作业代写：A flaw in big data

下面为大家整理一篇优秀的essay代写范文- A flaw in big data，供大家参考学习，这篇论文讨论了大数据的缺陷。如今，虽然大数据对于我们的生活有非常大的帮助，但考虑到我们拥有的数据量，有时甚至使用有缺陷的模型来产生有用的结果。所以有时侯对自己的技术过于自负，当模型出现故障时，大数据的结果就会变得非常难看。毕竟数据科学和大数据是领域知识、数学、统计专业知识和编程技能的复杂组合。

The problem is that big data is too big. Given the amount of data we have, we sometimes use flawed models to produce useful results. Sometimes the technique is too conceited, and when the model fails, the result becomes very ugly.

Google launched the big data service in 2008 to predict flu outbreaks in 25 countries. The logic is simple: analyze a Google search query for influenza in a particular area. Compare the search results with the history of flu activity in the area. Based on these results, activity levels are classified as low, medium, high, or extremely high. At first glance, this seems like a reasonable idea, but it is not. At the height of the flu season in 2013, Google's flu analysis was a mess. The reason is that the algorithm is flawed, not taking into account several factors. For example, searching for words like "cold" or "fever" doesn't necessarily mean that searchers are looking for flu symptoms. Google was unable to recover from the catastrophic failure that led to the project's collapse in 2013.

Here are some reasons why big data fail:

Often organizations don't fully understand the data they already have, but they still decide to start new projects on top of that. Lack of documentation, storage, policies, and other procedures for data processing. In this case, the big data consulting company can provide your company with a clear road map and guide, that should be how to deal with the data you have, this is the first step in the right over large data.

There are too many IT terms and marketing terms that are hard to understand, and there are so many big data products on the market that IT is difficult to choose the right product. Before making any decisions, it is important to find the services and techniques needed to achieve the goals. "Small data on big data" means that you should evaluate your big data architecture on a small amount of data to make sure you choose the right product.

Data science and big data are complex combinations of domain knowledge, mathematics, statistical expertise, and programming skills. However, it must also have commercial implications. Often IT departments and management cannot understand each other's changes. To ensure that your big data makes sense for IT and business leaders, ensure good communication between IT and business people in the project.

When you first start executing big data projects, there are many undefined factors, such as budgeting, technology, routes, and so on. Choose a small project and measure your chances of success. A good approach to benchmarking progress is to create prototypes or validation concepts to verify the work you have done. If the early stages are flawed, it makes no sense to move on to the next phase of the project. People who lack analysis personnel perform project must have industry background and data analysis ability, and not just the processing DaTiLiang data, with a focus on the analysis of digging out the data contains profound meaning.

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