Info
- Utilizzare la checkbox di selezione a fianco di ciascun documento per attivare le funzionalità di stampa, invio email, download nei formati disponibili del (i) record.
Big data in practice : how 45 successful companies used big data analytics to deliver extraordinary results / Bernard Marr
| Big data in practice : how 45 successful companies used big data analytics to deliver extraordinary results / Bernard Marr |
| Autore | Marr, Bernard |
| Descrizione fisica | xi, 308 p. ; 22 cm |
| Disciplina | 658.0557 |
| Soggetto topico |
Consumer behavior
Strategic planning Big data |
| ISBN | 9781119231387 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Machine generated contents note: Introduction 1 Walmart: How Big Data Is Used To Drive Supermarket Performance 2 CERN: Unravelling the Secrets of the Universe with Big Data 3 Netflix: How Netflix Used Big Data to Give Us the Programmes We Want 4 Rolls-Royce: How Big Data Is Used To Drive Success In Manufacturing 5 Shell: How Big Oil Uses Big Data 6 Apixio: How Big Data Is Transforming Healthcare 7 Lotus F1 Team: How Big Data Is Essential To The Success Of Motorsport Teams 8 Pendleton & Son Butchers: Big Data for Small Business 9 US Olympic Women's Cycling Team: How Big Data Analytics Is Used To Optimize Athletes' Performance 10 ZSL: Big Data In The Zoo And To Protect Animals 11 Facebook: How Facebook Use Big Data to Understand Customers 12 John Deere: How Big Data Can Be Applied On Farms 13 Royal Bank of Scotland: Using Big Data to Make Customer Service More Personal 14 LinkedIn: How Big Data Is Used To Fuel Social Media Success 15 Microsoft: Bringing Big Data To The Masses 16 Acxiom: Fuelling Marketing With Big Data 17 US Immigration and Customs: How Big Data Is Used To Keep Passengers Safe and Prevent Terrorism 18 Nest: Bringing the Internet of Things into The Home 19 GE: How Big Data Is Fuelling the Industrial Internet 20 Etsy: How Big Data Is Used In A Crafty Way 21 Narrative Science: How Big Data Is Used To Tell Stories 22 BBC: How Big Data Is Used In The Media 23 Milton Keynes: How Big Data Is Used To Create Smarter Cities 24 Palantir: How Big Data Is Used To Help The CIA And To Detect Bombs In Afghanistan 25 Airbnb: How Big Data Is Used To Disrupt the Hospitality Industry 26 Sprint: Profiling Audiences Using Mobile Network Data 27 Dickey's Barbecue Pit: How Big Data Is Used to Gain Performance Insights Into One Of America's Most Successful Restaurant Chains 28 Caesars: Big Data At The Casino 29 Fitbit: Big Data In The Personal Fitness Arena 30 Ralph Lauren: Big Data In The Fashion Industry 31 Zynga: Big Data In The Gaming Industry 32 Autodesk: How Big Data Is Transforming The Software Industry 33 Walt Disney Parks and Resorts: How Big Data Is Transforming Our Family Holidays 34 Experian: Using Big Data To Make Lending Decisions And To Crack Down On Identity Fraud 35 Transport for London: How Big Data Is Used To Improve And Manage Public Transport In London 36 The US Government: Using Big Data To Run A Country 37 IBM Watson: Teaching Computers To Understand And Learn 38 Google: How Big Data Is At The Heart Of Google's Business Model 39 Terra Seismic: Using Big Data To Predict Earthquakes 40 Apple: How Big Data Is At The Centre Of Their Business 41 Twitter: How Twitter And IBM Deliver Customer Insights From Big Data 42 Uber: How Big Data Is At The Centre Of Uber's Transportation Business 43 Electronic Arts: Big Data In Video Gaming 44 Kaggle: Crowdsourcing Your Data Scientist 45 Amazon: How Predictive Analytics Are Used To Get A 360-Degree View Of Consumers Final Thoughts About the Author Acknowledgements Index . |
| Record Nr. | UNISALENTO-991000349779707536 |
Marr, Bernard
|
||
| Materiale a stampa | ||
| Lo trovi qui: Univ. del Salento | ||
| ||
Data analytics for organisational development : unleashing the potential of your data / / Uwe H. Kaufmann, Amy BC Tan
| Data analytics for organisational development : unleashing the potential of your data / / Uwe H. Kaufmann, Amy BC Tan |
| Autore | Kaufmann Uwe H. |
| Pubbl/distr/stampa | West Sussex, England : , : Wiley, , [2021] |
| Descrizione fisica | 1 online resource (364 pages) |
| Disciplina | 658.0557 |
| Soggetto topico |
Business enterprises - Data processing
Organizational change |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-119-75837-8
