Three suggestions for maximizing the value of large data

Let's break through the sheer volume of management, and look at some of the drawbacks of the big data infrastructure, whether it's based on Hadoop, or based on Adbms, or a combination of both. Companies seeking to maximize the commercial value of large data must address these issues effectively.

Here are three suggestions to help your business maximize the business value of large data:

First, reduce the gap between business users and large data

The current implementation requires many of the same components, traditional http://www.aliyun.com/zixun/aggregation/8302.html "> Data warehousing/Business Intelligence (DW/BI) systems, including establishing the necessary data schemas and SQL queries. These are usually not directly accessible to large-scale parallel processing (output) of distributed file systems, such as Hadoop, route data batch processing mode. Along with the normal latency associated with the Data warehouse, this can lead to blind spots that affect real-time decisions.

Second, manage multiple sources at the same time

Data management systems typically manage data from one source at a time. As a result, complex relational data types are often missed. Information management and analysis isolation as a (very large) silo can produce only a subset, and there are potential problems more than the possibility of the answer. A complete picture of the information will be combined with other relevant records of the enterprise to achieve large data. Companies must reconsider their large data and refactor the analytical methods they use to get and analyze and solve problems so that unified data can be passed.

Third, the development of contextual services

Unstructured content, such as documents, e-mail, Web content, fluent text, SharePoint, call histories, and surveys, are neither categorized nor analyzed, combined with contextual understanding. Most large data systems assert that the data they "handle" is unstructured, but they do not perform textual analysis or unstructured content analysis that really combines context. Contextual business understanding helps to discover new business insights that must be developed.

The review of existing large data infrastructures confirms that the challenges facing businesses are far beyond the understanding and imagination of IT leaders. Using the techniques above, rather than just managing large data as your own silos, can help you gain new insights and system data.

(Responsible editor: The good of the Legacy)