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Making Big Data Work for Your Business

Book Description

A guide to effective Big Data analytics

  • Open up exciting new opportunities for your business with Big Data

  • Gain fresh insights into your customers and clients to make confident business decisions

  • A practical and accessible perspective on the most talked about development in business

  • In Detail

    Bridge the gap between data and decision.

    Big Data has brought about a revolution in the way we do business. Essential business decisions can today be informed by the wealth of data now at our disposal. However, while Big Data may appear to be the answer to every business problem, for many, gaining real value from data – gaining business insights is a difficult task. Big Data, for many, is a big problem itself, with many struggling to reap the rewards that it promises. In this accessible and stimulating guide, Sudhi Sinha, management, technology and sustainability expert, provides a unique perspective on Big Data and how to derive maximum value from it – with sharp and careful analytics.

    “this is a perfect starter for any manager who wants to understand and explore Big Data… The Big Data field is evolving quickly, and this book serves as a quick and practical introduction to the field.”

    AnHai Doan, Professor, University of Wisconsin; Chief Scientist at WalmartLabs USA

    This insightful and engaging book demonstrates that Big Data, to be most effective, needs to be weaved within the fabric of organization strategy. Without it, you are simply left with numbers and statistics, lacking purpose – lacking potency. Beginning with the essential stage of building a strategy framework for you Big Data analytics project, and integrating it within your wider business strategy, Sudhi provides you with the knowledge and insight to help you build a big data strategy that gets results.

    Big data is one of the biggest buzzwords in the world of business today. And while it is true that it has opened up huge opportunities for businesses of all sizes, it is nevertheless difficult for many businesses to turn the reserves of numbers and statistics at their disposal into clear insights that can inform important business decisions. Beginning with the creation of a Big Data strategy and the identification of the key opportunities that it has the potential to unlock, the book will then demonstrate how to implement and manage your project with the best team and the right technology for your needs. Once this is in place you will then find out how to get the most from your Big Data insights, with effective organizational alignment and change management.

    About the Author

    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.

