Your course of action should be defining goals and objectives, understanding existing processes, identifying stakeholders, data inventory, and data profiling.
What should inform your course of action?
Your course of action should include the following:
1. Define Goals and Objectives: Identify the specific business goals and objectives that the company aims to achieve through data-driven decision-making.
2. Understand Existing Processes: Gain a deep understanding of the current business processes, key performance indicators (KPIs), and areas where data-driven insights can make a significant impact.
3. Identify Stakeholders: Identify key stakeholders who will be involved in the decision-making process and gather their input on the data requirements.
4. Data Inventory: Conduct a thorough data inventory to identify the types of data stored in the database. This includes customer data, transaction data, operational data, and any other relevant information.
5. Data Profiling: Perform data profiling to understand the structure, quality, and completeness of the data. This involves examining patterns, distributions, and potential data issues.
6. Database Schema Analysis: Analyze the database schema to understand the relationships between different tables and how data is organized. This is crucial for forming queries to retrieve meaningful insights.
7. Data Dictionary: Create a data dictionary that documents the metadata and definitions for each data element. This helps in establishing a common understanding of the data across the organization.
8. Data Retrieval: Develop SQL queries or use appropriate tools to retrieve relevant data from the database. This may involve joining tables, filtering data, and aggregating information based on the business requirements.
9. Data Quality Assessment: Assess the quality of the retrieved data. Look for anomalies, inconsistencies, and missing values. Implement data cleansing and normalization processes if necessary.
10. Data Visualization: Use data visualization tools to create meaningful and insightful visual representations of the data. This helps in conveying information effectively to stakeholders.
11. Collaboration and Feedback: Collaborate with stakeholders to ensure that the retrieved data aligns with their expectations and requirements. Gather feedback for continuous improvement.
12. Implement Analytics and Machine Learning: Apply appropriate analytics and machine learning techniques to derive deeper insights from the data. This may involve building predictive models or identifying trends and patterns.
13. Monitor and Iterate: Implement a monitoring system to track the impact of data-driven decisions. Continuously iterate and refine the process based on feedback, changing business requirements, and emerging opportunities.
14. Data Security and Compliance: Ensure that data retrieval and analysis processes comply with data security and privacy regulations. Implement measures to protect sensitive information.
By following these actions, you can establish a robust foundation for data-driven decision-making within the local company, enabling informed and strategic choices based on actionable insights gained from the available data.
Complete question:
Assignment 3: From Data to Decisions: Data Analytics: You are given the task of data driven decision making by a local company, the company stores its data in the data base. What should be your course of action. How will you identify what kind of data exist, and how will you retrieve that data.