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.

DataOps for everyone at #DataOpticon

If there’s one thing that our CEO Steph Locke is passionate about, it’s data. Getting businesses’ data AI-ready, sharing knowledge around data skills and processes, and generally empowering people through data. Back in September 2019, Steph hosted the first ever DataOpticon in London, with a simple goal: to help people who work with data do it better.

Sealing the gap in education poverty with AI & EdTech

Could education be the industry that has seen the least change over the years? While we’ve seen big changes in the accessibility of education, there is still a long way to go, and as pointed out by The World Bank, being in school is not the same as learning. Often pupils are unengaged, teachers are failing to hold everyone’s attention in class, and drop out rates and grades are proving that the one-size-fits-all approach to learning is outdated.

The AI Hierarchy of Needs meets the Minimum Viable Product

Two of my favourite pyramids are the Data Science Hierarchy of Needs and the Minimum Viable Product. Combining them helps us build effective artificial intelligence (AI) proof of concepts in businesses. It also supports building AI competency at the same time as demonstrating Return on Investment (ROI).

How to score your first AI quick wins: Intelligent Insights

There’s no doubt that going ahead with Artificial Intelligence (AI) can be risky. We’ve seen numerous AI fails from major companies including IBM, Amazon and Microsoft which landed them in hot water, something big companies can often bounce back from, but could be more of a problem for the smaller players. The trick to getting started with AI is to start small, which is where our quick win AI projects come into play.

Mastering AI in manufacturing: the three levels of competency

Manufacturers have been facing continual pressure to improve their technology base, reduce costs, and improve quality since the Industrial Revolution. Manufacturers are used to change but not every manufacturer can or will embrace it at the same rate. Also, no manufacturer jumps straight to being an expert at the new thing they're needing to adopt. The same goes for Artificial Intelligence (AI) as an emerging change in manufacturing.

Industry IoT, smart factories and AI in manufacturing

The world of manufacturing is on the brink of another revolution due to the Internet of Things (IoT) and Artificial Intelligence (AI) applications. Aside from clear use cases like robotics and automation, big data applications are coming into play, thanks to industrial time series data collected by data historians. Thriving on all this data, AI systems can be built to send early warnings, optimise processes, predict maintenance and enforce quality control. By collecting the right data, manufacturers can get really creative with their AI solutions, and it can set them apart from the competition.

A partnership of Machine Learning and AI with healthcare professionals

Healthcare has always been a data-rich area, but with new technologies for processing and structuring, and new ways of collecting data, such as using sensors, like many other industries, the available data is growing exponentially. Artificial Intelligence (AI) makes it possible to analyse all this data in real-time by combing Machine Learning (ML) and Natural Language Processing (NLP), in order to gain valuable insights.

Expert Perspectives: Enhancing business with data and AI

This week we spoke to Dr. Leila Etaati, co-founder, data scientist, consultant and mentor at RADACAD, about what she thought was the key to success with AI for businesses, and how her business was implementing these beliefs. The RADACAD team work with other companies to deliver expert training and consulting around all things data, with a passion for helping businesses improve by listening to their data.

How to score your first AI quick wins: Social Listening

Marketing is a ripe area for Artificial Intelligence (AI) adoption, with all the data and the insights, but how do you make the jump into the AI pool when you look around and all you see is resistance? Quick win projects are an essential tool for building confidence among your team, particularly when introducing new concepts. That is why Nightingale HQ have created a guide of quick-win projects to help departments and companies ease into AI and build momentum for future, more complex projects. In this edition, we will discuss the benefits of practising social listening and how to pull it off as your first AI win.

How to score your first AI quick wins: Knowledge Worker Productivity

When you choose to introduce Artificial Intelligence (AI) in your organisation or department there can be a lot of resistance and uncertainty, which is why it is important to start small and win fast. By taking on smaller fail-proof projects, you can build up confidence among your team as they begin to see the value of the projects and stop fearing failure and resisting changes. In this project we discuss how to boost knowledge worker productivity, something employees will be able to track themselves and see the true value of. Building momentum in this way will pave the way for greater successes down the line.

Archived Content