Business analytics (BA) is the process of evaluating data in order to gauge business performance and to extract insights that may facilitate strategic planning. It aims to identify the factors that directly impact business performance, such as ie. revenue, user engagement, and technical availability. BA takes data from all business levels, from product and marketing, to operations and finance.
Organizations in the financial services sector face a unique set of challenges as they consider how to wrangle and process the vast amount of data they collect. During our Financial Services Summit, I was lucky enough to speak to Brian Anthony, chief data officer for the Municipal Securities Rulemaking Board (MSRB), to learn how the MSRB is integrating technologies such as artificial intelligence (AI) and machine learning to modernize its data.
For the next interview in our series speaking to technology and IT leaders around the world, we’ve welcomed experienced CIO and lead AI expert at BagsID, Erik Van Breusegem to share his thoughts on the current state of AI.
In part 1 of this blog post, we discussed the need to be mindful of data bias and the resulting consequences when certain parameters are skewed. Surely there are ways to comb through the data to minimise the risks from spiralling out of control. We need to get to the root of the problem. In 2019, the Gradient institute published a white paper outlining the practical challenges for Ethical AI.
Advances in the performance and capability of Artificial Intelligence (AI) algorithms has led to a significant increase in adoption in recent years. In a February 2021 report by IDC, they estimate that world-wide revenues from AI will grow by 16.4% in 2021 to USD $327 billion. Furthermore, AI adoption is becoming increasingly widespread and not just concentrated within a small number of organisations.
In my main position, as a data scientist at SIL International, I work on expanding language possibilities with AI. Practically this includes applying recent advances in Natural Language Processing (NLP) to low resource and multilingual contexts. We work on things like spoken language identification, multilingual dialogue systems, machine translation, and translation quality estimation.
For the newest instalment in our series of interviews asking leading technology specialists about their achievements in their field, we’ve welcomed Matthew Renze. Matthew is a specialist in artificial intelligence who has given over 100 talks on this very subject and is the author of numerous AI courses on Pluralsight.
The word “data” is ubiquitous in narratives of the modern world. And data, the thing itself, is vital to the functioning of that world. This blog discusses quantifications, types, and implications of data. If you’ve ever wondered how much data there is in the world, what types there are and what that means for AI and businesses, then keep reading!
Much of my focus over the last couple of decades has been in analytics, big data, and AI, and Joe DosSantos and I discussed the progression of these fields over time in a recent Data Brilliant podcast episode. My subtitle for that episode might be, “The Promise and Perils of a Hot New Field,” as we addressed several aspects of how these popular concepts have evolved in the first fifth of the 21st century.
