Chapter_12

Chapter 12: Analytics and IS

Introduction

• Overview of the importance of analytics in Management Information Systems (MIS).

• Analytics involves acquiring, analyzing, and publishing data to discover patterns and inform business decisions.

Learning Objectives

• Key questions addressed:

  • Q12-1: What is analytics and its importance in business?

  • Q12-2: Objectives of the analytics process?

  • Q12-3: Key components of an analytics information system (IS)?

  • Q12-4: How do analytics IS support analytics activities?

  • Q12-5: Support of business processes by analytics IS?

  • Q12-6: Definition and usage of Big Data analytics IS?

  • Q12-7: Risk management in analytics IS?

  • Q12-8: How SAP utilizes analytics?

  • Q12-9: Future IS impacting the analytics process by 2031.

Understanding Analytics

Definition and Importance

• Analytics: The process of data handling focused on pattern discovery to guide business decisions.

  • Key activities include: acquiring, analyzing, and reporting data.

Types of Analytics

1. Descriptive:

   • Involves sorting, categorizing, and summarizing data to understand what happened.

2. Diagnostic:

   • Analysis of historical data to find out why events occurred.

3. Predictive:

   • Provides predictions about future outcomes based on data trends.

4. Prescriptive:

   • Offers recommendations and multiple action paths based on analysis results.

Objectives of the Analytics Process

• Focus on maximizing effectiveness and efficiency in analytics processes.

  • Importance of setting clear objectives to use analytics effectively.

Key Components of an Analytics Information System (IS)

Overview

• Five Components:

  • Business Intelligence IS: Tools for data analysis and decision-making.

  • Hardware: Scalable servers often residing in cloud environments.

  • Software: Varies per activity; software is user-friendly for ease of data analysis.

  • Data: Sourced from both internal and external avenues; stored in a data warehouse.

  • Procedures: Technical training required; increasing self-service capabilities for analytics.

  • People: Diverse knowledge, education, and experience differences that enhance collaboration.

Key Attributes of Analytics IS

• Scalability, ease of use, dependency on data warehouses, collaboration skills among users.

Supporting Analytics Activities

1. Acquisition: Data collection, cleansing, organizing, and cataloging.

2. Analyzing: Applying statistical and analytical techniques to the prepared data.

3. Publishing/Reporting: Presenting results through visualization and reporting tools; including operations like sorting, filtering, and grouping data:

   • RFM Analysis: Examines recent purchase behavior, frequency of purchases, and monetary value.

   • OLAP (Online Analytical Processing): Supports complex analytical queries including slicing and dicing data and drill-down options.

4. Data Mining:

   • Regression: Examining the relationship between variables.

   • Market Basket Analysis (MBA): Identifying cross-selling opportunities.

   • Text Mining: Sentiment analysis to gauge customer opinions about products and services.

Big Data Analytics Information Systems

• Big Data Characteristics: Volume, velocity, variety.

• Technologies involved:

  • NoSQL databases, MapReduce, Hadoop, SAP HANA (in-memory databases).

• Supporting processes utilized by Big Data analytics IS.

Risk Management in Analytics IS

Risks Associated with Business Intelligence

1. Data Issues: Inaccuracies and management problems with analytics data.

2. People Problems: User resistance, misunderstanding results, and oversimplification leading to misinterpretation.

3. Analysts' Challenges: Over-reliance on data, lack of critical questioning, and biases affecting analysis.

4. Leadership Failures: Poor scope definition, insufficient funding, and overselling results can lead to analytics failure.

Conclusion

• Understanding analytics and its systems is crucial for businesses to utilize data effectively, maintaining both operational efficiency and strategic advantage.