To keep pace with ever-present business and technology change and challenges, organizations need operating models built with a strong data and analytics foundation. Here’s how your organization can build one incorporating a range of key components and best practices to quickly realize your business objectives.
Building an Effective Data & Analytics Operating Model A Data Modernization G...Mark Hewitt
This is the age of analytics—information resulting from the systematic analysis of data.
Insights gained from applying data and analytics to business allows large and small organizations across diverse industries—be it healthcare, retail, manufacturing, financial, or others—to identify new opportunities, improve core processes, enable continuous learning and differentiation, remain competitive, and thrive in an increasingly challenging business environment.
The key to building a data-driven practice is a Data and Analytics Operating Model (D&AOM) which enables the organization to establish standards for data governance, controls for data flows (both within and outside the organization), and adoption of appropriate technological innovations.
Success measures of a data initiative may include:
• Creating a competitive advantage by fulfilling unmet needs,
• Driving adoption and engagement of the digital experience platform (DXP),
• Delivering industry standard data and metrics, and
• Reducing the lift on service teams.
This green paper lays out the framework for building and customizing an effective data and analytics operating model.
Building an Effective & Extensible Data & Analytics Operating ModelCognizant
This document provides a framework for building an effective and extensible data and analytics operating model. It outlines a 3-step methodology: 1) Develop a business model focused on data; 2) Design the operating model focusing on integration and standardization of processes and data; 3) Design the operating model architecture detailing how people, processes and technology are organized. It identifies 9 core components of the operating model including managing processes, data, analytics services, and governance. The document provides examples of how to detail the subcomponents and design rules to integrate and standardize data across the organization.
How to Strengthen Enterprise Data Governance with Data QualityDATAVERSITY
If your organization is in a highly-regulated industry – or relies on data for competitive advantage – data governance is undoubtedly a top priority. Whether you’re focused on “defensive” data governance (supporting regulatory compliance and risk management) or “offensive” data governance (extracting the maximum value from your data assets, and minimizing the cost of bad data), data quality plays a critical role in ensuring success.
Join our webinar to learn how enterprise data quality drives stronger data governance, including:
The overlaps between data governance and data quality
The “data” dependencies of data governance – and how data quality addresses them
Key considerations for deploying data quality for data governance
Activate Data Governance Using the Data CatalogDATAVERSITY
This document discusses activating data governance using a data catalog. It compares active vs passive data governance, with active embedding governance into people's work through a catalog. The catalog plays a key role by allowing stewards to document definition, production, and usage of data in a centralized place. For governance to be effective, metadata from various sources must be consolidated and maintained in the catalog.
This practical presentation will cover the most important and impactful artifacts and deliverables needed to implement and sustain governance. Rather than speak hypothetically about what output is needed from governance, it covers and reviews artifact templates to help you re-create them in your organization.
Topics covered:
- Which artifacts are most important to get started
- Important artifacts for more mature programs
- How to ensure the artifacts are used and implemented, not just written
- How to integrate governance artifacts into operational processes
- Who should be involved in creating the deliverables
Linking Data Governance to Business GoalsPrecisely
This document discusses linking data governance to business goals. It begins with an example of a typical governance program that loses business support over time. It then advocates taking a business-first approach to accelerate programs and increase ROI. Successful programs link governance to business goals, outcomes, stakeholders and capabilities. The document provides examples of how different business goals map to governance objectives and capabilities. It emphasizes quantifying value at strategic, operational and tactical levels. Finally, it discusses Jean-Paulotte Group's Chief Data Officer implementing a working approach driven by business value through an iterative process between a Data Management Committee and Working Groups.
This document reviews several existing data management maturity models to identify characteristics of an effective model. It discusses maturity models in general and how they aim to measure the maturity of processes. The document reviews ISO/IEC 15504, the original maturity model standard, outlining its defined structure and relationship between the reference model and assessment model. It discusses how maturity levels and capability levels are used to characterize process maturity. The document also looks at issues with maturity models and how they can be improved.
Data Catalogs Are the Answer – What is the Question?DATAVERSITY
Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data.
Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.
Building an Effective Data & Analytics Operating Model A Data Modernization G...Mark Hewitt
This is the age of analytics—information resulting from the systematic analysis of data.
Insights gained from applying data and analytics to business allows large and small organizations across diverse industries—be it healthcare, retail, manufacturing, financial, or others—to identify new opportunities, improve core processes, enable continuous learning and differentiation, remain competitive, and thrive in an increasingly challenging business environment.
The key to building a data-driven practice is a Data and Analytics Operating Model (D&AOM) which enables the organization to establish standards for data governance, controls for data flows (both within and outside the organization), and adoption of appropriate technological innovations.
Success measures of a data initiative may include:
• Creating a competitive advantage by fulfilling unmet needs,
• Driving adoption and engagement of the digital experience platform (DXP),
• Delivering industry standard data and metrics, and
• Reducing the lift on service teams.
This green paper lays out the framework for building and customizing an effective data and analytics operating model.
Building an Effective & Extensible Data & Analytics Operating ModelCognizant
This document provides a framework for building an effective and extensible data and analytics operating model. It outlines a 3-step methodology: 1) Develop a business model focused on data; 2) Design the operating model focusing on integration and standardization of processes and data; 3) Design the operating model architecture detailing how people, processes and technology are organized. It identifies 9 core components of the operating model including managing processes, data, analytics services, and governance. The document provides examples of how to detail the subcomponents and design rules to integrate and standardize data across the organization.
How to Strengthen Enterprise Data Governance with Data QualityDATAVERSITY
If your organization is in a highly-regulated industry – or relies on data for competitive advantage – data governance is undoubtedly a top priority. Whether you’re focused on “defensive” data governance (supporting regulatory compliance and risk management) or “offensive” data governance (extracting the maximum value from your data assets, and minimizing the cost of bad data), data quality plays a critical role in ensuring success.
Join our webinar to learn how enterprise data quality drives stronger data governance, including:
The overlaps between data governance and data quality
The “data” dependencies of data governance – and how data quality addresses them
Key considerations for deploying data quality for data governance
Activate Data Governance Using the Data CatalogDATAVERSITY
This document discusses activating data governance using a data catalog. It compares active vs passive data governance, with active embedding governance into people's work through a catalog. The catalog plays a key role by allowing stewards to document definition, production, and usage of data in a centralized place. For governance to be effective, metadata from various sources must be consolidated and maintained in the catalog.
This practical presentation will cover the most important and impactful artifacts and deliverables needed to implement and sustain governance. Rather than speak hypothetically about what output is needed from governance, it covers and reviews artifact templates to help you re-create them in your organization.
Topics covered:
- Which artifacts are most important to get started
- Important artifacts for more mature programs
- How to ensure the artifacts are used and implemented, not just written
- How to integrate governance artifacts into operational processes
- Who should be involved in creating the deliverables
Linking Data Governance to Business GoalsPrecisely
This document discusses linking data governance to business goals. It begins with an example of a typical governance program that loses business support over time. It then advocates taking a business-first approach to accelerate programs and increase ROI. Successful programs link governance to business goals, outcomes, stakeholders and capabilities. The document provides examples of how different business goals map to governance objectives and capabilities. It emphasizes quantifying value at strategic, operational and tactical levels. Finally, it discusses Jean-Paulotte Group's Chief Data Officer implementing a working approach driven by business value through an iterative process between a Data Management Committee and Working Groups.
This document reviews several existing data management maturity models to identify characteristics of an effective model. It discusses maturity models in general and how they aim to measure the maturity of processes. The document reviews ISO/IEC 15504, the original maturity model standard, outlining its defined structure and relationship between the reference model and assessment model. It discusses how maturity levels and capability levels are used to characterize process maturity. The document also looks at issues with maturity models and how they can be improved.
Data Catalogs Are the Answer – What is the Question?DATAVERSITY
Organizations with governed metadata made available through their data catalog can answer questions their people have about the organization’s data. These organizations get more value from their data, protect their data better, gain improved ROI from data-centric projects and programs, and have more confidence in their most strategic data.
