The future of data analytics will move towards a more pervasive, real-time data analytics, which means "getting the right information for the right person, at the right time", in the sense that it has transcended the technology itself, and is much closer to the business level.
This is the first time I've seen a real-time analyzer.
The ability to integrate, analyze, and act on data is essential to a successful business. Companies that don't have the ability to integrate, analyze, and act on data are at risk of being eliminated sooner or later. With the drastic changes in the business environment, any enterprise must be prepared in big data planning so that it can discover new trends in the market ahead of its competitors.
Three Capabilities
We recommend that companies and government organizations build data integration capabilities, analytical capabilities, and operational capabilities. For the management of any company, it is important to fully recognize the importance of data, and after the management fully recognizes the importance of data, it is important to have enough people and capabilities internally to integrate, build and improve the data management infrastructure. With massive data, data analysts are able to analyze and mine it to produce the desired value.
The ability to analyze data can be obtained through a certain methodology. This methodology, from a macro perspective, is to explore the effective business value through data integration, which in turn can accurately assist in the development of business strategies or service enhancement strategies, effectively take the right actions to assist in the growth of the business and quality of service, or to solve the business known, uncertain, or discover unknown problems.
In addition, data to achieve universal, not only in the hands of management, in the data security and rights management mechanism, each person in the enterprise or unit to understand their own business specifically what happened, why it happened, predict what will happen, so as to faster and better decision-making, and ultimately to achieve the wisdom of the management of a number of proactive events, to produce the right actions
Five Steps
Today, big data is far beyond the realm of IT, meaning that all sectors are in the realm of big data utilization.
There are five steps to big data planning, starting with a business-driven perspective, where the relevant department selects the business scenarios to be addressed and generated. In response to the demand for processing and take the integration of these scenarios need big data. Of course, the focus of the choice is how to make the information quickly generate value. Scenarios vary by need and are all-encompassing: for example, an organization's ability to increase business growth in terms of precision marketing, statistical analysis of the golden path of its customers before they buy which products, and so on.
Second, the value generated directly needs to be combined and correlated with existing CRM, customer transaction, and other data to generate overall key value benefits for the organization. For example, which users do go through the golden path summarized in the statistics above before making a purchase, and what is the historical relationship between those users and the business to provide the business with the next precise action priorities, etc.
Third, the entire enterprise should establish a support system for big data analysis, a culture of analysis, and talent for analyzing data, and thoroughly form the enterprise's comprehensive management, exploration, and **** knowledge of big data. The construction of big data capabilities is the topic of up and down and cross-departmental within the enterprise or government unit on how to provide more intelligent services and products to users.
Fourth, as the scope of big data exploration expands, enterprises need to establish standards for big data, unify data formats, collection methods, and usage, set a vision and purpose for *** enjoyment, and then follow staged goals to realize the vision. For example, the storage and processing of data in question has long revolved around relational structured data, and the provision of smarter services and products is required to incorporate data that was difficult to process and analyze in the past, such as text, images, and so on. Data content is rapidly evolving, so the ability to govern data standards, formats, collection, tools, methods, etc. must evolve with the times.
Fifth, the ultimate goal is to build a "unified data architecture" within an enterprise or government unit, with the ability to integrate (collect, store, and roughly process) multiple structured data sources from a variety of needs. On this basis, it builds data exploration and analysis capabilities (to quickly explore the value from the integrated massive data), and then how to effectively, real-time, and accurately combine it with the existing business data to generate accurate business action capabilities (for more in-depth utilization and provision of smarter services), so as to achieve the goal of "providing the right information to the right people, at the right time, in the right way". The goal is to provide the right information to the right people, at the right time, in the right way.