Data Strategy

Data Strategy and Governance

Discover essential resources for building and implementing effective data strategies in your organisation. From data governance frameworks to fostering data literacy, we provide comprehensive guidance on creating a data-driven culture.

Our data strategy content covers critical topics including data governance, data security and privacy, DataOps practices, data platform architecture, and building robust data warehouses. Learn how to establish proper data collection and storage practices, ensure data quality, and implement security measures that protect sensitive information.

Whether you’re developing your first data strategy or maturing existing data capabilities, our resources help you navigate challenges around data culture, literacy, and science. We focus on practical approaches that balance innovation with responsible data management, helping organisations unlock the full value of their data assets through business intelligence, analytics, and effective visualisation.

AI Winters and hype

This is not the first time AI has been all the rage in the business world. In particular, AI was big in the eighties with solutions called expert systems. Will AI be a passing fad now?

How IoT technology can be used to improve UK public transport

There is no shortage of possible applications when it comes to Artificial Intelligence (AI) in the public sector, but while the UK government is investing heavily in AI in the private sector, what are they actually doing to implement it themselves? Some fear that governments using AI will result in a dystopian future of constant surveillance, but in reality, public sector applications of AI are far more pragmatic.

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.

FBS Small Business Awards 2020

FBS Small Business Awards 2020 Tell us briefly about you and your business Nightingale HQ is a platform for businesses to adopt AI. As the supply of data in all industries increases exponentially, we help businesses get AI-ready so that they can fully harness and utilise the data available to them to solve business problems. Nightingale HQ can help get your business the training and connections they need to start practising data science.