Corporate Training

Why Invest in Citizen Data Scientist Training?

Most analytic projects, up to 85% according to McKinsey, fail to meet expectations. The reasons for these analytic lapses have been studied, and we believe that citizen data scientist training can address these top issues identified by researchers Andriole and Barsky (2022):

1)      Poor Data Quality – Subject matter experts are closest to the data, but they are not familiar with how machine learning works. When they learn how data is used to make predictions, the quality of that data should improve.

2)      Small Basic Talent Pools – The world needs more data scientists, but institutions of higher learning haven’t been able to meet the need. Citizen data scientists can act as a force multiplier and extend the reach of your technical data scientists.

3)      Over Simplicity – Because of the sheer number of possibilities in machine learning, many times we make time/effort tradeoffs that result in oversimplified models that fail to support decision making. The CitizenAnalytics approach eliminates any concerns about simplification because of the Dual-Experiment Data Mining Process Flow with automatic modeling for both classification and regression.

4)      Weak Support from Key Stakeholders – In the same way that subject matter experts can become familiar with machine learning requirements, so can their managers and directors. Everyone can become a citizen data scientist and improve operations at all levels.

5)      Poor User Interface – Our No Code promise holds true for all modeling, and the PyCaret interface is the easiest for creating and maintaining machine learning models from genesis to expiration, and from data inspection to extensive evaluation for better decision making.