However, I've observed that some actually lack the necessary mathematical intuition and framework to get useful results. This is the main reason I decided to write this blog post.

A new TDWI Checklist Report examines best practices for data engineering and management to support machine learning with a focus on collecting, cleansing, transforming, and governing new and big data for analysis. In a new TDWI Checklist Report, "Five Data Engineering Requirements for Enabling Machine Learning," Fern Halper, vice president and senior director of TDWI Research for advanced analytics, notes how a new generation of data is reinvigorating interest in AI and machine learning -- and providing new challenges to enterprises of all sizes. Machine learning does what its name implies -- it is a system that learns to identify patterns by examining data. There are two approaches: supervised (where the system is given the desired target and learns to predict the same outcome based on attributes) and unsupervised (where there are no predefined outcomes, and once trained, the model is tested against additional data to make sure the model is valid). Although still in the early mainstream phase of adoption, machine learning is being deployed in a wide range of use cases, including recommendation engines, fraud detection, churn analysis, and cybersecurity.

In this special guest feature, Marc Alacqua, CEO and founding partner of Signafire, discusses a useful approach to data – known as data fusion – which is essentially alchemy-squared, turning not just one but multiple raw materials in to something greater than the sum of their parts. It goes beyond older methods of big data analysis, like data integration, in which large data sets are simply thrown together in one environment. Marc is a decorated combat veteran of the U.S. Army Special Operations Forces. For his service during Operation Iraqi Freedom, he was cited for "exceptionally conspicuous gallantry" and awarded two Bronze Star Medals and the Army Commendation Medal for Valor. A 20-year veteran and Lieutenant Colonel, Marc has extensive command experience in both combat and peace time, having commanded airborne and light infantry as well as special operations units.

Thanks to the rise of AI and the failings of data management, the new face of marketing technology is both more and less human. For years, marketers have been talking about data, collecting it and preparing for the coming revolution. But something new has happened on the road to utopia: the volume and variety of data have exploded. No longer rare and hard to gather, data is everywhere, and organizations are failing to keep up with its variability and quantity. No longer data deprived, these companies have become data blind, over their heads in information they can't properly distill or draw insight from.

The Commonwealth Bank of Australia (CBA) has filed its defence in response to the civil proceedings commenced by the Australian Transaction Reports and Analysis Centre (Austrac), admitting that disparate datasets contributed to a contravention of the Anti-Money Laundering and Counter-Terrorism Financing Act 2006. Under Section 82(1) of the Act, CBA is required to identify, mitigate, and manage the risk a reporting entity may reasonably face that might involve or facilitate money laundering or the financing of terrorism if it has adopted a standard anti-money laundering and counter-terrorism financing program. "CBA admits that at various times between about 20 October 2012 and 12 October 2015, due to an error in the process of merging data from two systems, its account level automated transaction monitoring did not operate as intended in respect of 778,370 accounts," the bank wrote in its claim. "CBA admits that this deficiency in its automated transaction monitoring over that period constituted a contravention of s82(1) of the Act." Part A of the Act also requires the bank to undertake risk assessments of the inherent risk that new products and services -- including new channels and technologies for delivering those products and services -- might involve or facilitate money laundering or terrorism financing, and keep those risk assessments up to date.