Information Governance Principles and Practices for a Big Data Landscape Information Governance Principles and Practices for a Big Data Landscape

Information Governance Principles and Practices for a Big Data Landscape

発行者による作品情報

This IBM® Redbooks® publication describes how the IBM Big Data Platform provides the integrated capabilities that are required for the adoption of Information Governance in the big data landscape.

As organizations embark on new use cases, such as Big Data Exploration, an enhanced 360 view of customers, or Data Warehouse modernization, and absorb ever growing volumes and variety of data with accelerating velocity, the principles and practices of Information Governance become ever more critical to ensure trust in data and help organizations overcome the inherent risks and achieve the wanted value.

The introduction of big data changes the information landscape. Data arrives faster than humans can react to it, and issues can quickly escalate into significant events. The variety of data now poses new privacy and security risks. The high volume of information in all places makes it harder to find where these issues, risks, and even useful information to drive new value and revenue are.

Information Governance provides an organization with a framework that can align their wanted outcomes with their strategic management principles, the people who can implement those principles, and the architecture and platform that are needed to support the big data use cases. The IBM Big Data Platform, coupled with a framework for Information Governance, provides an approach to build, manage, and gain significant value from the big data landscape.

ジャンル
コンピュータ/インターネット
発売日
2014年
3月31日
言語
EN
英語
ページ数
280
ページ
発行者
IBM Redbooks
販売元
International Business Machines Corp
サイズ
4.9
MB
Building Big Data and Analytics Solutions in the Cloud Building Big Data and Analytics Solutions in the Cloud
2014年
Data Science For Dummies Data Science For Dummies
2021年
Engineering Agile Big-Data Systems Engineering Agile Big-Data Systems
2022年
Python Data Wrangling for Business Analytics Python Data Wrangling for Business Analytics
2024年
The Data Warehouse Toolkit The Data Warehouse Toolkit
2013年
Data Analytics. Fast Overview. Data Analytics. Fast Overview.
2017年
IBM Watson Content Analytics: Discovering Actionable Insight from Your Content IBM Watson Content Analytics: Discovering Actionable Insight from Your Content
2014年
IT Service Management Best Practices Using IBM SmartCloud Control Desk IT Service Management Best Practices Using IBM SmartCloud Control Desk
2013年
IBM zEnterprise EC12 Technical Guide IBM zEnterprise EC12 Technical Guide
2015年
IBM and Cisco: Together for a World Class Data Center IBM and Cisco: Together for a World Class Data Center
2013年
Experiences with Oracle Database 12c Release 1 on Linux on System z Experiences with Oracle Database 12c Release 1 on Linux on System z
2014年
Oracle to DB2 Conversion Guide: Compatibility Made Easy Oracle to DB2 Conversion Guide: Compatibility Made Easy
2014年
Data Warehousing and Big Data #OOW16 Data Warehousing and Big Data #OOW16
2016年
Enterprise Data Warehouse Optimization with Hadoop on IBM Power Systems Servers Enterprise Data Warehouse Optimization with Hadoop on IBM Power Systems Servers
2018年
The Complete Review Of Data Warehousing and Big Data From OpenWorld 2018 The Complete Review Of Data Warehousing and Big Data From OpenWorld 2018
2018年
The State of Risk-Based Security Management The State of Risk-Based Security Management
2012年
What Is Big Data What Is Big Data
2015年
Continuous Security Monitoring for DevOps Continuous Security Monitoring for DevOps
2016年