18/03/2018
What is "Data Science"
Here is a short list of common data science deliverables:
>Prediction (predict a value based on inputs)
>Classification (e.g., spam or not spam)
>Recommendations (e.g., Amazon and Netflix >recommendations)
>Pattern detection and grouping (e.g., classification without known classes)
>Anomaly detection (e.g., fraud detection)
>Recognition (image, text, audio, video, facial, …)
>Actionable insights (via dashboards, reports, visualizations, …)
>Automated processes and decision-making (e.g., credit card approval)
>Scoring and ranking (e.g., FICO score)
>Segmentation (e.g., demographic-based marketing)
>Optimization (e.g., risk management)
>Forecasts (e.g., sales and revenue)
Each of these is intended to address a specific goal and/or solve a specific problem. The real question is which goal, and whose goal is it?
For example, a data scientist may think that her goal is to create a high performing prediction engine. The business that plans to utilize the prediction engine, on the other hand, may have the goal of increasing revenue, which can be achieved by using this prediction engine.
While this may appear to not be an issue at first glance, in reality the situation described is why the first pillar (business domain expertise) is so critical. Often members of upper management have business-centric educational backgrounds, such as an MBA.
While many executives are exceptionally smart individuals, they may not be well versed on all the tools, techniques, and algorithms available to a data scientist (e.g., statistical analysis, machine learning, artificial intelligence, and so on). Given this, they may not be able to tell a data scientist what they would like as a final deliverable, or suggest the data sources, features (variables), and path to get there.