Join Bob Seiner for this lively webinar where he will talk about the value of a data catalog and how to build the use of the catalog into your stewards’ daily routines. Bob will share how the tool must be positioned for success and viewed as a must-have resource that is a steppingstone and catalyst to governed data across the organization.
Data Governance and Data Science to Improve Data QualityDATAVERSITY
Data Science uses systematic methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Data Science requires high-quality data that is trusted by the organization and data scientists. Many organizations focus their Data Governance programs on improving Data Quality results. These three concepts (governance, science, and quality) seem to be made for each other.
In this RWDG webinar, Bob Seiner and his special guest will discuss how the people focusing on Data Governance and Data Science must work together to improve the level of confidence the organization has in its most critical data assets. Heavy investments are being made in Data Science but not so much for Data Governance. Bob will talk about how Data Governance and Data Science must work together to improve Data Quality.
1. Enterprise Data Management (EDM) is the ability of an organization to precisely define, easily integrate and effectively retrieve data for both internal applications and external communication. It involves managing various types of data across the enterprise.
2. EDM includes areas like master data management, reference data management, metadata management, data governance, data quality, data analytics, data privacy, data integration, and data architecture.
3. The document discusses definitions and concepts for each of these areas, including roles, processes, and technologies involved. It provides overviews of fundamental concepts, principles, dimensions and processes for data quality, data governance, data privacy and other areas.
The right approach to data governance plays a crucial role in the success of AI and analytics initiatives within an organization. This is especially true for small to medium-sized companies that must harness the power of data to drive growth, innovation and competitiveness.
This guide aims to provide SMB organizations with a practical roadmap to successfully implement a data governance strategy that ensures data quality, security and compliance. Use it to unlock the full potential of your data assets.
This introduction to data governance presentation covers the inter-related DM foundational disciplines (Data Integration / DWH, Business Intelligence and Data Governance). Some of the pitfalls and success factors for data governance.
• IM Foundational Disciplines
• Cross-functional Workflow Exchange
• Key Objectives of the Data Governance Framework
• Components of a Data Governance Framework
• Key Roles in Data Governance
• Data Governance Committee (DGC)
• 4 Data Governance Policy Areas
• 3 Challenges to Implementing Data Governance
• Data Governance Success Factors
Metadata is hotter than ever, according to a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.
This presentation reports on data governance best practices. Based on a definition of fundamental terms and the business rationale for data governance, a set of case studies from leading companies is presented. The content of this presentation is a result of the Competence Center Corporate Data Quality (CC CDQ) at the University of St. Gallen, Switzerland.
This document provides an overview of Visual Analytics Session 3. It discusses data joining and blending in Tableau. Specifically, it explains why joining or blending data is necessary when data comes from multiple sources. It then describes the different types of data joins in Tableau - inner joins, left joins, right joins, and outer joins. An example is provided to demonstrate an inner join using a primary key to connect related data between two tables. The goal is to understand how to connect different but related data sources in Tableau using common keys or variables.
Data governance is a framework for managing corporate data through establishing strategy, objectives, and policy. It consists of processes, policies, organization, and technologies to ensure availability, usability, integrity, consistency, auditability, and security of data. Implementing data governance addresses the needs of different groups requiring different data definitions, ethical duties regarding privileged data, organizing data inventories, and staying compliant with rules and other databases. Data governance is important for increasing customer demands, adapting to technology and market changes, and addressing increasing data volumes and quality issues.
The document discusses dimensional modeling and data warehousing. It describes how dimensional models are designed for understandability and ease of reporting rather than updates. Key aspects include facts and dimensions, with facts being numeric measures and dimensions providing context. Slowly changing dimensions are also covered, with types 1-3 handling changes to dimension attribute values over time.
Business impact without data governanceJohn Bao Vuu
This document discusses the importance of data governance and outlines its benefits. It notes that without data governance, bad or incorrect data can proliferate throughout an organization, leading to increased costs and inaccurate decision making. Some key benefits of data governance include improved data quality, a single source of truth, more consistent and fact-based decision making, and alignment with business strategy. The document also provides an overview of how to implement a data governance program, including establishing roles and responsibilities, developing governance processes and policies, and using tools and systems to operationalize governance.
DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace: digital transformation, marketing, customer centricity, and more. This webinar will help de-mystify Data Strategy and Data Architecture and will provide concrete, practical ways to get started.
A business-friendly approach to data governance is imperative to engage all users and accommodate diverse business use cases spanning analytics, operational improvements, and compliance requirements. To increase adoption and collaboration, business and technical data users across your organisation need to have a common, agreed-upon, and documented understanding of which data is most important, what it’s called, and where it’s used.
Watch this on-demand webinar, where we explore the concept of business-first Data Governance, an approach that promotes adoption by the organisation, lays the foundation for data integrity and consistently delivers business value in the long term.
We also look at how Oripharm, one of the dynamic healthcare players in the Nordics and international markets, choose a data governance solution:
• to improve personalisation of products and services
• to achieve accurate and timely credit-risk analysis
• to increase user productivity by improving time-to-insights
• to mitigate risk and facilitate regulatory compliance and reporting
Speakers:
Mikkel Holmgaard - Data Governance Lead, Orifarm
Emily Washington - Sr. Vice President, Product Management, Precisely
Data Governance PowerPoint Presentation Slides SlideTeam
Presenting this set of slides with name - Data Governance Powerpoint Presentation Slides. This PPT deck displays twenty five slides with in depth research. Our topic oriented Data Governance Powerpoint Presentation Slides deck is a helpful tool to plan, prepare, document and analyse the topic with a clear approach. We provide a ready to use deck with all sorts of relevant topics subtopics templates, charts and graphs, overviews, analysis templates. Outline all the important aspects without any hassle. It showcases of all kind of editable templates infographs for an inclusive and comprehensive Data Governance Powerpoint Presentation Slides. Professionals, managers, individual and team involved in any company organization from any field can use them as per requirement.
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
Building a Data Strategy Your C-Suite Will SupportReid Colson
Being a data leader in any industry is an advantage that creates measurable financial benefits. Many studies have shown this – I’ve seen them from Bain, McKinsey, MIT and more. Since most firms are measured on profit, getting good at making data driven decisions is a key to being competitive. You can't get there without a plan. That is where a data strategy comes in.
In speaking with ~300 firms who indicated that their organizations were effective in using data and analytics, McKinsey found that construction of a data strategy was the number one contributing factor to their success. Being good at using data to drive decisions creates a meaningful profit advantage and those who are leaders indicated that the number one driver of their success was their data strategy.
This presentation will cover what a data strategy is, how to construct one, and how to get buy in from your executive team. The author is a former Fortune 500 Chief Data Officer and has held senior data roles at Capital One and Markel.
Here are a few helpful links for your data journey:
Free Data Investment ROI Template:
https://www.udig.com/digging-in/roi-calculator-for-it-projects/
Real world data use cases:
https://www.udig.com/our-work/?category=data
Contact Me:
https://www.udig.com/contact/
The document discusses data governance and why it is an imperative activity. It provides a historical perspective on data governance, noting that as data became more complex and valuable, the need for formal governance increased. The document outlines some key concepts for a successful data governance program, including having clearly defined policies covering data assets and processes, and establishing a strong culture that values data. It argues that proper data governance is now critical to business success in the same way as other core functions like finance.
Master Data Management's Place in the Data Governance Landscape CCG
This document provides an overview of master data management and how it relates to data governance. It defines key concepts like master data, reference data, and different master data management architectural models. It discusses how master data management aligns with and supports data governance objectives. Specifically, it notes that MDM should not be implemented without formal data quality and governance programs already in place. It also explains how various data governance functions like ownership, policies and standards apply to master data.
Enterprise Information Management Strategy - a proven approachSam Thomsett
Access a proven approach to Enterprise Information Management Strategy - providing a framework for Digital Transformation - by a leader in Information Management Consulting - Entity Group
This document provides an overview of strategies for accelerated data conversion and effective long-term data management. It discusses the importance of understanding data lifecycles and having tools that can validate data during conversion, enable ongoing maintenance, and allow for reorganization during business changes. Key aspects include configuring validation rules during conversion, developing "get clean, stay clean" processes for maintenance, and having repeatable strategies for rollouts and reorganizations to continually update data as an organization evolves. Managing data through its entire lifecycle is critical to reducing risks and ensuring project and business success.