9781119758334 1-119-75832-7 1-119-75831-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Foreword -- Preface -- About the Authors -- Introduction: Why Data Analytics is Important -- Why This Book Has Been Written -- How This Book Is Structured -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- What Tools Are Used -- Activating and Using MS Excel's Analysis ToolPak -- Downloading and Using MS Power BI -- Downloading and Using R and R Studio -- What Is Provided -- Which Cases Should I Study? -- References -- List of Figures and Tables -- Chapter 1 Introduction to Data Analytics and Data Science -- Components of Data Analytics -- Big Data and its Relationship to Data Analytics -- Data Analytics - The Foundation for Data Science and Artificial Intelligence -- Practice -- Phases of Data Analytics -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- Analytical Story Telling -- Deploying Analytics Tools -- Practice -- Competencies of a Data Scientist -- Competencies Needed in Data Analytics Phases -- Key Roles of Today's Managers and Leaders -- References -- List of Figures and Tables -- Chapter 2 Customer Domain - Customer Analytics -- Why Customer Analytics? -- Listen to the Voice of Your Existing Customers -- Understanding Customer Expectations -- Studying the Complete Customer Experience -- Designing Customer Surveys -- Determine the Purpose of your Survey -- Use Proven Questionnaires -- Use Proven Scales -- Test Your Survey Questionnaire -- Decide on the Distribution of the Questionnaire -- Select an Appropriate Timing for your Survey -- Begin with the End in Mind -- Some More Considerations -- Conclusion -- Practice -- Case 1: Great, We Have Improved . . . or Not? -- The Problem with Sampling -- Understanding Confidence Intervals -- Means Are Lies -- Business Question.
Data Collection -- Data Processing -- Data Analysis -- Business Decision -- Analytical Storytelling -- What If We Had All The Data? -- Deploying Analytics Tools -- Practice -- Case 2: What Drives our Patient Satisfaction? -- Patient Satisfaction in an Outpatient Clinic -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- Analytical Storytelling -- Practice -- Deploying Analytics Tools -- Case 3: How to Create a Patient Satisfaction Dashboard -- Deciding about Metrics to Illustrate our Clinic Performance -- Building a Clinic Dashboard with MS Power BI and R -- Using MS Power BI for Analytical Storytelling -- Conclusion -- Practice -- References -- List of Figures andTables -- Chapter 3 Process Domain - Operations Analytics -- Why Operations Analytics? -- Dimensions of Operations Analytics -- Process Design Using Analytics -- Defining Measures for Analytics -- Process Management Using Analytics -- Process Improvement Using Analytics - The Power of DMAIC -- Roles and Deployment of Operations Analytics -- Conclusion -- Practice Questions -- Case 4: Which Supplier has the Better Product Quality? -- Business Question -- Data Acquisition -- Data Analysis -- Business Decision -- Deploying Analytics Tools -- Practice -- Case 5: Why Does Finance Pay Our Vendors Late? -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- Deploying Analytics Tools -- Practice -- Case 6: Why Are We Wasting Blood? -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- Deploying Analytics Tools -- Practice -- References -- List of Figures and Tables -- Chapter 4 Workforce Domain - Workforce Analytics -- Why Workforce Analytics? -- Why has the topic "workforce analytics" developed into a priority? -- Dimensions of Workforce Analytics. Putting Workforce Analytics into Practice -- Using Descriptive and Predictive Workforce Analytics in Workforce Planning -- Workforce Planning for Transactional Processes -- Workforce Planning for Less Transactional Processes -- Workforce Planning from the Workforce Perspective -- Getting the Intent Right -- a) Connect HR Data and Business Outcomes -- b) Determine Information Needed and Collect Data -- c) Analyse the Data -- d) Derive and Formulate a Business Answer - Tell a Story -- Workforce Analysts' Paradise is Employees' Nightmare - Managing the Change -- Summary -- Practice -- Case 7: Do We Have Enough People to Run Our Organisation? - Workforce Planning Inside-Out -- Data Acquisition and Data Wrangling -- Understanding the Demand Pattern -- Predicting a Potential Future Problem -- Understanding the Activity Pattern -- Planning the Workforce -- "Fighting