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...

Six factors for modelling with confidence in 2019

Find out why Confidence Scores will become increasingly important in 2019.In the marketing arena the increased use of big data is a dead cert with UK investment in analytics set to double by the start of according to new research by OC&C. The problem with data...

2019: Data will be the new IP

Research from Dresner Advisory Services reveals that 53 per cent of organisations are using predictive analytics to help them enhance marketing communications, reduce risk, detect fraud and optimise operations. Airlines use it to set ticket prices, insurers use it to...

Viewpoint: The Importance of Confidence Scores

How confident are you about your modeled data? If you reply honestly, the answer to the question is likely to be akin to sticking your finger in the air and seeing which way the wind is blowing. The problem with modeled data is its very nature – it’s modeled....