Data Governance and Data Science to Improve Data QualityDATAVERSITY
Data Science uses systematic methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Data Science requires high-quality data that is trusted by the organization and data scientists. Many organizations focus their Data Governance programs on improving Data Quality results. These three concepts (governance, science, and quality) seem to be made for each other.
In this RWDG webinar, Bob Seiner and his special guest will discuss how the people focusing on Data Governance and Data Science must work together to improve the level of confidence the organization has in its most critical data assets. Heavy investments are being made in Data Science but not so much for Data Governance. Bob will talk about how Data Governance and Data Science must work together to improve Data Quality.
1. Enterprise Data Management (EDM) is the ability of an organization to precisely define, easily integrate and effectively retrieve data for both internal applications and external communication. It involves managing various types of data across the enterprise.
2. EDM includes areas like master data management, reference data management, metadata management, data governance, data quality, data analytics, data privacy, data integration, and data architecture.
3. The document discusses definitions and concepts for each of these areas, including roles, processes, and technologies involved. It provides overviews of fundamental concepts, principles, dimensions and processes for data quality, data governance, data privacy and other areas.
The right approach to data governance plays a crucial role in the success of AI and analytics initiatives within an organization. This is especially true for small to medium-sized companies that must harness the power of data to drive growth, innovation and competitiveness.
This guide aims to provide SMB organizations with a practical roadmap to successfully implement a data governance strategy that ensures data quality, security and compliance. Use it to unlock the full potential of your data assets.
This introduction to data governance presentation covers the inter-related DM foundational disciplines (Data Integration / DWH, Business Intelligence and Data Governance). Some of the pitfalls and success factors for data governance.
• IM Foundational Disciplines
• Cross-functional Workflow Exchange
• Key Objectives of the Data Governance Framework
• Components of a Data Governance Framework
• Key Roles in Data Governance
• Data Governance Committee (DGC)
• 4 Data Governance Policy Areas
• 3 Challenges to Implementing Data Governance
• Data Governance Success Factors
Metadata is hotter than ever, according to a number of recent DATAVERSITY surveys. More and more organizations are realizing that in order to drive business value from data, robust metadata is needed to gain the necessary context and lineage around key data assets. At the same time, industry regulations are driving the need for better transparency and understanding of information.
While metadata has been managed for decades, new strategies & approaches have been developed to support the ever-evolving data landscape, and provide more innovative ways to drive business value from metadata. This webinar will provide an overview of metadata strategies & technologies available to today’s organization, and provide insights into building successful business strategies for metadata adoption & use.
This presentation reports on data governance best practices. Based on a definition of fundamental terms and the business rationale for data governance, a set of case studies from leading companies is presented. The content of this presentation is a result of the Competence Center Corporate Data Quality (CC CDQ) at the University of St. Gallen, Switzerland.
This document provides an overview of Visual Analytics Session 3. It discusses data joining and blending in Tableau. Specifically, it explains why joining or blending data is necessary when data comes from multiple sources. It then describes the different types of data joins in Tableau - inner joins, left joins, right joins, and outer joins. An example is provided to demonstrate an inner join using a primary key to connect related data between two tables. The goal is to understand how to connect different but related data sources in Tableau using common keys or variables.
Data governance is a framework for managing corporate data through establishing strategy, objectives, and policy. It consists of processes, policies, organization, and technologies to ensure availability, usability, integrity, consistency, auditability, and security of data. Implementing data governance addresses the needs of different groups requiring different data definitions, ethical duties regarding privileged data, organizing data inventories, and staying compliant with rules and other databases. Data governance is important for increasing customer demands, adapting to technology and market changes, and addressing increasing data volumes and quality issues.
The document discusses dimensional modeling and data warehousing. It describes how dimensional models are designed for understandability and ease of reporting rather than updates. Key aspects include facts and dimensions, with facts being numeric measures and dimensions providing context. Slowly changing dimensions are also covered, with types 1-3 handling changes to dimension attribute values over time.
Business impact without data governanceJohn Bao Vuu
This document discusses the importance of data governance and outlines its benefits. It notes that without data governance, bad or incorrect data can proliferate throughout an organization, leading to increased costs and inaccurate decision making. Some key benefits of data governance include improved data quality, a single source of truth, more consistent and fact-based decision making, and alignment with business strategy. The document also provides an overview of how to implement a data governance program, including establishing roles and responsibilities, developing governance processes and policies, and using tools and systems to operationalize governance.
DAS Slides: Building a Data Strategy - Practical Steps for Aligning with Busi...DATAVERSITY
Developing a Data Strategy for your organization can seem like a daunting task. The opportunity in getting it right can be significant, however, as data drives many of the key initiatives in today’s marketplace: digital transformation, marketing, customer centricity, and more. This webinar will help de-mystify Data Strategy and Data Architecture and will provide concrete, practical ways to get started.
A business-friendly approach to data governance is imperative to engage all users and accommodate diverse business use cases spanning analytics, operational improvements, and compliance requirements. To increase adoption and collaboration, business and technical data users across your organisation need to have a common, agreed-upon, and documented understanding of which data is most important, what it’s called, and where it’s used.
Watch this on-demand webinar, where we explore the concept of business-first Data Governance, an approach that promotes adoption by the organisation, lays the foundation for data integrity and consistently delivers business value in the long term.
We also look at how Oripharm, one of the dynamic healthcare players in the Nordics and international markets, choose a data governance solution:
• to improve personalisation of products and services
• to achieve accurate and timely credit-risk analysis
• to increase user productivity by improving time-to-insights
• to mitigate risk and facilitate regulatory compliance and reporting
Speakers:
Mikkel Holmgaard - Data Governance Lead, Orifarm
Emily Washington - Sr. Vice President, Product Management, Precisely
Data Governance PowerPoint Presentation Slides SlideTeam
Presenting this set of slides with name - Data Governance Powerpoint Presentation Slides. This PPT deck displays twenty five slides with in depth research. Our topic oriented Data Governance Powerpoint Presentation Slides deck is a helpful tool to plan, prepare, document and analyse the topic with a clear approach. We provide a ready to use deck with all sorts of relevant topics subtopics templates, charts and graphs, overviews, analysis templates. Outline all the important aspects without any hassle. It showcases of all kind of editable templates infographs for an inclusive and comprehensive Data Governance Powerpoint Presentation Slides. Professionals, managers, individual and team involved in any company organization from any field can use them as per requirement.
How to Build & Sustain a Data Governance Operating Model DATUM LLC
Learn how to execute a data governance strategy through creation of a successful business case and operating model.
Originally presented to an audience of 400+ at the Master Data Management & Data Governance Summit.
Visit www.datumstrategy.com for more!
Building a Data Strategy Your C-Suite Will SupportReid Colson
Being a data leader in any industry is an advantage that creates measurable financial benefits. Many studies have shown this – I’ve seen them from Bain, McKinsey, MIT and more. Since most firms are measured on profit, getting good at making data driven decisions is a key to being competitive. You can't get there without a plan. That is where a data strategy comes in.
In speaking with ~300 firms who indicated that their organizations were effective in using data and analytics, McKinsey found that construction of a data strategy was the number one contributing factor to their success. Being good at using data to drive decisions creates a meaningful profit advantage and those who are leaders indicated that the number one driver of their success was their data strategy.
This presentation will cover what a data strategy is, how to construct one, and how to get buy in from your executive team. The author is a former Fortune 500 Chief Data Officer and has held senior data roles at Capital One and Markel.
Here are a few helpful links for your data journey:
Free Data Investment ROI Template:
https://www.udig.com/digging-in/roi-calculator-for-it-projects/
Real world data use cases:
https://www.udig.com/our-work/?category=data
Contact Me:
https://www.udig.com/contact/
The document discusses data governance and why it is an imperative activity. It provides a historical perspective on data governance, noting that as data became more complex and valuable, the need for formal governance increased. The document outlines some key concepts for a successful data governance program, including having clearly defined policies covering data assets and processes, and establishing a strong culture that values data. It argues that proper data governance is now critical to business success in the same way as other core functions like finance.
