CalledData-driven wisdomThe concept is new energy model new perspective collect insights from large amounts of data. This article intelligent model data-driven intelligence and more familiar with the application-driven comparison.
One of the earliest IT name is "data processing", it includes demand and processing of the data, which focus on dealing with or compute-centric dominated. Data from birth (created) to death (deletion), are within the control of most of the data in the application. Of course, after the data analysis of the data to create applications already exist (such as business intelligence process), but these applications only a fraction of the actual use of IT.
In the ‘remodeling discovery ", author Michael Nielsen discusses the data-driven intelligence, and were compared with artificial intelligence and human intelligence. He will define intelligent data-driven ability to extract meaning from the data for the computer. He will distinguish it with artificial intelligence, artificial intelligence, he said implementation of the human good at tasks designed to mimic or improve human performance (such as chess) and human intelligence (for example, our ability to process visual information). According to Nielsen’s statement, data-driven intelligence by solving different types of problems to supplement human intelligence.
Let’s IT perspective to study its meaning. Application-driven intelligence tend to create, read, update and delete data in order to achieve the initial purpose, such as managing order processing, shipping and workflow processes receivables. In contrast, the conventional data driving intelligent data (manually or machine-generated) for a secondary or additional purposes, such as performing analyzes or electronic discovery using external information collected from the network email large data files to increase sell or cross-sell customers. First create sensory information (such as a meter) or a machine / computer-generated information (e.g., logs), and then the downstream processes (which may be real-time) analysis (as appropriate).
From an IT perspective, the skill set of application development and developer may vary; From an operational perspective, service level agreements (SLA), such as performance and recoverability of data, you may have a different way planning. Resources (servers, network, storage) planning must also be different. It is familiar with intelligence applications based on application-driven, but must know how to deal with more intelligent data-driven applications, such as large data.
The world is nothing new. Data-driven intelligence (for example, using regression analysis, linear programming and simulation modelingMachine learning techniquesThe statistical analysis) has been around a long time. Later, there have been new concepts, including data warehousing, online analytical processing and data mining. The problem is that advanced analytics, business intelligence and big data companies and other terms are regarded as valuable, but they are as isolated IT silos exist. However, to see these isolated (or at most overlapping) work and consider this work from the perspective of intelligent data-driven, you can combine them in order to emphasize the importance of data-centric focus.
Yes, the concept can be mixed applications. Data-driven intelligence can be inserted into an operating system, such as retail credit card to check if there is fraud, or inserted into various points in the supply chain.
Data-driven intelligenceIs an additional point of view, it broadens our understanding, and not a substitute for application-driven intelligence. letIntelligent SoftwareContinues to grow exponentially, and increase our understanding of the value we derive.