive/data mining, decision-making/prescriptive analytics, pattern recognition, and forecasting. Methodological and practical aspects of knowledge discovery algorithms will also be covered including: data preprocessing, k-nearest neighborhood algorithm, machine learning (e.g. decision trees, artificial neural networks), predictive modeling, cognitive computing, clustering and market segmentation, association rule mining techniques, and time series forecasting. The focus of this course is on understanding the potential of these analytical techniques in various organizational settings.
Select At Least One (1) From The Following:
I. Knowledge & Discovery Approaches *
This course follows the Analytics Applications and Techniques Course, and will focus on the hands-on application of data mining, text mining, cognitive computing, artificial intelligence, and big data products/tools/software in solving real world business and operational problems. A variety of popular knowledge discovery software products (both professional/industrial and free/open source) will be used to demonstrate a wide range of interesting application scenarios. This course will provide participants with an in-depth understanding of the trade-offs that exist in identifying, designing and implementing knowledge discovery projects. It concentrates on building hands-on skills to apply appropriate techniques to discover hidden knowledge in corporate and external databases (both structured and unstructured) to help managers make near-real time intelligent strategic and operational business decisions. The main goal of this course is to provide candidates with not only a well-grounded understanding and appreciation of the methods and methodologies but also help candidates develop hands-on experiences in applying them to real world problems and data sets.
II. Deploying Blockchain Technologies *
Candidates completing this Certificate will also be entilted to obtain a GBA Certification.
As Blockchain emerges as an essential technology across every industry (well beyond Bitcoin), with all of the buzzwords flying around it can be difficult to separate Blockchain hype from business reality. This foundational technical course will enable IT candidates to understand the essential concepts of the distributed ledger, relevant Blockchain terminology, real world Blockchain use cases, and technology management considerations for carrying out Blockchain projects.
This course will also explore the concept of anonymous consensus and how it is essential to ensuring that the blocks in a Blockchain contain the single version of the truth, as well as learning the mechanics of Blockchain validation and how consensus can eliminate errors that otherwise require reconciliation. How identities work inside of Blockchain and the dependencies that Blockchain Oracles have on Smart Contracts are covered in detail.
Specific and generic industry examples and emerging applications and blockchain technologies (e.g., Bitcoin, Ethereum, Ripple, The Hyperledger Foundation which is actually 6 Blockchain’s including Fabric from IBM, Multichain, EOS, Corda), and approaches for deploying blockchain will be the focus of this course. At the end of this course candidates will be prepared to engage in the technical management and development responsibilities necessary to effectively and efficiently implement blockchain initiatives.
Candidates should consider taking other GIIM Blockchain courses.
III. Deploying AI Technologies *
Artificial Intelligence (AI) has progressed from a specialized research field to a core enterprise capability reshaping every industry. While AI’s academic foundations span decades, contemporary usage now encompasses a broad spectrum of technologies—from predictive and generative models to autonomous agents, cognitive systems, robotics, and large‑scale machine learning platforms. Organizations increasingly depend on AI to enhance decision‑making, automate complex workflows, improve customer experiences, and unlock new sources of competitive advantage. This shift has created urgent demand for leaders who can design, deploy, and manage AI systems responsibly and on a scale.
This course provides the technical, architectural, and operational foundation required to successfully deploy AI in modern enterprises. Integrating principles from computer science, data science, cognitive psychology, software engineering, and organizational strategy, the course prepares candidates to lead AI initiatives from concept through enterprise‑wide deployment.
Course Focus and Learning Outcomes
Participants will develop a deep understanding of:
- Understanding the differences, concepts, and algorithms for Machine Learning, Predictive, Generative, and Agentic AI concepts and algorithms used across industries
- AI techniques and cognitive computing approaches for real-world problem solving
- Deep Learning architectures, including artificial neural networks and multi-layer data abstraction
- Natural Language Processing (NLP) using leading libraries such as NLTK, spaCy, TensorFlow, Keras, and PyTorch
- Python-based AI development, with exposure to alternative languages and platforms
- Industry-specific AI applications, emerging technologies, and deployment strategies
- Robotic Process Automation (RPA) and cognitive automation as components of the broader AI ecosystem
Through hands-on exercises and applied projects, candidates will learn to build, train, and evaluate machine learning and deep learning models; work with unstructured data; and leverage modern AI frameworks to develop scalable solutions.
Industry Integration
The course emphasizes practical implementation. Learners will explore:
- AI use cases across sectors such as healthcare, finance, retail, logistics, and cybersecurity
- Current and emerging AI technologies shaping enterprise transformation
- Best practices for deploying AI systems responsibly, efficiently, and ethically, including organizational and governance considerations
- Understand the changes to traditional application development and deployment
- Approaches for integrating AI into existing business processes and technical infrastructures
Real-world examples, case studies, and project-based learning ensure that participants can translate theory into actionable technical solutions.
Outcome and Professional Readiness
Upon completion, candidates will be equipped to:
- Contribute to the technical development, evaluation, and deployment of AI systems
- Support AI-driven transformation initiatives within their organizations
- Engage confidently in technical management roles involving AI strategy, governance, architecture, and implementation
- Apply machine learning, deep learning, and NLP techniques to solve complex business and engineering challenges
Graduates of this course will be prepared to advance their careers as AI engineers, data scientists, technical AI managers, or specialists responsible for guiding enterprise AI adoption.