Master Data Management's Place in the Data Governance Landscape CCG
This document provides an overview of master data management and how it relates to data governance. It defines key concepts like master data, reference data, and different master data management architectural models. It discusses how master data management aligns with and supports data governance objectives. Specifically, it notes that MDM should not be implemented without formal data quality and governance programs already in place. It also explains how various data governance functions like ownership, policies and standards apply to master data.
Enterprise Information Management Strategy - a proven approachSam Thomsett
Access a proven approach to Enterprise Information Management Strategy - providing a framework for Digital Transformation - by a leader in Information Management Consulting - Entity Group
This document provides an overview of strategies for accelerated data conversion and effective long-term data management. It discusses the importance of understanding data lifecycles and having tools that can validate data during conversion, enable ongoing maintenance, and allow for reorganization during business changes. Key aspects include configuring validation rules during conversion, developing "get clean, stay clean" processes for maintenance, and having repeatable strategies for rollouts and reorganizations to continually update data as an organization evolves. Managing data through its entire lifecycle is critical to reducing risks and ensuring project and business success.
Turning your Excel Business Process Workflows into an Automated Business Inte...OAUGNJ
Many organizations have evolved key internal business processes built on top of Microsoft Excel. These cross-functional workflows involve several organizational units responsible for collecting business system transactions, modifying this raw data, consolidating, transforming, pivoting and preparing data into a published set of Reports & Graphs – all in MS Excel. Such workflows are a burden to organizations – not repeatable, costly, time-consuming, inflexible and hard to scale, and evolve to become more complex over time. Business critical processes such as financial analysis, operational analysis and revenue analysis are often supported this way. Attempting to replace such systems can be quite daunting and a barrier to replace. The goal of this session is to present an easy to understand methodology and use cases to demonstrate how to move from an operational workflow in Excel to truly automated Business Intelligence.
data-model-mastery-a-systematic-approach-to-organizing-your-companys-informat...Data & Analytics Magazin
The document discusses the importance of implementing a systematic data model for companies. It outlines the key steps in creating a systematic data model which include identifying key data elements, creating a logical data model to define relationships between elements, implementing a physical data model in a specific technology, and maintaining effective data governance and management. By following these steps to organize and manage data in a structured way, the document states that companies can improve data quality, increase efficiency, reduce costs, enable better decision making and gain a competitive advantage.
Itlc hanoi ba day 3 - thai son - data modellingVu Hung Nguyen
https://www.facebook.com/events/535707009911719/
(ITLC HN) BA DAY3: CHIẾN LƯỢC THIẾT KẾ MÔ HÌNH DỮ LIỆU
1.Thời gian: 18:30 - 21:00, 10/9/2015 (Tối thứ 5)
2. Địa điểm: HATCH - Tầng 14 - 195B Đội Cấn (http://nest.hatch.vn/nest-14.html)
3. Tổ chức: Ban tổ chức sự kiện ITLC Hà Nội
4. Chương trình:
18:30 - 18:45: Đón khách
18:45 - 19:00: Nguyễn Mạnh Cường (Fis) Giới thiệu ITLC Hà Nội
19:00 - 19:30: Thái Sơn chia sẻ “Một số mô hình dữ liệu mẫu trong phân tích nghiệp vụ”
19:30 - 19:50: Lê Phú Cường chia sẻ “Chiến lược lưu giữ dữ liệu lịch sử”
19:50 - 20:50: Panel cùng với: Thái Sơn, Lê Phú Cường, Lê Văn Duy
20:50 - 21:00: Tổng kết sự kiện và chụp hình kỷ niệm
5. Đăng ký: theo form sau đây http://topi.ca/baday3
6. Phí tham gia: 100K
7. Liên hệ, giải đáp: Lê Đại Nam: 0902-261-239
Xem thêm sự kiện BA1 tại đây: https://www.facebook.com/events/1616821285258614/
Xem thêm sự kiện BA2 tại đây: https://www.facebook.com/events/1669594633274443/
This document outlines a playbook for implementing a data governance program. It begins with an introduction to data governance, discussing why data matters for organizations and defining key concepts. It then provides guidance on understanding business drivers to ensure the program aligns with strategic objectives. The playbook describes assessing the current state, developing a roadmap, defining the scope of key data, establishing governance models, policies and standards, and processes. It aims to help clients establish an effective enterprise-wide data governance program.
This document discusses the impact of data mining on business intelligence. It begins by defining business intelligence as using new technologies to quickly respond to changes in the business environment. Data mining is an important part of the business intelligence lifecycle, which includes determining requirements, collecting and analyzing data, generating reports, and measuring performance. Data mining allows businesses to access real-time, accurate data from multiple sources to improve decision making. Using business intelligence and data mining techniques can help businesses become more efficient and make better decisions to increase profits and customer satisfaction. The expected results of applying business intelligence include improved decision making through accurate, timely information to support organizational goals and strategic plans.
This document introduces the Data Management Capability Model (DCAM) created by the Enterprise Data Management Council. The DCAM defines the capabilities required for effective data management. It addresses strategies, organization, technology, and operational best practices. The DCAM is organized into eight core components: data management strategy, business case, program, governance, architecture, technology architecture, data quality, and data operations. Each component defines goals and requirements for sustainable data management. The DCAM aims to help organizations assess their current data management capabilities and identify areas for improvement.
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environment. What ensues ends up being an agenda based on “how do we build it”, not a “why are we doing this”. This needs to change. In this discussion paper, I explore the issues of data quality in data warehouse, business intelligence and analytic environments, and propose an approach based on "Data Quality by Design"
Enterprise Architecture (EA) can be used as an effective change management agent by providing a consistent description of the business, including processes, services, technology, risks and other important elements. This centralized description in an "Enterprise Repository" enables rapid impact analysis of potential changes. It increases efficiency by sharing best practices, reduces risks through impact assessment, and expedites project management by centralizing important information. An EA approach supports change initiatives through visibility and understanding of the entire organization and how different elements are related.
Running head Database and Data Warehousing design1Database and.docxhealdkathaleen
Running head: Database and Data Warehousing design 1
Database and Data Warehousing Design 3
Database and Data Warehousing Design
Thien Thai
CIS599
Professor Wade M. Poole
Strayer University
Feb 20, 2020
Database and Data Warehousing Design
Introduction
Technology has highly revolutionized the world of business –hence presenting more challenges and opportunities for businesses. Companies which fail to embrace and incorporate technology in their operations risks being edged out of the market due to stiff competition witnessed in the market today. On the flipside, cloud-based technology allows businesses to “easily retrieve and store valuable data about their customers, products, and employees.” Data is an important component that help to support core business decisions. In today’s highly competitive and constantly evolving business world, embracing cloud-based technology business managers an opportunity to make informed and result-oriented decisions regarding day-to-day organizational operations (Dimitriu & Matei, 2015).
Notably, business growth and competitiveness depends on its ability to transform data into information. Data warehousing and adoption of relational databases are some of cloud-based technologies which have positively impacted on businesses. The two technologies have had a strategic value to companies –helping them to have the extra edge over their competitors. Both data warehousing and relational databases help businesses to “take smart decisions in a smarter manner.” However, failure to adopt these cloud-based technologies has hindered business executives’ ability to make experienced-based and fact-based decisions which are vital to business survival. Both “databases and data warehouses are relational data systems” which serve different and equally crucial roles within an organization. For instance, data warehousing helps to support management decisions while relational databases help to perform ongoing business transactions in real-time. Basically, embracing cloud-based technologies within the organization will help to give the company a competitive advantage in the market. However, the adoption and maintenance of such technologies require full support and endorsement of the business management. Organizational management must understand the feasibility, functionality, and the importance of embracing such technologies. Movement towards relational databases and data warehousing requires a lot of funding –hence the need to convince the management to support and fund them. This paper seeks to explore the concepts of data warehousing, relational databases, their importance to the business, as whey as their design.
