Big data analysis needs to analyze the current situation through data analysis, and predict and optimize the improvement through model and predictive analysis techniques. Domestically, whether it is state-owned or private enterprises, the data analysis results are based on business decision-making. Several industries such as banking, insurance, telecommunications and e-commerce.
Has the era of big data arrived?The trend is a ridiculous and respectable force: Today, if you open any media, if you don't mention "big data," I am afraid I am embarrassed to publish. This trend is overwhelming, and even national leaders are no exception. The question is: Why do everyone say big data?
The value of data, as the geometric progression of data volumes grows, can no longer be seen through traditional charts, which is why business intelligence has not yet gained popularity, and it has been squeezed out of the stage by "data analysis." Because value is hidden in the data, data analysis is required to release these values.
The level of data analysis ability determines the quality and success of the value discovery process. It can be said that without data analysis, "big data" is just a bunch of IT inventories, with high costs and zero returns. However, the “big data†concept of the domestic boom is still in the initial stages of data collection, sorting, storage and simple reporting. There are only a few companies in a few industries that can perform basic analysis and application of big data.
For the domestic data analysis market, we feel as follows:
The market is huge, and many companies (whether the Internet is a new or traditional enterprise) are discussing this, there are practical needs and willing to pay for it, but the system is not systematic. At present, the industries with the strongest data demand are: financial institutions (from funds to banks to insurance companies to P2P companies), Internet companies represented by advertising and e-commerce, etc.
There is no platform-level company model (this may be the chaotic period before the big market or big opportunity)
The atmosphere of ToB service has not yet been fully formed in China. For some capable technology companies, if the data demand is strong, considering the improvement of their own capabilities and data security, they will not outsource or adopt external modules, but tend to build themselves. This business
In the future, BAT and JD, 58 and Didi taxis will be the big players in the data field with their massive data. However, the whole industry is very large and the demand is strong. Even if there is no opportunity for the startup to have a platform-level giant enterprise, it will leave a variety of market segments for everyone to get their own territory.
Data precipitationIn the vernacular, it is data grabbing. There are currently four ways to get data.
Web crawler, developed its own crawler platform with Python and Go, and crawled dozens of websites to get relevant information.
Wi-Fi access solution, we have developed a complete software and hardware solution, the advantage is the high ROI (return on investment ratio), and free to the property manager to help them make money by net fee and promotion fee to make money . Obtain user data based on consultations with them. This is mainly the development of OpenWRT and the development of some intelligent hardware and client.
Provide some image APIs, image search and face search to meet some of the customer's needs in image processing and image recognition. The development mainly uses some algorithms of Machine Learning and Deep Learning, using C++/Open CV/Matlab.
Data service demanders provide their own.
Data miningIn the vernacular, it is the use of data analysis to generate deep and valuable understanding. Based on the data obtained in the above various ways, we can do the simplest statistical analysis, user and brand understanding, user portraits, relationships between brands or product models, etc., to understand the present and history and strive to predict the future.
Commonly used tools are Python/R/SPSS, etc. The algorithms include the simplest statistics, the slightly more complicated Machine Learning, the now-famous Deep Learning, and the CollaboraTIve Filtering.
In the vernacular, it is to present the results of the analysis in the most aesthetically pleasing and understandable way (icons or graphics). Currently, we have several forms:
Website (compatible with PC and mobile): Provided to paid B-side customers, not open to the public, the general situation is as follows
A public cloud platform of SaaS makes it easy for everyone to use their tools to make a graphic report that is easy to spread on the Internet, especially on the mobile side. It will be online soon, and the general form is shown below. The logic of the product is very simple: the demand for reading and reading is getting stronger and stronger, but there is no such tool or platform to create such content, even Excel, can not produce graphic content suitable for network communication.
The commonly used technology is JS+Node JS+MongoDB and so on.
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