Maximizing Business Value with Data Governance

Real-Life Applications of AR and Data

• Tourism: AR gives personal tips and helps with navigation.
• Smart Cities: AR and data help plan cities and manage traffic.
• Social Media: It tracks user activity to respond quickly and send the right messages.
• Retail: It uses data to set better prices, predict demand, and improve customer service.

What is Data Governance?

Data Governance is the process of managing data properly to make sure it is used well, safely, and helps make better decisions.

The Four Main Steps of Data Governance

1. Design: Plan the data project. Identify the people involved, what is needed, and what data is available. Set goals, metrics, and decide how to collect data.
2. Development: Build or add tools needed to collect and analyze data.
3. Analysis: Study the data based on the design plan. Look for useful insights and patterns.
4. Assessment: Check the results. See if the goals were met and how to improve next time.

The Design Phase

The Design Phase involves planning your data project. It helps you get ready before you collect or analyze any data.

Key Steps in Design:

1. Identify Stakeholders: Know who cares about the results (e.g., clients, teams).
2. Understand Needs: Learn what those people want from the data.
3. Find the Data: See what data you already have and how useful it is.
4. Set Goals and Metrics: Decide how you will measure success (like KPIs).
5. Plan Data Collection: Choose the best way to gather the right data.
6. Pick Analysis Methods: Decide how you will study the data (past trends, predictions, etc.).
7. Choose Visual Tools: Plan how to show the results (charts, graphs, dashboards).

Technical Data Governance Workflow

1. Data Sources: Collect data from different systems (Excel, CRM, websites, etc.).
2. Integration: Clean and prepare the data for use.
3. Storage: Save data in a secure place like a Data Warehouse or Data Lake.
4. Analysis and Visualization: Use tools to study the data and turn it into charts or reports.
5. User Presentation: Show the results to users on websites or mobile apps in a clear, interactive way.

Data Analytics vs. Data Science

Data Analytics helps understand what happened. It looks at past data to find patterns and support business decisions. It works mostly with structured data (like Excel tables) and uses tools like Excel, Tableau, Power BI, SQL, Google Analytics, Python, and R.

Data Science helps predict what will happen and why. It goes deeper by using AI, machine learning, and statistics to predict the future and solve complex problems. It works with both structured and unstructured data (like text, images) and uses tools like Python, R, SPSS, TensorFlow, scikit-learn, Hadoop, and Spark.