IN THE PREVIOUS CHAPTER WE BUILT the bridge between our business strategy and our AI strategy. In this chapter we will introduce the second transformational capability necessary for industrial-scale enterprise machine learning (ISEML): the data capability. We will cover how to build essential data capabilities for your artificial intelligence program. We will also differentiate between various data needs. Finally, we will also distinguish between data management and data management for AI.
Well-performing data management programs were critical even before the advent of the AI into mainstream. But solid data management programs have now become a must-have for companies to succeed. However, legacy data management does not necessarily encapsulate the specific requirements for data management for the AI era. It also does not easily relate to how to build a company around AI. We need to go beyond what the traditional or legacy data management entails. Traditional data management organizations would require restructuring to modern data organizations. We introduce one such model.
WHO IS RESPONSIBLE FOR THE DATA CAPABILITY?
The data capability is managed by the data organization. Many companies have established chief data officer (CDO) positions (Noh, 2016; Samuels, 2015). Many data executives are data management specialists. Many others come from different fields. Data science is often confused with data management. They are not the same. While data science deals with ...
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