Data Stewardship in Action E-Book

Pui Shing Lee

• Herausgeber: Packt Publishing
• Kategorie: Lebensstil
• Sprache: Englisch
• Veröffentlichungsjahr: 2024

Seitenzahl: 444

Beschreibung

In the competitive data-centric world, mastering data stewardship is not just a requirement—it's the key to organizational success. Unlock strategic excellence with Data Stewardship in Action, your guide to exploring the intricacies of data stewardship and its implementation for maximum efficiency.
From business strategy to data strategy, and then to data stewardship, this book shows you how to strategically deploy your workforce, processes, and technology for efficient data processing. You’ll gain mastery over the fundamentals of data stewardship, from understanding the different roles and responsibilities to implementing best practices for data governance. You’ll elevate your data management skills by exploring the technologies and tools for effective data handling. As you progress through the chapters, you’ll realize that this book not only helps you develop the foundational skills to become a successful data steward but also introduces innovative approaches, including leveraging AI and GPT, for enhanced data stewardship.
By the end of this book, you’ll be able to build a robust data governance framework by developing policies and procedures, establishing a dedicated data governance team, and creating a data governance roadmap that ensures your organization thrives in the dynamic landscape of data management.

Leseprobe

Data Stewardship in Action

A roadmap to data value realization and measurable business outcomes

Pui Shing Lee

Data Stewardship in Action

Copyright © 2024 Packt Publishing

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To my beloved wife, Annie, the beacon of my life who has always been my strongest pillar of support and my ceaseless source of inspiration. Your patience and love have been the guiding lights in my journey of writing this book.

To our precious newborn baby, Lok Yan, the newest spark of joy in our lives. Your arrival has filled our hearts with immense happiness and has given me renewed strength and motivation. Every word penned in these pages is a testament to the hope and dreams we hold for you. You are the future; the future is yours.

To my cherished family, who have stood by me through thick and thin, always encouraging me, believing in me, and cheering me on. Your unwavering faith in me has been my greatest strength. I am forever grateful for your love and support.

And lastly, to my three lovely fur babies, BallBall, Brownie, and Creamy. Your purrs and cuddles have been the best stress-relievers during the most challenging times of this journey. Your feline antics have brought joy and laughter to our home, making the process of writing this book a delightful experience.

This book is a product of all the unconditional love, support, and patience that you have generously given me. I dedicate it to you all, with my deepest gratitude and love. Thank you!

In memory of BallBall

– Pui Shing Lee

Foreword

I have over 30 years of experience in the data industry, from databases to artificial intelligence, and in recent years, I established the Data Literacy Association, with the goal of empowering users to effectively utilize data and leverage its business potential in their daily work and lives. This book on data stewardship is extremely timely. As more users incorporate data into their everyday routines and data becomes an integral part of their lives, it is crucial for them to understand the significance of data stewardship and implement it within their companies.

It’s comparable to having a smart home equipped with intelligence, convenience, and personalized services. If you fail to secure the door and properly manage the security of your home devices while safeguarding your day-to-day data, disaster may strike one day. I urge you to read this book attentively and take action before it’s too late. It’s not just a book for technical and data professionals; it’s also relevant to business leaders and executives, as well as anyone who uses data in their daily lives.

I extend my gratitude to the book author, Pui Shing Lee, for investing significant effort in writing this book and spending time to distill his invaluable experiences and presenting them in an easily understandable manner. Thanks for your contribution to the world of data literacy.

Dr. Toa Charm

Founding Chairman

Data Literacy Association

Contributors

About the author

Pui Shing Lee is a visionary leader with two decades’ experience in FinTech, data, AI, and cloud across Europe, the US, and APAC. He is a cloud solution strategist (Data & AI) at Microsoft. With a passion for deriving actionable insights from data, he provides comprehensive solutions for customers’ journeys, ensuring tangible business outcomes.

Shing holds industry-leading certifications including DAMA CDMP and EDMC CDMC V1. His professional experience includes roles as chief data officer at Hang Seng Index, head of data governance at HKEX, and APAC director at IHS Markit. As the co-founder of the Data Literacy Association, Shing advocates for a culture-fit data strategy, self-service models, and automated governance on robust cloud platforms.

I want to thank the Packt team who have been very supportive. Vandita, Kirti, and Tejashwini, thanks for the coordination and guidance.

A heartfelt thank you to my friend Lance for being the reviewer; your insightful advice has elevated this book to its greatest potential.

I extend my deepest gratitude to Dr. Toa Charm for graciously writing the foreword of my book; your esteemed perspective has truly enhanced its value.

About the reviewers

Anthony A Afolabi is a principal consultant on major data management and governance engagements, with over 12 years of experience in the financial services, consulting, banking, and capital market sectors. He is deeply experienced in the implementation of design principles for banking applications, enterprise risk data architectures, and data warehouse solutions, planning, and mobilization, the execution of global programs, and operational risk management. He has been integral to the implementation of BCBS 239 at major banking groups, G-SIBs, and D-SIBs across Europe and North America, where he was responsible for both the end-to-end delivery and post-implementation assurance frameworks for the first, second, and third lines of defense.

