A multinational retail corporation is establishing an Analytics Center of Excellence (CoE) to standardize practices and drive innovation. The CoE is struggling with adoption because individual business units, such as Marketing and Supply Chain, are accustomed to their own tools and methods. They perceive the CoE as a bureaucratic hurdle rather than an enabler. Which strategy should the CoE lead prioritize to foster trust and demonstrate value to the business units?
A healthcare provider plans to analyze patient journey data to reduce hospital readmission rates. The necessary data is fragmented across several systems: the Electronic Health Record (EHR) system (SQL database), a patient satisfaction survey platform (CSV exports), and a third-party billing system (API access). A significant challenge is that patient identifiers are inconsistent across these sources. Which data sourcing tasks are critical to creating a viable, unified dataset for this analysis? (Select TWO)
An analyst is performing exploratory data analysis (EDA) on a customer dataset to prepare for a segmentation project. They create a box plot for the 'Customer_Age' variable and notice a large number of data points extending far beyond the upper whisker of the plot. What is the most appropriate interpretation and immediate next step?
A non-profit organization wants to increase donations from its existing donor base. The leadership team believes that younger donors are contributing less frequently than older donors. They want to launch a targeted campaign but have a limited budget. Which of the following is the most effective research question to guide an initial data analysis?
An analytics team presented findings from a sales forecasting model to regional sales managers. The presentation included the model's accuracy metrics (RMSE, MAE) and a list of the top 10 feature importances. During the Q&A, a manager stated, "I don't understand what 'feature importance' means, and your forecast for my region seems too low based on my team's current pipeline. I don't trust this model." This reaction is a primary symptom of which challenge in analytics adoption?
An insurance company has used analytics to identify that customers who have both auto and home insurance policies have a 30% lower churn rate. The analytics team recommends a new business goal: 'Increase the number of customers with bundled policies by 15% over the next fiscal year by offering a targeted 10% discount.' Why is this considered an effective, data-driven recommendation?
A large enterprise is in the early stages of developing its analytics capabilities. Different departments have purchased their own BI tools, leading to data silos and inconsistent reporting. The CIO wants to create a cohesive organizational strategy for analytics. Which of the following should be the foundational first step?
When sourcing data for a predictive modeling project, an analyst discovers that a key numerical feature, 'Years_of_Experience', has approximately 20% missing values. The business stakeholder confirms that this data is difficult to collect retroactively. Which approach for handling the missing data is most appropriate to consider first?
True or False: In regression analysis, a high R-squared value always indicates a good, reliable model that will perform well on new data.
A business analyst is defining the scope for an analytics project aimed at optimizing inventory levels for a fast-fashion retailer. The primary business need is to reduce holding costs without causing stockouts of popular items. Which framework would be most effective for framing the business situation and ensuring all key perspectives are considered?