Today, take everyone from the micro perspective to analyze the big data industry.

"Big Data Analytics, Visualization, and BI Domains" Although these three areas have their own functions and applications, they can be considered complementary: through the basic tools of big data analytics, researchers can obtain the logic and results of the data. However, these results are often too complex or hard to interpret, making it difficult for data scientists and business managers to quickly understand and adjust business activities. This is where big data visualization comes into play. Most visualization solutions have traditionally been an extension of data analysis tools. However, some companies have taken a different approach, using non-traditional methods to make data visualization more aligned with user needs. Business Intelligence (BI) combines big data analytics and visualization with real-world business scenarios. As an internal management tool, BI has significantly increased the value of enterprises and has become a crucial part of the big data application field. Big data analytics is moving towards being more user-friendly and easier to develop. Many big data analytics companies today integrate data analysis, visualization, and data collection, management, and integration into platforms. For example, Fractal Analytics not only offers data analysis capabilities but also provides automated data cleansing and validation services that return standardized structured data. Voyager Labs collects and analyzes billions of global data points in real time to help users make predictions. These typical companies mainly offer customized full-process services for large enterprises, with customer prices sometimes reaching up to $10 million. For instance, Fractal Analytics serves clients like Philips and Kimberly-Clark, and its high pricing and quality service keep small businesses behind. However, as big data technology becomes more widespread, SaaS-based big data analytics services are becoming a clear development direction. This will greatly reduce the usage threshold, allowing SMEs to gradually access big data analysis capabilities and realize the value of their underlying resources. Additionally, enterprise data security and the data protection market will expand into new spaces with the rise of SaaS. The current trend of simplifying, reducing costs, and making big data technology more accessible is evident in some companies' product strategies. For example, Domino's products allow data scientists to focus on analysis without worrying about infrastructure setup and maintenance. Datameer further develops its products to hide the complexity of big data analysis through a spreadsheet-like visual interface, enabling employees to get started quickly. RapidMiner Studio allows zero-code operation to implement machine learning, data mining, text mining, and predictive analysis. As big data analysis becomes more popular, the development of technologies to improve performance and optimize results is also advancing rapidly. For example, SigOpt uses a self-developed Bayesian Optimization algorithm to adjust model parameters, resulting in faster, more stable, and easier-to-use solutions than traditional grid searching techniques. SigOpt not only allows users to test different variables but also provides recommendations for future tests to help them continuously optimize and improve their analysis results. It's encouraging to see that some companies in the big data analytics field have challenged traditional data analysis theories and used unique methods for data analysis. These companies' technologies complement traditional methods and have successfully applied in specific areas. One such company is Ayasdi, founded by three top mathematicians worldwide. It uses topological data analysis techniques and hundreds of machine learning algorithms to process complex datasets, efficiently capturing high-dimensional spatial topological information and discovering small classifications that traditional methods might miss. This method is currently playing a major role in gene and cancer research. For example, a doctor used Ayasdi's data analysis technology to discover 14 variants of breast cancer. Ayasdi has gained significant customers in the financial services and healthcare industries. Visualization technology is gradually becoming automated and intelligent. Big data visualization is the best way to connect data analysis results with the human brain. Therefore, the level of visualization technology has become an important factor in the ability of big data companies to attract customers. Currently, the development direction of visualization aligns with big data analytics, with efforts focused on simplicity, automation, and intelligence. Companies like Alteryx, a startup providing a one-stop data analysis platform, allow users to perform data entry, modeling, and data graphing on the same platform, seamlessly integrating data operations and beautiful visuals. They can also perform statistical analysis in a way similar to SAS and R languages. Visualization technology also helps users achieve real management capabilities. German big data company Celonis uses process mining technology to extract data from daily records, identify key factors, and reveal company performance in business, helping client companies increase productivity by 30%. Today, visualization technology is not limited to traditional analysis results but can directly convert unstructured data like text and images into visualizations. For example, Quid uses machine intelligence to read a large amount of text and then converts