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What-is-the-Difference-Between-MIS-and-Data-Analytics

Understanding the difference between MIS (Management Information Systems) and Data Analytics is crucial for businesses and professionals aiming to leverage data for decision-making. While MIS focuses on managing business operations through structured data reporting, Data Analytics involves advanced techniques like data visualization, predictive analytics, and big data processing to extract meaningful insights.<br><br>In this detailed PPT, we explore:<br>u2705 Key differences between MIS and Data Analytics<br>u2705 Roles & career opportunities in MIS vs. Data Analytics<br>u2705 Importance of data-driven decision-making.

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What-is-the-Difference-Between-MIS-and-Data-Analytics

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  1. What is the Difference Between MIS & Data Analytics? Demystifying two crucial fields in the information age. This presentation explores their distinct roles, skills, and applications. Understand which path aligns with your career goals.

  2. Defining Management Information Systems (MIS) Focus Goal Key Activities Efficient management of information resources. To support decision-making and operational efficiency. Systems design, implementation, and maintenance. Implementing an ERP system is an example of streamlining business processes with MIS.

  3. Core Functions of MIS Data Management 1 Collecting, storing, and securing data. Information Processing 2 Processing data and generating reports. Decision Support 3 Supporting managerial decision-making at all levels. Automation 4 Automating routine tasks to improve efficiency.

  4. Required Skills for MIS Professionals Technical Skills Business Acumen Project Management Database management, system administration. Understanding business processes and strategy. Planning and executing IT projects. Communication Translating technical information.

  5. Defining Data Analytics Focus Goal Key Activities Extracting insights from data. Improving business outcomes. Data mining, statistical analysis. Analyzing sales data is an example of identifying top-performing products with data analytics.

  6. Core Functions of Data Analytics Data Collection Collecting and cleaning data. Statistical Analysis Analyzing and modeling data. Visualization Data visualization and reporting. Insights Identifying actionable insights.

  7. Required Skills for Data Analysts Statistical Analysis 1 Regression, hypothesis testing. Programming Skills 2 Python, R, SQL. Data Visualization 3 Tableau, Power BI. Critical Thinking 4 Interpreting data and conclusions.

  8. MIS vs. Data Analytics: Head-to-Head Feature MIS Data Analytics Focus Managing existing data Analyzing data for insights Approach Operational Strategic Role Maintain systems Explore data

  9. Real-World Examples MIS Data Analytics Implementing a CRM system to track customer interactions. Analyzing purchase history to personalize marketing campaigns. Securing a hospital network and protecting patient data. Predicting patient readmission rates to improve healthcare.

  10. Conclusion Key Takeaway MIS Role 1 2 MIS and Data Analytics are essential for modern organizations. Ensures efficient operations and data management. Data Analytics Role Career Choice 3 4 Drives strategic decision-making and innovation. Depends on your skills, interests, and aspirations.

  11. THANK YOU Thank you for joining us today. We hope you found this presentation valuable. We wish you all the best. For more information, or visit our website:- Attitude Academy

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