DUBLIN--(BUSINESS WIRE)--Research and Markets (http://www.researchandmarkets.com/research/kw8v6c/big_data_comes_of) has announced the addition of the "Big Data Comes of Age" report to their offering.
In the Information Technology (IT) industry, 2012 has been the year of Big Data. From a standing start toward the end of the last decade, Big Data has become one of the most talked about topics in IT. There is hardly a vendor who does not have a solution or, at least, a go-to-market strategy. Beyond IT, even the financial and popular press discusses its merits and debates its drawbacks. And yet, the debate of exactly how to define Big Data remains. In this joint study between EMA's Shawn Rogers and John Myers, and 9sight Consulting's Dr Barry Devlin, respondents clearly indicated that their Big Data solutions range far beyond social media and machine-generated data to include all types of traditional transactional business data.
The concept of Big Data has evolved in two key directions. First, was the growing understanding that while size is important, the technology implications of data structure and processing speed are at least as important. Second, what really matters for Big Data is what systemic business cases it supports and what real analytic and operational value can be extracted from it.
Big Data has driven change in our traditional data management strategies and has found a home in an expanding information ecosystem that many companies struggle to manage today. This landscape was once dominated by the enterprise data warehouse (EDW) on the informational side and an array of largely monolithic transaction processing systems on the operational side. This has now given way to an array of data management platforms, including NoSQL platforms like Hadoop.
Key Topics Covered:
1 Executive Summary
1.1 Key Findings
2 Big Data Comes of Age
2.1 Big Data - the Technological Evolution
2.2 Big Data - the Emergence of Systemic Business Value
2.3 Big Data - the Holistic View
2.4 Big Data - Where Next?
3 Hybrid Data Ecosystem
3.1 Nodes within the Hybrid Data Ecosystem
3.2 Shift from a Single Platform to an Ecosystem
4 Big Data Adoption
4.1 Overall Implementation
4.2 Ongoing Programs vs One Time Projects
4.3 Industry Breakdown
4.4 Adoption Curve
4.5 Use Cases
4.6 Implementation Sponsors
4.7 Implementation User Base
4.8 Implementation User Base by Industry
5 Big Data Requirements: Beyond Buzzwords
5.1 The Speed of Business
5.2 Inexpensive is not Free
5.3 Refining Data into Information
5.4 How Big is Big?
5.5 Another Man's Treasure
6 Methodology and Demographics
For more information visit http://www.researchandmarkets.com/research/kw8v6c/big_data_comes_of