Difference between revisions of "Big data"
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==Implementation Guide== | ==Implementation Guide== | ||
| + | |||
==Success Factors== | ==Success Factors== | ||
| − | Involve | + | Involve users in the definition and creation of the data model and system, ensure that the model and system align with and support operations. |
| + | |||
| + | Train the users in the data model and reporting tools. | ||
==Common Pitfalls== | ==Common Pitfalls== | ||
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Training staff who will use the system to create reports is key and is often over-looked. Staff must understand the data model and how the system works and fits together so that accurate, reliable reports can be created. | Training staff who will use the system to create reports is key and is often over-looked. Staff must understand the data model and how the system works and fits together so that accurate, reliable reports can be created. | ||
| − | Other pitfalls are those common to any technology implementation: ensure that it aligns with the organization's goals, objectives, and processes; involve users in the definition and creation of the data model and system. | + | Having/obtaining adequate resources: human (people trained and experienced in big data), financial (to obtain the hardware and software necessary for big data), and time (to plan and implement and maintain big data). This includes establishing the necessary processes and governance for big data. |
| + | |||
| + | Other pitfalls are those common to any technology implementation: ensure that it aligns with the organization's goals, objectives, and processes; involve users in the definition and creation of the data model and system; include change management activities in the roll-out and implementation. | ||
==Related articles== | ==Related articles== | ||
[[Business analytics]] | [[Business analytics]] | ||
| + | ==External Articles== | ||
| + | #[http://www.ey.com/Publication/vwLUAssets/EY_-_Ready_for_takeoff/$FILE/EY-Ready-for-takeoff.pdf EY: Ready for takeoff? Overcoming the practical and legal difficulties in identifying and realizing the value of data] | ||
[[Category:Data mining]] | [[Category:Data mining]] | ||
Revision as of 10:39, 7 March 2016
Contents
Definition
An all-encompassing term for any collection of data sets so large and complex that it becomes difficult to process them using traditional data processing applications ( http://en.wikipedia.org/wiki/Big_data Wikipedia)
Purpose & Benefits
Big data benefits the organisation through better decision making at all levels. It provides strategic, operational and tactical information to decision makers. Big data applications report, analyse and present data, ideally previously stored in a data warehouse although it may have been stored in other, less structured and scalable environments.
Description
Variations
Big Data can also be referred to as Business Intelligence.
Implementation Guide
Success Factors
Involve users in the definition and creation of the data model and system, ensure that the model and system align with and support operations.
Train the users in the data model and reporting tools.
Common Pitfalls
Common pitfalls are those concerning creating and understanding the data model that is used, i.e. ensuring the source of the data is trustworthy and has integrity, that it represents the operational activities of the organization and provides accurate reporting. Taking time to plan and create the data model is key.
Training staff who will use the system to create reports is key and is often over-looked. Staff must understand the data model and how the system works and fits together so that accurate, reliable reports can be created.
Having/obtaining adequate resources: human (people trained and experienced in big data), financial (to obtain the hardware and software necessary for big data), and time (to plan and implement and maintain big data). This includes establishing the necessary processes and governance for big data.
Other pitfalls are those common to any technology implementation: ensure that it aligns with the organization's goals, objectives, and processes; involve users in the definition and creation of the data model and system; include change management activities in the roll-out and implementation.
