As drastic changes occur in the organization of professional activities, as a result of the enormous change caused in the life of companies by the Digital Transformation process, it is common to find innovation systems that seek to create synergies between different teams to escape from watertight departments. From the point of view of both the employee experience and the incorporation of new skills and solutions to problems of increasing complexity, multidisciplinary professionals equipped with soft skills are increasingly valuable.
However, internal competition between departments is not only healthy but also compatible with the previous statement. Internal competition is also present when it comes to 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 that concerns us today in light of the recent publication of the sixth annual study by the services 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 insights from the market study, it should be mentioned that data mining, advanced algorithms and predictive analytics are among the highest priority projects for companies that have adopted Artificial Intelligence (AI, ditto) and machine learning (ML, ditto) throughout this 2019. In addition, reporting and dashboarding, data integration and advanced visualization are the top technologies and strategic initiatives applied to Business Intelligence (BI, ditto) today. However, AI-based BI, also called cognitive BI, still ranks relatively low among the priorities.
Specifically, reporting and dashboarding are cited as "business critical" by nearly half of the survey respondents, and as "very important" by another 35%, with only 1 in 5 considering it relatively or not at all important. Data integration is cited as "critical" or "very important" by 3 out of 4 respondents. The list of priorities is closed by disciplines such as the Internet of Things (IoT), the so-called Edge Computing and video analytics, which are cited as relatively or not at all important strategic initiatives by 3 out of 5 participants, and even more in the case of the last option.
But as we said above, market studies such as this one also serve to measure the pressure that various functional departments of companies exert when it comes to adopting new technologies. In the case of data science and machine learning, the areas of marketing and sales are once again crowned with the laurels of winners, since their members are the ones who in the highest percentage claim these technologies as something "critical": 2 out of 5 say so. They are followed by those responsible for research and development, and below them, with around 25% support, we can find managers and executives, followed by the technical area (IT) and finally the financial area. In the latter case, 3 out of 10 participants say 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 the 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 sinks to second to last place, with only a quarter claiming to be using these technologies effectively. And the first place is occupied, and prominently, by the functional areas of R&D, which soar to a not inconsiderable 70% affirmative response. Financial departments are also in the red, with just over 15% saying that they are using them, and only another 15% saying that they are evaluating software for future incorporation.
It can get even more complicated if we look not at the departments within a company... but at the main industrial sectors of business activity. Because in this case we could say that the tables are turned: financial services (and insurance) lead the way in ML and data technology acquisition, with nearly 3 out of 10 companies in the sector claiming it as "critical", and more than 40% further highlighting it 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 fixated in this regard.
However, in terms of actual use, it is the telecommunications companies that have a certain advantage, as they are leaders in the effective use of these technologies in 4 of the 14 characteristic functionalities that the authors of the study have identified for these technologies: recommendation engines, governance and model management, principal component analysis (PCA) and geospatial analysis. On the other hand, retail, although it seems to want to capture leading technologies, occupies the last effective position in 10 of these 14 specific functionalities.
In any case, 2019 is seen as a record year for companies' interest in data science, AI and machine learning features, which they perceive as the most necessary additions to achieve their business strategies and goals. Specifically, most companies expect support for a variety of regression models, hierarchical clustering functions and text statistics functions for descriptive statistics. For 11 of the 14 features identified, interest is the highest recorded to date, after (recall) six editions of this same study.
Photo by Thomas Griesbeck on Unsplash
