Blagoeva, Kalina Trenevska; Belsoska, Marina Mijoska
March 2019
Knowledge: International Journal;Mar2019, Vol. 30, p197
Academic Journal
The expansion of the digital economy and the rapid developments of technology induced the creation of new products and industries and drove a significant increase in data resources. The information products industry, including products based on data, information and knowledge, is intensely dynamic in terms of growth and the pace of new product introduction. The complexity in the variety of product offerings and the number of firms offering those products in this industry is increasing exponentially every day. Data-driven innovation forms a key pillar in the 21 century sources of growth. Large data sets are becoming a core asset in the economy, fostering new industries, processes, and products and creating significant competitive advantages (OECD, 2015). The past two decades have brought several reconfigurations of the information and knowledge economy. The recent technological breakthroughs have driven the emergence and the exponential growth of a digital economy with vast data assets. The changes have been accompanied by ongoing attempts to make sense of all the data through the use of analytics. Analytics add substantial value to intangible assets by making them easier to understand and apply. In a world in which information alone has become ubiquitous and somewhat commoditized, analytics provide a means of making information more useful and valuable. In this paper, we focus on new analytical capabilities and data assets that together form value-added information product offerings and new possibilities for emerging markets. These offerings are often called data products. In general, a data product is digital information that can be purchased. Data products incorporate data science into the operation of a product or service, using data in smart ways to provide value. In research, a data product is a large data set in a format that requires little or no processing or programming. Typical data products are predictive, descriptive or prescriptive models, as well as insights. The future of new product development reflects both developing new innovative products and data driven products typical for emerging markets that are large economies. For any organization, there is not only revenue, but competitive advantage to be gained in developing data products and new innovative products. Creating an effective development process for data products requires following well-established steps and data analytics helps adding a few new ones, which are explained in the paper. Further, this paper will focus on three important decisions for innovative process: decision 1- key enablers of emerging market innovation (R&D capability, market opportunity and executive champions), decision 2 - what product to develop (market need, portfolio fit, and product-capability fit) and decision 3 - how to develop the product (the decision matrix and bootstrapping). Organizations can follow several approaches to monetize their data like improving internal business processes and decisions, focusing information around core products and services, and selling information offerings to emerging and existing markets. Few remarks for our country will be made as further paths of development.


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