The rapid development of the big data industry has brought about a more intuitive and effective impact on the social subject of the enterprise, in addition to changing all aspects of people's lives and promoting rapid social progress.
Analysis of large enterprises development status and application data overview of
a <br>, China's large enterprise data industry development status <br> <br> recent years, enterprise-class big data applications increasing popularity, consumer behavior analysis, precision marketing, new business new Product promotion, advertising push, spokesperson choice, social media, visualization, premium income, inventory management, credit insurance and other related applications are constantly enriched. With the rapid growth of a large number of big data startups dedicated to business and enterprise application services, big data is more widely used in enterprises in various fields.
Business transformation is a common demand of most enterprises today. Big data analysis can not only optimize access, speed up decision making, maximize availability, but also assist in business transformation.
At present, more and more executives in Chinese enterprises have begun to pay attention to IT, not limited to CIOs. In the era of information explosion, companies need more data scientists to conduct data analysis, and even some companies have set up CDO (CIO) positions to control big data and analysis separately. This is relative to companies that do not have data to provide reference, often rely on intuition and past experience to make decisions, they can easily enter into irreversible misunderstandings, and the use of big data and analysis can better and faster business and market.
Although big data applications are highly valued in emerging companies, the biggest application prospects for enterprise big data transactions in the future will be in traditional industries. This is not only because companies in almost all traditional industries are rapidly Internetizing, but also because traditional industries still account for the vast majority of GDP. Big data fairs help these traditional businesses complete transformation and upgrade faster.
At present, in the traditional industry, financial, telecommunications, manufacturing, transportation, and medical enterprises have become the main force for the use of big data analysis. Take manufacturing companies as an example. Traditional manufacturing companies can obtain market terminal sales through big data transactions, understand their own and competitors' market performance and consumer preferences; obtain data through user buying habits and purchase evaluations, and can target different types. Accurate marketing of customized production in different regional consumer groups; the industry chain data obtained through transactions can reduce production costs and enhance the overall competitiveness of enterprises.
Taking emerging Internet finance as an example, through the acquisition of user information, we can comprehensively judge from the latitudes of wealth, security, compliance, consumption, socialization, etc., build credit reports for users, and form a massive database based on big data. To help companies reduce credit risk.
In addition, more companies are using big data analytics to help companies make decisions, enhance the user experience, and create more and more new business models with customer focus.
(1) What is the core value of
enterprise data <br> <br>
large enterprise big data is the business after the collection, storage and analysis of massive data, through mining and analysis of these data, in order to improve operational efficiency, Provide business reference and guide new business development, and provide support for the company's future development strategy to achieve the overall competitiveness of the company. The “cheap, speed, and optimization†of big data makes its overall cost optimal compared to other existing technologies.
(2) China enterprise big data industry development track <br> <br> application of Chinese enterprises to large data can be mainly divided into three stages: the first stage between 2010 and 2012, big data and machine data applications concern The relationship is limited to traditional IT thinking, but it has been tagged with big data tags on many small data applications. The second phase from 2013 focuses on the relationship between data and people. Visualization and predictive applications have become the darling of the market. After 2014, the focus of big data applications has shifted to analyzing the relationship between data and data, which requires open innovation in enterprise big data applications: from data openness, sharing and transactions, to basic processing and analytics platforms. Open, and then open to value extraction capabilities.
As the business extension extends from the internal to the external and to the industrial chain and ecosystem of the enterprise, the data view of the enterprise is also wider and wider. From the main focus on the internal data of the enterprise, it has been extended to pay attention to social data, including Enterprise data such as transaction data, synthetic data, machine data, and social network data are constantly being re-recognized.
(3) the development of enterprise big data meaning <br> <br> For businesses, big data application solutions are mainly three aspects of value. First, it can handle massive amounts of data that were previously unprocessable or that cannot be processed in real time and quickly, including both structured and unstructured data. Second, companies can use big data solutions to extract, organize, and analyze massive amounts of data distributed across various networks, such as social networks and video networks, and then gain new insights from these new data. Convergence with the details of known businesses to promote the marketing of corporate products and services. Third, you can also use the big data that you have accumulated or exist on the Internet to launch a variety of new products and services.
The significance of enterprise big data, in addition to reshaping customer behavior, using customer interaction data to reshape customer behavior, such data enables companies to predict and guide the market's outstanding demand, thereby creating new profits, and more to enhance the data ecosystem. The vision of the system, because companies can obtain complementary data from other companies in the ecosystem, based on an appropriate cooperative strategy.
The existence of big data development (4) landed yet to business problems â‘ <br> <br> with the concept of big data continues to promotion, the company now talk about big data if, it will make people feel out of date, business people feel The management level has fallen behind the times. This phenomenon, of course, has the theory of preemptive conceptualization caused by practice. Although enterprises have already felt the urgent need to use data in depth to help enterprises enhance their core competitiveness, how to apply these data is still in the exploration stage.