Lance Yeung is the data governance lead at the Hong Kong Science and Technology Parks Corporation (HKSTP), where he steers organizations from various sectors towards success in the digital economy. His strategic leadership ensures robust data integrity and compliance, which are vital for innovation in industries such as banking, insurance, logistics, transportation, healthcare, hospitality, and government. Lance’s seasoned background includes impactful stints at the Hong Kong Monetary Authority (HKMA) and Hong Kong Exchanges and Clearing Limited (HKEX). This multidimensional exposure equips him with insights to craft data governance frameworks and strategies that enable businesses to navigate and excel in a data-centric global marketplace.

Table of Contents

Preface

Part 1: Why Data Stewardship and Why Me?

1

From Business Strategy to Data Strategy to Data Stewardship

Understanding the strategic, tactical, and operational value of data stewardship

Bridging the gap between data strategy and data operation

Unlocking business value with data stewardship

Understanding business strategy

Understanding data strategy

Operationalizing your data strategy via data stewardship

Exploring the mindset and skillset gap

Translating strategy into execution

Data collection

Data governance framework

Analytics and reporting tools

Tracking progress

Decoding data governance, management, and stewardship

Data stewards wear different hats

Summary

2

How Data Stewardship Can Help Your Organization

Defining data stewardship

The work scope of a data steward

Understanding the role of a data steward

Types of data stewards

Comparing strategic data stewardship with a standard operating procedure

Using business cases for storytelling and value realization

Creating a competitive edge with data stewardship

Summary

3

Getting Started with the Data Stewardship Program

Defining the origin and destination of your data stewardship program

Getting buy-in of data stewardship from stakeholders

Building a prioritization matrix

The impact-effort matrix

The RICE method

MoSCoW analysis

Comparing different prioritization matrices

Assessing data maturity

Building the foundation of your data stewardship program

Summary

Part 2: How to Become a Data Steward and Shine!

4

Developing a Comprehensive Data Management Strategy

What is a data strategy?

Assessing your current data environment for creating a data strategy – Where are you now?

Data maturity assessment

The CDMC framework

Fulfilling the business and data strategy – Where do you want to go?

Spider web diagram

Introducing the people, process, and technology – How do we get there?

Making the impact visible to your stakeholders - Feedback loop to measure and report progress

Engagement model

Summary

5

People, Process, and Technology

Empowering people for an effective data stewardship program

Roles and responsibilities

Skills and training

Stakeholder engagement

Demonstrating the return on investment (ROI) of a data stewardship program

Standardizing processes to ensure consistent data operation

Data governance framework

Data compliance and risk management

Leveraging technology to fast-track your data journey

Data infrastructure

Data integration

Data security and privacy technologies

Investment strategy on technology for data stewardship

Fostering the data culture

Cultivating a data-driven culture

Overcoming resistance

Measuring cultural change

Understanding the TOM – From strategy to operation

Designing the TOM

Implementing the TOM

Evaluating and adjusting the TOM

Summary

6

Establishing a Data Governance Organization

Establishing data governance bodies

Building your team

Team structure and hierarchy

Mode of data stewardship

Data is a team sport

Creating a data governance roadmap

Creating short-term and long-term roadmaps

Risk management and mitigation

Defining KPIs

Measuring and reporting on KPIs

Using KPIs for continuous improvement

Reviewing the fitness of your data stewardship mode

Summary

7

Data Steward Roles and Responsibilities

Understanding high-level roles and responsibilities

Day-to-day activities for data stewards

RACI matrix for data governance

Establishing data quality and lineage principles and practices

Data quality

The DQM cycle

Data lineage

Setting up data classification, access control, and security

Data classification

Data access control

Data security and protection

Monitoring and ensuring data privacy and compliance

Summary

8

Effective Data Stewardship

Establishing data stewardship principles and standardizing data incident management

The data life cycle

Principles and policies

Data incident management

Defining and implementing data ownership

Defining and designing a data domain

Assigning an owner to a data domain

Day 2 for data ownership and domain

Defining a target state – What does good data stewardship look like?

Understanding the level of data complexity

Defining a target state

What does good look like for data owners?

Summary

9

Supercharge Data Governance and Stewardship with GPT

Pairing data and AI

Leveraging AI and GPT for data governance

Enhancing data quality and trust

Automation and enrichment

Driving innovation and insight

Understanding the challenges and limitations

Embracing a responsible AI framework

Best practices for responsible AI in data governance

Operationalizing a responsible AI Framework

Future of AI for data governance

Summary

Part 3: What Makes Data Stewardship a Sustainable Success?

10

Data Stewardship Best Practices

Rolling out a people-first operational model

Aligning data mindset and continuous learning

Creating a culture of accountability and ownership

Executing day-to-day data processes

Who does what, by when, and approved by whom?