    Table of Contents

    1. Making Big Data Work for Your Business
      1. Making Big Data Work for Your Business
      2. Credits
      3. Foreword
      4. About the Author
      5. Acknowledgments
      6. About the Reviewers
      7. Preface
        1. What this book covers
        2. Who this book is for
        3. Conventions
        4. Reader feedback
        5. Piracy
      8. 1. Building Your Strategy Framework
        1. Using Big Data analytics to identify where to play and how to win, to grow your business
        2. Understanding the changing landscape
        3. Identifying the strategic implications
        4. Spotting and simulating growing influences
          1. Simulating your organization's use of data
          2. Understanding competitive actions
          3. Establishing correlations
        5. Integrating new possibilities into planning
        6. Developing strategies
          1. The Balanced Score Card approach
          2. Force Field Analysis
        7. Aligning existing initiatives
        8. Cascading your strategy
        9. Summary
      9. 2. Creating an Opportunity Landscape and Collecting Your Gold Coins
        1. Building your Data Catalog
        2. The Gold Coin approach
          1. Identifying your Gold Coins
            1. Qualification
            2. Benefit assessment
            3. Strategic advantage assessment
              1. Gold Coin examples
                1. Problem 1 – service parts historical analysis
                2. Problem 2 – customer records aggregation
                3. Problem 3 – mining corporate social media to understand employee engagement
        3. Assessing your Gold Coin project
          1. Prioritizing your Gold Coins
            1. Developing the prioritization framework
        4. Building your Gold Mine
        5. Summary
      10. 3. Managing Your Big Data Projects Effectively
        1. Recognizing how Big Data Analytics projects are different
          1. Scope fluidity
          2. Business case certainty
          3. Focus specificity
          4. Initiation and progression
          5. Learning tolerance
          6. Data complexity
          7. Functional transaction processing
        2. Defining unique success criteria
        3. Creating an Explore, Validate, Amplify framework for Big Data Analytics projects
          1. Explore
            1. Building use cases
            2. Identifying data sources
            3. Ingressing data
            4. Deciding your analytics models
            5. Applying analytical models on your selected data sets
            6. Testing your hypothesis
          2. Validate
            1. Identifying more data sets
            2. Retesting your use case
            3. Identifying adjacent data types, sources, and data sets
            4. Refining and extending your use case
            5. Validating your modified use case
            6. Identifying output data needs
          3. Amplify
            1. Building an enterprise data model
            2. Refining the ingress process
            3. Developing a functional user prototype
            4. Developing repeatable analytical algorithms
            5. Developing an application package
            6. Hosting your data and application
            7. Developing a user guide
            8. Developing a communication package
        4. Establishing a governance model
        5. Treating customer-facing applications differently
          1. Intellectual property protection
          2. User experience excellence
        6. A comprehensive approach for building an organizational Big Data Analytics infrastructure
          1. The enterprise data map
          2. The enterprise data ingestion infrastructure
          3. Scalable data storage
          4. The analysis engine
          5. Benefits map
          6. The platform framework
        7. Summary
      11. 4. Building the Right Technology Landscape
        1. Designing Big Data storage
          1. Evolution of storage technology
          2. Big Data storage architecture
          3. Big Data storage calculations
          4. The hardware and operating system needs for Big Data
        2. Identifying the different technology layers
          1. Quality check
          2. Cleansing
          3. Correlation
          4. Enrichment
          5. Data cataloging
          6. Modeling for analysis
          7. Classification and clustering
          8. Statistical summary for preliminary insights
          9. Human explorations
        3. Selecting from your technology choices
          1. An overview of key Big Data technology components
            1. Hadoop
            2. MapReduce
            3. Programming languages
              1. Java
              2. Python
              3. Pig Latin
              4. R
              5. Hive
              6. Mahout
              7. ZooKeeper
            4. NoSQL
            5. Other Hadoop components
          2. Technology choices by layers
          3. Making the right technology choices
        4. Creating a visualization of your Big Data
        5. Understanding the difference between Enterprise Data Warehouse and Big Data
        6. Summary
      12. 5. Building a Winning Team
        1. Understanding the distinctive skills you need
          1. Data scientist
            1. Skills of a data scientist
            2. Sourcing data scientists
          2. Experimental analyst
            1. Skills of an experimental analyst
            2. Sourcing experimental analysts
          3. Application developer
            1. Skills of an application developer
            2. Sourcing application developers
          4. Infrastructure specialist
            1. Skills of an infrastructure specialist
            2. Sourcing infrastructure specialists
          5. Change leader
            1. Skills of a change leader
            2. Sourcing the change leader
          6. The project manager
            1. Skills of a project manager
            2. Sourcing the project manager
        2. Defining the team and structure
        3. Building an extended ecosystem
          1. Educational institutes
            1. The engagement framework
            2. Best practices
          2. Consulting organizations
            1. The engagement framework
            2. Best practices
        4. Improving team alignment and performance
          1. Training and orientation
          2. Quick wins
          3. Rotational leadership
        5. Motivating your team towards progress
        6. Summary
      13. 6. Managing Investments and Monetization of Data
        1. Understanding how data creates value
          1. Insights and influence
          2. Immediate and future value
          3. Value creation for data
        2. Capturing the value of data
          1. Identifying the value
          2. Building a value catalog
        3. Understanding and capturing your Big Data costs
          1. Data collection costs
          2. Storage and processing costs
          3. Software licensing costs
          4. People costs
          5. Infrastructure and administrative costs
          6. Maintenance costs
            1. Capturing the costs
            2. Cost Context Framework
        4. Monetizing your Big Data
          1. Direct business impact
          2. Selling data externally
          3. Valuation of Big Data
        5. Managing your Big Data investments
        6. Summary
      14. 7. Driving Change Effectively
        1. Understanding changes caused by Big Data
          1. The significance of changes
        2. Applying the IMMERSE framework to manage change
          1. Identify
          2. Modulate
          3. Mitigate
          4. Educate
          5. Role play
          6. Show
          7. Effect
        3. Creating stakeholder groups to drive change
          1. Project group
          2. Work group
          3. Review group
        4. Summary
      15. 8. Driving Communication Effectively
        1. Identifying your communication needs
          1. Communicating with the internal audience
            1. Providing a general overview of Big Data
            2. Sharing the strategic cascade
              1. Communicating about Gold Coin projects
          2. Communicating with the external audience
            1. Engaging with customers
            2. Swaying the business influencers
            3. Roping in your ecosystem partners
          3. Communicating with the shareholders
        2. Selecting your communication channels
          1. Communication channels
          2. Channel effectiveness
        3. Building your communication strategy
        4. Building your communication plan
        5. Engaging executives effectively
        6. Monitoring and modulating your communication program
          1. Effectiveness metrics
          2. Measuring effectiveness
        7. Summary