② data silos <br> <br> data silos enterprise development is the biggest problem facing the Big Data industry. On the one hand, industries, companies and governments are doing everything they can to collect data, possess data and use data. On the other hand, most of the data is blocked by various industries, enterprises, institutions and governments, forming a “data island†that cannot be freely circulated and there is no connection between the data.
â‘¢ <br> <br>
technology gap in the current era of data is king, the enterprise to leverage existing resources and strive for greater market, consumers must be ownership of big data. However, the limited access to big data creates a new monopoly and technology gap. The application of big data also has a double gap between access and skills. This not only wastes data resources, but also brings problems to the precise marketing of enterprises.
War <br> <br> â‘£ of SMEs for SMBs, big data is likely to be they do not want to be lifted in the "scars": big data technology is the enterprise development plays a vital skill, But for them, the lack of funds and the immature data links make them unable to use big data very well.
Big data is not just a patent for large companies, it is also an opportunity for small businesses. In many cases, big data is very suitable for small businesses. But if companies are not able to act flexibly, even the most savvy insights become worthless. Small businesses often have the flexibility to adapt to data-driven trends quickly and efficiently. Implementing a data strategy in an intelligent, structured way is the biggest difference between a big data-driven enterprise and a company that simply uses data based on temporary ideas. For small, flexible, and evolving companies, these foundations are not significantly different from those of industry giants that have used big data for many years.
⑤ how companies should take advantage of big data <br> <br> in the era of big data, companies face a number of new data sources and massive amounts of data, the ability to make decisions based on analysis of these data, thus turning it into an enterprise competition The source of the advantage is the challenge to the top of the company. Faced with the many benefits that big data brings to enterprises, the current problem facing enterprises is how to obtain and analyze data so that enterprises can be invincible. The Internet is a major source of big data. However, it is difficult for some offline traditional enterprises to obtain. For enterprises, the following strategies can be adopted to obtain data support.
First, companies must make cultural adjustments and establish a culture of data-driven decision making. Big data is first and foremost a matter of concept, that is, to provide the basis for decision-making through objective and rational data. In traditional enterprises, especially those that have achieved success, they often form a fixed corporate culture and management experience, processes and systems. To establish a culture of data-driven decision-making, we must break the original decision-making mechanism with experience, process and system as the core, and make the decision-making process data, objectification and flattening. Under the new competitive market and rules, historical experience has often lagged behind. Especially after entering the Internet era, the idea that the Internet is customer-centric and the ecological chain is the operating mode has already had a subversive impact on traditional enterprises. Therefore, only by establishing a flat decision-making mechanism driven by objective data can we respond to rapidly changing market and customer requirements.
Second, the enterprise must establish the organizational structure of the corresponding data management center. Without a relatively complete and professional data management team, it is difficult to play the role of big data analysis. The data itself is just information. If you can't turn this useful information into a valuable decision-making basis for the company, the data is just a pile of waste paper stored in the warehouse. To transform data and information into decision-making information that is useful to the business, a professional data management team must be established that includes data collection and processing staff, data analysts, and data communication and display personnel.
Third, enterprises must establish and implement top-level data architecture design. In the planning of information system construction, there needs to be a top-level information strategy planning, in which the core part is the data architecture design and implementation road map. The main purpose of data architecture design is to ensure that all data links of the enterprise have a unified standard, a unique data design dictionary, and a core master data management system to ensure the integrity, consistency and effectiveness of enterprise data. After having the top-level data architecture design, establishing a suitable implementation roadmap can help enterprises gradually build various information systems under a clear data architecture framework.
Ensure that the same object corresponds to a unique data source, eliminate information silos, and improve data consistency and effectiveness.
Fourth, enterprises must establish a sound data governance system. If there is no perfect data management system, even if there is a good top-level data architecture design and strict system implementation, if the data governance system is lacking, the quality of the data will soon be disappointing, and it will be difficult to complete the mission of driving decision-making. The bad situation of Rubbishin, RubbishOut". A good data governance system that covers the complete lifecycle management of data, including data owner responsibilities and rights, data formats and standards, data creation and change processes, data usage systems, data security systems, and data destruction processes.
Fifth, companies must establish appropriate technology platforms and teams for data analysis. This part is an area that is familiar to the traditional information department and is generally the least difficult part. The difficulty is how to design compatible with traditional internal data analysis and the current demand for massive external data analysis, to establish a technology platform at the lowest effective cost, and to meet future scalability requirements.
The last is to work with big data analytics and mining companies. At present, many traditional enterprises do not have the ability to analyze massive amounts of data, but they can cooperate with companies that already have big data analysis and mining services, such as UFIDA and IBM, which are already available on the market. This is what traditional enterprises can do with big data analysis. power.
All in all, companies should do data management, or just a company that hoards data. Transforming big data into a "small number" that can provide advice for business development. If enterprises can stand on the cusp of big data, they may really take off.
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