Artificial Intelligence based Decision Support.
Descriptive, Predictive, and Prescriptive Machine Learning.
Innovative Data Modeling and Analysis.
- Objective data-driven decision making.
Organizations are facing ever increasing challenges to make timely, accurate decisions due to the extensive scope, volume, and variety of data being collected by modern ‘data-driven’ business operations. As a result, business decision making complexities have increased way beyond the capabilities of human decision makers.
Artificial Intelligence (AI) and Machine Learning (ML) technologies are able accelerate decision-making process. They’re able to efficiently compute and analyze vast quantities of data in reaching accurate decisions. This has been spearheaded as result of accessibility to greater volumes and varieties of data, affordable data storage solutions, and easier access to cloud hosted computational processing power.
ML decision making solutions are able to unlock value from massive sets of complex data in a way that’s simply impossible for humans to do so. As a result, ML they’re guiding organizations to make better decisions and take intelligent courses of action with minimal human intervention.
ML solutions implement sophisticated computer algorithms that have been trained and tested with relevant data to provide intelligence on decision-making trends. Once the ML decision maker has proven itself by reaching a certain threshold of correct suggestions, it can be allowed to make decisions automatically without the need for human intervention. Allowing to reach quicker decisions, which in turn accelerate business processes and workflows across the organization.
There are three fundamental types of machine learning decision making techniques: descriptive, predictive and prescriptive.
Descriptive decision making solutions are constructed around human developed statistical models which attempt to derive ‘what happened and may be why?’. They focus upon past events, classifying and clustering the data by the reporting business dimensions.
Predictive and prescriptive decision making solutions are constructed with AI technologies and techniques. They process data using machine learning heuristics to forecast future business events independent of human intervention.
Predictive ML decision making focuses upon ‘what is likely to happen and when?’. They're commonly implemented with time series, regression analysis, and other similar modeling techniques to aid the business in forecasting future business trends such as customer sentiments and propensity to purchase.
Prescriptive ML decision making proposes possible courses of action, together with supporting rationale to justify its decision. The ‘what to do and why?’. Inherent feedback loops enable prescriptive ML solutions to self-sufficiently evaluate and improve their decision making capabilities. These are the most complex ML decision making solutions to develop and maintain. The accuracy and quality of the decisions need to be continually monitored, with humans having sufficient insight to understand how and why the judgment calls made the technology.
The reliability of ML decision making depends upon the accuracy of the solution’s algorithms, and the quality of the data over which they are trained, tested, and executed. The data is required to be sufficiently complete, high quality, unbiased, relevant, and covariant to support ML solutions. The notion of ‘garbage in, garbage out’ is not an option; the consequences of poor data in a ML decision making solution can be catastrophic.
Data governance practices are essential for formal oversight over the provisioning of the data to service the organization’s ML decision making solutions. This ensures the accuracy and quality of the source data is maintained to the required standards. Any variances from the set thresholds have to be immediately brought to attention and addressed in order to mitigate against exposing the business to operational, financial, and other forms of risk.