Data mapping and metadata management

Data risk assessment and mitigation

Optimizing your data journey with strategic technological integration

Data stewardship tools and technologies

Data catalog and metadata

Blockchain and AI

Valuing and protecting data as an asset

Realizing short- and long-term business value via data

Summary

11

Theory versus Real Life

Understanding why there is a gap between theory and reality

Discovering the gaps

Identifying the gaps

Bridging the gap between theory and reality

Critical thinking in bridging theory and reality

Integrating data stewardship into daily operations

Gap #1 – Standard operating procedure is written but not followed

Gap #2 – Insufficient commitment from stakeholders

Gap #3 – Data governance operating model cannot keep up with ever-changing regulatory requirements

Gap #4 – Technical debt

Future-proofing your data stewardship program

Benchmarking with DAMA and EDMC surveys

Enhancing data stewards’ skills with skillset matrix

Cultivating resilience and adaptability in data stewardship

Evolving theoretical models with real-life experiences

Summary

12

Case Studies

Nurturing a data culture with a data mindset – case study #1

A plan of action

Data stewardship in action

Outcome

Streamlining fund performance and reporting – case study #2

A plan of action

Data stewardship in action

Outcome

Summary

Index

Other Books You May Enjoy

Preface

In an age where data is often heralded as the new oil, the role of a data steward has become increasingly critical. As organizations navigate the complexities of digital transformation, the need for a comprehensive understanding of data stewardship is more pressing than ever. This book is a response to that need—a guide that demystifies the role of data stewards, equips aspiring professionals with actionable insights, and serves as a beacon for those who wish to master the art and science of data management.

The journey of writing this book began with a simple observation: while there is a wealth of information on data science, engineering, and analytics, there is a noticeable gap when it comes to the nuanced field of data stewardship. This gap is not just academic; it reflects a real-world disconnect that organizations grapple with daily. The role of a data steward is often misunderstood, undervalued, and yet, absolutely vital to the health and success of any data-driven enterprise.

This book aims to bridge that gap by providing a comprehensive and practical guide to the world of data stewardship. It is crafted for individuals who recognize the importance of data as a strategic asset and are seeking to either step into the role of a data steward or enhance their existing data management practices.

As you turn these pages, you will embark on a journey that begins with the alignment of business strategy to data strategy and culminates in the implementation of robust data stewardship programs. You will learn not just the theory but also the practicalities of managing data effectively. Each chapter is designed to be both informative and engaging, offering real-life examples, case studies, and best practices that have been tested and proven in the field. Moreover, this book recognizes the ever-evolving nature of technology. It delves into how emerging tools such as Generative Pre-trained Transformer (GPT) models can revolutionize data governance and stewardship, providing a glimpse into the future of data management.

The preface would be incomplete without acknowledging the collective wisdom that has shaped this book. Insights from industry experts, feedback from peers, and the experiences of those on the front lines of data stewardship have all contributed to its creation. This book is a testament to the power of collaboration and the shared vision of elevating the practice of data stewardship.

Who this book is for

This book is for individuals interested in the role of a data steward and seeking to advance in the field of data management. It targets existing data team members, chief data officers, chief digital officers, strategy managers, and newcomers to the field aiming to gain a deeper understanding of data stewardship responsibilities, best practices, and implementation strategies.

What this book covers

Chapter 1, From Business Strategy to Data Strategy to Data Stewardship, explores the transformation from business strategy to data strategy and, ultimately, to data stewardship, emphasizing skill development and execution practices for effective data management programs.

Chapter 2, How Data Stewardship Can Help Your Organization, explains data stewardship’s role in enhancing competitive advantage by improving data accuracy, security, and management, leading to cost reduction and increased customer satisfaction.

Chapter 3, Getting Started with the Data Stewardship Program, guides you on starting a data stewardship program, highlighting stakeholder buy-in, key program elements, and strategies for addressing initial data management challenges.

Chapter 4, Developing a Comprehensive Data Management Strategy, takes you through creating a data management strategy, from assessing current states to defining future goals and executing governance, quality, and security plans.

Chapter 5, People, Process, Technology, delves into the interplay of people, processes, and technology in data stewardship, showing ROI and improving practices through automation and artificial intelligence (AI).

Chapter 6, Establishing a Data Governance Structure, outlines the steps for establishing a data governance structure, fostering a data culture, and defining KPIs, and provides tools for creating and measuring a successful governance program.

Chapter 7, Data Steward Roles and Responsibilities, defines data steward roles, emphasizing data quality, access control, security, and compliance, equipping you with frameworks for effective data governance.

Chapter 8, Effective Data Stewardship, discusses principles of effective data stewardship, including data ownership, role assignment, accountability, and leveraging continuous training for a successful data governance roadmap.

Chapter 9, Supercharge Data Governance and Stewardship with GPT, explores using GPT for enhancing data governance and stewardship, automating management tasks, and ensuring data privacy, security, and the prevention of bias in AI applications.

Chapter 10, Data Stewardship Best Practices, shares best practices for data stewardship, aligning mindsets, upskilling teams, fostering accountability, and ownership, and integrating emerging technologies for effective data management programs.

Chapter 11, Theory versus Real Life, addresses the gap between theory and real-life data stewardship, emphasizing collaboration, continuous monitoring, and improvement for bridging this divide and ensuring program success.

Chapter 12, Case Studies, presents two case studies on effective data stewardship: the Bank of East Asia fosters a data-driven culture for operational efficiency, while Fencore’s solution streamlines fund management, showcasing the practical impact of data strategies.

To get the most out of this book

We assume you have a basic understanding of data life cycle and process flow management in the context of enterprises. You should also be familiar with the basic data and cloud technology for data ingestion, transformation, and storage. Ideally, you should have experience in fostering a new working culture for an organization and a strong interest in extracting the utmost value from raw data.

