Social Impact Returns. Filling the Finance Gap with Data Value

Sustainability, regulation and environmental issues such as climate change and resource scarcity are emerging as key trends with decisive impact on company’s Risk management, value creation and growth strategy. This combination represents one of the biggest opportunities to Society as a whole, including organizations, Governments and citizens. Typically, companies possess vast amounts of data, most of it unutilized. Many are now making investments in digital transformation, which generates even more data. The issue is how to generate social impact returns. The use of data and data analytics is centuries old, but with Artificial Intelligence (AI), Machine Learning (ML), jointly with other distributed ledger technologies (Blockchain, Cloud) that are advancing rapidly, there are major opportunities to capture value better, cheaper and faster. Speed is of the essence, and success depends on how fast organizations understand the need for non-financial risks management and respond to data-driven intelligence by reallocating resources to accomplish what needs to be done more efficiently. The reason for impact returns is understanding the benefit as a common value, not exclusive to companies, but it also has to distribute value among individuals, communities, and why not, to contribute to regenerate our planet based on a new economy

Automatic Tools for Software Quality Analysis in a Project-Based-Learning Course

Over the last decade, the “Anytime, anywhere” paradigm has gained pace in Higher Education teaching, leading many universities to innovate in pedagogical strategies based on Internet and Web access technologies. Development of remote access technologies has enabled teachers to achieve higher levels of efficiency while students can access tools and resources no longer constrained by time or location. Additionally, students can submit their assignments, be evaluated and be provided feedback remotely. In this context arises the need for faculty to dispose of automatic tools that ease and support the evaluation process whilst facilitating the provision of student feedback. Project Based Learning (PBL) has emerged as a pedagogical strategy that can contribute to measure software quality and thus evaluate students in a more accurate and comprehensive way by devoting importance to a broad set of components, not just focused on functional aspects. This paper analyzes how the introduction of innovative automatic diagnosis and feedback tools, based on quantitative methods, can contribute towards a continuous process of student software quality enhancement and higher efficiency programming in PBL courses, without compromising functional aspects, as students are provided practical guidelines by instructors on a timely basis

An approximation to systematize innovation

Impact. This is the end result companies are looking to achieve. There is no-one accepted approach towards innovation, and although the first known use of the word “Innovation” dates from 1548 [1] the understanding of this concept has evolved overtime. Specifically, innovation nowadays is seen as a toolkit for companies to drive superior performance. However the lack of systematization and control in this arena may lead businesses to fall short in execution or dissatisfaction with the way they pursue and benefit from innovation. In the search of means suited for those companies that want to become more resourceful or increase the returns on their investments in innovation, this paper contributes with the application of a framework that looks to standardize and systematize a series of activities that are crucial and repeatedly pursued along innovation processes. The proposed approach leverages on the concept of “Ecosystem” (general framework) and is oriented towards setting a structure and common procedure for companies to secure their business goals in terms of growth, value creation and competitive advantage