جستجوی مقالات مرتبط با کلیدواژه « Data Economy » در نشریات گروه « کتابداری و مدیریت اطلاعات »

The main purpose of this research is to review and Redefine about data-driven business according to data-driven economies features in order to help policymakers in the domains of ICT Industry, Entrepreneurship, Iran Digital Economy and Iran Digital Ecosystems.

This paper is a kind of Narrative literature review in which the documentary technique is used.

In this study, a new definition of data-driven Business is presented based on eight principles from previous studies.

The researchers believe that the result of the text can be exploited in the following ways: Supreme Council of Cyberspace, in particular the efforts of content, technology and regulation departments , the Vice President of Science and Technology of the Presidency of the Islamic Republic of Iran, in particular the efforts of policy, innovation and commercialization departments, the project of Iran Big Data Roadmapping with a code of 904550100 at the Communication and Information Technology Research Institute in and Data Governance Program of the Ministry of Science, Research and Technology.

The aim of research is to comprehensively investigate various types of Data-Driven Business Patterns in order to help entrepreneurship and innovation actors, especially digital businesses efforts.This paper is a critical-argumentative study using a narrative review with a documentary approach. In order to collect and extract information, a comprehensive search was carried out on documents in the Iranian, international scientific journals databases and some of the most famous management consulting groups in the world. The statistical population of this study was all documents related to the keyword Data-Driven Business Model in the period 2000 to 2018.After studying documents, in the literature section, 26 patterns of Data-Driven Business Model were indexed. After analyzing similarities, 20 patterns were counted. In the second review of these cases, 13 different patterns were identified, and in the third review period, the necessity of the need for these 13 patterns was identified by 5 complementary tools. Data-driven business model patterns can help innovators and entrepreneurs utilize the patterns in proportion to their customer groups or even think of the worldchr('39')s most successful businesses using these patterns. In this study, for the first time, all types of data-driven business models were collected from different perspectives. If companies want to actually use these patterns, they should also be equipped with a set of complementary tools. Major complementary tools play a role in data ethics, identification of data stakeholders, and business-data alignment.