International Institute for Analytics

Inquiry:

We’re in the insurance industry and are interested in identifying other sources of data that could help us inform which companies may be more interested in our offerings than others. We already have the standard financial information that everybody can get. Any ideas on alternative data sources that might be helpful for us?

If you want to hire students from universities with strong analytical skills, you need to know the landscape of available programs and skills. For companies hiring graduates of analytics master’s degrees in business schools, it’s important to be aware of the differences among programs. This blog discusses the skills you should consider when hiring analytics graduates.

Learn strategies to motivate, coach and train the “demand” side of the analytics partnership to ask better questions, make better decisions, and do their jobs more efficiently using data and advanced analytics.

Inquiry:

We have a new analytics team that I’m trying to transition to Agile. Unfortunately, the data scientists in particular have no experience with Agile and are resistant.

This article describes the potential for AI to augment risk estimation for both individual investors and financial market assets. AI processes vast amounts of a variety of data to identify patterns underpinning processes and metrics. Evolving data resources including digital touch points provide AI with attributes that can enhance risk estimation to ultimately augment elements of modern portfolio theory.

Artificial Intelligence (AI) today merely has specific intelligence as opposed to generalized intelligence. This means that an AI process can appear quite intelligent within very specific bounds yet fall apart if the context in which the process was built is changed. In this blog I will discuss why adding an awareness of context into an AI process – and dealing with that context – may prove to be the hardest part of succeeding with AI. In fact, handling context may be the Achille’s heel of AI!

Inquiry:

We’re a service and product provider within the financial sector. We recently unveiled a new online portal and are in the process of moving our clients to it. At the same time, we need to start advancing on the next iteration of the platform that will use advanced analytics capabilities such as AI and machine learning. We’re having a rough transition. Any tips?

Studies show that only 26% of data-related jobs are held by women. I attended the Women in Technology International meeting featuring a panel discussion with Rehgan Avon, Katie Sasso and Kristen Stovell on the “Future of Data Analytics”. This blog covers insights from the event across a wide range of topics including machine learning, educational resources, responsibilities of data scientists, and managing big data

Inquiry:

We’re pushing more and more into AI and ML, and we’d like your perspective on risks, governance practices, and any other best practices that are developing.

Many organizations struggle with implementing agile or don’t see the expected results because they treat agile as just another process. If you are considering the use of agile development for analytics, it is critical to remember that agile is much more than a process – it is a philosophical and cultural approach to delivering customer value.