Data Management Strategy at Microsoft: Best Practices from a Tech Giant's Decade-Long Data Transformation Journey
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Birmingham
Packt Publishing, Limited
2024
|
| Ausgabe: | 1st ed |
| Schlagwörter: | |
| Links: | https://ebookcentral.proquest.com/lib/hwr/detail.action?docID=31472503 |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Umfang: | 1 Online-Ressource (270 Seiten) |
| ISBN: | 9781835466933 |
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| 245 | 1 | 0 | |a Data Management Strategy at Microsoft |b Best Practices from a Tech Giant's Decade-Long Data Transformation Journey |
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| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 650 | 4 | |a Business-Data processing-Management | |
| 650 | 4 | |a Database management | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Plotnikovs, Aleksejs |t Data Management Strategy at Microsoft |d Birmingham : Packt Publishing, Limited,c2024 |z 9781835469187 |
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Datensatz im Suchindex
| _version_ | 1818992326043762688 |
|---|---|
| any_adam_object | |
| author | Plotnikovs, Aleksejs |
| author_facet | Plotnikovs, Aleksejs |
| author_role | aut |
| author_sort | Plotnikovs, Aleksejs |
| author_variant | a p ap |
| building | Verbundindex |
| bvnumber | BV049876709 |
| collection | ZDB-30-PQE |
| 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 -- 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 |
| ctrlnum | (ZDB-30-PQE)EBC31472503 (ZDB-30-PAD)EBC31472503 (ZDB-89-EBL)EBL31472503 (OCoLC)1439600056 (DE-599)BVBBV049876709 |
| dewey-full | 658.4038 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.4038 |
| dewey-search | 658.4038 |
| dewey-sort | 3658.4038 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Wirtschaftswissenschaften |
| edition | 1st ed |
| format | Electronic eBook |
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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</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">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</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Business-Data processing-Management</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Database management</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="a">Plotnikovs, Aleksejs</subfield><subfield code="t">Data Management Strategy at Microsoft</subfield><subfield code="d">Birmingham : Packt Publishing, Limited,c2024</subfield><subfield code="z">9781835469187</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-PQE</subfield></datafield><datafield tag="943" ind1="1" ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-035216159</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://ebookcentral.proquest.com/lib/hwr/detail.action?docID=31472503</subfield><subfield code="l">DE-2070s</subfield><subfield code="p">ZDB-30-PQE</subfield><subfield code="q">HWR_PDA_PQE</subfield><subfield code="x">Aggregator</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV049876709 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-20T20:24:23Z |
| institution | BVB |
| isbn | 9781835466933 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035216159 |
| oclc_num | 1439600056 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (270 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | Packt Publishing, Limited |
| record_format | marc |
| spelling | Plotnikovs, Aleksejs Verfasser aut Data Management Strategy at Microsoft Best Practices from a Tech Giant's Decade-Long Data Transformation Journey 1st ed Birmingham Packt Publishing, Limited 2024 ©2024 1 Online-Ressource (270 Seiten) txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources 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 Business-Data processing-Management Database management Erscheint auch als Druck-Ausgabe Plotnikovs, Aleksejs Data Management Strategy at Microsoft Birmingham : Packt Publishing, Limited,c2024 9781835469187 |
| spellingShingle | Plotnikovs, Aleksejs Data Management Strategy at Microsoft Best Practices from a Tech Giant's Decade-Long Data Transformation Journey 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 Business-Data processing-Management Database management |
| title | Data Management Strategy at Microsoft Best Practices from a Tech Giant's Decade-Long Data Transformation Journey |
| title_auth | Data Management Strategy at Microsoft Best Practices from a Tech Giant's Decade-Long Data Transformation Journey |
| title_exact_search | Data Management Strategy at Microsoft Best Practices from a Tech Giant's Decade-Long Data Transformation Journey |
| title_full | Data Management Strategy at Microsoft Best Practices from a Tech Giant's Decade-Long Data Transformation Journey |
| title_fullStr | Data Management Strategy at Microsoft Best Practices from a Tech Giant's Decade-Long Data Transformation Journey |
| title_full_unstemmed | Data Management Strategy at Microsoft Best Practices from a Tech Giant's Decade-Long Data Transformation Journey |
| title_short | Data Management Strategy at Microsoft |
| title_sort | data management strategy at microsoft best practices from a tech giant s decade long data transformation journey |
| title_sub | Best Practices from a Tech Giant's Decade-Long Data Transformation Journey |
| topic | Business-Data processing-Management Database management |
| topic_facet | Business-Data processing-Management Database management |
| work_keys_str_mv | AT plotnikovsaleksejs datamanagementstrategyatmicrosoftbestpracticesfromatechgiantsdecadelongdatatransformationjourney |