Variation" -- Rethinking and Innovating the Process -- Conclusion -- Practice -- Deploying Analytics Tools -- Case 8: What Makes Our Staff Innovate? -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision and Storytelling -- Deploying Analytics Tools -- Background -- Case 9: What Does Our Engagement Survey Result Mean? -- Why We Should not Trust this Data Easily -- Performing a Proper Data Analysis -- Making a Better Decision -- Practice -- Deploying Analytics Tools -- Case 10: What Drives Our Staff Out? - Logistic Regression for Prediction and Decision Making -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- Summary -- Practice -- Deploying Analytics Tools -- References -- Table of Equations, Figures, Tables -- Chapter 5 Implementing Data Analytics for Organisational Development -- Making Better Decisions - Knowing the Risk of Being Wrong -- There is No Difference, and We Decide There Is None. There is No Difference, and We Decide There Is One - Type I Error -- There Is a Difference, and We Decide There Is One -- There Is a Difference, but We Decide There Is None - Type II Error -- Making Better Decisions - Do not Trust Statistics Blindly -- Significant Difference Does Not Mean Important Difference -- A Non-Significant Difference Could Be Important for The Organisation -- Data Analytics Does Not Take Over Decision Making -- Ensuring the Success of Your Data Analytics Journey -- Steps for Implementing Data Analytics -- Ensuring the Management Walks and Talks Analytics -- Creating Excitement for Data Analytics and its Benefits -- Developing a Body of Knowledge - Start Small -- Using Analytics to Breakdown Silos -- Closing the Analytics Loop - Sustaining the Gains -- Calibrating Your Data Analytics Implementation -- Outlook -- References -- List of Figures and Tables -- Materials for Download -- Index -- EULA. |
| Record Nr. | UNINA-9910554855403321 |
|
Kaufmann Uwe H.
|
||
| West Sussex, England : , : Wiley, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Data analytics for organisational development : unleashing the potential of your data / / Uwe H. Kaufmann, Amy BC Tan
| Data analytics for organisational development : unleashing the potential of your data / / Uwe H. Kaufmann, Amy BC Tan |
| Autore | Kaufmann Uwe H. |
| Pubbl/distr/stampa | West Sussex, England : , : Wiley, , [2021] |
| Descrizione fisica | 1 online resource (364 pages) |
| Disciplina | 658.0557 |
| Soggetto topico |
Business enterprises - Data processing
Organizational change |
| ISBN |
1-119-75837-8
1-119-75832-7 1-119-75831-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover -- Title Page -- Copyright Page -- Contents -- Foreword -- Preface -- About the Authors -- Introduction: Why Data Analytics is Important -- Why This Book Has Been Written -- How This Book Is Structured -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- What Tools Are Used -- Activating and Using MS Excel's Analysis ToolPak -- Downloading and Using MS Power BI -- Downloading and Using R and R Studio -- What Is Provided -- Which Cases Should I Study? -- References -- List of Figures and Tables -- Chapter 1 Introduction to Data Analytics and Data Science -- Components of Data Analytics -- Big Data and its Relationship to Data Analytics -- Data Analytics - The Foundation for Data Science and Artificial Intelligence -- Practice -- Phases of Data Analytics -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- Analytical Story Telling -- Deploying Analytics Tools -- Practice -- Competencies of a Data Scientist -- Competencies Needed in Data Analytics Phases -- Key Roles of Today's Managers and Leaders -- References -- List of Figures and Tables -- Chapter 2 Customer Domain - Customer Analytics -- Why Customer Analytics? -- Listen to the Voice of Your Existing Customers -- Understanding Customer Expectations -- Studying the Complete Customer Experience -- Designing Customer Surveys -- Determine the Purpose of your Survey -- Use Proven Questionnaires -- Use Proven Scales -- Test Your Survey Questionnaire -- Decide on the Distribution of the Questionnaire -- Select an Appropriate Timing for your Survey -- Begin with the End in Mind -- Some More Considerations -- Conclusion -- Practice -- Case 1: Great, We Have Improved . . . or Not? -- The Problem with Sampling -- Understanding Confidence Intervals -- Means Are Lies -- Business Question.
