Essay精選範文:A flaw in big data

下面是Fanessay提供的一篇essay範文--A flaw in big data，這篇文章主要討論的是大數據的不足。在現在的生活中，我們離不開網際網路帶給我們的便利，其中，大數據就帶給了我們很大的幫助。雖然大數據能帶給我們便利，可是它也擁有著不足，會給我們帶來一定的損害。所以我們不能對自己的技術太自信，因為大數據包括了很多領域的知識。

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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