The Value of Big Data Analytics and How to Analyze It

Survey data on China's Big Data market trends will be analyzed to explain China's Big Data market and technology trends. Also, we will explain China's big data market trends and the impact of big data on IT technology, architecture, management, and landscape through online seminars and Chinese readers. Combined with the research data and analysis of China's big data market, CBi will explain "China's Big Data Value and Trends" in four series.

In the previous 3 series, CBi analyzed the impact of big data analytics on the next 24 months, the focus of big data analytics investments in enterprises, and the demand of big data analytics on IT resources. In this series, we'll cover big data analytics approaches and technologies.

Business value and data types of big data analytics

More and more organizations are recognizing the value that big data analytics can bring to their business. According to the results of a multi-option survey conducted by CBi (Figure 1), the main business values that enterprises believe big data analytics can bring are, in order of priority: improving resource utilization in the production process and reducing production costs; improving the accuracy of business intelligence based on business analytics and reducing the business risk of traditional "sense-based" decision-making; optimizing profits and growth through dynamic pricing; and acquiring quality customers. growth; access to quality customers. This shows that big data has a direct impact on the cost of enterprises, business decisions, profits. Another set of research data from CBi shows that more and more enterprise-level users are considering moving from batch analytics (the first phase of big data value creation) to near real-time analytics (the second phase) to improve IT's ability to create value. Meanwhile, data analytics is rapidly evolving from business intelligence to user intelligence. The Chinese market is gradually evolving from big data to reduce costs to big data to accelerate business growth, increase profits, and breakthrough innovation.

Figure 1. Key Business Values of Big Data Analytics

Currently, Chinese users are mainly using data analytics to improve operational efficiency and reduce operational costs across the enterprise. From the results of the survey on data types in Figure 2, at present, data analysis in Chinese enterprises is still dominated by structured data, such as database or transactional data. In addition, office documents, computer/network log files, text/messages, etc. are also the main sources of enterprise data growth, and also the types of data that can capture the value.

Figure 2. Big Data Analytics Data Types

A survey of data sources that contribute to the Big Data problem shows (Figure 3) that there is no doubt that databases bear the brunt of Big Data in the enterprise; while semi-structured and unstructured data such as software and weblogs, sensory data, communities, etc. have also been incorporated into the main scope of enterprise data analysis, which indicates that enterprises have realized the importance of these data for the business, which is also the key to achieving the goal of Big Data analytics. business, which is necessary to realize the transition from the first phase of (big) data analytics to the second phase of big data analytics. It has also become the focus of IT investments for users to create value through IT over the next 24 months.

Figure 3. Big Data Analytics Data Sources

Big Data Analytics Methods for the Chinese Market

After understanding the sources and types of big data in the enterprise, how to take an effective approach to analyze these data to maximize the value of the data and transform it into the most informed business decisions for the benefit of the enterprise's business operations is the purpose of the enterprise's analysis of big data. Looking at the current analytical approach to big data analytics in China (Figure 4), 33.8% of enterprises chose to adapt a common database for specific workloads; 22.0% of respondents chose data analytics cloud computing services (such as Software-as-a-Service and/or Infrastructure-as-a-Service); and 20.7% chose to customize their developed solutions. Only 4.8% use parallel processing (MPP) analytic databases and 3.3% use symmetric processing (SMP) analytic databases. This result shows that most Chinese enterprises are still in the first stage of data analysis. Moreover, most Chinese users currently use general-purpose databases, cloud computing, or custom-developed solutions and database tools as a big data analytics methodology, without choosing to go for software for data analytics.

Figure 4. Big Data Analytics Methods

MapReduce allows users to integrate semi-structured and unstructured data into data processing and analytics platforms, evolving from traditional core-based data distribution to cluster or grid-based data distribution. From the survey results on data processing and analytics platforms in Figure 5, commonly used distributed computing environments (29.0%), custom-developed solutions (27.7%), SMP (symmetric processing) databases (16.0%), and public cloud platforms (10.5%) are the more commonly adopted data processing and analytics platforms in the current big data environment, while the use of MapReduce enterprises account for a relatively low percentage (4.8%). This indicates that Chinese enterprises currently have limited recognition of MapReduce, which not only affects the speed of evolution of the three phases of data analysis, but also constrains the management of data collection, and furthermore, the later of the four segments of big data analytics.

Figure 5. Big data processing and analysis platform

The above is the value of big data analytics and the analysis of the way the editor shares the relevant content, more information can be concerned about the Global Green Ivy share more dry goods