When we stop hyping big data, the big data era is coming.

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Since 2015, big data has been removed from Gartner's new technological Hype Curve. The word "Big Data" was first published in Gartner's emerging technological Hype Curve in August 2011, at that time, Gartner expected that big data technology would take two to five years to enter the actual production applications of enterprises. Since then, big data has quickly been hyped up by the market, and eventually disappeared from Gartner's new technological Hype Curve in 2015.

In 2016, big data has already entered the practical production and application of enterprises, and is promoting the digital transformation of enterprises. Another market research firm, IDC, stressed that in the next five years, global data-driven enterprises will receive an additional revenue of more than $2 trillion, they are from three fields, namely cost reduction, productivity improvement, and revenue growth, which are distributed in business operations, customer experience, enterprise innovation, and operation support.

So how can we realize the value of 2 trillion of the data? Dan vesset, vice president of IDC Data Analysis and Information Management Group, said that cloud-based Data Analysis and Management Solutions play an important role in promoting enterprises' digital transformation, in the next five years, data analysis and management solutions based on cloud services will grow 4.5 times faster than local solutions. This means that more and more enterprises will adopt cloud-based data analysis and management services. The big data era has come!

　　Data Analysis on demand

According to IDC observations, the popularity of big data has been significantly reduced over the past few years. Whether in various meetings, forums or media, the importance of the discussion is not the "big" or "type" of data. IDC data shows that 23% of enterprises in the United States have deployed hadoop and 32% of enterprises have deployed nosql databases. They have basically gone through a new round of technical deployment cycle for data management and analysis, I have a better understanding of the advantages and disadvantages of various big data technologies.

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(: Data-driven enterprises will receive an additional revenue of more than $2 trillion, IDC 2015)

In fact, there are already a variety of technical options today, from local deployment to cloud deployment and hybrid deployment, plus data from internal and external sources of the enterprise, the demand for self-service data management and analysis is also growing. In an interview with IBM, Dan vesset said that the current big data application focus of enterprises has shifted to the strategic architecture of data management and analysis, it is mainly embodied in providing users with a data management and analysis service that can respond to data needs as needed.

IDC believes that from now until 2020, the self-help visual data exploration and data processing market will grow by 2.5 times or more than traditional non-self-help solutions. In particular, self-service data acquisition and processing services will be increasingly valued by the market. The reason is very simple, that is, for many non-professional users, they may be accounting, marketing personnel, human resources management personnel, or even ordinary project personnel in the enterprise, however, data analysis and processing are also required.

Data management and analysis services are provided in the form of cloud services, which enables various users to use big data technology at a low cost, high efficiency and faster speed. More importantly, cloud service-based big data management and analysis provides users with new interactive interfaces and interfaces, which are visual data management and analysis, in particular, it can "consume" data and data analysis on smart terminals such as smartphones at any time. This will make big data everywhere and always absent, so that we can truly use the "connection" capabilities of mobile phones.

Cloud service-based data and analysis solutions share big data capabilities across the enterprise, and even partners, suppliers, and customers can share enterprise big data, this allows big data to be further extended to the entire enterprise-related ecological environment. In addition, the IOT solution allows members in the enterprise ecosystem to use data and analysis for their own business. On the other hand, they can also perceive and understand various data trends throughout the enterprise ecosystem at any time, by connecting "small" analysis with "big" analysis, you can make better business decisions.

　　Turning big data into cloud services

At the beginning of this year, IBM Chief Executive ginni Rometty publicly stated that it would transform to a cognitive computing and cloud computing platform. After nearly half a year's efforts, cloud data services, a cloud data service system that combines development platforms, cloud services, and open-source data tools, in China, the cloud database product cloudant was first launched through cooperation with century internet.

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(The IBM bluemix public cloud will be officially launched in China on September 10, October 19, 2016, and more IBM cloud data services will be launched)

In fact, IBM has been increasing its investment since 2004 and has spent nearly $20 billion to create a comprehensive and rich big data analysis capability. Now, IBM wants to provide these structured data processing, unstructured data processing, and big data analysis capabilities to enterprises through cloud services. This year, IBM proposed the "analytics platform Services" strategy and set up relevant departments to allow more users to use IBM data services on the cloud.

