Big data analytics tends to focus on what is easily available, which is by and large data about what has already happened, the implicit assumption being that past behavior will predict future behavior. However, organizations already possess data they aren’t exploiting.
Barry Singleton and Richard Goyder demonstrate how, with the right tools, the latent variables hidden within call recordings, email records, social media, and video can be extracted and used to develop models that are far more predictive because they identify personality, the root of motivation. Barry and Richard explain why this is important and how companies can exploit this data toward actionable outcomes.
Richard Goyder is the head of data science at IMC Business Architecture, where he helps companies change the way they think about and use their big data through AI. Richard came to big data as a user, running data-intensive departments, credit portfolio management, and performance management and compensation for Canada’s largest bank. Previously, he worked in consulting with BCG and in financial services in the UK and Canada. Richard holds degrees from the University of Oxford and INSEAD.
Barry Singleton is the vice president of client engagement at innovative AI company IMC Business Architecture, which is pioneering behavioral AI. He helps some of the world’s largest companies design and implement AI that changes the way they understand their big data and is responsible for establishing commercial and academic partnerships in the UK. Most notably, Barry helped companies including large financial institutions, the NHS, the Leeds Institute of Medical Education, the University of Leeds, and a number of British charities better understand the latent data all organizations possess. Previously, he worked with High Street retailers and globally recognized brands to transition from analog to digital technologies.
©2018, O’Reilly UK Ltd • (800) 889-8969 or (707) 827-7019 • Monday-Friday 7:30am-5pm PT • All trademarks and registered trademarks appearing on oreilly.com are the property of their respective owners. • firstname.lastname@example.org