On the list of the most talked about topics in the IT landscape, Data Science, Big Data Analytics, Artificial Intelligence and Machine Learning are now pervasive phenomena of everyday experience.
Data Science, or the set of techniques for collecting, managing and analyzing information in order to extract value-based insights, is the driving force behind any business organization and a recurring element of personal experience.
The explosion of Big Data Analytics
The advent of digitization and in particular the growing popularity of the Internet Of Things are driving up the amount of information.
According to IDC, the world will generate 163 ZB of data in 2025, and companies are preparing to handle the challenge. Based on last year’s statistics, 67 percent of enterprises are building Data Management capabilities and 43 percent Data Monetization.
In short, organizations are preparing to become data-driven, aiming to manage any operational and decision-making process through information analytics.
The identikit of the Data Scientist
The figure of the Data Scientist, capable of managing Big Data and extracting relevant information from it, is counted among the most sought-after professions of the moment and with a bright future.
Typically graduated in Engineering, Computer Science, Economics, Mathematics or Statistics, the data scientist has built a multidisciplinary background that includes technical skills in IT, knowledge of programming and machine learning, business skills, and modeling abilities. Thus, a hyperspecialized figure with heterogeneous skills.
The Evolution of Data Science
In short, technologies and people are reshaping the future of Data Science, which will enable companies to target new competitive goals.
Typically, the Data-Driven Enterprise follows a path by maturation steps based on the ability to harness the potential of analytical tools.
To summarize, one can count five different stages involving the use of analytics for different purposes:
- performance monitoring;
- extraction of insights (hidden evidence) for understanding phenomena;
- optimization of operational and strategic processes;
- monetization of knowledge;
- metamorphosis of organizational and business models.
The success of Data Science initiatives is linked to the ability to engage the entire corporate structure. The use of analytics must be extended to any resource and process, regardless of function.
Executives, Data Scientists, Business Managers, technicians and operators. Everyone in the company must have access to data and insights, espousing the culture of innovation and analytics.
What to expect from Data Science
The future of Data Science will see an increasingly pervasive use of analytical tools, an increase in Data Monetization and business transformation initiatives, and a massive use of artificial intelligence.
In particular, Machine Learning techniques will be refined with the goal of automating algorithm correction processes. And, achieving an increasing level of accuracy.
Further advances will involve model management technologies to ensure rapid deployment of designs from the laboratory to the production environment. Data Visualization will be a cornerstone of any Data Science initiative to extend the use of analytics to users with no specific background.
Data science projects are progressively shifting focus. Initially, analytics were performed for purely descriptive (monitoring results) and diagnostic (to identify and correct contingent problems) purposes.
Today, the purposes are becoming increasingly predictive, with data and algorithms allow anticipating future situations, and prescriptive. Thus, artificial intelligence suggests and even activates response mechanisms to possible critical issues.