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The launch of ChatGPT has taken the hype around artificial intelligence (AI) to unprecedented heights. Such hypes often come with confusion, though. Building a data-driven organization is still a complex challenge that requires more than linking up with an AI chatbot. Rather than ask ChatGPT, let us clear the fog on which profiles you need on your journey to become data-driven.
Business Intelligence (BI) analysts help management make smarter decisions by creating insightful dashboards. Data analysts work in many different domains such as marketing, operations, and human resources, and visualize their results in reports or slide decks. These two profiles mostly analyze data from the past. Data scientists, on the other hand, use machine learning algorithms to predict and understand future events. In the most data mature organizations, machine learning engineers fine-tune, operationalize, and monitor these algorithms such that they can be integrated into products.
Data platforms enable analytical teams to do their work. The key role here is the data engineer, who makes the data flow reliably from data sources (e.g., real-time stock prices) to data products (e.g., dashboards or decision engines). Data architects analyze organizations’ data infrastructure and design the corresponding data models, data platforms, and solutions for storing, accessing, and managing data. Cloud architects design the infrastructure of cloud-based solutions and ensure that all services are logically connected. Cloud engineers implement these plans by building infrastructure and platforms, ideally as code. Finally, MLOps engineers provide the platform to manage the machine learning lifecycle and to address the needs of business stakeholders, machine learning engineers, and information managers in AI projects.
Large volumes of data require organization. Information managers set out the lines for data access management, data documentation, and data quality. They help data stewards establish data governance practices that allow employees easy access, but only to the data they need. They also continuously work to clear up ambiguity around business concepts and their translation into data. Finally, they set up controls and resolve issues around data quality.
When building data-driven organizations, it’s important to remain focused on the organizations’ business objectives. Analytics translators integrate data products into daily operations. They convert business problems into analytical problems and analytical solutions back to business solutions. Their more mature counterparts, data strategists, align organizations around a shared vision for data and create ambitious data roadmaps. In many organizations, data strategists also manage projects. Designing and executing a data strategy is often the main responsibility of the chief data officer. This person also connects the data talent throughout the organization.
Most organizations understand that working with data involves risks. Data protection officers (DPO) will make sure that organizations use sensitive data only after getting consent from data subjects, preceded by a transparent explanation of how their data will be used.
To become data-driven, organizations need employees with different backgrounds. The main challenge is to make these diverse data teams communicate in one common language – the language of data.
A special thanks to Diederik Lauwers and Samuel Franssens for collaborating on this article.