Using AI and Analytics to Extract Meaning and Insight From Data
In this short article, Colin Shearer, OPEX's Principal Advisor for Predictive Solutions, provides a brief overview of how AI can help to make sense of huge volumes of data.
Artificial Intelligence – AI – means computers doing things that, if a human did them, would be judged to be intelligent.
Most applications of AI are based on the masses of data that represent operations in today’s digitally enabled world. This data holds huge value, but its sheer size and complexity makes it impossible for humans to understand. Analytics, ranging from traditional statistics to AI-based machine learning algorithms, extract meaning and insights from data, helping to improve decision making and optimise outcomes.
There are several levels of analytics:
Descriptive Analytics – understanding what has been going on, up to and including this point in time.
Diagnostic Analytics – discovering the underlying causes of what has been going on.
Predictive Analytics – being able to make accurate predictions and assessments about current or future cases.
Prescriptive Analytics – recommending the best actions to take in a particular case, or to deliver optimal results across a more complex situation.
Data scientists are the human drivers of analytics. Following approaches such as the CRISP-DM methodology, they break operational or business goals down into analytical tasks, identify and prepare the relevant data, analyse it, test and validate results, and deliver them to inject intelligence into future decisions.
To find subtle or complex insights, and to take analytics to the predictive and prescriptive levels, data scientists depend heavily on machine learning algorithms and other advanced analysis and optimisation techniques.
Colin has been a pioneer and thought leader in AI and advanced analytics for over 25 years.