Key Elements of a Big Data Strategy

1) Visualisation and Discovery is an important capability. Organizations need to understand the scope and content of their data sources. Federated search, discovery and navigation tools enables access to information, irrespective of where it is located or format, restricting access to those with the authority to view the content. The ability to enrich the content, e.g. by adding comments, rating, tagging can be used to add a social dimension to how data is presented to the end user. The ability to quickly examine, explore and discover data relationships is can create a competitive advantage.

2) Hadoop enables organisations to reduce the cost of their data management infrastructure by offloading structured and unstructured data not suitable for traditional data warehouse for deep cost effective deep analytics.

3) What can you do when analyzing stored data is not good enough? Stream computing enables analysis of realtime streaming data of multiple formats; prefiltering (using complex calculations) and selective storing of high velocity data in realtime.

4) What can be done to accelerate the adoption of data warehousing and analytics capabilities that can offer a competitive advantage? As IBM has demonstrated with it's Netezza technology, there is a competitive advantage to be derived from purpose built systems for complex analytics workloads. These Expert Integrated Systems are designed with simplicity in mind, minimal administration required, and ability to perform complex analytics on large volumes of structured data at blistering speeds. Additionally, expertise can be built into this systems by packing pre-built analytics and visualizations applicable to specific industry applications. This library of pre-built functions, and tools that enable custom functions to be built, accelerate the time to value for analysis of data in native formats, where it lives.

5) Finally Security and Governance are key aspects of Big Data management. Sensitive data needs to be protected, retention policies need enforcing and data quality governed. Information lifecycle and master data management, along with data quality and governance services, are very important considerations in operating a Big Data platform.