Algorithmic Bias, Discrimination and a Call to Action

As the adoption of AI by businesses gathers momentum, addressing the growing issue of Algorithmic Bias is becoming increasingly important. Recognising that it isn’t just the problem of data scientists, but of society as a whole is key.Four years ago, the software...

Enriching traditional insurance models with AI

Solving common insurance issues such as customer churn and managing risk through the application of AI, can provide more accurate and predictive outcomes.Insurance has long been a sector which has appreciated the value of its data. No wonder, since in comparison to...

AI Implementation Drives Customer Value

Read our pragmatic guide for businesses considering AI implementation as a driver of customer value.Considered the fourth industrial revolution, AI is changing the way organisations do business. The opportunity it affords is vast and research shows that using AI to...

Forget Digital Transformation, Data Transformation is Key

As digital transformation moves into maturity, we look to the emerging discipline of data transformation and what it means for businesses.Digital transformation has commanded thousands of column inches over the past few years and millions of hours of time, meeting and...

Why is Explainability important and how can it be achieved?

By law, organisations must take accountability for how they are using and processing data. Exlainability is key to this. Picture this: you are a data scientist and you are reviewing a neural network architecture built by one of your colleagues. The model is highly...

Five important considerations for Data Scientists

Data science as a strategic business tool is growing in prominence.It is not surprising that a recent study by Deloitte found that many businesses are planning on tripling their data science teams over the next 24 months. With the advent of GDPR and increasing...