Data Management Strategy at Microsoft : Best Practices from a Tech Giant's Decade-Long Data Transformation Journey

Data Management Strategy at Microsoft Best Practices from a Tech Giant's Decade-Long Data Transformation Journey

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...

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Detalles Bibliográficos
Otros Autores: Plotnikovs, Aleksejs, author (author)
Formato: Libro electrónico
Idioma:Inglés
Publicado: Birmingham, England : Packt Publishing Ltd [2024]
Edición:First edition
Materias:
Database management.
Business > Data processing > Management.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009841206506719
Tabla de Contenidos:
  • 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
  • Summary and key takeaways
  • 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
  • Summary and key takeaways
  • 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
  • Summary and key takeaways
  • 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
  • Summary and key takeaways
  • 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
  • Summary and key takeaways
  • 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?
  • Summary and key takeaways
  • 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
  • Summary and key takeaways
  • 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…
  • Summary and key takeaways
  • 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
  • Summary and key takeaways
  • 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
  • Summary and key takeaways
  • 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
  • Summary and key takeaways
  • Takeaway #1 - AI governance and AI ethics
  • Takeaway #2 - AI for Data
  • Takeaway 3 - AI over Data
  • Index
  • Other Books You May Enjoy.

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