The other day, the Los Angeles Police Department in the United States began using big data to predict crime, an example of how big data can help people make forward-looking decisions. Yet big data is much more than that. In the business world, big data is also a major reference for business managers to make decisions. This article describes how business decision makers can collect data and use big data to make decisions.
In recent years, global data has been growing at an unprecedented rate, and the types of data have become more and more diverse. On the one hand, the huge amount of diverse data poses a challenge to the effective storage and fast retrieval of information, and on the other hand, the huge business value embedded in it has triggered a huge demand for data processing and analysis.
To date, there is no clear definition of the concept of big data that has been widely adopted by the industry. Based on the connotation of the concept of big data and the industry's general agreement on the characteristics of big data, we propose the following concept: big data refers to the massive and diversified transaction data, interaction data, and sensing data that need to be rapidly acquired, processed, and analyzed in order to extract value from them.
Wherein, massive and diversified is the definition of the data volume and data type of big data; fast is the requirement for the speed of acquiring, processing and analyzing big data; value is the significance and purpose of acquiring, processing and analyzing big data; transactional data, interactive data and sensing data are the sources of big data, and the transactional data come from the ERP system of the enterprise, all kinds of POS terminals, as well as online payment systems and other business systems; the interactive data come from the business systems such as ERP system, all kinds of POS terminals, as well as online payment systems. Transaction data comes from enterprise ERP systems, various POS terminals, and online payment systems; interaction data comes from mobile communication records and social media; and sensing data comes from GPS devices, RFID devices, and video surveillance devices.
The utilization of big data will become the key for enterprises to improve their core competitiveness and seize market opportunities. Big data will promote the application of information technology in various industries to produce two important trends:
One is the assetization of data, the information sector will be shifted from a cost center to a profit center. In the era of big data, data penetrate all industries, gradually becoming a strategic corporate asset. The scale and activity of owning data, as well as the ability to collect and utilize data, will determine the core competitiveness of enterprises.
The second is decision-making intelligence, enterprise strategy will shift from business-driven to data-driven. Intelligent decision-making is the direction of future development of enterprises. In the past, many enterprises analyzed their own business development only at the level of simple summary of data and information, and lacked in-depth analysis of customers, business, marketing, competition and other aspects.
In the era of big data, enterprises can predict market demand and conduct intelligent decision-making analysis by mining the information contained in a large amount of internal and external data to develop more effective strategies.
So for industry users, how should they formulate a big data strategy to fully utilize the huge business value of big data?
On the one hand, data centralization should be achieved through cloud platforms to form enterprise data assets. For large group enterprises, the ERP systems of subsidiaries and branches at all levels are generating a large amount of transaction data and business data every day. The data scattered in each business system cannot form a centralized resource pool and interconnection, which will seriously affect the unified management and value mining of big data. Realizing data centralization is the first step in the utilization of big data.
On the other hand, the value of big data should be y analyzed and mined to promote intelligent decision-making. Industry users should pay attention to the in-depth analysis and mining of the value of big data, to promote the enterprise decision-making mechanism from business-driven to data-driven transformation, to improve enterprise competitiveness. According to the prediction, big data mining and application can create more than a trillion dollars of value, data will become the source of profit for enterprises, mastering the data also masters the competitiveness. Enterprises must pay more attention to the collection, organization, extraction and analysis of data.
In the next 3-5 years, the gap between those enterprises that truly understand big data and can utilize it for value mining and those that do not pay enough attention to big data value mining will further widen. Companies that can truly leverage big data and turn its value into productivity will have a strong competitive advantage to become industry leaders.