According to Ji yanyong, General Manager of the big data and analysis platform of the IBM China Development Center, IBM cloud data services covers almost all of IBM's core big data and analysis technical capabilities, which can be divided into five aspects: database, data analysis, enterprise content management, data integration and insight services. In particular, the open-source platform of IBM cloud data services enables enterprises to manage and analyze data in a self-service manner.

In terms of comprehensiveness, IBM cloud Data Services provides 25 popular open-source big data technologies and IBM's own database products, including MongoDB, PostgreSQL, elasticsearch, redis, rethinkdb, etcd, rabbitmq, hosted database cloudant based on the open-source couchdb architecture, optimized Apache spark and hadoop, and IBM's own DB2, DB2 Blu, dashdb, Informix, and so on.

Ji yanyong said that the core competitiveness of IBM cloud data services lies in having the source code of these databases and providing management services (managed services) to users at the source code level ), this greatly reduces the burden on enterprise IT and developers. IBM hopes to combine open-source technology with IBM's advantages in the big data field to create a relatively complete data service environment for users.

In addition, IBM cloud data services is a platform-level service for enterprises, allowing enterprise users to receive technical support services around the clock. At present, IBM provides 24x7 Enterprise Services through teams in the United States, Britain, and China to quickly respond to enterprise user needs and ensure business continuity.

For the Chinese market, IBM will launch the cloud data services-related Big Data Analysis Service on the IBM bluemix PAAs public cloud in October through its cooperation with 21 vianet, developers can directly call IBM data analysis and processing services on the bluemix platform.

　12 years of data analysis

Ji yanyong has been working in the IBM China Development Center for the past 10 years. The IBM China development center was established in 1999 to develop IBM's core products.

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(JI yanyong, General Manager of the big data and analysis platform for the IBM China Development Center)

Before December 2004, Ji yanyong was mainly responsible for the development of IBM e-commerce and related products. From 2004 to 2010, in order to provide stronger database technical support to domestic bank users, Ji yanyong established a database development team of the IBM China Development Center to work on structured data solutions. In addition, Ji yanyong is also responsible for the establishment of the IBM unstructured data and Enterprise Content Management Development Team.

In 2011, Because IBM acquired companies such as SPSS and Cognos, Ji yanyong formed a business analysis team. Now, the structured data team, unstructured data team, and analysis team of the big data and analysis platform of the IBM China Development Center are complete. As the market demand for Big Data talents is getting stronger and stronger, IBM has jointly established a big data analysis major with Xi'an Jiao Tong University since 2012. Ji yanyong also serves as the Director of the Department.

In the past two years, IBM's technology has changed rapidly, focusing on cognitive computing and cloud platform development. Therefore, in 2016, IBM released the department responsible for cloud development, and Ji yanyong was responsible for all cloud development for big data and analysis. At present, Ji yanyong is mainly responsible for implementing big data analysis capabilities on the cloud, and his team is also responsible for providing cloud data service O & M support for IBM Global users to ensure no downtime.

Ji yanyong said that the cloud data service platform that IBM hopes to build should first integrate all the data management and analysis technical capabilities of IBM and then cooperate with each other to form an overall service, it is then output as a cloud service. IBM is also preparing to accelerate R & D and increase investment in data science and machine learning to improve the cloud data service platform.

The IBM cloud data services cloud data service has been deployed in China and is also led by JI yanyong's team. Ji yanyong said that IBM needs to adapt to and comply with relevant data security regulations in China, accelerate the implementation of cloud data services-related services on this basis, and increase cooperation with local enterprises, solve various problems encountered during the implementation process as soon as possible, and continuously promote the process of Service implementation.

Ji yanyong stressed that the Chinese market is developing very fast, and many places are undergoing rapid development, as is big data. With the launch of the IBM cloud data services cloud data service, you only need to subscribe to the cloud service online to provide big data to enterprises, developers, and end users in the form of cloud services, without the need to install complex infrastructure and management software, this makes data analysis on demand and meets the characteristics of China's rapid development.

With enterprises and users using big data and realizing the value of big data, the big data era has come. (Text/Ning Chuan, Cloud technology age no.: cloudtechtime)

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When we stop hyping big data, the big data era is coming.