The three main reasons why domestic BI firms passively treat big data

When large data, namely big and cloud computing, mobile applications and social networks, became popular in the 2012, it was the most fashionable word in the field of information technology. IBM, Oracle, SAP, Microsoft and other vendors, like the search for a new gold mine, began to strongly promote the big data concept. As a result, many vendors flocked to grab the big data market, especially SAP's Hana and Oracle's exalytics. In contrast, some of the companies that push bi are not up to the level of large data, sticking to the position of the report analysis. In fact, is big data really as powerful as we think? Why does BI not run the "last mile"? Big data and bi are at the same time encountering the embarrassment of being questioned.

Why can't bi quickly step into the big data age?

The confusion of a newly entered BI consultant, or a certain representation, expresses the current state of BI.

"Today with the customer demo system, demo finished, the customer asked me a question: business intelligence in the end smart where?" What is the difference between the things that bi and reporting tools do? For this question, I believe that the bi people have asked themselves, but also gave themselves a lot of answers, but not really is 100 points of the answer. Just like today, although I have given some examples to explain and illustrate these issues, I still feel that I am not thorough and persuasive. I think the important reason for this problem is that BI itself is too broad, and the various reporting tools in the market now call themselves BI solutions, and the various projects are on the BI, making the concept of bi very vague. The second reason is that most of the BI projects actually require a query and report that satisfies the data, bi or whatever. ”

The concept of BI is so broad that a few years ago, many companies thought that Bi was dispensable. In recent years, large data has brought a great impact on BI, accelerated its pace of development, and even a bold prediction, the next decade, business intelligence analysis will lead the development of management information.

The difference between bi and large data is that large data can be processed based on BI tools for High-volume and unstructured data, compared to traditional transaction based data warehouse systems, large data analysis focuses not only on structured historical data, but also on web, social networks, RFID sensors and other unstructured mass data analysis, large data is undoubtedly a perfect complement to BI.

Why do most bi vendors show a "cold" attitude towards big data?

As a result, it is difficult to deal with and analyze unstructured data: Whether it is large interactive data or large transaction data, processing and analysis of unstructured data is the BI industry, or even large data processing, has been facing difficulties. Many manufacturers of BI products, their technical capabilities can not reach the height required by large data.

Reason two, the enterprise will not strong: Now a lot of enterprises do bi, and not fully reflect the intelligence, at most just the existing data used to present the report, the development of the report is also very simple, most users do not want from the development of the BI system to explore more value awareness.

For three reasons, the value of BI cannot be measured: Big data does have value, but the value is too great to measure accurately. New values are mined from some large data, but this value is only added value and is an imaginary space. For example, there may be gold in the desert, but it is not that the desert must be able to dig up gold.

Big Data How to pick out the "new bottle of Old wine" label?

It was said that "big data is a relative concept, a new bottle of old wine." "The legend of the large data processing way, is only to fashion, in the existing scheme packaging, new bottles of old wine." The vast data age has not revolutionized the number of companies, and before MapReduce and Hadoop, there are companies that can easily perform large-scale parallel computations of data, and the presence of NoSQL is only a way of making it more likely to handle the data.

So, judging from the results, there is no more doubt about the big data than BI, and the embarrassment of "one kilometer short" is also encountered.

The large data analysis lacks the mature practical experience, its way method and the traditional data warehouse and the BI system have certain difference. Before implementing a large data analysis project, the enterprise should not only know what technology to use, but should also know when and where to use it. What are the correlations between the data? Which data is trustworthy? How to dig out valuable, easy-to-use customer information from massive amounts of data?

To answer these questions, the enterprise needs a single, complete, and trusted view of the customer data, creating a single, complete, and trusted view of the customer data, and data integration is the key. There is no integrated data, its commercial value is zero. Data integration enables organizations to combine traditional transaction data with new interactive data to gain insight and value that cannot be achieved in other situations.

It is certain that with the development of Internet technology, the future of the big Data era, must be a variety of information to the rapid growth of the state, how to obtain useful information is the key, intelligent analysis tools will become more and more important, can override multiple management systems, databases, how to through more flexible, controllable bi tools, It is a common challenge for large data and bi to really tap into the value of the big data age.

(Responsible editor: Fumingli)