Data and analytics have climbed to the top of Wall Street’s wish list. Together, they promise to transform the way investors view the market, cutting through dense information to uncover new sources of returns unseen by top-down stock picking funds.
An enterprise AI platform paves the way for an organization to deliver intelligent services and products, empowering not just AI scientists but all workers and customers to tap into the tool or combination of tools they need.
Understandable AI combines the technical expertise of engineers with the design usability knowledge of UI/UX experts as well as the people-centric design of product developers.
The data governance necessary to comply with the GDPR will likely prove helpful in advancing an information governance program’s ability to move toward automation of its policies and procedures.
As more women take on fintech roles at almost every level, their influence is driving user-centric product design and rapid development timelines.
The transition to becoming a data samurai will require new skills and responsibilities that may be unfamiliar to many data analysts.
The EU will commit 1.5 billion euros of funds to AI over the next 2 years, which will trigger an extra 2.5 billion euros from partnership projects with companies on big data and robotics.
The rise of pure-play cloud platforms has enticed lines of business like sales, marketing, customer service, and HR to move their supporting services to the cloud. Most organizations have been multi-cloud for years, only 21% of organizations used a single cloud to operate as of 2016.
The regulatory-driven origin of CDOs creates certain challenges as the scope of the role evolves and expands.
There’s little doubt that integrating data can improve the investment operational process, but access to vast quantities of data without well-defined data fidelity, governance and proper data engineering can be problematic.