As drastic changes take place in the organization of professional activities, resulting from the enormous change that the Digital Transformation process is causing in the life of companies, it is common to find innovation systems that seek to create synergies between different teams in order to escape from siloed departments. From the point of view of both employee experience and the incorporation of new skills and tackling increasingly complex problems, multidisciplinary professionals equipped withsoft skills are becoming increasingly valuable.
However, internal competition between departments is not only healthy but also compatible with the above statement. Internal competition also occurs when addressing the speed with which different areas of companies assimilate, incorporate, or demand new technologies to improve their day-to-day operations. And that is, in part, the issue at hand today in light of the recent publication of the sixth annual study by the service company Dresner on the use of data science and machine learning. This is a market study that analyzes the impact of 37 technologies related to these two disciplines.
Among the key findings of the market study, it should be noted that data mining, advanced algorithms, and predictive analytics are among the highest priority projects for companies that have adopted Artificial Intelligence (AI) and Machine Learning (ML) throughout 2019. In addition, reporting and dashboards, data integration, and advanced visualization are the main technologies and strategic initiatives currently applied to Business Intelligence (BI). However, AI-based BI, also called cognitive BI, still ranks relatively low among priorities.
Specifically, reporting and dashboards are cited as "critical" to the business by almost half of the study participants, and as "very important" by another 35%, with only 1 in 5 considering them relatively or not at all important. Data integration is cited as "critical" or "very important" by three out of four respondents. The list of priorities is rounded off by disciplines such as the Internet of Things (IoT), Edge Computing, and video analytics, which are cited as strategic initiatives of little or no importance by three out of five participants, and even more so in the case of the latter.
But as we mentioned above, market studies such as this one also help us measure the pressure that various functional departments within companies exert when it comes to adopting new technologies. In the case of data science and machine learning, the marketing and sales departments once again come out on top, as their members are the ones who most often claim these technologies are "critical": two out of five say so. They are followed by those responsible for research and development, and below them, with support of around 25%, we find managers and executives, followed by the technical area (IT) and finally the financial area. In the latter case, 3 out of 10 participants maintain that these technologies are of little or no importance to their objectives.
However, the pressure exerted is not always directly related to the actual use of technologies. And in the case of data science and ML, this reality is clearly evident. Thus, if we measure their actual use, marketing and sales sink to second-to-last place, as only a quarter claim to be using these technologies effectively. The top spot is occupied, by a wide margin, by the functional areas of R&D, which shoot up to a not inconsiderable 70% of affirmative responses. Financial departments also bring up the rear on this occasion, as only slightly more than 15% say they use these technologies, and barely another 15% say they are evaluating software in this regard for future incorporation.
The situation becomes even more complex when we consider not just departments within a company, but the main industrial sectors of business activity. Because in this case, we could say that the tables have turned: financial (and insurance) services lead the way in the acquisition of data and ML technology, with almost 3 out of 10 companies in the sector claiming them to be "critical," and more than 40% additional companies highlighting them as "very important." Telecommunications, technology, health sciences, and retail companies also appear in relevant positions in this regard, while education and manufacturing-oriented companies seem to be the least focused in this regard.
However, in terms of actual use, telecommunications companies have a certain advantage, as they are leaders in the effective use of these technologies in four of the 14 functionalities identified by the authors of the study: recommendation engines, governance and model management, principal component analysis (PCA), and geospatial analysis. For its part, the retail sector, despite appearing to want to embrace cutting-edge technologies, ranks last in 10 of these 14 specific functionalities.
In any case, 2019 is considered a record year in terms of companies' interest in data science, artificial intelligence, and machine learning features, which they perceive as the most necessary additions to achieve their business strategies and objectives. Specifically, most companies expect to support a variety of regression models, hierarchical clustering functions, and statistical text functions for descriptive statistics. In 11 of the 14 features identified, interest is at its highest level to date, after (remember) six editions of this same study.
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