“Importance of Data Warehousing and Relational Databases”
Today, technology has changed the market landscape. Business are striving to adopt cloud-based technology in order to improve efficiency in business functions –among them analytical queries as well as transactional operations. Both relational databases a ...
Running head Database and Data Warehousing design1Database and.docxtodd271
Running head: Database and Data Warehousing design 1
Database and Data Warehousing Design 3
Database and Data Warehousing Design
Thien Thai
CIS599
Professor Wade M. Poole
Strayer University
Feb 20, 2020
Database and Data Warehousing Design
Introduction
Technology has highly revolutionized the world of business –hence presenting more challenges and opportunities for businesses. Companies which fail to embrace and incorporate technology in their operations risks being edged out of the market due to stiff competition witnessed in the market today. On the flipside, cloud-based technology allows businesses to “easily retrieve and store valuable data about their customers, products, and employees.” Data is an important component that help to support core business decisions. In today’s highly competitive and constantly evolving business world, embracing cloud-based technology business managers an opportunity to make informed and result-oriented decisions regarding day-to-day organizational operations (Dimitriu & Matei, 2015).
Notably, business growth and competitiveness depends on its ability to transform data into information. Data warehousing and adoption of relational databases are some of cloud-based technologies which have positively impacted on businesses. The two technologies have had a strategic value to companies –helping them to have the extra edge over their competitors. Both data warehousing and relational databases help businesses to “take smart decisions in a smarter manner.” However, failure to adopt these cloud-based technologies has hindered business executives’ ability to make experienced-based and fact-based decisions which are vital to business survival. Both “databases and data warehouses are relational data systems” which serve different and equally crucial roles within an organization. For instance, data warehousing helps to support management decisions while relational databases help to perform ongoing business transactions in real-time. Basically, embracing cloud-based technologies within the organization will help to give the company a competitive advantage in the market. However, the adoption and maintenance of such technologies require full support and endorsement of the business management. Organizational management must understand the feasibility, functionality, and the importance of embracing such technologies. Movement towards relational databases and data warehousing requires a lot of funding –hence the need to convince the management to support and fund them. This paper seeks to explore the concepts of data warehousing, relational databases, their importance to the business, as whey as their design.
“Importance of Data Warehousing and Relational Databases”
Today, technology has changed the market landscape. Business are striving to adopt cloud-based technology in order to improve efficiency in business functions –among them analytical queries as well as transactional operations. Both relational databases a.
This document discusses how business intelligence can benefit financial institutions. It defines business intelligence and describes how it involves collecting and analyzing data to improve business decisions. It then provides examples of how business intelligence can help various parts of the financial industry, including retail banking, insurance, and investment banking, by identifying profitable customers, optimizing marketing, reducing costs and risks, and improving customer service.
This document discusses business intelligence (BI) in financial institutions. It defines BI as gathering meaningful information to help with analysis and conclusions. An ideal BI system gives employees easy access to needed information and the ability to analyze and share it. The document contrasts traditional reporting with BI and analytic applications. It also discusses identifying BI opportunities by evaluating where it could improve decision making. The benefits of BI include improved operational and strategic decisions from timely information. The document outlines the layers of a BI infrastructure from operational data to delivering intelligence to users.
The document discusses business modeling and how modeling systems can help businesses redesign processes to cut costs. It states that a business model must be adaptable to changing customer needs and priorities. The modeling system allows businesses to link IT systems to organizational information and processes in a relational way to facilitate redesigning processes.
Tips --Break Down the Barriers to Better Data AnalyticsAbhishek Sood
1) Analytics executives face challenges in collecting, analyzing, and delivering insights from data due to a lack of skills, cultural barriers, IT backlogs, and productivity drains.
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Building an effective and extensible data and analytics operating model
1. Digital Business
Building an Effective & Extensible
Data & Analytics Operating Model
To keep pace with ever-present business and technology change and challenges,
organizations need operating models built with a strong data and analytics
foundation. Here’s how your organization can build one incorporating a range of
key components and best practices to quickly realize your business objectives.
Executive Summary
Tosucceedintoday’shypercompetitiveglobaleconomy,
organizationsmustembraceinsight-drivendecision-making.
Thisenablesthemtoquicklyanticipateandenforcebusiness
changewithconstantandeffectiveinnovationthatswiftly
incorporatestechnologicaladvanceswhereappropriate.The
pivottodigital,consumer-mindednewregulationsaround
dataprivacyandthecompellingneedforgreaterlevelsof
dataqualitytogetherareforcingorganizationstoenact
bettercontrolsoverhowdataiscreated,transformed,stored
andconsumedacrosstheextendedenterprise.
Chiefdata/analyticsofficerswhoaredirectlyresponsibleforthe
sanctityandsecurityofenterprisedataarestrugglingtobridge
thegapbetweentheirdatastrategies,day-to-dayoperations
andcoreprocesses.Thisiswhereanoperatingmodelcanhelp.
Itprovidesacommonview/definitionofhowanorganization
shouldoperatetoconvertitsbusinessstrategytooperational
design.Whilesomematureorganizationsinheavilyregulated
sectors(e.g.,financialservices),andfast-pacedsectors(e.g.,
retail)aretweakingtheirexistingoperatingmodels,younger
organizationsarecreatingoperatingmodelswithdataand
analyticsasthebackbonetomeettheirbusinessobjectives.
This white paper provides a framework along with a set of
must-have components for building a data and analytics
operating model (or customizing an existing model).
Cognizant 20-20 Insights
August 2018
2. The starting point: Methodology
Eachorganizationisunique,withitsown
specificdataandanalyticsneeds.Differentsetsof
capabilitiesareoftenrequiredtofilltheseneeds.For
thisreason,creatinganoperatingmodelblueprintis
anart,andisnotrivialmatter.Thefollowingsystematic
approachtobuilding itwillensurethefinalproduct
worksoptimallyforyourorganization.
Buildingtheoperatingmodelisathree-stepprocess
startingwiththebusinessmodel(focusondata)
followedbyoperatingmodeldesignandthen
architecture.However,thereisaprecursorystep,
called“thepivots,”tocapturethecurrentstateand
extractdatapointsfromthebusinessmodelpriorto
designingthedataandanalyticsoperatingmodel.
Understandingkeyelementsthatcaninfluencethe
overalloperatingmodelisthereforeanimportant
considerationfromtheget-go(asFigure1illustrates).
Theoperatingmodeldesignfocusesonintegration
andstandardization,whiletheoperatingmodel
architectureprovidesadetailedbutstillabstractview
oforganizinglogicforbusiness,dataandtechnology.
Insimpleterms,thispertainstothecrystallizationofthe
designapproachforvariouscomponents,including
theinteractionmodelandprocessoptimization.
Preliminary step: The pivots
No two organizations are identical, and the
operating model can differ based on a number
of parameters — or pivots — that influence the
operating model design. These parameters fall into
three broad buckets:
❙
❙ Design principles: These set the foundation
for target state definition, operation and
implementation. Creating a data vision
statement, therefore, will have a direct impact
on the model’s design principles. Keep in mind,
effective design principles will leverage all
existing organizational capabilities and resources
to the extent possible. In addition, they will be
reusable despite disruptive technologies and
industrial advancements. So these principles
should not contain any generic statements, like
“enable better visualization,” that are difficult to
measure or so particular to your organization
that operating-model evaluation is contingent
upon them. The principles can address areas
such as efficiency, cost, satisfaction, governance,
technology, performance metrics, etc.
Cognizant 20-20 Insights
2 / Building an Effective & Extensible Data & Analytics Operating Model
Sequence of operating model development
The pivots
(current state)
Preliminary
Business
model
Step 1 Step 2
Operating model
architecture
Step 3
Operating
model
Figure 1
3. Cognizant 20-20 Insights
3 / Building an Effective & Extensible Data & Analytics Operating Model
❙
❙ Current state:Gaugingthematurityofdataand
relatedcomponents—whichisvitaltodesigning
therightmodel—demandsatwo-pronged
approach:topdownandbottomup.Thereason?