To fully engage with the practical elements of this book, especially in chapters discussing the integration of AI in data stewardship, you may need to set up an OpenAI/Azure OpenAI account for access to GPT models.

Additionally, while not mandatory, registering with professional bodies such as the EDM Council (EDMC) or DAMA International could enhance your understanding and provide valuable resources aligned with the advanced topics covered in this book. These platforms offer a wealth of knowledge and community support that can complement your learning journey.

Conventions used

There are a number of text conventions used throughout this book.

Keyword: This indicates a new word or phrase when it is first introduced. Here is an example: “Data stewardship is the practice of managing data ethically and responsibly.”

Italics: This is used to add emphasis to a sentence or add a figure, table, chapter, or other reference. Here is an example: “We have several key building blocks, as illustrated in Figure 2.2 in Chapter 2.”

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Part 1:Why Data Stewardship and Why Me?

Here, you will begin the foundational journey of aligning your business strategy with data strategy and stewardship in the initial chapters of this insight-packed book. You will discover how to unlock the latent value of data, develop the required mindset and skillset, and transition seamlessly from strategic planning to execution. These chapters are crafted to guide you through the inception of data stewardship, demonstrating its organizational benefits and providing a practical roadmap to initiate a robust data stewardship program.

This part has the following chapters:

Chapter 1, From Business Strategy to Data Strategy to Data StewardshipChapter 2, How Data Stewardship Can Help Your OrganizationChapter 3, Getting Started with the Data Stewardship Program

1

From Business Strategy to Data Strategy to Data Stewardship

“Data is the new oil”

“Data is the new currency”

“Data is the common language”

All these buzz-phrases fail to resonate when you are trying to secure a budget for data programs in a management meeting. Distracted stakeholders, lack of impact, and stagnating initiatives often become the norm as data quality issues mount and data compliance challenges multiply.

Everyone recognizes the need for a data-driven culture, yet nobody wants to own the responsibility. Conducting a lot of data workshops will not bring you closer to your objectives if data stewardship and strategy are not aligned with the business strategy.

The struggle is real and the cycle is repetitive – once you leave the organization after years of struggle, your successor restarts the data stewardship program, and the story replays.

Does this sound familiar to you?

As a data professional, it is unrealistic for you to expect other business functions to dedicate time to understanding data intricacies, just as you would not spend days learning about marketing, HR, or accounting.

The key question for your audience is the following: What is in it for me?

Consider redefining your data initiatives, including a data stewardship program, as a solution to practical problems: freeing up Friday nights spent on report consolidation, introducing efficient self-service data analytics tools, and offering potential bonuses tied to meeting data quality metrics.

Now we are talking.

Data stewardship is not limited to a data workshop or two. It’s a direction, a mindset, and a problem-solving mechanism. In the first chapter, we’ll emphasize the pivotal role of data stewardship in aligning data initiatives with business strategy and solving real business issues.

Then how does data stewardship relate to the business strategy?

Again, think from the C-level perspective – what is in it for the senior executives if they invest x amount of money and y amount of head counts into data stewardship initiatives?

So, what is data stewardship and why is it essential? We will explore these questions and a few more in this chapter.

In this chapter, we will discuss the following:

Understanding the strategic, tactical, and operational value of data stewardshipUnlocking business value with data stewardshipExploring the mindset and skillset gapTranslating strategy into executionDecoding data governance, management, and stewardship

Understanding the strategic, tactical, and operational value of data stewardship

Data stewardship is the practice of managing data ethically and responsibly. Data stewards are responsible for ensuring that data is used in a way that respects the rights of individuals and meets the business objectives of the organization. Data stewardship requires a holistic approach to data management. It involves developing a data strategy that aligns with the organization’s business strategy, establishing data governance policies and procedures, and designing data models and architectures that can support the implementation of an automated data operating model on the cloud. All of these only make sense if you can tie them to business objectives.

Now, we will look at how data stewardship is related to business strategy and how it can help businesses achieve their goals. We will also discuss how to develop a data strategy that aligns with the business strategy and how to identify the key stakeholders involved in the data stewardship process. Finally, we will discuss how to develop a data stewardship program that will help businesses unlock the value of their data.

Bridging the gap between data strategy and data operation

Data stewardship is not a tick-box exercise. It is a continuous refinement of your data operation to support your new business model. You need to tackle from strategic, tactical, and operational levels (Figure 1.1) to make it a sustainable program and ensure that it meets both short- and long-term business objectives horizontally and vertically:

Strategic: It’s designed with the entire organization in mind and begins with an organization’s mission. This will also influence the culture within an organization.Tactical: It describes the series of plans to achieve the ambitions outlined in the strategy.Operational: It is highly specific with a measurable metric and usually couples with standard operating procedures (SOPs) to ensure the consistency of operation excellence.

Figure 1.1 shows how a high-level strategy gets broken down into mid-level tactical plans, and then finally into on-the-ground operations.

Figure 1.1 – An organizational view of data stewardship

It is a business-friendly way to visualize how things get done in a big organization.

Your organization’s business strategy drives your data, AI, marketing, cybersecurity, and all other strategies. From there, specifically for data, you need the data governance and stewardship program to link up strategic and operational levels. Then you have the data operating model and related SOPs to make sure that data is utilized and protected in the right way in day-to-day activities.

