Straive is a market leading Content, Data and e-learning/Ed-Tech solutions company providing Research & Education content creation services, e-learning course design, Data intelligence & operations, unstructured data solutions and platform-based technology solutions.
Straive provides data solutions and subject matter expertise, and end-to-end technology support to financial services firms. We offer text and public data and ESG Data Solutions, LIBOR transitioning, to caters ESG space through ESG data that aid in benchmarking and financial information.
ESG Data Solutions - Straive’s ESG solutions can help you streamline and optimize any ESG Rating workflow to fit into your investment operations.
Our proprietary end-to-end cloud-centric and modular Data Lifecycle Management Platform - Straive Data Platform (SDP). The platform helps to Extract, Transform and Enrich - collecting, curating, and organizing the data into data elements, keywords, indexes, classifications, and deliver into the desired format.
Blogs : 11/2/2021
Unstructured data solutions - Unstructured data offer financial institutions hitherto undiscovered actionable insights—of customer preferences, unmet customer needs, and market and process gaps. Banks, for instance, can use these insights to amplify customer experiences, enable new products and services, and conceptualize improved operating models.
Blogs : 10/8/2021
In environmental and social governance (ESG) parlance, greenwashing is the deliberate practice of making a company appear more sustainable (or green) than it really is.
Blogs : 10/8/2021
Reliable environmental and social governance (ESG) scores require a lot of data that need to be parsed, analyzed, and translated into benchmark scores.
Blogs : 8/12/2021
Socially conscious or sustainable investing has spread across the world. It has a long-term financial implication in increasing a firm's chances to survive and thrive by anticipating and resolving surprises arising from ESG failures.
Whitepapers : 8/11/2021
Integrating ESG datasets helps blend finance and investment values. However, integrating ESG datasets is challenging with the existing business operations. Data available from firms vary in quality, quantity, and relevance for investment decisions.