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Paper 47012020Using Analytics to Predict Tax Recovery and Prioritized Audits and Investigations in Canada With: SAS Enterprise Miner and Enterprise Guide Jason A. Oliver, Senior Compliance Analyst,

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How to fill out using analytics to predict

1. To fill out using analytics to predict, follow these steps:
2. Define the problem: Identify what you want to predict or forecast using analytics. This could be anything from customer behavior to sales trends.
3. Gather data: Collect relevant data that is needed to make predictions. Ensure that the data set is comprehensive and includes all necessary variables.
4. Preprocess the data: Clean and preprocess the data to ensure its quality. This may involve removing duplicates, handling missing values, and normalizing the data.
5. Select an appropriate analytics technique: Choose the predictive analytics technique that is most suitable for your problem. This could be regression analysis, time series forecasting, or machine learning algorithms.
6. Build the predictive model: Apply the selected analytics technique to the preprocessed data and build a model that can make predictions.
7. Validate the model: Assess the accuracy and performance of the predictive model by using validation techniques such as cross-validation or holdout validation.
8. Make predictions: Once the model is validated, use it to make predictions on new data. Analyze the results and interpret the predictions to gain insights and make informed decisions.
9. Refine and improve the model: Continuously refine and improve the predictive model by incorporating new data, adjusting parameters, and exploring different analytics techniques.
10. Monitor and evaluate: Monitor the performance of the predictive model over time and evaluate its effectiveness. Make adjustments as necessary to ensure accurate and meaningful predictions.
11. Communicate and act on the predictions: Share the predictions with relevant stakeholders and take appropriate actions based on the insights gained from the analytics predictions.

Who needs using analytics to predict?

1. Various industries and professionals can benefit from using analytics to predict, including:
2. - Retailers: Analytics can help retailers predict customer demand, optimize inventory management, and personalize marketing strategies.
3. - Financial institutions: Predictive analytics can aid in fraud detection, credit scoring, and risk assessment in the banking and insurance sectors.
4. - Healthcare providers: Analytics can assist in predicting disease outbreaks, patient readmissions, and treatment outcomes to improve healthcare delivery.
5. - Marketing and advertising agencies: Predictive analytics can optimize advertising campaigns, identify target audiences, and predict customer behavior.
6. - Supply chain and logistics companies: Predictive analytics can improve demand forecasting, route optimization, and inventory planning.
7. - Manufacturing companies: Analytics can help in predictive maintenance, quality control, and supply chain optimization.
8. - Sports teams and coaches: Predictive analytics can aid in player performance analysis, game strategy planning, and talent scouting.
9. In summary, anyone who wants to make data-driven predictions, make informed decisions, and gain a competitive advantage can benefit from using analytics to predict.

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