Data stewardship is a core element of the tactical layer but it can also cover some of the of operational activities too. The key takeaway is you have to align the expected outcomes and prioritize the deliverables with the stakeholders along with relevant and measurable outcomes. You should make sure it can glue the high-level strategy and the ground-level execution together.

In the upcoming section, we will also talk about not just the three horizontal layers but also the vertical bonding so that team resources and outcomes are aligned.

Data stewardship is not another IT project that runs for 15 months and into a never-ending user acceptance test (UAT) of new software. Then, stakeholders do not even appreciate your team’s effort when you think you have been going the extra mile. No one is happy in this case and it happens quite often in the corporate world. You do not want to run into this scenario.

Let’s now discuss how data stewardship helps create more business value by translating strategy into operation.

Unlocking business value with data stewardship

Let’s explore the importance of data stewardship in unlocking business value. Data stewardship can help your organization to make informed decisions based on trustworthy data. This is the cornerstone of all your data analytics and AI initiatives because data is the ultimate fuel.

Let’s understand what a business strategy is and why it’s relevant to data strategy and data governance.

Understanding business strategy

Let’s zoom in on the concept of business strategy and how it is used to drive business outcomes. Since it is not a book about business strategy, we will just lightly touch on how a business strategy is developed.

A business strategy is a plan of action that outlines how a business will achieve its goals and objectives. It involves setting objectives, identifying resources, analyzing the competitive environment, and creating a plan of action to reach the desired outcome. The strategy should be tailored to the company’s unique strengths and weaknesses and should take into account the external environment, such as customer needs, competitive pressures, and market trends.

Here are the steps to help create an effective business strategy:

To understand how your organization’s business executives develop a business strategy, you need to identify your organization’s goals and objectives. These should be SMART:Specific: Your objective should clearly state what you or the team needs to achieve. For example, you can set a target to boost the quarterly sales revenue by 15% by introducing new data-driven marketing strategies.Measurable: You must have a quantitative way of measuring what you have effectively achieved. For example, improve your customer satisfaction score from 75% to 85% over the next six months by utilizing customer feedback data to improve your services.Achievable: Your objective must be possible to achieve and you should secure the resources required to achieve it. Assessing the achievability of an objective necessitates a thorough evaluation of available resources, including finances, personnel, your team’s expertise, time, technology, and past experience with similar objectives. Say you want to mitigate data breach risks in your company. This could be achieved by implementing a robust data encryption protocol within the next three months, utilizing the expertise of your in-house data security team.Relevant: A goal is relevant to your organization’s objectives when it aligns with the broader vision, mission, and strategic direction of the company. It should contribute to the advancement of key business priorities, whether that’s expanding market share, increasing revenue, or improving operational efficiency. For example, a the sales manager, increase cross-selling opportunities by 20% over the next quarter by leveraging customer purchase data.Time-bound: Data stewardship is a continuous operation but you still need to set start and end times to achieve the objective for each milestone. For example, launch a data-driven customer feedback system by the end of Q1 to proactively address customer issues and improve overall satisfaction.These examples illustrate how data stewardship can contribute toward business strategy and broader organizational goals such as revenue growth, customer satisfaction, and risk management.Once the goals and objectives have been established, the next step is to analyze the competitive environment. This involves researching the industry, understanding the competitive landscape, and identifying opportunities and threats.The next step is to identify the resources needed to achieve the goals and objectives. This includes financial resources, human resources, and technological resources:Financial resources: These are the funds required to achieve your goals. For example, if your objective is to implement a new data management system, you need to consider costs such as software purchases or subscriptions, hardware upgrades, and potential training costs for staff. Budgeting accurately for these expenses is vital to ensure financial feasibility.Human resources: This refers to the personnel needed to carry out tasks. If your goal is to improve data quality, you’ll likely need data analysts, data scientists, and possibly data stewards. Assess the current team’s capabilities and determine whether additional hiring or training is needed. Also, consider the time commitment required from each team member.Technological resources: These are the tools and systems needed to achieve your objectives. If your aim is to increase data security, you may need to invest in advanced encryption software or a secure data storage system. Evaluate the current technological infrastructure and identify what upgrades or additions are necessary.Once the resources have been identified, the next step is to create a plan of action. This involves setting milestones, establishing timelines, and outlining the steps that need to be taken to reach the desired outcome. For example, if your goal is to implement a new data governance framework, the milestones could be the following:Completing audit – by the end of Q1Designing framework – by the end of Q2Training staff – throughout Q3Implementing framework – by the end of Q4Reviewing framework – by Q1 the following yearFinally, the strategy should be monitored and evaluated to ensure that it is achieving the desired results. This includes tracking progress, making adjustments as needed, and ensuring that the strategy is aligned with the company’s goals and objectives. Along the journey, communication with stakeholders is critical. Let’s say the strategy is to implement a new data management system. Progress can be tracked by monitoring system setup and integration stages, staff training completion, and the successful migration of data into the new system.

Let me now introduce you to the concept of data strategy and how it is used to support a business strategy. We will look at the different components of a data strategy, such as data architecture, data governance, and data stewardship.

Understanding data strategy

Data is a strategic asset that organizations must manage strategically. By recognizing its value, we can leverage data to gain competitive advantages and drive growth.

