基于企业信息工厂的商务智能数据管理
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摘要
随着内外部环境中数据量的激增,人们开始面对众多的数据管理问题。频繁出现的数据管理事件,更突显了数据管理的重要性。数据管理活动和人们所从事的各类生产劳动活动一样,借助相关工具技术,是有可能提高活动效率和活动价值的。商务智能(BI, Business Intelligence)便是这样一种数据管理工具,其关键是从企业运作系统的数据中析取出有用的数据,并对这些数据进行清理,从而确保数据的正确性,然后经过ETL过程(析取(extraction)、转换(transformation)和装载(load)),合并到目的数据仓库里,由此得到企业数据的全局视图,在全局视图的基础上利用商务智能技术工具对其进行分析和处理,最后将结果呈现给管理者,为管理者的决策过程提供支持。
但是,商务智能理论和工具也有其局限性,在商务智能数据管理过程中,视阈的局限,造成数据管理全生命周期过程中部分阶段的缺失,使得数据管理活动片面,从而不具有完整性,由此提供的数据服务在可信度和效率性方面也缺乏说服力,也就使得层出不穷的数据问题的出现成为了可能。企业信息工厂是数据管理的全过程逻辑架构,基于企业信息工厂来开展商务智能数据管理课题研究,可以从一定程度上解决上述困境。商务智能试图为管理者提供可靠的决策支持,数据服务优劣是决定商务智能决策成败的关键所在。
数据管理的问题层出不穷,本研究提出的商务智能数据管理对策具有普遍适用性,其中包括了理论层面的解决对策数据生态系统(数据反馈流和数据质量管理框架)和执行层面的解决对策数据治理政策和数据管理工作流程设计。其中,数据质量管理框架包括了数据血统分析(数据评价)、数据整合(ECTL)、数据标准化、数据匹配与巩固以及持续监控。
科学技术的不断完善,使得商务智能相关技术处于持续升级换代之中。商务智能从理论的萌芽阶段,到理论的提出阶段,再到理论的发展完善阶段,数据存储技术、数据库、数据仓库、数据集市、联机分析处理技术、数据挖掘、信息可视化技术等都是伴随着这三个发展阶段逐步向前推进的。可以说,企业信息工厂(CIF)、商务智能(BI)理论和这些数据收集、处理、分析技术是相辅相成、互相影响的,理论的提出可以为技术的发展,确定基调及指明方向；而技术的不断更新改进,又为理论的有效性验证和完善创造了条件。数据处理活动与人类从事的其他生产劳作活动类似,借助商务智能这一利器可以提高效率,提升效用。同时,可视化技术的出现和不断完善,使商务智能以及商务智能数据管理的商业产业链形成了一个从“数据整合”、经“数据分析”、“数据挖掘”、到最后“数据展示”的完整“闭环”。
商务智能数据管理产品层出不穷,并且各具特色,占据了各自目标市场的份额,现阶段的商务智能数据管理产品并非完美无缺,无论是现实的应用需求还是科研理论的需要,都对商务智能数据管理产品提出了迫切的改进需求。
本研究试图从理论层面(数据生态系统和数据质量框架)和执行层面(数据治理政策和工作设计)来解决商务智能数据管理实践活动中的问题随着相关技术,特别是数据仓库技术、ETL技术、OLAP技术、数据挖掘技术、信息可视化技术的飞速发展,技术层面的优化改进基础已经具备,而基于信息工厂的商务智能数据管理可以很好的结合技术层面的成果并提出相应的理论层面的优化改进对策,真正实现为商务智能决策提供高效、可信数据服务的目的。
现代企业所面临的商业竞争日趋激烈,数据作为企业重要资产之一的效用体现的越来越明显。高效可信的数据服务,为企业在激烈的竞争的赢得一席之地提供了有效的帮助。在企业中,建立基于企业信息工厂的商务智能数据管理机制,实施理论层面(数据生态系统和数据管理框架)和执行层面(数据治理政策和数据管理工作流程)相结合的革新对策,对于案例中的某大型电子科技公司的数据管理活动起到了明显的改善作用,并且至少实现了预期的为管理层商务决策提供较之以往更为高效可信的数据服务的目标：为所有应用程序主动监控和清洗数据,保持数据清洁；使业务部门能够分担数据质量和数据治理的职责；借助可信的企业数据实现更好的业务成果。
本研究通过总结现有主流的商用智能数据管理产品在市场定位及功能点方面的特性,分析现状得出不足；并对商务智能数据管理的发展趋势研究热点进行简单介绍,例如目前影响较大的大数据管理课题。
With the surge of the amount of data, people began to face the numerous problems of data management (DM). The problems highlight the importance of data management. Business Intelligence (BI) is a kind of data management technology which is to clean the useful date which is extracted from the enterprise operation system. The data merged into a data warehouse through extraction, transformation and load (the ETL process) to ensure the correctness of it. Then for the data analysis and processing with BI tools, the result presented to the managers for support of their decision-making.