Findingswillrevealkeyleversthatrequireattention
andaroundofprioritization,whichinturncan
movedecision-makerstoseeifintermediate
operatingmodels(IOMs)arerequired.
❙
❙ Influencers: Influencers fall into three broad
categories: internal, external and support.
Current-state assessment captures these
details, requiring team leaders to be cognizant of
these parameters prior to the operating-model
design (see Figure 2). The “internal” category
captures detail at the organization level.
”External” highlights the organization’s focus
and factors that can affect the organization. And
“support factor” provides insights into how much
complexity and effort will be required by the
transformation exercise.
Operating model influencers
• Data & analytics vision
• Geography, spread
& culture
• Organization setup
(flat vs. consensus;
product-driven vs.
function-driven)
• Position in value chain
• Value proposition
• Customer/business
segment & communication
channels
• Competition (monopoly
vs. oligopoly)
• Regulatory influence
Internal
• Change impact index
(employee)
• Technology landscape
• Revenue & headcount
• Management commitment
and funding
Support factor
External
Figure 2
4. Cognizant 20-20 Insights
4 / Building an Effective & Extensible Data & Analytics Operating Model
First step: Business model
A business model describes how an enterprise
leverages its products/services to deliver value,
as well as generate revenue and profit. Unlike a
corporate business model, however, the objective
here is to identify all core processes that generate
data. In addition, the business model needs to
capture all details from a data lens — anything that
generates or touches data across the entire data
value chain (see Figure 3).
We recommend that organizations leverage one
or more of the popular strategy frameworks, such
as the Business Model Canvas1 or the Operating
Model Canvas,2 to convert the information
gathered as part of the pivots into a business model.
Other frameworks that add value are Porter’s Value
Chain3 and McKinsey’s 7S framework.4 The output
of this step is not a literal model but a collection of
data points from the corporate business model and
current state required to build the operating model.
Second step: Operating model
The operating model is an extension of the business
model. It addresses how people, process
and technology elements are integrated and
standardized.
❙ Integration: This is the most difficult part, as it
connects various business units including third
parties. The integration of data is primarily at
the process level (both between and across
processes) to enable end-to-end transaction
processing and a 360-degree view of the
customer. The objective is to identify the core
processes and determine the level/type of
The data value chain
Data ingestion/
data integration
Data modeling/
reporting
Analytics &
Visualization
Data preparation/
data synthesis
Data
provisioning
Data acquisition/
Data generation
Business value
Enterprise data management Enterprise data analytics
Figure 3
5. Cognizant 20-20 Insights
5 / Building an Effective & Extensible Data & Analytics Operating Model
integration required for end-to-end functioning
to enable increased efficiency, coordination,
transparency and agility (see Figure 4).
A good starting point is to create a cross-functional
process map, enterprise bus matrix, activity-
based map or competency map to understand
the complexity of core processes and data. In our
experience, tight integration between processes and
functions can enable various functionalities like self-
service, process automation, data consolidation, etc.
❙
❙ Standardization: During process execution,
data is being generated. Standardization ensures
the data is consistent (e.g., format), no matter
where (the system), who (the trigger), what
(the process) or how (data generation process)
within the enterprise. Determine what elements
in each process need standardization and the
extent required. Higher levels of standardization
can lead to higher costs and lower flexibility, so
striking a balance is key.
Creating a reference data & analytics
operating model
The reference operating model (see Figure 5) is
customizable, but will remain largely intact at this
level. As the nine components are detailed, the
model will change substantially. It is common to see
three to four iterations before the model is elaborate
enough for execution.
Figure 4
Integration & standardization
Cross-functional process map
Data standardization requirements
Data map: field services
Business process/data domain mapping
Customer
Product
Equipment
Order
Work management
Billing & invoice
Employee
Location
Transmission & distribution
Field services
Metering
Installation services
Services
Customer interaction
Billing & payment processing
Customer administration
Service request management
Third-party settlement
Receivables management
<attributelevelstandardizationrules&processlevelstandardizationrequirements>
<attributelevelstandardizationrules&processlevelstandardizationrequirements>
<attributelevelstandardizationrules&processlevelstandardizationrequirements>
<attributelevelstandardizationrules&processlevelstandardizationrequirements>
<attributelevelstandardizationrules&processlevelstandardizationrequirements>
<attributelevelstandardizationrules&processlevelstandardizationrequirements>
<attributelevelstandardizationrules&processlevelstandardizationrequirements>
<attributelevelstandardizationrules&processlevelstandardizationrequirements>
x x x x
x x x
x x x x x x x
x x x x x
x x x
X x x x x
x x x x x x x
x x x x
X x x
Customer
Product
Equipment
Order
Work
management
Billing
&
invoice
Employee
Location
6. Cognizant 20-20 Insights
6 / Building an Effective & Extensible Data & Analytics Operating Model
Figure 5
Reference data & analytics operating model (Level 1)
For anyone looking to design a data and analytics
operating model, Figure 5 is an excellent starting
point as it has all the key components and areas.
Final step: Operating model
architecture
Diverse stakeholders often require different views
of the operating model for different reasons. As
there is no one “correct” view of the operating
model, organizations may need to create variants
to fulfill everyone’s needs. A good example is
comparing what a CEO will look for (e.g., strategic
insights) versus what a CIO or COO would look
for (e.g., an operating model architecture). To
accommodate these variations, modeling tools like
Archimate
5
will help create those different views
quickly. Since the architecture can include many
objects and relations over time, such tools will help
greatly in maintaining the operating model.
The objective is to blend process and technology
to achieve the end objective. This means using
documentation of operational processes aligned to
industry best practices like Six Sigma, ITIL, CMM, etc.
for functional areas. At this stage it is also necessary
to define the optimal staffing model with the right
skill sets. In addition, we take a closer look at what the
organization has and what it needs, always keeping
value and efficiency as the primary goal. Striking the
right balance is key as it can become expensive to
attain even a small return on investment.
Each of the core components in Figure 5 needs
to be detailed at this point, in the form of a
checklist, template, process, RACIF, performance
Business units / functions Customer / employee Third party (vendor/supplier) Regulatory compliance Industry value chain
Manage governance
Manage project lifecycle
It
Business
Process
Organization
Channel
Data
Technology
People 5a.
Strategy,
Plan & align
5b.
Analyze &
define
5c.
Architecture
design
5d.
Detailed
design
5e.
Iterative
development
5f.
Iterative
Testing
5g.
Rollout &
transition
5h.
Sustain &
govern
Manage technology/platform
A. Manage demand/requirements + b. Manage channels
Manage process
Manage data
Manage data services
4a. Data management services 4b. Data analytics services
1
2
3
4
5
6
9
Manage
support
Manage
change
3a. Data acquisition/
data generation
3b. Data provisioning/
data storage
3c. Data preparation/
data synthesis
3d. Data ingestion/
data integration
3e. Data modeling/
reporting
3f. Data analytics &
visualization
8
7
7. Cognizant 20-20 Insights
7 / Building an Effective & Extensible Data & Analytics Operating Model
Figure 6
Sampling of subcomponents: An illustrative view
metrics, etc. as applicable. the detailing of three
subcomponents one level down. Subsequent
levels involve detailing each block in Figure 6 until
task/activity level granularity is reached.
The operating model components
The nine components shown in Figure 5 will be
present in one form or another, regardless of the
industry or the organization of business units.
Like any other operating model, the data and
analytics model also involves people, process and
technology, but from a data lens.
❙ Component 1: Manage process: If an
enterprise-level business operating model exists,
this component would act as the connector/
Component 1: Manage Process: If an enterprise-
level business operating model exists, this
component would act as the connector/bridge
between the data world and the business world.
Every business unit has a set of core processes
that generate data through various channels.
Operational efficiency and the enablement
of capabilities depend on the end-to-end
management and control of these processes.
For example, the quality of data and reporting
capability depends on the extent of coupling
between the processes.
❙ Component 2: Manage demand/require-
ments & manage channel: Business units are
normally thirsty for insights and require different
types of data from time to time. Effectively
managing these demands through a formal
prioritization process is mandatory to avoid
duplication of effort, enable faster turnaround
and direct dollars to the right initiative.
