大数据时代下的财务分析工作-英国硕士论文代写－r51due

英国essay论文精选范文：“大数据时代下的财务分析工作”，这篇论文讨论了大数据时代下的财务分析工作。大数据时代，企业的经营环境、决策环境都发生巨大的变化。大数据，对于财务工作来说，不仅是一门技术，更是一种全新的模式，财务分析的架构、工具、方法、理念均将随之改变，只有充分认识到大数据所带来的影响，抓住这一趋势，才能充分发挥财务分析在企业经营决策中应有的价值，促进企业提高竞争力，在激烈的市场竞争中取得优势。

Victor in the "big data age" in the large data analysis: to do not cause and effect, to the whole do not sample, to efficiency is not absolutely accurate. For financial purposes, accounting treatment, accounting assumptions, accounting estimates need to have a sufficient basis, relying solely on the relevance will not be enough to accept the letter, and civil audit, government audit, capital market and so on the reasons for each account There is an extraordinary concern, causality has long been the financial staff into the habit, to be related to the traditional financial thinking will not be a huge challenge. In the precise and efficient sort, the financial will usually be ranked in the first place, but also with the big data thinking is different, the financial work of the data accuracy requirements are extremely high, especially accounting work, "there must borrow, borrowing will be equal" Is the accounting calculation of the basic principles of balance method, cannot tolerate the slightest mistake.

Pei Pei to large data into structured data and unstructured data 2 categories, structured data can be simply understood as the line data, that can be used to express the two-dimensional table to logically express data, and not easy to use two-dimensional table to express Of the data is unstructured data, including all format text, pictures, audio / video and so on. Large data age, structured data accounted for only about 10%, the traditional financial analysis to study the structured data known, especially in the financial analysis of the most respected ratio analysis, all based on structured data to study, and for 90% of the non- Structured data, traditional analytical methods are powerless.

With the acquisition and processing of data magnitude and data types of diversification, Excel and other financial staff of the usual office software performance will not be able to meet the needs: First, data collection links, the traditional point-to-point data manual interaction means, can only solve a small amount of low frequency Data needs, it is difficult to meet the large data analysis of large, high-frequency acquisition needs; in data storage, large data source data is composed of structural and unstructured data, the traditional line-type relational database will be powerless, Structured data processing, the disadvantage is particularly prominent; in data mining, will face greater challenges, large data widely used Hadoop distributed storage technology, requiring users to be proficient in using a programming language, such as Python, Matlab, etc., in this regard, most of the financial staff is almost zero basis.

Traditional financial analysis habits along the "find the problem - analyze the problem - to solve the problem" of the idea of analysis, to be clear that because of the fruit, persuasive, easy to be accepted. And large data applications cannot be found in advance of the problem, but cannot effectively analyze the problem of the case, according to the relevance of business efficiency improvement programs, such as beer and diapers case. If the decision makers and performers fail to change their minds, they still go deep into the cause and effect, and the results of the financial analysis will be difficult to adopt, and the financial will not be able to influence the decision and cannot be personally practiced, so that the value cannot be reflected. The data will be a paper empty talk.

Sampling will mean that the data information that is not being drawn will be missed, in order to ensure that the accounting information is objective, fair and financially estimated on the loss and the benefit. Partial caution Large data age, computer technology has made the full amount of research possible, and effectively solve the problem of insufficient sampling, data analysis will be more comprehensive and objective evaluation of business activities. The current financial analysis structure, more biased towards structured data, in the era of large data, with more efficient data storage and mining technology, financial analysis of the framework must be the expansion of unstructured data research content, covering industry, Inside and outside the full amount of data to carry out research to improve the accurate estimates of losses and benefits, rather than overestimate the loss, underestimated the cautious estimate of earnings.

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In the era of large data, both data sources and data processing techniques are fundamentally different from the small data age, and large amounts of data will inevitably lead to inaccurate data due to the diversity of data sources and data types. Inaccurate data does not necessarily prevent us from recognizing the totality, but also for the analysis, the deviation from the common sense of the phenomenon and data, but with higher research value, different quality of data together, will help us more comprehensive understanding of the overall the true situation. It is important to emphasize that the inaccuracy of objective knowledge of data is not laissez-faire, allowing data to be inaccurate, allowing for uncontrollable data to be deviated, and for manageable data, fine management is still required for large data age The

With the popularity of information, office automation, paperless into the standard of the enterprise, a large number of non-institutionalized business data generated in real time, the production of these data can be any person, any time, any way, because of its arbitrary, Which is the lack of "dirty data", creating a low value of the value of large data; and its immediate, diverse features, given us to grasp the real-time dynamic, multi-dimensional analysis of the possibility of digging large data, will give financial analysis and management To a new perspective.

Large data platform to build a system engineering, build a complete and efficient large data platform has a high technical threshold, and put into large, time-consuming, slow, in view of this, can be easy after the first, and gradually in-depth. Because Hadoop is a distributed computing framework that can run on a large number of inexpensive hardware devices, 3-4 general office computers can be built with a simple Hadoop large data storage platform to meet the needs of early unstructured analysis, Using the Hadoop distributed file system to store massive source data, through the MapReduce distributed computing model to deal with these massive source data, and then use Hbase distributed database storage processing of massive data, in order to achieve the massive unstructured data analysis and management.

The early application of the scene, you can start from the management of hot start recommendation, because the big data recommendation technology is more mature, a molding algorithm can learn from the first use of Chinese word segmentation technology to establish information retrieval index, unstructured data for automatic classification, At the same time with large data recommendation algorithm to achieve the hot information crawl, high repetitive entries to help analysts quickly focus on business hot research. When the small application value is affirmed, large-scale deployment of Hadoop cluster environment will be possible, and scattered in the major information island data, buried in the computer terminal data will also be expected to share integration, and gradually achieve large data, big value.

Decision-making functions will be re-layout, financial analysis need to both industry and wealth. Large data age, relying solely on subjective decision-making will not be able to cope with complex environments, long-term rely on experience, theory and thinking of decision-making will give way to accurate data analysis. The role of decision-making participants in the big data will change, the staff at all levels can easily obtain the information needed for decision-making, decision-making is no longer a minority of senior managers of the exclusive, the traditional decision-making division will be adjusted, more tactical Decision-making down, corporate executives have more time and effort to plan strategic decisions, but also need to do more dimensions of consideration, and real-time, dynamic control of each tactical implementation, in order to develop a reasonable strategic decision. Financial analysis To continue to fulfill the role of senior decision-making staff, we must take advantage of the situation, close to the strategic needs, to a more comprehensive perspective, to carry out both business integration analysis.

Financial integration analysis is the need for financial reporting disclosure and risk management. Large data-driven management model, from decision-making to implementation time will be significantly shortened, the basis for decision-making from the data, the implementation of the results of the reaction to data, away from the financial analysis of the business, will not be able to give timely risk and value assessment, Report disclosure is the business of investors, governments, regulators, obligations, and will not change because of changes in corporate decision-making, and vice versa may put forward higher requirements. Therefore, the financial analysis needs to be fully involved in the decision-making process, reasonable evaluation of the decision-making compliance and effectiveness, and timely implementation of the results and financial statements to establish a dynamic link to meet the performance evaluation and external disclosure needs.

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