Data strategy is the process of developing a plan to leverage data and analytics to achieve business objectives. It involves understanding the data needs of the organization, assessing the current data landscape, and creating a plan to acquire, manage, and use data to drive business decisions.

Data strategy is used to support a business strategy by providing insights into customer needs, competitive pressures, and market trends. By leveraging data and analytics, organizations can gain a better understanding of their customers, their competitors, and their industry. This helps them to make more informed decisions and create strategies that are tailored to their unique needs. It also helps organizations to identify opportunities and risks. By understanding the data landscape, organizations can identify areas of opportunity and potential risks. This helps them to create strategies that are more likely to succeed and to avoid potential pitfalls.

With a good data strategy, organizations create more efficient and effective processes. By leveraging data and analytics, organizations can streamline processes, reduce costs, and increase efficiency. This helps them to create strategies that are more cost-effective and efficient.

From strategy to operation, you need data stewardship to glue people, processes, and technology together. This is where business strategy, data strategy, and data stewardship align and collaborate to ensure the quality, availability, security, and usability of data across the organization as a day-to-day operation.

Having explored the importance of a well-defined data strategy and its alignment with business objectives, we now step into the practical aspect of this journey. In the next section, we will discuss how to transform your data strategy from a conceptual framework into a functional, day-to-day operation that drives organizational efficiency and effectiveness.

Operationalizing your data strategy via data stewardship

Data stewardship being a key component of any successful business and data strategy helps businesses unlock the value of their data by ensuring that data is accurate, secure, and accessible. Carefully crafted data stewardship helps businesses gain insights into their customers, markets, and operations that can help them make better decisions supported by quality data and achieve their business goals in a compliant way. Data stewards are the key actors who oversee and coordinate the data life cycle, from collection to analysis to dissemination.

Data stewardship is closely linked to business strategy. To develop a successful data stewardship program, a business must first understand its business strategy and how data can help it achieve its goals. This requires the business to identify its key stakeholders, such as customers, suppliers, and employees, and understand its needs and objectives. It also requires the business to understand its current data assets and how they can be used to meet its goals. The other direction is to gather a list of burning pain points and tackle them one by one. We will discuss the use of a prioritization matrix in the upcoming chapters to help you identify the low-hanging fruit to get stakeholders’ buy-in, and also pinpoint the long-term strategic data initiatives for your organization’s sustainable success.

Once a business has identified its stakeholders and data assets, it can develop a data strategy that aligns with its business strategy. This data strategy should include objectives, such as increasing customer satisfaction, improving operational efficiency, and reducing costs. It should also include a plan for how to use data to achieve these objectives. This will also drive the scope of your data collection and cleaning requirements. Beware that we do not want to boil the ocean. It is not realistic to clean every data cell from your data sources. More importantly, the ROI is low and it is just not worth the effort. Instead, you should develop the definition of critical data elements (CDEs) for your organization. Focusing on CDE, which may be 10-20% of your total data assets, can resolve 80% of your organization’s data pain points. We will get into the details of CDE in the next few chapters.

Finally, a business must develop a data stewardship program that will help it achieve its data strategy objectives. This program should include policies and procedures for data governance, data quality, and data security. It should also include a roadmap for how to implement the program and measure its success so that you can report it in a business-friendly format to your stakeholders.

By developing a data stewardship program that aligns with its business strategy, a business can unlock the value of its data and achieve its goals.

To ensure that data is managed ethically, we must adhere to the principles of fairness, transparency, and privacy. These principles are essential for building trust and safeguarding the integrity of data, enabling us to unlock its true potential.

So now, finally, stakeholders understand how data stewardship is relevant to the overall business.

Often, we focus too much on the data skillset enablement and overlook the importance of aligning the right data mindset.

Let’s now talk about mindset and skillset.

Exploring the mindset and skillset gap

Mindset is the direction; skillset is your muscle.

With the right direction and strong muscles, you are capable of doing the right things in the right way.

Data stewardship is a critical component of any successful business and data strategy. It requires a combination of the right mindset and the right skillset to ensure that data is managed and used effectively. In this section, we will explore the importance of having the right mindset and skillset when it comes to data stewardship.

The right mindset is essential to data stewardship. It is important to understand the value of data stewardship and how it can help the organization achieve its goals. This means having an understanding of the importance of data governance, data quality, and data security. It also means being able to identify the right data sources and datasets to use and understanding the impact of data on the organization. One of the most important elements in the data mindset is asking the right question. Having the perfect solution to the wrong question does not seem right at all. With the data mindset, we should ask ourselves and others: can we make an informed decision based on data and not (just) instinct?

How about a skillset for data stewardship?

Well, data storytelling skill is remarkably important. It includes having the ability to analyze data, visualize data, and secure data. It also includes understanding the technologies and tools used to manage data, such as data warehouses, data lakes, and data marts. Finally, it includes having the ability to develop and implement a data stewardship program, including setting up a data governance team and creating a data governance roadmap. With the right tooling and upskilling, data users should be able to drive insights from the data in a consistent and future-proof way.

