Leverage your data as a business asset, from readiness to actionable insights, and drive exceptional performance Key Features Learn strategies to create a data-driven culture and align data initiatives with business goals Navigate the ever-evolving business landscape with a modern data platform and unique Data IP Surpass competitors by harnessing the true value of data and fostering data literacy in your organization Purchase of the print or Kindle book includes a free PDF eBook Book Description Microsoft pioneered data innovation and investment ahead of many in the industry, setting a remarkable standard for data maturity. Written by a data leader with over 15 years of experience following Microsoft's data journey, this book delves into every crucial aspect of this journey, including change management, aligning with business needs, enhancing data value, and cultivating a data-driven culture. This book emphasizes that success in a data-driven enterprise goes beyond relying solely on modern technology and highlights the importance of prioritizing genuine business needs to propel necessary modernizations through change management practices. You'll see how data-driven innovation does not solely reside within central IT engineering teams but also among the data's business owners who rely on data daily for their operational needs. This guide empower these professionals with clean, easily discoverable, and business-ready data, marking a significant breakthrough in how data is perceived and utilized throughout an enterprise. You'll also discover advanced techniques to nurture the value of data as unique intellectual property, and differentiate your organization with the power of data. Its storytelling approach and summary of essential insights at the end of each chapter make this book invaluable for business and data leaders to advocate for crucial data investments. What you will learn Develop a data-driven roadmap to achieve significant and quantifiable business goals Discover the ties between data management and change management Explore the data maturity curve with essential technology investments Build, safeguard, and amplify your organization's unique Data Intellectual Property Equip business leaders with trustworthy and high value data for informed decision-making Unleash the value of data management and data governance to uplift your data investments Who this book is for This book is for data leaders, CDOs, CDAOs, data practitioners, data stewards, and enthusiasts, as well as modern business leaders intrigued by the transformative potential of data. While a technical background isn't essential, a basic understanding of data management and quality concepts will be helpful. The book avoids twisted technical, engineering, or data science aspects, making it accessible and insightful for data engineers and data scientists to gain a wider understanding of enterprise data needs and challenges.
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Contents
Cover
Title Page
Copyright and Credits
Dedicated
Contributors
Table of Contents
Preface
Part 1: Thinking Local, Acting Global
Chapter 1: Where's My Data and Who's in Charge?
The journey begins
Forging collaboration
Unveiling the ownership
The birth of MAL
Development overview of MAL
Summary and key takeaways
Takeaway 1 - becoming the change agent
Takeaway 2 - discovering the killer feature
Takeaway 3 - building the power of a virtual team
Chapter 2: We Make Data Business-Ready
The power of one sentence
Locally inspired, globally connected
Introducing a global request-tracking tool
Moving ahead
The rise of Data Management Organization
My personal story - Data Management Organization announcement
Takeaway #1 - crafting an inspiring motto for transformation
Takeaway #2 - scaling from local to global with trust
Takeaway #3 - the formula for a centralized data team
Chapter 3: Thousands to One - from Locally Siloed to Globally Centralized Processes
The opening story
Five inventory perspectives
One-stop shop
Aligning with role experiences
Corporate applications and tools
Shadow IT
Background work
The next steps
Consolidation paths
Getting started - streamlining from over 1,000 data services to 72
The first path - data enhancement through applications
The second path - no-code solutions
The third path - data platform solutions
The fourth path - handling exceptions
Enabling globally but with a local twist
Technology - the cornerstone of global data management
Processes - the core of data management
People - the pillars of success
Takeaway 1 - approaching the inventory from five diverse perspectives
Takeaway 2 - paths to consolidate effectively.
Takeaway 3 - people, processes, and technology
Chapter 4: "Reactive! Proactive? Predictive."
Addressing urgency
Let's get proactive
Path to predictive data management
Takeaway #1 - addressing urgency and data demand, with quick and impactful actions, to win the time for the next steps
Takeaway #2 - add proactive capabilities, converging from an initial and reactive approach to a solid set of data services
Takeaway #3 - path to predictive data maintenance - as your maturity grows, you will be ready to tap into the next evolutional step
Part 2: Build Insights to Global Capabilities
Chapter 5: Mastering Your Data Domains and Business Ownership
The path toward domain thinking
Defining data and business domains
Ownership - business teams versus the data team
The shift-left principle
Takeaway #1 - integration of data and business domains
Takeaway #2 - empowering business ownership with data
Takeaway #3 - evolving operational principles with shift left
Chapter 6: Navigating the Strategic Data Dilemma
Setting up a global outsourced data operation
Attempt #2
Count to three
Where to start?