the data into interactive visual maps, saving time that was previously spent on reading searches. Origami helps marketers integrate and analyze cross-platform data such as CRM, social media, email marketing, and survey reports, making it simple, intuitive, and visual so everyone can be efficient and practical. At the same time, hardware innovations in data analysis and visualization are also underway. Kinetica, which develops GPU relational database services, has received $50 million in Series A funding. MapD, which uses the same technology route, has achieved 100 times faster query speeds and visualizes big data. BI technology is moving away from "chicken ribs," offering real-time convenience and efficiency for governments and enterprises. The development of BI technology has a long history, but due to technical limitations, its actual impact has been limited. With the popularity of data collection and application, and the promotion of big data analytics and visualization technology, BI, which enhances enterprise operational efficiency through data dashboards and intelligent decision-making, has once again gained favor in the capital market. Tableau, a representative BI company, now has a market value exceeding $4.8 billion, while another representative company DOMO has reached a valuation of $2 billion, growing faster than traditional commercial software companies. Compared to visualization technology, BI focuses more on practical applications. Through templating, SaaS, and de-coding, BI applications are no longer limited to data scientists and corporate executives. It is foreseeable that every employee in the enterprise can use BI tools to learn about their own and departmental data, improving the way and direction of work. Looker has raised a total of $177 million to enable users to query large data sets using natural language, lowering the threshold for querying big data. GoodData provides enterprises with big data analysis SaaS services, achieving 100% cloud-based data analysis, allowing existing enterprise data to be imported into the GoodData cloud platform for tracking, segmentation, visualization, analysis, and processing. An interesting case in the BI field is Qlik's products, which are highly praised by China's General Administration of Customs. The General Administration of Customs conducts massive data analysis daily. Qlik uses graphical data displays to allow customs management personnel to analyze long-span multi-view data without being restricted by platform or time, greatly promoting inspection results. Enterprise big data retrieval, product big data analysis, big data consulting and forecasting, big data platforms, and machine learning fields. Enterprise big data retrieval can fully exploit and release the potential of enterprise data; product big data analysis makes user behavior an important reference factor in product design and operation; the combination of big data technology and consulting business has had a great impact on the consulting industry, with data technology-oriented consulting becoming the mainstream choice for the future industry; big data service support platform companies have made great contributions to the popularization and practical application of big data technology, becoming an indispensable part of the big data technology ecosystem; finally, machine learning, as the underlying technical method of big data analysis, is gradually becoming widely used. First, we list the typical enterprises in the five fields of enterprise big data retrieval, product big data analysis, big data consulting and forecasting, big data platforms, and machine learning. Next, we will introduce them in detail. Enterprise big data retrieval: The popularity of mobile Internet and the rise of SaaS services have led to an exponential increase in the amount of data deposited by enterprises. However, the current value of enterprise data is still at a shallow level, and the true big data analysis capability has not yet been applied. Therefore, how to do a good job in the value of internal data information has become a key first step. A typical enterprise that improves the ability of enterprise data mining and retrieval and lowers the technical threshold of search is Algolia. Its products now include intelligent fault tolerance for keyword input and provide search ranking configuration, allowing ordinary employees to find what they need as needed. Data information. At the same time, Algolia also provides an offline search engine for mobile devices. Its C++ SDK can be embedded in the application server so that it can provide search functions even without a network connection application. At the same time as the emergence of SaaS services, enterprises use a variety of software to cause internal data to be disconnected, forming data islands. According to analysis by Internet Queen Mary Meeker, companies in different industries use an average of 25 to 91 SaaS services, requiring cross-platform data retrieval and analysis services. Maana Knowledge Graph, a data search and discovery platform developed by Maana, has the advantage of collecting data from multiple systems or "islands" and converting it into operational recommendations for a wide range of industries. Product big data analysis: Product big data analysis is less focused than other applications, but it has many features. By collecting user browsing, clicking, and purchasing behaviors, it not only allows macroscopic awareness of changes in user group preferences but also constructs user portraits at the micro level, enabling customized product recommendations and marketing to effectively