However, the theory and tool of BI has its limitations. And also the missing data which is made by limit of managers'sight will cause defects in the data services. Based on the BI of Corporate Information Factory will solve the above-mentioned issues.
A universal applicability of BIDM will be put forward in this paper including the data ecosystem of decision-making (data feedback flow and data quality management framework), the data management policies of decision-making and the design of data management workflow.
With the continuous development of science and technology, business intelligence technology is being continually updated. Data storage technology, databases, data warehouses, data marts, online analytical processing, data mining and information visualization techniques are moving forward accompanied by the development of BI theory step by step. So, the theory of Corporate Information Factory (CIF) and Business Intelligence (BI) and those techniques are complementary and influenced each other.
The products of BIDM emerged in endlessly, and each has its own characteristics. But they are not perfect whether on the needs of practical application or research activities.
With the help of data ecosystem build the framework of data quality, the data management policies and the working flow to provide an efficient and reliable data service of business intelligence decision-making which is based on CIF.
This study attempts to solve the problem in the business intelligence data management practices from the theoretical level and the implementation level.
Modern enterprises are facing increasingly fierce competition; the utility of data is becoming more and more important.
Efficient and reliable data services provide effective helps for the development of enterprises in the fierce competition. And to achieve the expected goal:(1)active monitoring and data cleaning for all applications;(2) business units can share the responsibility of control data quality and data management;(3) with the help of credible data to achieve better results.
This study summarizes and analysis the present situation of the existing BIDM products' characteristics in market position and function, and also analysis and forecasts the development trend of BIDM while a brief introduction is made to introduce the large data management issues.
但是,商务智能理论和工具也有其局限性,在商务智能数据管理过程中,视阈的局限,造成数据管理全生命周期过程中部分阶段的缺失,使得数据管理活动片面,从而不具有完整性,由此提供的数据服务在可信度和效率性方面也缺乏说服力,也就使得层出不穷的数据问题的出现成为了可能。企业信息工厂是数据管理的全过程逻辑架构,基于企业信息工厂来开展商务智能数据管理课题研究,可以从一定程度上解决上述困境。商务智能试图为管理者提供可靠的决策支持,数据服务优劣是决定商务智能决策成败的关键所在。
数据管理的问题层出不穷,本研究提出的商务智能数据管理对策具有普遍适用性,其中包括了理论层面的解决对策数据生态系统(数据反馈流和数据质量管理框架)和执行层面的解决对策数据治理政策和数据管理工作流程设计。其中,数据质量管理框架包括了数据血统分析(数据评价)、数据整合(ECTL)、数据标准化、数据匹配与巩固以及持续监控。
科学技术的不断完善,使得商务智能相关技术处于持续升级换代之中。商务智能从理论的萌芽阶段,到理论的提出阶段,再到理论的发展完善阶段,数据存储技术、数据库、数据仓库、数据集市、联机分析处理技术、数据挖掘、信息可视化技术等都是伴随着这三个发展阶段逐步向前推进的。可以说,企业信息工厂(CIF)、商务智能(BI)理论和这些数据收集、处理、分析技术是相辅相成、互相影响的,理论的提出可以为技术的发展,确定基调及指明方向；而技术的不断更新改进,又为理论的有效性验证和完善创造了条件。数据处理活动与人类从事的其他生产劳作活动类似,借助商务智能这一利器可以提高效率,提升效用。同时,可视化技术的出现和不断完善,使商务智能以及商务智能数据管理的商业产业链形成了一个从“数据整合”、经“数据分析”、“数据挖掘”、到最后“数据展示”的完整“闭环”。
商务智能数据管理产品层出不穷,并且各具特色,占据了各自目标市场的份额,现阶段的商务智能数据管理产品并非完美无缺,无论是现实的应用需求还是科研理论的需要,都对商务智能数据管理产品提出了迫切的改进需求。
本研究试图从理论层面(数据生态系统和数据质量框架)和执行层面(数据治理政策和工作设计)来解决商务智能数据管理实践活动中的问题随着相关技术,特别是数据仓库技术、ETL技术、OLAP技术、数据挖掘技术、信息可视化技术的飞速发展,技术层面的优化改进基础已经具备,而基于信息工厂的商务智能数据管理可以很好的结合技术层面的成果并提出相应的理论层面的优化改进对策,真正实现为商务智能决策提供高效、可信数据服务的目的。
