Business Analytics 25259

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Across
  1. 3. The agricultural supply chain stage involving transportation, storage and distribution of crops.
  2. 8. In marketing: predicted net profit expected from a customer over the entire relationship.
  3. 9. The process of cleaning data to detect and correct (or remove) corrupt or inaccurate records.
  4. 10. The step in the analytical decision‐making process where you break down a business problem into key questions.
  5. 12. The five Vs of Big Data: Volume, Velocity, Variety, Veracity and ___ .
  6. 14. Analytics that asks “What will happen?” by forecasting future events.
  7. 17. Decision-support systems in healthcare that offer recommendations based on patient data and guidelines.
  8. 18. Process of segmenting employees or customers into groups based on similar characteristics.
  9. 19. Analytics type used to identify and prevent fraudulent transactions in finance or healthcare.
  10. 21. Using satellite imagery, weather patterns and soil data to forecast agricultural output.
  11. 22. In HR: measuring factors like job satisfaction, motivation and commitment.
  12. 23. Analytics that asks “Why did it happen?” by examining causes of past outcomes.
  13. 26. In agriculture: using sensors, drones, IoT to optimise farming practices
  14. 28. In agriculture: tracking animal health, feeding patterns and productivity.
  15. 29. In healthcare: detecting fraudulent activities in claims processing and billing.
Down
  1. 1. The data-driven approach where decisions are made based on factual data rather than intuition.
  2. 2. Supply chain analytics helps optimise the flow of goods from suppliers to customers by forecasting.
  3. 4. Analytics used to forecast equipment failure and schedule maintenance ahead of time.
  4. 5. In operations: anticipating equipment failures before they happen to reduce downtime.
  5. 6. The concept dividing raw facts into: data → information → knowledge.
  6. 7. Analytics in operations that focuses on reducing waste and defects using approaches like Six Sigma.
  7. 11. In finance: models used to evaluate creditworthiness of individuals or businesses.
  8. 13. Metric predicting the net profit from a customer over their entire relationship.
  9. 15. Analytics that asks “What happened?” by summarising past data.
  10. 16. In agriculture/environment: measuring water usage, carbon emissions, soil degradation.
  11. 20. The process of identifying which marketing channels contributed to a conversion or sale.
  12. 24. In agriculture: predicting demand, price fluctuations and market trends.
  13. 25. The comparison: Business Intelligence vs ___ (which uses advanced algorithms & machine learning).
  14. 27. Tools for interactive charts & dashboards: e.g., Excel, Tableau, Power BI and ___.