Big Data Imperatives

Big Data Imperatives, focuses on resolving the key questions on everyone's mind: Which data matters? Do you have enough data volume to justify the usage? How you want to process this amount of data? How long do you really need to keep it active for your analysis, marketing, and BI applications?Big data is emerging from the realm of one-off projects to mainstream business adoption; however, the real value of big data is not in the overwhelming size of it, but more in its effective use.This book addresses the following big data characteristics: Very large, distributed aggregations of loosely structured data often incomplete and inaccessible Petabytes/Exabytes of data Millions/billions of people providing/contributing to the context behind the data Flat schema's with few complex interrelationships Involves time-stamped events Made up of incomplete data Includes connections between data elements that must be probabilistically inferred Big Data Imperatives explains 'what big data can do'. It can batch process millions and billions of records both unstructured and structured much faster and cheaper. Big data analytics provide a platform to merge all analysis which enables data analysis to be more accurate, well-rounded, reliable and focused on a specific business capability.Big Data Imperatives describes the complementary nature of traditional data warehouses and big-data analytics platforms and how they feed each other. This book aims to bring the big data and analytics realms together with a greater focus on architectures that leverage the scale and power of big data and the ability to integrate and apply analytics principles to data which earlier was not accessible. This book can also be used as a handbook for practitioners; helping them on methodology,technical architecture, analytics techniques and best practices. At the same time, this bookintends to hold the interest of those new to big data and analytics by giving them a deep insight into the realm of big data.

Vendors and platforms agnostic there by bringing in deep understanding of key areas viz., Big data platforms Implementation best practices, etc; Numerous industry use cases about big data and its implications to businesses.

Autorentext

Soumendra Mohanty? is a Partner and leads Accenture?s Global Information Management Services practice. He is an expert within the Information Management area, focusing primarily on BI architectures, data warehouse, CRM/Customer Insight, MDM, Analytics and PCM solutions.? He is experienced in leading project teams through the lifecycle of a project, and has successfully helped sell and delivered BI and DW projects in multiple industries, including products, CPG, brokerage, banking, telecommunications, and retail.Soumendra has authored several books on data warehousing and Analytics and published numerous journals in DM Review. He has also presented in numerous international forums. His functional expertise ranges from Big Data Analytics, BI Architectures, Data Warehouse, CRM/Customer Insight, Supply Chain Analytics, Marketing Insights, & MDM.

Klappentext

Big Data Imperatives, focuses on resolving the key questions on everyone s mind: Which data matters? Do you have enough data volume to justify the usage? How you want to process this amount of data? How long do you really need to keep it active for your analysis, marketing, and BI applications? Big data is emerging from the realm of one-off projects to mainstream business adoption; however, the real value of big data is not in the overwhelming size of it, but more in its effective use. This book addresses the following big data characteristics: Very large, distributed aggregations of loosely structured data often incomplete and inaccessible Petabytes/Exabytes of data Millions/billions of people providing/contributing to the context behind the data Flat schema's with few complex interrelationships Involves time-stamped events Made up of incomplete data Includes connections between data elements that must be probabilistically inferred Big Data Imperatives explains 'what big data can do'. It can batch process millions and billions of records both unstructured and structured much faster and cheaper. Big data analytics provide a platform to merge all analysis which enables data analysis to be more accurate, well-rounded, reliable and focused on a specific business capability. Big Data Imperatives describes the complementary nature of traditional data warehouses and big-data analytics platforms and how they feed each other. This book aims to bring the big data and analytics realms together with a greater focus on architectures that leverage the scale and power of big data and the ability to integrate and apply analytics principles to data which earlier was not accessible. This book can also be used as a handbook for practitioners; helping them on methodology,technical architecture, analytics techniques and best practices. At the same time, this bookintends to hold the interest of those new to big data and analytics by giving them a deep insight into the realm of big data.

Zusammenfassung
Big Data Imperatives, focuses on resolving the key questions on everyone's mind: Which data matters? Do you have enough data volume to justify the usage? How you want to process this amount of data? How long do you really need to keep it active for your analysis, marketing, and BI applications? Big data is emerging from the realm of one-off projects to mainstream business adoption; however, the real value of big data is not in the overwhelming size of it, but more in its effective use.This book addresses the following big data characteristics:

• Very large, distributed aggregations of loosely structured data often incomplete and inaccessible
• Petabytes/Exabytes of data
• Millions/billions of people providing/contributing to the context behind the data
• Flat schema's with few complex interrelationships
• Involves time-stamped events
• Made up of incomplete data

• Includes connections between data elements that must be probabilistically inferred Big Data Imperatives explains 'what big data can do'. It can batch process millions and billions of records both unstructured and structured much faster and cheaper. Big data analytics provide a platform to merge all analysis which enables data analysis to be more accurate, well-rounded, reliable and focused on a specific business capability.Big Data Imperatives describes the complementary nature of traditional data warehouses and big-data analytics platforms and how they feed each other. This book aims to bring the big data and analytics realms together with a greater focus on architectures that leverage the scale and power of big data and the ability to integrate and apply analytics principles to data which earlier was not accessible. This book can also be used as a handbook for practitioners; helping them on methodology,technical architecture, analytics techniques and best practices. At the same time, this bookintends to hold the interest of those new to big data and analytics by giving them a deep insight into the realm of big data.

  Inhalt

1. The New Information Management Paradigm
2. Big Data's Implication for Businesses
3. Big Data Implications for Information Management
4. Defining Big Data Architecture Characteristics
5. Co-Existent Architectures
6. Data Quality for Big Data
7. Data Security and Privacy Considerations for Big Data
8. Big Data and Analytics
9. Big Data Implications for Practitioners