IV. Implementing the Cloud Data Center
Implementing the Cloud Data Center (e.g., security, systems integration)
This course provides an introduction to the concepts and principals needed to build and support an enterprise cloud data center. Candidates will investigate the emerging technologies (e.g. Business Analytics, mobile, social media/networks, security, internet of things, crowd-sourcing) and how they can be integrated, infrastructure deployments, organizational approaches and operational considerations that underlay managing the cloud data center. It will delve into how a cloud data center differs from a traditional one. In additional the course will look at methods for orchestrating public and private clouds, and how the cloud can be leveraged to delivery more cost efficient infrastructure and IT services to the enterprise.
Topics covered include: Cloud infrastructure principals as they relate to a deployment, emerging cloud technology standards, building scalable cloud networks, changing data center operations models and best practices, The Open Group Architecture Framework (TOGAF), emerging Cloud technologies, integrating technologies, managing for scale and elasticity, leveraging hybrid public and private cloud models, cloud readiness skills, develops concepts and how to apply them in support of a cloud, and finally cloud data center deployment life cycles.
V. Engineering Applications for the Cloud
The purpose of this course is to prepare candidates with the strategic, tactical, and operational competencies necessary to derive and carry out strategies, plans, and processes for migrating existing applications to the Cloud and how to build new applications that run effectively and efficiently in the Cloud.
When deriving strategies and plans for migrating to Cloud (whether insourced, outsourced, or hybrid) an area often not anticipated or understood is how to leverage this important infrastructure with the existing application portfolio and the anticipated future application portfolio. The purpose of this course is to provide the underpinnings of best practices that will enable the attendees to derive and deploy a successful Cloud applications strategy including howto build new and integrate existing applications, considerations for managing/leveraging data, how to organize activities and tasks of people and systems systematically into effective business processes, and how to evaluate cloud service offerings.
Industry Specific Courses
B. Advanced BI
Candidates that have experience (1-3 years) in BI/Big Data projects are often preparing for more arduous initiatives. This courses focusing on the more complex cutting edge approaches to BI/Big Data.
The significant amount of corporate information available requires a systematic and analytical approach to selecting the most important information and anticipating major events. Statistical learning algorithms facilitate this process for understanding, modeling, and forecasting the behavior of major corporate variables. This course prepares candidates that do not have the important foundation of statistics.
This course introduces time series, and statistical and graphical models used for inference and prediction. The emphasis of the course is in the learning capability of the algorithms and their application to several business areas.
The course also provides an understanding of the basic methods underlying multivariate analysis through computer applications using regression/multivariate analysis.
Topics covered include principal components analysis, factor analysis, structural equation modeling, multidimensional scaling, correspondence analysis, cluster analysis, multivariate analysis of variance, discriminant function analysis, logistic regression, and other methods used for dimension reduction, pattern recognition, classification, and forecasting.
Participants should have a basic knowledge of probability theory, and linear algebra prior to taking this course.
In essence it takes a similar perspective as course f below, but instead of focusing on the role of the CDO (Chief Data Officer), it focuses on the role of the CAO (Chief Analytics Officer).
This course addresses what the Analytics and Cognitive Computing functions should look like by focusing on the management, organizational, and human resource considerations for leveraging analytics. It addresses the emerging job roles of data governance, data stewards, data curators, data scientist, master data architects, data security & privacy, data engineers & architects, and data scientists, as well as centers of excellence/ competency. Managing data as an asset requires significant transformation at many companies. There are cultural issues that must be dealt with, and learning how to manage transformation is a critical skill. Topics such as where the group should report, how they are assessed, the necessary skills and how to source them, key data/analytics processes, integration strategies, data governance, data-driven innovation in products and services, data security/privacy and standards, IT and non-IT roles, customer and competitor drivers, and understanding how the preceding can be used to improve the quality and speed of business decisions and processes, and the risks/challenges for implementing them to leverage data as a strategic asset are fundamental. By concentrating on ITs data and analytics responsibilities, in essence this course puts the candidate in the role of the CAO (Chief Analytics Officer) as they build the management processes and organization/skills necessary to deploy these data driven strategies.
In essence it takes a similar perspective as course e above, but instead of focusing on the role of the CAO, it focuses on the role of the CDO (Chief Data Officer).
This course addresses the role of a “Chief Data Officer” (CDO) in an enterprise. The course focuses on the management, organizational, and human resource considerations for data and analytics. It addresses how big data assets fits with other information assets of the firm, and the emerging job characteristics of data governance, data scientists, master data architects, and data security and privacy. The key is how organizations can leverage information assets to provide demonstrable business value.
Managing data as an asset frequently requires significant transformation at many enterprises including cultural and political considerations and learning how to manage the complexity of change. Topics include the alternatives for where the position should report, the necessary skills (executive and staff, IT and non-IT), governance processes, and defining an appropriate set of strategic, tactical, and operational objectives. By considering information assets from an organization-wide perspective, in essence this course puts the candidates in the role of Chief Data Officer as they build the management processes and organization/skills necessary to get the full advantage from data.