enhance consumer spending levels and satisfaction levels. Mixpanel is a company that offers similar products that enable enterprise users to track user habits and provide real-time analysis of products with user dynamics (Trends), behavioral funnel models (Funnels), user activity (Cohorts), and single-user behavior. Several modules, such as People, comprehensively cover possible user behaviors and scenarios. Big data consulting forecast: Nowadays, the development of big data technology has made event analysis and forecasting possible, with high accuracy and processing speed. The situation of traditional consulting companies is similar to that of Wall Street analysts who are now facing AI threats and may soon be replaced. Therefore, with the emergence of big data consulting companies, traditional consulting companies have also cooperated with big data technology companies and even set up their own data business departments. Opera Solutions is a consulting firm based on big data analytics. Its founders are veterans of the consulting industry and have founded business consulting firms Mitchell Madison and Zeborg. Currently, Opera is committed to data analysis consulting in the financial field, providing advice to customers through modeling and quantitative analysis to solve customer business problems. For example, its computer system can collect billions of pieces of data at once, including real-time data from real estate and car prices to brokerage accounts and supply chains, through analysis to get signals about how consumers, markets, and the entire economy will act or opinion. Its clients include consulting firms and companies such as Citibank, and recently also provided Morgan Stanley with a business that helps brokers provide investment advice to their clients. The combination of new technologies, machine learning, and consulting and forecasting industries has become a small hot spot in the industry compared to using only big data analytics technology to achieve better results. For example, Endor based on the principle of social physics can generate a unified human behavior data set based on a small amount of data, and make pattern recognition and judgment earlier than traditional massive data analysis methods. In an experiment to identify an ISIS-controlled account on Facebook, Endor efficiently identified a new ISIS suspect account with satisfactory accuracy based on a small number of known ISIS account features. Big data service support platform: At present, there are many platform-oriented companies that are developing around the big data technology and big data industry ecological chain. These companies have certain technical level, but they mainly exist by serving big data technology companies and researchers. An indispensable part of the ecology. Dataiku has created a cloud platform designed to make it easier for data scientists and employees to access the big data collected by the company and to shorten the time required by experts and data analysts through machine learning libraries. Algorithmia's platform provides general algorithms including machine learning, semantic analysis, text analysis, etc. Once the user finds the algorithm they want to use, just add a few lines of simple algorithm query code to the application, and the Algorithmia server will connect to the application. Avoid duplication of work by developers. At present, some platform-based enterprises that have transitioned to the developer community business development have been favored by industry giants because of their resources. Kaggle, which was acquired by Google, is an example. By holding online competitions around data science, Kaggle has attracted a large number of data scientists. The participation of machine learning developers to find data-based algorithm solutions for a variety of real-world business problems. At the same time, Kaggle provides a full range of services for its community, including well-known recruitment services and code sharing tools Kernels. Machine learning: Machine learning is a technical means of pattern recognition, statistical learning, and data mining. It is also the underlying technology in the fields of computer vision, speech recognition, and natural language processing. You can see in the introduction of the attachment, Microsoft Azure, Google Cloud Platform, and AWS. Both have launched their own machine learning products, and many machine learning startups compete by providing differentiated technologies or services. Attivio, which has accumulatively received $79 million in financing, focuses on emotional analysis using textual learning techniques through text, providing supervised machine learning and unsupervised machine learning techniques to help companies model emotions by identifying documents in the corporate corpus. and analyze. Through Attivio's intelligent system, Cisco enables salespeople to recommend suitable products based on their emotions, spending power, etc., when working with customers, saving millions of sales and operating expenses while saving sales teams 15-25% of the time. DataRobot's business is to search through millions of possible combinations of algorithms, perform pre-processing, feature calculations, transformations, and tuning parameters to provide the best model for users' data sets and forecasting targets, so that users can build great models in minutes without a data science professional background. For example, banks use Datarobot to automatically build very accurate predictive models and identify fraudulent financial transactions to avoid losses.

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