现代企业所面临的商业竞争日趋激烈,数据作为企业重要资产之一的效用体现的越来越明显。高效可信的数据服务,为企业在激烈的竞争的赢得一席之地提供了有效的帮助。在企业中,建立基于企业信息工厂的商务智能数据管理机制,实施理论层面(数据生态系统和数据管理框架)和执行层面(数据治理政策和数据管理工作流程)相结合的革新对策,对于案例中的某大型电子科技公司的数据管理活动起到了明显的改善作用,并且至少实现了预期的为管理层商务决策提供较之以往更为高效可信的数据服务的目标：为所有应用程序主动监控和清洗数据,保持数据清洁；使业务部门能够分担数据质量和数据治理的职责；借助可信的企业数据实现更好的业务成果。
本研究通过总结现有主流的商用智能数据管理产品在市场定位及功能点方面的特性,分析现状得出不足；并对商务智能数据管理的发展趋势研究热点进行简单介绍,例如目前影响较大的大数据管理课题。
With the surge of the amount of data, people began to face the numerous problems of data management (DM). The problems highlight the importance of data management. Business Intelligence (BI) is a kind of data management technology which is to clean the useful date which is extracted from the enterprise operation system. The data merged into a data warehouse through extraction, transformation and load (the ETL process) to ensure the correctness of it. Then for the data analysis and processing with BI tools, the result presented to the managers for support of their decision-making.
However, the theory and tool of BI has its limitations. And also the missing data which is made by limit of managers'sight will cause defects in the data services. Based on the BI of Corporate Information Factory will solve the above-mentioned issues.
A universal applicability of BIDM will be put forward in this paper including the data ecosystem of decision-making (data feedback flow and data quality management framework), the data management policies of decision-making and the design of data management workflow.
With the continuous development of science and technology, business intelligence technology is being continually updated. Data storage technology, databases, data warehouses, data marts, online analytical processing, data mining and information visualization techniques are moving forward accompanied by the development of BI theory step by step. So, the theory of Corporate Information Factory (CIF) and Business Intelligence (BI) and those techniques are complementary and influenced each other.
The products of BIDM emerged in endlessly, and each has its own characteristics. But they are not perfect whether on the needs of practical application or research activities.
With the help of data ecosystem build the framework of data quality, the data management policies and the working flow to provide an efficient and reliable data service of business intelligence decision-making which is based on CIF.
This study attempts to solve the problem in the business intelligence data management practices from the theoretical level and the implementation level.
Modern enterprises are facing increasingly fierce competition; the utility of data is becoming more and more important.
Efficient and reliable data services provide effective helps for the development of enterprises in the fierce competition. And to achieve the expected goal:(1)active monitoring and data cleaning for all applications;(2) business units can share the responsibility of control data quality and data management;(3) with the help of credible data to achieve better results.
This study summarizes and analysis the present situation of the existing BIDM products' characteristics in market position and function, and also analysis and forecasts the development trend of BIDM while a brief introduction is made to introduce the large data management issues.
引文
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