Internal misalignment on what business data actually drives performance
Lack of trust in dashboards and reports due to data integrity and quality issues
Priorities misaligned between the business and IT
Difficulty in extracting timely data from business systems
Challenges related to the integration of data from siloed applications
Trouble leveraging data from cloud systems
Too much data to manage effectively
Decentralized analytics with redundant reporting for individual departments
Inconsistent application of analytics business rules and calculations across the enterprise
Insufficient resources to stay on top of analytics requests
Too many spreadsheet-based reporting solutions
Undocumented business rules and procedures
Inability to experiment with new ideas due to rigid infrastructure and processes
Inability to forecast future results optimally
Inability to identify patterns, trends, and correlations that lead to actionable insights
Difficulty handling very large data sets for prediction
