
Making Big Data Work for Your Business
Description
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Who this book is for
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Person
Sudhi Sinha is a business leader with over 17 years of global experience in technology and general management. He started his career designing and developing database management systems and business intelligence systems. Currently, he is the Vice President for product development and engineering for Building Technology and Services in Johnson Controls. He is also responsible for several Big Data initiatives. He has worked in technology consulting, engineering, sales, strategy, operations, and P&L roles across US, Asia, and Europe. He has written extensively on various technical and management topics including applying Big Data to different aspects of business. Sudhi holds a degree in Production Engineering from Jadavpur University, Kolkata, India. He resides in Mumbai with his wife, Sohini who is an entrepreneur and a fashion designer.
Content
- Cover
- Copyright
- Credits
- Foreword
- About the Author
- Acknowledgments
- About the Reviewers
- Contents
- Preface
- Chapter 1: Building Your Strategy Framework
- Using big data analytics to identify where to play and how to win, to grow your business
- Understanding the changing landscape
- Identifying the strategic implications
- Spotting and simulating growing influences
- Simulating your organization's use of data
- Understanding competitive actions
- Establishing correlations
- Integrating new possibilities into planning
- Developing strategies
- The Balanced Score Card approach
- Force Field Analysis
- Aligning existing initiatives
- Cascading your strategy
- Summary
- Chapter 2: Creating an Opportunity Landscape and Collecting Your Gold Coins
- Building your Data Catalog
- The Gold Coin approach
- Identifying your Gold Coins
- Qualification
- Benefit assessment
- Strategic advantage assessment
- Assessing your Gold Coin project
- Prioritizing your Gold Coins
- Developing the prioritization framework
- Building your Gold Mine
- Summary
- Chapter 3: Managing Your Big Data Projects Effectively
- Recognizing how Big Data Analytics projects are different
- Scope fluidity
- Business case certainty
- Focus specificity
- Initiation and progression
- Learning tolerance
- Data complexity
- Functional transaction processing
- Defining unique success criteria
- Creating an Explore, Validate, Amplify framework for Big Data Analytics projects
- Explore
- Building use cases
- Identifying data sources
- Ingressing data
- Deciding your analytics models
- Applying analytical models on your selected data sets
- Testing your hypothesis
- Validate
- Identifying more data sets
- Retesting your use case
- Identifying adjacent data types, sources, and data sets
- Refining and extending your use case
- Validating your modified use case
- Identifying output data needs
- Amplify
- Building an enterprise data model
- Refining the ingress process
- Developing a functional user prototype
- Developing repeatable analytical algorithms
- Developing an application package
- Hosting your data and application
- Developing a user guide
- Developing a communication package
- Establishing a governance model
- Treating customer-facing applications differently
- Intellectual property protection
- User experience excellence
- A comprehensive approach for building an organizational Big Data Analytics infrastructure
- The enterprise data map
- The enterprise data ingestion infrastructure
- Scalable data storage
- The analysis engine
- Benefits map
- The platform framework
- Summary
- Chapter 4: Building the Right Technology Landscape
- Designing Big Data storage
- Evolution of storage technology
- Big Data storage architecture
- Big Data storage calculations
- The hardware and operating system needs for Big Data
- Identifying the different technology layers
- Quality check
- Cleansing
- Correlation
- Enrichment
- Data cataloging
- Modeling for analysis
- Classification and clustering
- Statistical summary for preliminary insights
- Human explorations
- Selecting from your technology choices
- An overview of key Big Data technology components
- Hadoop
- MapReduce
- Programming languages
- NoSQL
- Other Hadoop components
- Technology choices by layers
- Making the right technology choices
- Creating a visualization of your Big Data
- Understanding the difference between Enterprise Data Warehouse and Big Data
- Summary
- Chapter 5: Building a Winning Team
- Understanding the distinctive skills you need
- Data scientist
- Skills of a data scientist
- Sourcing data scientists
- Experimental analyst
- Skills of an experimental analyst
- Sourcing experimental analysts
- Application developer
- Skills of an application developer
- Sourcing application developers
- Infrastructure specialist
- Skills of an infrastructure specialist
- Sourcing infrastructure specialists
- Change leader
- Skills of a change leader
- Sourcing the change leader
- The project manager
- Skills of a project manager
- Sourcing the project manager
- Defining the team and structure
- Building an extended ecosystem
- Educational institutes
- The engagement framework
- Best practices
- Consulting organizations
- The engagement framework
- Best practices
- Improving team alignment and performance
- Training and orientation
- Quick wins
- Rotational leadership
- Motivating your team towards progress
- Summary
- Chapter 6: Managing Investments and Monetization of Data
- Understanding how data creates value
- Insights and influence
- Immediate and future value
- Value creation for data
- Capturing the value of data
- Identifying the value
- Building a value catalog
- Understanding and capturing your Big Data costs
- Data collection costs
- Storage and processing costs
- Software licensing costs
- People costs
- Infrastructure and administrative costs
- Maintenance costs
- Capturing the costs
- Cost Context Framework
- Monetizing your Big Data
- Direct business impact
- Selling data externally
- Valuation of Big Data
- Managing your Big Data investments
- Summary
- Chapter 7: Driving Change Effectively
- Understanding changes caused by Big Data
- The significance of changes
- Applying the IMMERSE framework to manage change
- Identify
- Modulate
- Mitigate
- Educate
- Role play
- Show
- Effect
- Creating stakeholder groups to drive change
- Project group
- Work group
- Review group
- Summary
- Chapter 8: Driving Communication Effectively
- Identifying your communication needs
- Communicating with the internal audience
- Providing a general overview of Big Data
- Sharing the strategic cascade
- Communicating with the external audience
- Engaging with customers
- Swaying the business influencers
- Roping in your ecosystem partners
- Communicating with the shareholders
- Selecting your communication channels
- Communication channels
- Channel effectiveness
- Building your communication strategy
- Building your communication plan
- Engaging executives effectively
- Monitoring and modulating your communication program
- Effectiveness metrics
- Measuring effectiveness
- Summary
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