Shulou(Shulou.com)06/03 Report--

With the enrichment of life, there are more and more devices that generate data, and the volume of data that needs to be processed is also increasing day by day, and various institutions have also turned their attention to the term "big data". In the gold rush of this data, many companies have returned with a full load, but there are many companies that have failed to invest in "big data", so there is a previous article "Why Big Data Projects Fail". In this regard, Jim King, director of research and development of business intelligence tools and senior adviser to esProc and esCalc, denied this view and took Google as an example to give some warnings to big data investors.

The following is the translation:

First of all, let's take a look at big data's successful model Google and see how they play big data:

1. Collect data, capture each website, email, Cookie content, and extract key information.

two。 Create a composite index for information. Needless to say, advertising-related indexes are essential.

3. Store directories and content in a distributed server.

4. When users browse the site and search for or visit e-mail, Google makes complex transformations to these requests, and several index entries are determined.

5. Query the data in the server according to the index and return search results or advertisements.

It is not difficult to find here that there are only 3 and 5 related to Hadoop, that is, data storage and query. These two items are also the easiest to implement. For example, Hadoop is a solution with good scalability and low cost.

So if you achieve 3 and 5, you can play big data like Google? Obviously not, because the key options 2 and 4 are not implemented, and 2 and 4 are so-called business analysis algorithms. These algorithms are carefully built by business experts according to data, business knowledge and market trends, and are the important means and core of business strategy formulation for many enterprises. This is the "Value" in 4V theory.

This is also the reason why many big data investments fail, because the current big data only provides strategies for data storage and query, and lacks business analysis solutions to improve the competitiveness of enterprises, which is precisely the most important. In fact, today's big data tools are built for IT experts, who can implement MapReduce functions through C++ or Java, but can't provide valuable business algorithms.

Therefore, the key to big data's success does not lie in the successful deployment of Hadoop, but in the formulation of algorithms that are helpful to the business. At a time when there is a serious shortage of talents, we might as well start with data tools. Lower the threshold for the use of tools, so that business experts can participate in it, in order to play the real role of big data, and immediately improve the business.

Summary

Anyone can deploy the tools, the key lies in the formulation of business algorithms, so that business experts seamlessly participate in the analysis of data is the beginning of success.

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