Taking the driver's seat
Our wins - embracing outsourcing as a key enabler
Building trust and partnership
Educational foundations
Documentation and pilot projects - essential tools
Fostering quality, upskilling, and collaboration
Choosing your approach
Contracts and KPIs - the triple-A approach
Navigating challenges and pitfalls
Evolution of outsourcing and insourcing
Outsourcing data engineering and beyond
Embracing outsourced education and data literacy
Data science - a selective outsourcing strategy
Outsourcing innovation and incubations
Achieving maximum performance - nearshore versus offshore.
Insourcing - a strategic counterbalance
Shadowing and knowledge transition
Talent management
The integral roles of data engineering, data science, and data analytics - life learnings
Our real-life learnings
Takeaway #1 - a dynamic and collaborative journey
Takeaway #2 - a balanced ecosystem of outsourcing and insourcing
Takeaway #3 - a fair approach to technology and business
Chapter 7: Unique Data IP Is Your Magic
Defining data IP
Documentation
Outsourcing
Community
Technology
Processes
People
Evolving, scaling, modernizing, and governing your data IP
Embracing interactive and in-depth feedback
Comprehensive tracking and celebration of each step forward
Fostering community participation
Seeking external inspiration
Creating a team that loves to learn and share
Protecting and navigating when managing change
Federate and share knowledge
Rely on the steady parts
Show how data helps the business
Executive summary and key takeaways
Takeaway #1 - define your IP, with six dimensions in mind
Takeaway #2 - evolve, modernize, and govern
Takeaway #3 - protect your company
Chapter 8: Pareto Principle in Action
Solid at the core, flexible at the edge
Data management is a team sport with a focus on people
The discipline of change management is key for landing the value of data
Any and all feedback is a learning opportunity
Listening to your partners and customers is critical to drive incremental value
DQ by design, must be implemented to instantly align with strategic and connected data work at the enterprise
Prioritize the demand and run an agile service portfolio
Get solid at the core first, before becoming flexible at the edge
What to avoid - personal experience
Addressing top enterprise data issues.
Case study - the creation of the Unified Support service
The first idea
Unexpected turn
And off we go
We did it - what did we learn?
Takeaway #1 - using the Pareto principle as your compass
Takeaway #2 - practical application of the Pareto principle
Takeaway #3 - case study - building a multi-billion-dollar business
Part 3: Intelligent Future
Chapter 9: Exploring Master Data Management
Setting the stage
The legacy of Microsoft Organizations
The rise and fall of Microsoft Individuals and Organizations
Hello Mr. Jarvis
A meme? No, a MOM (aka Microsoft Org Master)!
Dos and don'ts
Takeaway #1 - start small, with high relevance
Takeaway #2 - business stakeholders are part of the solution
Takeaway #3 - be a Chief Orchestration Officer
Chapter 10: Data Mesh and Data Governance
Taking a look at a typical enterprise-"Data Mess"
From "Data Mess" to Data Mesh - how?
Data Governance = Data Excellence
Where is our data? Again…
Takeaway #1 - digital transformation is the ultimate driver of change
Takeaway #2 - Data Excellence that everybody loves
Takeaway #3 - if you don't have Data Governance, these three Fs will help
Chapter 11: Data Assets or Data Products?
The challenge we face today with data
The magnificent shine of data products
Raw data deserves appreciation too
Takeaway #1 - need for a modern data estate
Takeaway #2 - several sources of inspiration for data products
Takeaway #3 - the naked truth of data assets
Chapter 12: Data Value, Literacy, and Culture
Introduction to three pivotal disciplines
Data Economics
Data Literacy
Data Culture
Unveiling the true worth of enterprise data
Data Literacy has no end state.
Data culture for everyone
Takeaway #1 - data value is coming out of the shadows
Takeaway #2 - embark on the data literacy journey
Takeaway #3 - data culture is what we need
Chapter 13: Getting Ready for GenAI
From pre-AI times to today's aspirations
The strategic role of data in AI
AI for Data
AI governance and ethics
AI-powered data governance - revolutionizing data management
AI over Data
Custom LLMs and orchestrators - the future of AI
Small versus large models
Custom and private models versus public LLMs
The role of RAG and orchestrators in AI
Human-reinforced input for AI success
Takeaway #1 - AI governance and AI ethics
Takeaway #2 - AI for Data
Takeaway 3 - AI over Data
Index
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ISBN
9781835466933 ((electronic bk.))
OCLC
1439600056
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