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Welcome to Tom Khabaza’s website.  Read on for straightforward explanations of data mining and predictive analytics.

 

Data mining is an analytical business process which applies business knowledge to data in order to achieve business goals, creating new business knowledge and often using predictive modelling algorithms.  Predictive analytics is a family of business solutions which embed data mining and predictive modelling into a business process.  Data mining is the “predictive core” of predictive analytics.

 

Tom’s mission is to create an effective data mining and predictive analytics capability in the business community, based on a widespread understanding of the nature and benefits of data mining.

 

To contact Tom, please email info@khabaza.com.

 

 

 

Copyright (c) Tom Khabaza 2010-13.

 

Data Mining & Predictive Analytics

Tom Khabaza
Text Box: News  Tom Khabaza will speak on Predictive Analytics Strategy in a UNICOM webinar on 2nd November 2015. Predictive Analytics assists the achievement of business objectives; a Predictive Analytics Strategy indicates what business objectives will be achieved through Predictive Analytics, how they will be achieved, and how the business benefit will be measured. Many organisations are in need of a strategy for Predictive Analytics, and Tom will address this need in two ways: by listing and organising the elements which make up such a strategy, and by outlining the activities needed to develop such a strategy in a specific business situation. Also including pitfalls in Predictive Analytics Strategy, using examples from commercial and government organisations. This is one of 3 webinars in association with the UNICOM Data Analytics conference, also including big data challenges in financial services and predictive analytics for TV advertising performance. ———————————————- The How and Why of Data: Methodology and Science in Analytics Monday 26th October 2015, 6-8pm Venue: Campus London, 4-5 Bonhill St, Shoreditch, London EC2A 4BX, UK Do we need a methodology or process for data analytics, and why do all analytics projects have so much in common? Tom Khabaza explain the reasons for using a standard process and describe the CRISP-DM data mining and analytics process standard, then go on to explain the common properties of data analytics using the 9 Laws of Data Mining. The lecture will be followed by a discussion period. This is the first of a new series of SocDM events to take place monthly in London; come along and take part in the discussion to help shape the future of data and the analytics profession. Register here: http://www.meetup.com/Society-of-Data-Miners/events/225390561/