In extremely fast changing circumstances, you want control over the algorithms that are in production and you want to be able to make timely adjustments that correct the model for the changing environmental factors. With a standardized software solution with a static structure this is often not possible. Even if the possibility is there, you remain dependent on the software provider and there is a good chance that the turnaround time will not be fast enough.
The solution can be found in a dynamic data science structure, where the best of consultancy and software come together. Within this unambiguous and manageable structure, the algorithms can be quickly and easily adapted to the new reality. Looking at consultancy, the lack of a manageable structure makes this a time-consuming and complex task. Data science and personalization work optimally when both humans and algorithms are put in their power.
Human monitoring is decisive for timely intervention. The algorithm is self-learning and automatically adapts to gradual changes. But in extremely fast changing times, the combination of human creativity, business knowledge and algorithms is an unbeatable combination. ‘Alerts’ can help in recognizing abnormal behavior. If, for example, you see that your personalized recommendations no longer yield the expected conversions, then this can be a sign to intervene. By thinking from the business context and validating this from the data, these deviations can be explained. These insights can then be added to the algorithm by, for example, drawing up new business rules and/or selecting more relevant data. In this way, the algorithm learns to understand the new context. This keeps you relevant for your consumer and avoids misplaced recommendations.