Mindset and skillset are the two cornerstones of data stewardship. To ensure that data stewardship is successful, organizations must invest in measurable upskilling programs to guarantee that their data stewards have the necessary skills and knowledge. This includes providing training on data analysis, data visualization, data security, and data privacy. It also includes providing guidance on best practices for data governance, data quality, and data security.

By having the right mindset and skillset, an organization can ensure that its data stewards are well-equipped to develop and maintain a successful data stewardship program. This will enable it to achieve its business goals and objectives while ensuring that its data is managed and used effectively.

With all these solid foundations in place, let us explore how to execute data stewardship as a consistent data operating model.

Upon understanding the significance of the right mindset and skillset for effective data stewardship, we are now well-equipped to take the next step. We will discuss how to transform your set of plans into actionable steps, ensuring that your data stewardship efforts become a seamless part of your organization’s operational model.

Translating strategy into execution

In this section, we will explore the process of transforming a business strategy into a data strategy and then into data stewardship. We will provide you with an understanding of how to create a feedback loop between business strategy and data stewardship to ensure continuous improvement.

Executing a data strategy involves putting the plan into action. This involves implementing the data collection and management processes, developing the analytics and reporting tools, and creating the necessary infrastructure to support the data strategy.

The following subsections list the steps to execute a data strategy, common challenges faced, and recommendations to tackle those challenges.

Data collection

The first step in executing a data strategy is to collect the necessary data. Again, do not boil the ocean, and try to use the concept of CDE to scale down the complexity when you start a pilot. This involves setting up the data scoping process, such as setting up data collection systems, developing data pipelines, and creating data warehouses.

The key challenge in collecting data is data silos. A data silo is a repository of fixed data that remains under the control of one department and is isolated from the rest of the organization, leading to a lack of transparency, collaboration, and overall efficiency. Data silos can hinder the ability to derive meaningful insights from the data and create barriers to effective data management and governance.

To overcome this, we should deploy a data catalog, which is a structured collection of data assets, equipped with metadata, descriptions, and data lineage. The cataloging process helps in organizing and making sense of large volumes of data. This offers two benefits:

Enhancing transparency: By creating a central point of access for all data assets, data cataloging reduces the opacity caused by data silos. All users can view the data sources and understand their purpose, thus promoting transparency.Facilitating collaboration: Data cataloging enables different departments to access and share data, facilitating collaboration. This cross-functional accessibility breaks down the barriers of data silos and encourages a data-driven culture.

Data governance framework

After the data is collected, the next step is to manage the data. This involves developing data governance policies, setting up data quality processes, and creating data security protocols.

One of the key challenges in implementing a data governance framework is aligning business goals with IT capabilities. Often, there is a disconnect between what the business side of an organization wants to achieve with data and what the IT department can realistically deliver. This misalignment can lead to ineffective data governance, with policies and procedures that do not support the organization’s objectives.

To overcome the challenge of aligning business goals with IT capabilities, we should establish clear lines of communication between these two entities by doing the following:

Involving both sides in planning: Both the business and IT departments should be involved in the planning stages of the data governance framework. This helps to ensure that the framework supports both business objectives and technical feasibility.Regular meetings and updates: Regular meetings and updates between the business and IT departments can help to keep everyone on the same page and facilitate the alignment of goals.Training and education: Providing the necessary training and education can help both sides understand each other’s perspectives better, leading to more effective collaboration.Implementing a data governance council: Establishing a data governance council or committee that includes representatives from both sides can help in making decisions that align with both business and IT needs.

Analytics and reporting tools

Once we have access to data, we need to develop the analytics and reporting tools to derive insights. This involves creating dashboards, developing algorithms, and creating data visualizations.

Once the analytics and reporting tools are in place, we should then create the necessary infrastructure to support the data strategy. This includes setting up data warehouses, creating and developing data lakes.

Here, the predominant challenge is dealing with the complexity and volume of data. As the scale of data increases, its complexity also grows, making it challenging to manage and analyze. The vast amount of data generated from various sources can be overwhelming, and if not managed properly, it can lead to inefficiencies and inaccuracies in reporting and analytics.

Hence, we need a future big data platform, among others that are listed as follows:

Big data platforms: Big data platforms on the cloud can efficiently process and analyze large volumes of data. They are designed to handle the scale and complexity of big data.Data processing automation: Automating data processing tasks can significantly reduce the time spent on data preparation and increase efficiency. Machine learning algorithms can be particularly useful for this.Data visualization tools: Utilizing data visualization tools can help to simplify complex data and make it more understandable. Tools such as Tableau, Power BI, and QlikView can provide interactive visualizations that make it easier to extract insights from the data.

Tracking progress

Last but not least, the data strategy should be monitored and evaluated to ensure that it is achieving the desired results. This includes tracking progress, making adjustments as needed, and ensuring that the data strategy is aligned with the company’s goals and objectives.

Measuring the success of the data strategy is one of the challenges you may encounter. It can be difficult to quantify the impact of a data strategy and to determine whether it is delivering the expected benefits. This is especially true if the organization does not have clear metrics or key performance indicators (KPIs) to evaluate the effectiveness of the data strategy.

We should align on the desired outcome of a future state. Clearly defined KPIs that align with the organization’s goals and objectives can provide a tangible way to measure the success of the data strategy. These could include metrics related to data quality, data usage, data governance, or business outcomes. Conducting regular reviews of the data strategy can help to identify any areas that need improvement and ensure that the strategy continues to align with the organization’s goals.