Data/Information governance
Manage governance
Manage technology/platform
Technology, application, platform
a. Manage demand/requirements
Data checklist & templates
Business/Systemrequirements
Requirementstraceabilitymatrix
Business IT alignment
Process/Data flows
Infrastructure
Support requirements
Regulatory & compliance
Effort & ROI estimation
Value proposition matrix
Business case justification
RACI matrix
Wireframes/Templates
Meta model representation
Infrastructure
Tool/Vendor selection model
Staffing
Application inventory
Policy, process & procedures
KPI/Metrics
RACI/CRUD
Standards & controls
Framework & methodology
Issue management
DG organization model
Charter
Capacity
Backup & disaster recovery
Data center operations
License & renewals
Backup & disaster recovery
Security & access management
Usability requirements
Benefit/Value realization
Impact/Dependency analysis
2 6
9
8. Business units are normally thirsty for insights and require different
types of data from time to time. Effectively managing these
demands through a formal prioritization process is mandatory to
avoid duplication of effort, enable faster turnaround and direct
dollars to the right initiative.
Cognizant 20-20 Insights
8 / Building an Effective & Extensible Data & Analytics Operating Model
❙
❙ Component 3: Manage data: This component
manages and controls the data generated by
the processes from cradle to grave. In other
words, the processes, procedures, controls and
standards around data, required to source, store,
synthesize, integrate, secure, model and report it.
The complexity of this component depends on
the existing technology landscape and the three
v’s of data: volume, velocity and variety. For a fairly
centralized or single stack setup with a limited
number of complementary tools and technology
proliferation, this is straightforward. For many
organizations, the people and process elements
can become costly and time-consuming to build.
To enable certain advanced capabilities, the
architect’s design and detail are major parts of
this component. Each of the five subcomponents
requires a good deal of due diligence in
subsequent levels, especially to enable “as-a-
service” and “self-service” capabilities.
❙
❙ Component 4a: Data management services:
Data management is a broad area, and each
subcomponent is unique. Given exponential
data growth and use cases around data, the
ability to independently trigger and manage
each of the subcomponents is vital. Hence,
enabling each subcomponent as a service adds
value. While detailing the subcomponents,
architects get involved to ensure the process
can handle all types of data and scenarios. Each
of the subcomponents will have its set of policy,
process, controls, frameworks, service catalog
and technology components.
Enablement of some of the capabilities as a
service and the extent to which it can operate
depends on the design of Component 3. It is
common to see a few IOMs in place before the
subcomponents mature.
❙
❙ Component 4b: Data analytics services:
Deriving trustable insights from data
captured across the organization is not easy.
Every organization and business unit has its
requirement and priority. Hence, there is no
one-size-fits-all method. In addition, with
advanced analytics such as those built around
machine-learning (ML) algorithms, natural
language processing (NLP) and other forms of
artificial intelligence (AI), a standard model is
not possible. Prior to detailing this component,
it is mandatory to understand clearly what the
business wants and how your team intends to
deliver it. Broadly, the technology stack and data
foundation determine the delivery method and
extent of as-a-service capabilities.
Similar to Component 4a, IOMs help achieve the
end goal in a controlled manner. The interaction
model will focus more on how the analytics
team will work with the business to find, analyze
and capture use cases/requirements from the
industry and business units. The decision on the
setup — centralized vs. federated — will influence
the design of subcomponents.
9. Cognizant 20-20 Insights
9 / Building an Effective & Extensible Data & Analytics Operating Model
❙
❙ Component 5: Manage project lifecycle: The
project lifecycle component accommodates
projects of Waterfall, Agile and/or hybrid
nature. Figure 5 depicts a standard project
lifecycle process. However, this is customizable
or replaceable with your organization’s existing
model. In all scenarios, the components require
detailing from a data standpoint. Organizations
that have an existing program management
office (PMO) can leverage what they already
have (e.g., prioritization, checklist, etc.) and
supplement the remaining requirements.
The interaction model design will help support
servicing of as-a-service and on-demand data
requests from the data and analytics side during
the regular program/project lifecycle.
❙
❙ Component 6: Manage technology/
platform: This component, which addresses
the technology elements, includes IT services
such as shared services, security, privacy and
risk, architecture, infrastructure, data center and
applications (web, mobile, on-premises).
As in the previous component, it is crucial to
detail the interaction model with respect to how
IT should operate in order to support the as-a-
service and/or self-service models. For example,
this should include cadence for communication
between various teams within IT, handling of live
projects, issues handling, etc.
❙
❙ Component 7: Manage support: No matter
how well the operating model is designed, the
human dimension plays a crucial role, too. Be it
business, IT or corporate function, individuals’
buy-in and involvement can make or break the
operating model.
The typical support groups involved in the
operating-model effort include BA team
(business technology), PMO, architecture
board/group, change management/advisory,
10. IOMs help achieve the end goal in a controlled manner. The
interaction model will focus more on how the analytics team will
work with the business to find, analyze and capture use cases/
requirements from the industry and business units. The decision
on the setup – centralized vs. federated – will influence the design
of subcomponents.
Cognizant 20-20 Insights
10 / Building an Effective & Extensible Data & Analytics Operating Model
training and release management teams, the
infrastructure support group, IT applications team
and corporate support group (HR, finance, etc.).
Organization change management (OCM) is a
critical but often overlooked component. Without
it, the entire transformation exercise can fail.
❙
❙ Component 8: Manage change: This
component complements the support
component by providing the processes,
controls and procedures required to manage
and sustain the setup from a data perspective.
This component manages both data change
management and OCM. Tight integration
between this and all the other components is
key. Failure to define these interaction models
will result in limited scalability, flexibility and
robustness to accommodate change.
The detailing of this component will determine
the ease of transitioning from an existing
operating model to a new operating model
(transformation) or of bringing additions to the
existing operating model (enhancement).
❙
❙ Component 9: Manage governance:
Governance ties all the components together,
and thus is responsible for achieving the
synergies needed for operational excellence.
Think of it as the carriage driver that steers the
horses. Although each component is capable
of functioning without governance, over time
they can become unmanageable and fail. Hence,
planning and building governance into the DNA
of the operating model adds value.
The typical governance areas to be detailed
include data/information governance
framework, charter, policy, process, controls
standards, and the architecture to support
enterprise data governance.
Intermediate operating models (IOMs)
As mentioned above, an organization can create as
many IOMs as it needs to achieve its end objectives.
Though there is no one right answer to the question
of optimal number of IOMs, it is better to have no
more than two IOMs in a span of one year, to give
sufficient time for model stabilization and adoption.
The key factors that influence IOMs are budget,
regulatory pressure, industrial and technology
disruptions, and the organization’s risk appetite.
The biggest benefit of IOMs lies in their phased
approach, which helps balance short-term priorities,
manage risks associated with large transformations
and satisfy the expectation of top management to
see tangible benefits at regular intervals for every
dollar spent.
11. Cognizant 20-20 Insights
11 / Building an Effective & Extensible Data & Analytics Operating Model
To succeed with IOMs, organizations need a tested
approach that includes the following critical
success factors:
❙
❙ Clear vision around data and analytics.
❙
❙ Understanding of the problems faced by
customers, vendors/suppliers and employees.
❙
❙ Careful attention paid to influencers.
❙
❙ Trusted facts and numbers for insights and
interpretation.
❙
❙ Understanding that the organization cannot
cover all aspects (in breadth) on the first attempt.
❙
❙ Avoidance of emotional attachment to the
process, or of being too detail-oriented.
❙
❙ Avoidance of trying to design an operating
model optimized for everything.
❙
❙ Avoidance of passive governance — as achieving
active governance is the goal.
Figure 7
DAOM (Level 2)
Manage
Support
Business units/functions Customer/Employee Third party (Vendor/Supplier) Regulatory compliance Industry value chain
Manage governance
Manage Project Lifecycle
IT
Business
Process
Organization
Channel
Data
Technology
People
5a.
Strategy
5b.
Analyze
5c.
Architecture
5d.
Design
5e.
Develop
5f.
