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As a Microsoft Fabric Analytics Engineer Associate, you are tasked with designing a comprehensive data analytics solution for a global supply chain management company aiming to optimize their inventory management process. The company operates in multiple regions and has a vast dataset comprising inventory levels, sales data, and supplier information across different formats and systems. Given the complexity and scale of the data, along with the need for real-time analytics to support decision-making, which of the following steps is MOST critical to initiate the planning of the data analytics environment? Choose the best option.
A
Immediately deploy the most advanced data processing and analytics tools available to handle the data volume and velocity, without initial assessment.
B
Start by designing a data visualization strategy to present insights, assuming the data is already clean and processed.
C
Identify and document the key performance indicators (KPIs) relevant to inventory management, such as stockout rates, inventory turnover, and carrying costs, to guide the analytics focus.
D
Skip the data source identification phase and directly move to data processing to save time.
E
All of the above