252 innovation strategy the subset of strategy in

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2.5.2Innovation StrategyThe subset of strategy in general, innovation strategy aims to align parts of organiza-tional behavior and resources with shared competitive goals in order to create some-thing new [Pisano, 2015]. According to Pisano, relevant points in formulating this strat-egy are:Understand needs. How possible innovations create value for the customer andorganization.Resource allocation, i.e. how to apply research strategy.Managing trade-offs in resource allocation.Innovation strategy has to evolve, keep it aligned with the strategy in general,and in line with organizational learning.Of different major management paradigms, innovation is the most recent [Putkiranta,2011; Kuula et al, 2012]. For how long this current pace of technological change can bemaintained remains to be seen, but considering the potential impact of some emergingtechnologies, the relevance of innovation paradigm and innovation strategy in manage-ment is likely to remain.2.5.3Some Ongoing Trends in TechnologyAs discussed at the beginning of this chapter, most current trends in technology havebeen facilitated by earlier advances in information technology. Therefore, it could beexpected that coming advances in IT will lead to bigger leaps in other areas as well. Assuch, it should be noted that IT is relevant regardless of context in general. This ten-dency can be seen in figure 5.
25Figure 5. The Innovation Landscape Map [adapted from Pisano, 2015].Data analyticsData analytics refers to the use of advanced analytics methods in research and busi-ness, in order to gain better understanding of observed phenomenon, i.e. data analyt-ics is about transformation of data to knowledge. The so called ‘big data’ in turn refersto volume, variety and velocity of this data, that because of its properties cannot be an-alyzed with traditional software tools [Laney, 2001].Machine learning plays a big part in this, as it can automate the building of analyticalmodels. These models are then used to monitor processes in question and gathermore data, that can be again used to improve the models.As almost anything numbers and data related can be automated, with time it shouldlead to automation of basic knowledge work. According to McKinsey Global Institutethis could offer the output of 110-140 million workers by 2025 [McKinsey, 2013].Data analytics has several possible applications, such as:Recommendation engines.
26Targeted advertising.Image and speech recognition.Logistics.Automated transportation and robots. As automated movement outside of con-trolled setting depends on responding to changes in the immediate environ-ment, autonomous car and robot depend on the machine learning.Despite advances in this field, true machine cognition, strong AI, still seems to be dec-ades away in the future [Wolfram, 2016].

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