Data Collection -- Data Processing -- Data Analysis -- Business Decision -- Analytical Storytelling -- What If We Had All The Data? -- Deploying Analytics Tools -- Practice -- Case 2: What Drives our Patient Satisfaction? -- Patient Satisfaction in an Outpatient Clinic -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- Analytical Storytelling -- Practice -- Deploying Analytics Tools -- Case 3: How to Create a Patient Satisfaction Dashboard -- Deciding about Metrics to Illustrate our Clinic Performance -- Building a Clinic Dashboard with MS Power BI and R -- Using MS Power BI for Analytical Storytelling -- Conclusion -- Practice -- References -- List of Figures andTables -- Chapter 3 Process Domain - Operations Analytics -- Why Operations Analytics? -- Dimensions of Operations Analytics -- Process Design Using Analytics -- Defining Measures for Analytics -- Process Management Using Analytics -- Process Improvement Using Analytics - The Power of DMAIC -- Roles and Deployment of Operations Analytics -- Conclusion -- Practice Questions -- Case 4: Which Supplier has the Better Product Quality? -- Business Question -- Data Acquisition -- Data Analysis -- Business Decision -- Deploying Analytics Tools -- Practice -- Case 5: Why Does Finance Pay Our Vendors Late? -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- Deploying Analytics Tools -- Practice -- Case 6: Why Are We Wasting Blood? -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- Deploying Analytics Tools -- Practice -- References -- List of Figures and Tables -- Chapter 4 Workforce Domain - Workforce Analytics -- Why Workforce Analytics? -- Why has the topic "workforce analytics" developed into a priority? -- Dimensions of Workforce Analytics. Putting Workforce Analytics into Practice -- Using Descriptive and Predictive Workforce Analytics in Workforce Planning -- Workforce Planning for Transactional Processes -- Workforce Planning for Less Transactional Processes -- Workforce Planning from the Workforce Perspective -- Getting the Intent Right -- a) Connect HR Data and Business Outcomes -- b) Determine Information Needed and Collect Data -- c) Analyse the Data -- d) Derive and Formulate a Business Answer - Tell a Story -- Workforce Analysts' Paradise is Employees' Nightmare - Managing the Change -- Summary -- Practice -- Case 7: Do We Have Enough People to Run Our Organisation? - Workforce Planning Inside-Out -- Data Acquisition and Data Wrangling -- Understanding the Demand Pattern -- Predicting a Potential Future Problem -- Understanding the Activity Pattern -- Planning the Workforce -- "Fighting Variation" -- Rethinking and Innovating the Process -- Conclusion -- Practice -- Deploying Analytics Tools -- Case 8: What Makes Our Staff Innovate? -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision and Storytelling -- Deploying Analytics Tools -- Background -- Case 9: What Does Our Engagement Survey Result Mean? -- Why We Should not Trust this Data Easily -- Performing a Proper Data Analysis -- Making a Better Decision -- Practice -- Deploying Analytics Tools -- Case 10: What Drives Our Staff Out? - Logistic Regression for Prediction and Decision Making -- Business Question -- Data Acquisition -- Data Preparation -- Data Analysis -- Business Decision -- Summary -- Practice -- Deploying Analytics Tools -- References -- Table of Equations, Figures, Tables -- Chapter 5 Implementing Data Analytics for Organisational Development -- Making Better Decisions - Knowing the Risk of Being Wrong -- There is No Difference, and We Decide There Is None. There is No Difference, and We Decide There Is One - Type I Error -- There Is a Difference, and We Decide There Is One -- There Is a Difference, but We Decide There Is None - Type II Error -- Making Better Decisions - Do not Trust Statistics Blindly -- Significant Difference Does Not Mean Important Difference -- A Non-Significant Difference Could Be Important for The Organisation -- Data Analytics Does Not Take Over Decision Making -- Ensuring the Success of Your Data Analytics Journey -- Steps for Implementing Data Analytics -- Ensuring the Management Walks and Talks Analytics -- Creating Excitement for Data Analytics and its Benefits -- Developing a Body of Knowledge - Start Small -- Using Analytics to Breakdown Silos -- Closing the Analytics Loop - Sustaining the Gains -- Calibrating Your Data Analytics Implementation -- Outlook -- References -- List of Figures and Tables -- Materials for Download -- Index -- EULA. |
| Record Nr. | UNINA-9910830067003321 |
|
Kaufmann Uwe H.
