Data governance | Eleven

Data governance

Among the many challenges that the exploitation of artificial intelligence (AI) poses for companies, those related to the governance of data and algorithms are among the most important. Data is the fuel of AI, while the algorithms are the engine. These challenges can be summarised in three main points: data management, the maintenance of models and algorithms over time, and the transparency of algorithms.

Data management

Businesses are faced with three main types of data management problems:

  • Data collection that results in a lack of depth in the data
  • Data engineering with scattered data as the main problem
  • The quality of the data, resulting in little or no usable data

Maintenance of models and algorithms over time

AI models are said to be self-learning and, therefore, are built from past data but also with the integration of new information flows that can modify the predictions made. To avoid degradation of model performance over time, you must be able to correct it regularly.

Addressing the issue of bias, trust and transparency

It is essential to ensure that AI recommendations or decisions are impartial, fully explainable and traceable. This helps to increase take-up by companies’ experts as well as protecting against any bias that could exist in the model.

Business cases

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