Data Science and Machine Learning
There is a curve of maturity and value in business intelligence and analytics. Organizations moved from: “what happened”, to “why did it happen”, to “can we predict what will happen”, to “can we influence outcomes”. This represents a journey from knowing the facts objectively, to understanding what causes events to happen, to predicting what will happen, to having insight about what to do to control circumstances and events.
Data Science and Machine Learning leverage ‘big data’ and inferential statistics to provide advanced, real time insights that can help organizations move up the data management and analytical value curve. A data science approach provides insights that would otherwise be inaccessible in standard analysis. This provides “first mover advantages”, predictive and behavioral economics opportunities, and insights into risk and loss prevention. These methods account for space and time complexity of outcomes, and the ability to recognize patterns is rapidly changing the way resources are allocated and how services are delivered. Informed decision making, automation, and AI are nudging organization towards greater efficiency and solving some of the most challenging problems by providing deeper intelligence where either large volumes or complex nexuses of data are concerned.
The process of implementing Machine Learning and Artificial Intelligence can help businesses act strategically, by providing information to decision makers to implement strategies for growth and optimization. This involves understanding your data, from basic statistics to cryptic relationships between variables. The process involves developing models using training data sets and, evaluating and improving the predictive nature of the model on unrelated data. A good model can accurately predict outcomes without overfitting, can identify key features and yield insight to high level decision makers who understand the constraints of the system they work in. Your data science and predictive models provide additional meta-data about your data that helps you to better understand what it means and how it will affect outcomes.
Data Scientists and Software Engineers use statistical analytical theory to create models that analyze data based on training and test populations. Once a model is trained on test data, it can be deployed against new data of the same type (domain and structure). The new data will be analyzed and scored/evaluated using the “intelligence” (statistical modeling predictions) of the trained data model.