Archived Content

This section contains our archived content, including historical resources, legacy documentation, and past offerings. While some specific details may have evolved, much of this content remains valuable for understanding our company’s journey and the development of our solutions.

You’ll find archived app documentation, technical glossaries, information about previous offerings, and technology stack overviews. These resources provide historical context and may still be useful for understanding foundational concepts and approaches.

Please note that current product information and documentation can be found in our main content sections. This archive is maintained for reference purposes and historical continuity.

How can you attract the best AI talent from a limited pool?

According to research by MMC Ventures - link no longer works, demand for AI talent has doubled in 24 months, faster than the talent pool can keep up. As of 2019 there was one AI professional for every two available jobs, so building a team of AI developers for your organisation requires both focused recruitment and a sound retention strategy.

Let your business strategy drive AI adoption

To reveal the tactics and behaviours of companies that are getting the most out of AI, MIT Sloan Management Review and Boston Consultancy Group undertook a survey of more than 2500 executives alongside 17 expert interviews in their 2019 report, Winning with AI - link no longer works. One of their findings was that while 9 out of 10 respondents saw AI as an opportunity for their company, the perceived risk of AI is on the rise, with 45% of respondents reporting perceived risk from AI (compared to 37% in 2017).

Decoding the hype around AI

As the powers and capabilities of Artificial Intelligence (AI) expand and evolve, the same cannot be said for the general understanding of the topic. This has resulted in AI becoming a blanket term that gets misused and thrown around for all things, including things that it’s not. People also have very unrealistic expectations of what AI can do leading in some cases to fear and paranoia over things like potential world domination, in others, disillusion when the AI doesn’t perform to the high standards they were hoping.

Can AI outperform medical professionals in diagnosis?

Last year the Guardian - link no longer works reported that AI is 'equal to humans in medical diagnoses' when interpreting images, referring to a study published in Lancet Digital Health. The study revealed that AI 'deep learning' systems were able to detect disease 87% of the time and correctly gave the all-clear in 93% of cases (the equivalent success rate in healthcare professionals is 86% and 93%). This means that AI in healthcare is on track to support medical professionals, leading to faster, cheaper diagnoses and drug development. This will allow healthcare professionals to achieve more with their time and help more people.

7 tips for building a data culture that will strengthen your business

Data analytics has taken off but not everyone is on the same page. While some companies are already making waves with data science, others are still struggling with the basics. Curating a healthy data culture is ever more important now to prevent the gap from growing between those who are embracing analytics and those who are lagging behind.

Project management: Are you backing the right AI projects?

As an executive with an influence over whether your company implements AI and which projects it embarks on, there’s a lot of pressure on you to be successful. The future of AI within your company could rest on you on how your chosen projects perform.

Do you really need big data to start using data science?

All businesses generate data. Even the smallest business has access to hundreds, if not thousands, of interesting data points that they could explore. But it is not uncommon for business owners to think their data is small, inferior and not yet worth analysing. This is where they are wrong every time. Starting small is the best thing you can do, so we say, the time to start your first data science projects is now.

How to get AI to work for your business and enhance operations

Enterprise cognitive computing is the application of AI to enhance business operations. It has a wide range of applications including call handling, fraud detection and maintenance scheduling. ECC systems automate repetitive tasks and improve efficiency through fast search and information processing.

Florence Nightingale: Lighting the way in much more than healthcare

Many may think of Florence Nightingale as the incredible woman who reformed healthcare in ways that still impacts the industry today, but at Nightingale HQ we admire her for an additional reason.

What is cloud data development?

Azure Data Factory is a cloud-based data integration service. It does not store data itself, but allows you to create and monitor automated workflows that collect, integrate, and (to some extent) transform large volumes of data from disparate sources, and pass them on to other services that can store, transform, analyse and use the data.

Archived Content