01652nas a2200277   4500008004100000022001400041245008100055210006900136260004200205300001000247490000600257520085500263653001301118653001701131653001901148653001601167653002001183653002701203653002201230653001801252653001701270100001501287700002401302700001801326856003001344       2019                        eng d  a1927-032100aUncovering Research Streams in the Data Economy Using Text Mining Algorithms0 aUncovering Research Streams in the Data Economy Using Text Minin  aOttawabTalent First Networkc11/2019  a62-740 v93 aData-driven business models arise in different social and industrial sectors, while new sensors and devices are breaking down the barriers for disruptive ideas and digitally transforming established solutions. This paper aims at providing insights about emerging topics in the data economy that are related to companies&rsquo; innovation potential. The paper uses text mining supported by systematic literature review to automatize the extraction and analysis of beneficial insights for both scientists and practitioners that would not be possible by a manual literature review. By doing so, we were able to analyze 860 scientific publications resulting in an overview of the research field of data economy and innovation. Nine clusters and their key topics are identified, analyzed as well as visualized, as we uncover research streams in the paper.10abig data10aData Economy10aData Ecosystem10aData Market10adigital economy10adigital transformation10aliterature review10aNetwork Graph10aText Mining.1 aAzkan, Can1 aSpiekermann, Markus1 aGoecke, Henry  utimreview.ca/article/1284