Augmented analytics augments human intelligence with AI to produce something all the more immediate and powerful, explains Karim Derrick of Kennedys
Artificial intelligence (AI), robotics, big data and machine learning are terms that have quickly become part of the lexicon used to describe contemporary approaches to machine-enabled decision making. While these approaches have begun to have an impact on many business processes, they continue to stop well short of the more extreme claims that have been made of AI.
When Fukoku Mutual Life Insurance in Japan decided to replace 34 claims handlers with IBM’s AI, Watson, many assumed wholesale disruption of the claims process was around the corner. Every image of android-like machines preparing legal documents offered up by software vendors helped perpetuate the idea that robots were about to take over.
Instead, as the hype around AI has given way to a growing sense of realism, approaches such as augmented analytics have risen to prominence in their place. Rather than replacing human intelligence with machine intelligence, augmented analytics augments human intelligence to produce something all the more immediate and powerful.
The definition of augmented analytics is to use machine learning or natural language processing (NLP) to automate the process of data preparation, to simplify complex data into a form that is significantly easier to interpret, allowing for greater confidence in the decision-making process. This confidence comes from the interface between the computing power of complex algorithms and the natural pattern spotting and inference abilities of the human brain.
Traditional business intelligence work involves manually exploring and preparing data, manually testing models, finding patterns in data and sharing those patterns with business leaders. The preparation aspect of the work can take upwards of 80% of a data scientist’s time. Those who do the analysis retain the power to decide what’s important, and anything outside of their purview is left unexamined.
Compared to traditional business intelligence work, augmented analytics improves the overall speed and accuracy, and, because more data can more readily be analysed, it can also potentially reduce data bias. Data discovery and preparation are faster as algorithms are applied to automatically search for patterns, while features, models and code are automatically selected.
Several products currently exist on the market from established data insight providers such as Qlik, DataRobot, and Smarten, among others. Augmented analytics platforms remain relatively immature, especially in the legal space where analytics can provide valuable insights into the vast array of data that is available and so speed up existing workflows.
At Kennedys, we are working on a number of products within Kennedys Intelligence (KI) that fall under the umbrella of augmented analytics, all designed to help our clients use lawyers less.
One important example concerns high-volume insurance claims from road traffic accidents. Where these result in personal injury, supporting evidence is required in the form of a written report from a medical professional. To help augment human decisio