Test
5g.
Transition
5h.
Sustain
Manage technology/platform
Manage
change
a. Manage Demand/Requirements + b. Manage Channels
Manage Process
Manage Data
Manage Data Services
1
2
3
4
5
6
9
8
7
Plan & Align Define Operate
Deliver
Design
Business
alignment
checklist
Business case,
Use case prioritization,
Requirements detailing
& analysis
IT / Data
alignment
checklist
Process definition
Stakeholder
involvement matrix
Delivery channel
(UX/UI)
Data identification
& collection
Use case elaboration
Workflow design
Process design
RACI / CRUD
UX/UI design
Proof of
concept (Pilot)
Workflow devl.
Process devl.
Configuration
KM base
Handover
Knowledge
transfer &
training
User
acceptance
testing
Process &
workflow
testing
UX/UI testing
IT solution
testing &
deployment
UX/UI
enhancements
Process
/Workflow
improvements
Enhancements
& upgrades
Data
provisioning
Development
&
configuration
OCM
impact
assessment
Training
IT solution design,
model design
& selection
Data profiling &
Data quality
Master data management
infrastructure
Operations Finance Fiserv FERC Generation Transmission Distribution
NERC
Potelco
Sales ...
4a. Data management services
4a.1. Metadata management
4a.2. Master data management
4a.3 Data quality management
4a.4. Information lifecycle management
4a.5. Data security & privacy
4a.6. Audit and compliance
4a.7 Backup & disaster recovery
Structured
data
Un-structured
data
4b. Data Analytics Services
4b.2 Search & discovery (Self-service)
4b.3 Visualization & UI/UX
4b.4. Cognitive
4b.5. Predictive & prescriptive
4b.6. Descriptive & diagnostic
4b.1. AI, IoT, Big data & blockchain
8a. Process
8b. Controls
8c. Framework
7a.
BA services
7b.
Architecture
7c.
Research
7d.
Release mgmt.
7e.
Infrastructure
7f. Training
7g. PMO
7h. OCM
7i. IT
7j. Security
Defensive
Offensive
3a. Data acquisition/
Data generation
3b. Data provisioning/
Data storage
3c. Data preparation/
Data synthesis
3d. Data ingestion/
Data integration
3e. Data modeling/
reporting
3f. Data analytics &
visualization
12. Cognizant 20-20 Insights
12 / Building an Effective & Extensible Data & Analytics Operating Model
Figure 8
Methodology: The big picture view
Step 1
Business
model
(Precursory
steps)
Step 2
Operating
model
(data
focused)
Step 3
Operating
model
architecture
Extract
data points
Reference
points
Level 1:
Customized data
& analytics operating
model (daom)
Level 2:
DAOM
Level 3:
DAOM
Data &
analytics vision
Integration &
standardization
Level 1 daom
(illustrative)
Reference data &
analytics model
Design
principles
Current state
(data) Influencers
Each subcomponent in level 2 is detailed as required from a people, process and
technology standpoint. The physicalization/implementation of the same for
some subcomponents can get into the next level of detail (level 4).
Data
domains
Core
processes
Entities &
relationships
Information
architecture
Manage support
Manage change
Manage governance
The
pivots
Manage process
Manage demand
Manage data
Manage data services
Manage project
lifecycle
Manage technology
Issue
Register/
tool
Governance
Structure
Raci,
r&r
& skillset
Conceptual &
Logical
data models
Kpi/
metrics,
thresholds
Issue
management
& reporting
Issue
resolution
process
Corporate business model (existing)
1
2
3
4
5
6 9
8
7
...
13. Cognizant 20-20 Insights
13 / Building an Effective & Extensible Data & Analytics Operating Model
Moving forward
Two factors deserve highlighting: First, as
organizations establish new business ventures and
models to support their go-to-market strategies,
their operating models may also require changes.
However, a well-designed operating model will
be adaptive enough to new developments that it
should not change frequently.
Second, the data-to-insight lifecycle is a very
complex and sophisticated process given the
constantly changing ways of collecting and
processing data. Furthermore, at a time when
complex data ecosystems are rapidly evolving and
organizations are hungry to use all available data
for competitive advantage, enabling things such
as data monetization and insight-driven decision-
making becomes a daunting task. This is where a
robust data and analytics operating model shines.
According to a McKinsey Global Institute report,
“The biggest barriers companies face in extracting
value from data and analytics are organizational.”
6
Hence, organizations must prioritize and focus on
people and processes as much as on technological
aspects. Just spending heavily on the latest
technologies to build data and analytics capabilities
will not help, as it will lead to chaos, inefficiencies
and poor adoption. Though there is no one-size-
fits-all approach, the material above provides key
principles that, when adopted, can provide optimal
outcomes for increased agility, better operational
efficiency and smoother transitions.
14. Cognizant 20-20 Insights
14 / Building an Effective & Extensible Data & Analytics Operating Model
Endnotes
1 A tool that allows one to describe, design, challenge and pivot the business model in a straightforward, structured way.
Created by Alexander Osterwalder, of Strategyzer.
2 Operating model canvas helps to capture thoughts about how to design operations and organizations that will deliver
a value proposition to a target customer or beneficiary. It helps translate strategy into choices about operations and
organizations. Created by Andrew Campbell, Mikel Gutierrez and Mark Lancelott.
3 First described by Michael E. Porter in his 1985 best-seller, Competitive Advantage: Creating and Sustaining Superior
Performance. This is a general-purpose value chain to help organizations understand their own sources of value — i.e., the
set of activities that helps an organization to generate value for its customers.
4 The 7S framework is based on the theory that for an organization to perform well, the seven elements (structure, strategy,
systems, skills, style, staff and shared values) need to be aligned and mutually reinforcing. The model helps identify what
needs to be realigned to improve performance and/or to maintain alignment.
5 ArchiMate is a technical standard from The Open Group and is based on the concepts of the IEEE 1471 standard. This
is an open and independent enterprise architecture modeling language. For more information: www.opengroup.org/
subjectareas/enterprise/archimate-overview.
6 The age of analytics: Competing in a data-driven world. Retrieved from www.mckinsey.com/~/media/McKinsey/
Business%20Functions/McKinsey%20Analytics/Our%20Insights/The%20age%20of%20analytics%20Competing%20
in%20a%20data%20driven%20world/MGI-The-Age-of-Analytics-Full-report.ashx.
References
• https:/
/strategyzer.com/canvas/business-model-canvas.
• https:/
/operatingmodelcanvas.com/.
• Enduring Ideas: The 7-S Framework, McKinsey Quarterly, www.mckinsey.com/business-functions/strategy-and-
corporate-finance/our-insights/enduring-ideas-the-7-s-framework.
• www.opengroup.org/subjectareas/enterprise/archimate-overview.
15. Cognizant 20-20 Insights
15 / Building an Effective Extensible Data Analytics Operating Model
About the authors
Jayakumar Rajaretnam
Senior Manager, AI and Analytics Practice, Cognizant Consulting
Jayakumar Rajaretnam is a Senior Manager within Cognizant Consulting’s AI and Analytics Practice. He
has 13 years of experience working on business intelligence (BI), data management, data governance
and operating model engagements across industries (hospitality, telecom, banking and utilities) and
geographies (India, APAC, Middle East, UK and the U.S.). Jayakumar is a Six Sigma green belt, holds an
MBA from SP Jain Center of Management, Dubai-Singapore and a B.Tech. from University of Madras. He
can be reached at Jayakumar.Rajaretnam@cognizant.com | www.linkedin.com/in/jayakumarr.
Mohit Vijay
Senior Consultant, AI and Analytics Practice, Cognizant Consulting
Mohit Vijay is a Senior Consultant within Cognizant Consulting’s AI and Analytics Practice. He has seven-
plus years of consulting and product/program management experience in providing BI, data strategy and
business analytics advisory to clients across industries including retail, hospitality and technology. Mohit
holds an MBA from SP Jain, Mumbai, and a B.Tech. from Rajasthan University. He can be reached at Mohit.
Vijay@cognizant.com | www.linkedin.com/in/mohit-vijay-57510b6/.