Data governance is the foundation of effective data management. It ensures that data is aligned with organizational goals and objectives, enabling better decision-making and mitigating risks. Through data governance, organizations can create policies and procedures to ensure data is secure, accurate, and compliant with regulations. Data governance also helps organizations maximize the value of their data by ensuring it is properly used and shared.

Let’s not forget about the feedback loop. Data initiatives should be aligned with the business strategy such that data operations are efficient and effective. With the feedback loop, you can better understand the gap and adjust your implementation.

Data governance, data management, and data stewardship. Confusing enough?

Let’s demystify them one by one in the next section.

Decoding data governance, management, and stewardship

Are data governance and data management different? What roles do data stewards play? You might have several questions at this point. Let’s define these terms to help you understand the key differences and relationships between them.

Data governance is the comprehensive management of the availability, usability, integrity, and security of the data employed in an enterprise. It involves establishing methods, policies, and procedures to ensure the data is well-managed throughout its life cycle.

Consider a large multinational bank. In this context, data governance involves setting up robust policies and procedures to ensure the safety and accuracy of financial data. The bank needs to establish clear rules about who can access the data, how it can be used, and how it should be secured. These rules are enforced through a combination of technologies, processes, and people. The bank also needs to ensure that its data governance policies comply with global financial regulations to avoid penalties.

On the other hand, data management is the practice of collecting, storing, and using data efficiently, effectively, and in a cost-efficient manner. It involves the development and execution of architectures, policies, practices, and procedures that properly manage the full data life cycle needs of an enterprise.

Let’s take an example of an e-commerce company. This company collects a lot of data about customers, such as their browsing history, purchase history, and feedback. Data management in this context involves storing this data in a structured and logical manner, such as in a database, so that it can be easily retrieved when needed. It also involves ensuring that the data is accurate and up-to-date and that it can be accessed quickly and securely. The company might use data management software to help with this process.

Lastly, data stewardship focuses on managing and supervising the data assets of an organization to ensure consistent access to high-quality data for business users. It is also the implementation of data governance policies. It would facilitate the data analytics capabilities for the organization, but data stewardship is not data analytics.

For instance, consider a hospital. In this context, a data steward might be responsible for ensuring the integrity and confidentiality of patient records. They would oversee the collection and storage of data, ensuring that it is entered correctly, stored securely, and used appropriately. They would also coordinate with various teams, such as IT and legal, to ensure that the hospital’s data practices comply with health information regulations.

While data governance sets the policy and strategy framework for managing data, data management is about executing those policies and strategies in a technical sense. Data stewardship, on the other hand, is the hands-on operation that ensures daily adherence to data governance policies and the upkeep of data management practices.

Data stewards wear different hats

Data stewardship within a data governance framework is an irreplaceable role. Data stewards are responsible for the management and fitness of data elements – both the content and metadata. They work hand-in-hand with data governance teams, ensuring policies and guidelines are adhered to while maintaining the quality and security of data.

For instance, in a large pharmaceutical company, data stewards might be responsible for ensuring the integrity and confidentiality of clinical trial data. They would work within the data governance framework, abiding by industry regulations and company policies. Their role might involve coordinating with various teams, such as IT and legal, to ensure data is correctly classified, stored, and used.

Comparing data stewardship in data governance and SMEs

While the core principles of data stewardship remain the same, the role can differ significantly between that of a comprehensive data governance framework and (small and medium-sized enterprises (SMEs).

In a large organization, data stewardship is often a dedicated role within a robust data governance structure. For example, in a multinational bank, data stewards would work to ensure the consistency, quality, and security of vast amounts of financial data. They would follow a detailed governance framework, which might involve complex data classification systems and rigorous compliance checks.

Contrastingly, in an SME, the role of a data steward could be part of someone’s broader job description. For example, in a small tech start-up, a product manager might take on the role of a data steward, ensuring that user data from the company’s app is handled correctly. Given the smaller scale and lower complexity, data stewardship in an SME might be less formalized and more intertwined with other business operations.

Exploring opportunities for data stewards in data quality and data governance

Regardless of the context, data stewards have a unique opportunity to enhance data quality and strengthen data governance. By overseeing data collection, storage, and usage practices, data stewards can directly impact the reliability and integrity of data.

For example, in a mid-sized e-commerce company, a data steward could initiate a project to clean up customer data. This might involve standardizing address formats, removing duplicate entries, and validating email addresses. This would not only improve the quality of the data but also optimize marketing efforts, leading to better customer targeting and higher conversion rates.

Similarly, a data steward in a hospital could work on enhancing the data governance around patient records. They could implement stricter access controls and audit trails, ensuring data privacy and compliance with health information regulations.

In both data governance and SME contexts, effective data stewardship can lead to improved decision-making, better regulatory compliance, and increased operational efficiency. It can also foster a data-driven culture, encouraging all employees to value and properly utilize data.

Summary

In conclusion our exploration of the journey from business strategy to data strategy to data stewardship, it is evident that a comprehensive approach to data management is required, one that fully aligns with the organization’s overarching business strategy. It’s the business strategy that sets the course for the data strategy, ultimately determining how data should be managed, used, and protected.