|
||
| West Sussex, England : , : Wiley, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
The impact challenge : reframing sustainability for businesses / / Alessia Falsarone
| The impact challenge : reframing sustainability for businesses / / Alessia Falsarone |
| Autore | Falsarone Alessia |
| Pubbl/distr/stampa | Boca Raton, FL : , : CRC Press, , 2022 |
| Descrizione fisica | 1 online resource (174 pages) |
| Disciplina | 658.0557 |
| Collana | Impactful Data Science Series |
| Soggetto topico |
Business enterprises - Data processing
Sustainable development |
| ISBN |
1-00-321222-0
1-000-56285-9 1-000-56283-2 1-003-21222-0 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Impact Challenge |
| Record Nr. | UNINA-9910774703203321 |
|
Falsarone Alessia
|
||
| Boca Raton, FL : , : CRC Press, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Monetising data? : how to uplift your business / / Andrea Ahlemeyer-Stubb, Shirley Coleman
| Monetising data? : how to uplift your business / / Andrea Ahlemeyer-Stubb, Shirley Coleman |
| Autore | Ahlemeyer-Stubbe Andrea |
| Pubbl/distr/stampa | Hoboken, New Jersey ; ; Chichester, West Sussex, England : , : Wiley, , 2018 |
| Descrizione fisica | 1 online resource (399 pages) : illustrations |
| Disciplina | 658.0557 |
| Soggetto topico |
Business - Data processing
Big data Corporations - Growth |
| ISBN |
1-119-12514-6
1-119-12515-4 1-119-12516-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910270897103321 |
|
Ahlemeyer-Stubbe Andrea
|
||
| Hoboken, New Jersey ; ; Chichester, West Sussex, England : , : Wiley, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Monetising data? : how to uplift your business / / Andrea Ahlemeyer-Stubb, Shirley Coleman
| Monetising data? : how to uplift your business / / Andrea Ahlemeyer-Stubb, Shirley Coleman |
| Autore | Ahlemeyer-Stubbe Andrea |
| Pubbl/distr/stampa | Hoboken, New Jersey ; ; Chichester, West Sussex, England : , : Wiley, , 2018 |
| Descrizione fisica | 1 online resource (399 pages) : illustrations |
| Disciplina | 658.0557 |
| Soggetto topico |
Business - Data processing
Big data Corporations - Growth |
| ISBN |
1-119-12514-6
1-119-12515-4 1-119-12516-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910831169903321 |
|
Ahlemeyer-Stubbe Andrea
|
||
| Hoboken, New Jersey ; ; Chichester, West Sussex, England : , : Wiley, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Security tokens and stablecoins quick start guide : learn how to build sto and stablecoin decentralized applications / / Weimin Sun, Xun (Brian) Wu, Angela Kwok
| Security tokens and stablecoins quick start guide : learn how to build sto and stablecoin decentralized applications / / Weimin Sun, Xun (Brian) Wu, Angela Kwok |
| Autore | Sun Weimin |
| Pubbl/distr/stampa | Birmingham ; ; Mumbai : , : Packt Publishing, , 2019 |
| Descrizione fisica | 1 online resource (226 pages) |
| Disciplina | 658.0557 |
| Soggetto topico | Blockchains (Databases) |
| ISBN | 1-83855-264-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910793422603321 |
|
Sun Weimin
|
||
| Birmingham ; ; Mumbai : , : Packt Publishing, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Security tokens and stablecoins quick start guide : learn how to build sto and stablecoin decentralized applications / / Weimin Sun, Xun (Brian) Wu, Angela Kwok
| Security tokens and stablecoins quick start guide : learn how to build sto and stablecoin decentralized applications / / Weimin Sun, Xun (Brian) Wu, Angela Kwok |
| Autore | Sun Weimin |
| Pubbl/distr/stampa | Birmingham ; ; Mumbai : , : Packt Publishing, , 2019 |
| Descrizione fisica | 1 online resource (226 pages) |
| Disciplina | 658.0557 |
| Soggetto topico | Blockchains (Databases) |
| ISBN | 1-83855-264-2 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910818138803321 |
|
Sun Weimin
|
||
| Birmingham ; ; Mumbai : , : Packt Publishing, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||