Big Data, All Data, or Just Data

 Big data is a hot topic these days, and it is extended to “All Data” recently. Actually, we are dealing with data from multi-sources, multi-channels, and multi-media; and the data can be from real-time business operation, data warehouse, back-office business data (e.g. financial, CRM, HR), social media, etc. The accumulated data amounts are enormous, although each individual data source can be big or small. The data correlations from different sources are as important as the analysis of “big data”. Therefore, we cannot ignore the importance of data management and processing.

 

Information and data management is getting more and more challenged due to the velocity and volume of data generation, as well as the tendency of increased data production. An important step for information and data management could be how and what data should be collected. The speedy growing of data could make people wonder even we can store them (e.g. using the new technologies like Hadoop), how we can catch up in processing them to make them really meaningful during analysis, e.g. to transform the data into meaningful information, and transform information into useful knowledge. Therefore, we need to improve the data collection mechanisms, e.g. to have cleaner data and better data organization mechanisms, with data/information correlation indicators. More meaningless data involved more burdens to the process. If data processing speeds continue lag behind data collection speeds, the data collected will not be as useful as expected, but add burden to the slow process. Yes, this is the new challenge in dealing with data, regardless big data or all data.

This is also published on LinkedIn:
https://www.linkedin.com/today/post/article/20140528003636-7206455-big-data-all-data-or-just-data?trk=prof-post

New Training Course: Architecture for Knowledge Management

We are providing a new training course: Architecture for Knowledge Management, which provide a Structure for a Cohesive KM Mechanism with Comprehensive Knowledge Sources. The abstract follows:

Internet provides us with explosive information, and enables us to collect and distribute information conveniently as well. However, information has to be organized and be comprehended to become knowledge. For an enterprise, in addition to its information and knowledge maintained internally, public Internet provides good information sources for knowledge extraction as well. The Architecture for Knowledge Management provides a structure and operation guidance for such cohesive knowledge management mechanism with comprehensive knowledge sources.

Information can be obtained through data processing, and knowledge can be obtained through information processing. The conversion from data to information usually doesn’t need to consider the information receivers. However, the conversion from information to knowledge has to consider the knowledge receivers. Knowledge to one person can be only information to another. In other words, information is target less, while knowledge has target and beholder. Therefore, for information to become knowledge, we have to interpret them for its targeted audience. In this regards, enterprise architecture is helpful in identifying the sources and targets for knowledge management. The architecture for knowledge management covers mainly three components: 1) knowledge capture, 2) knowledge management, and 3) knowledge distribution.