Managing Data Resources

Data Resource Management

Definition: Data Resource Management (DRM) is a critical managerial activity that involves the application of information systems technologies to manage an organization’s data resources effectively. This includes the use of database management systems, data warehousing, and other data management tools.

Importance:

Vital Organizational Resource:
- Data is a crucial asset for organizations, akin to other significant business resources such as capital, personnel, and infrastructure.
- Proper management of data ensures its quality, relevance, and availability, which are essential for informed decision-making.
Survival and Success:
- Modern businesses rely heavily on accurate and timely data regarding their internal operations and the external environment.
- Quality data supports strategic planning, operational efficiency, customer relationship management, and competitive advantage.

Components and Activities:

Database Management:
- Involves the use of database management systems (DBMS) to store, retrieve, and manage data efficiently.
- Ensures data integrity, security, and accessibility.
Data Warehousing:
- The practice of collecting and managing data from various sources to provide meaningful business insights.
- Supports data analysis, reporting, and business intelligence activities.
Data Management Tools:
- Includes software and technologies designed to handle data-related tasks such as data integration, data quality management, and data governance.
- Helps in maintaining data consistency, accuracy, and compliance with regulatory requirements.

Goals:

Meet Information Needs:
- Ensure that business stakeholders have access to accurate, relevant, and timely information to make informed decisions.
- Support various business functions such as marketing, finance, operations, and human resources with reliable data.
Optimize Data Utilization:
- Enhance the value derived from data by improving data management practices.
- Facilitate data-driven strategies and innovation within the organization.
Ensure Data Security and Compliance:
- Protect sensitive data from unauthorized access and breaches.
- Adhere to legal and regulatory standards related to data privacy and security

Type of Database

Operational Database

The type of database which creates and updates the database in real-time. It is basically designed for executing and handling the daily data operations in several businesses. For example, An organization uses operational databases for managing per day transactions. For example, a human resource database would include data identifying each employee and his or her time worked, compensation, benefits, performance appraisals, training and development status, and other related human resource data.

Distributed Database

Unlike a centralized database system, in distributed systems, data is distributed among different database systems of an organization. These database systems are connected via communication links. Such links help the end-users to access the data easily.

Homogeneous DDB: Those database systems which execute on the same operating system and use the same application process and carry the same hardware devices.
Heterogeneous DDB: Those database systems which execute on different operating systems under different application procedures, and carries different hardware devices.

Examples of the Distributed database are Apache Cassandra, HBase, Ignite, etc.

Advantages
– Protection of valuable data.
– Storage requirements: Often, a large database system may be distributed into smaller databases based on some logical relationship between the data and the location. For example, a company with several branch operations may distribute its data so that each branch operation location is also the location of its branch database. Because multiple databases in a distributed system can be joined together, each location has control of its local data while all other locations can
access any database in the company if so desired.

External Databases

External databases offer a vast array of information that can be accessed through websites, presenting an endless variety of hyperlinked multimedia documents in hypermedia databases. These resources are incredibly valuable for organizations and individuals seeking diverse types of data. For instance, statistical databanks provide crucial data on economic and demographic activities, which can be vital for market analysis, research, and policy-making. Additionally, bibliographic and full-text databases allow users to view or download abstracts or complete copies of numerous newspapers, magazines, newsletters, research papers, and other published materials.

Hypermedia Databases

The rise of websites on the Internet, along with corporate intranets and extranets, has led to a significant increase in the use of hypermedia databases. These databases store information as interconnected multimedia pages, including text, images, video clips, and audio segments. From a database management viewpoint, these pages form a network of hyperlinked multimedia elements rather than traditional data records, facilitating seamless navigation and richer user experiences.

Data warehouse

A data warehouse is a robust enterprise system specifically designed to facilitate the analysis and reporting of structured and semi-structured data from diverse sources such as point-of-sale transactions, marketing automation systems, and customer relationship management tools. By consolidating this data into a centralized repository, a data warehouse enables organizations to perform ad hoc analyses and generate custom reports efficiently.

A data warehouse serves as a central repository for data that has been cleaned, transformed, and cataloged to be utilized by managers and business professionals for activities such as data mining, online analytical processing (OLAP), business analysis, market research, and decision support.

Key Features and Processes of a Data Warehouse:

Data Marts: Data warehouses can be subdivided into data marts, which are focused subsets of data that cater to specific aspects of a company, such as individual departments or business processes.
Data Acquisition: Data from various operational and external databases are captured, cleaned, and transformed to enhance their usability for analysis. This acquisition process involves:
- Consolidating data from multiple sources.
- Filtering out irrelevant data.
- Correcting inaccuracies.
- Converting data into new elements.
- Aggregating data into new subsets.
Storage: The processed data is stored in the enterprise data warehouse. From here, data can be moved into data marts or an analytical data store, where it is organized in a format more suitable for specific types of analysis.
Metadata: Metadata, which defines the data within the data warehouse, is stored in a metadata repository and cataloged in a metadata directory. This ensures that users can understand the structure, origin, and purpose of the data.
Analytical Tools: A variety of analytical software tools are available to query, report, mine, and analyze the data. These tools enable business end-users to access and interpret data through Internet and intranet web systems effectively.

Data from various operational and external databases are captured, cleaned, and transformed to make them more suitable for analysis. This acquisition process involves several key activities:

Consolidation: Combining data from multiple sources into a unified dataset.
Filtering: Removing unwanted or irrelevant data.
Correction: Fixing any incorrect or inaccurate data.
Conversion: Transforming data into new data elements that are more useful for analysis.
Aggregation: Summarizing data into new subsets to facilitate analysis.

Once processed, the data is stored in the enterprise data warehouse. From there, it can be moved into data marts or an analytical data store, which organize the data in a format that is more conducive to specific types of analysis.

Metadata Management

Metadata, which defines and describes the data in the data warehouse, is stored in a metadata repository and cataloged in a metadata directory. This metadata provides essential context and understanding about the structure, origin, and usage of the data.

Analytical Tools

A variety of analytical software tools are available to query, report, mine, and analyze the data. These tools enable business end users to access and interpret data efficiently through Internet and intranet web systems.

Data mining involves analyzing data in a data warehouse to uncover hidden patterns and trends in historical business activity. This analysis can help managers make strategic decisions to gain competitive advantages in the marketplace. By discovering new correlations, patterns, and trends in large datasets (often several terabytes), data mining provides valuable insights that were previously unknown.

Key Aspects of Data Mining:

Pattern Recognition Algorithms: Data mining software employs advanced pattern recognition algorithms, along with various mathematical and statistical techniques, to sift through extensive data and extract strategic business information.

Applications of Data Mining:

Market-Basket Analysis: Identifies new product bundles by analyzing purchasing patterns.
Root Cause Analysis: Determines the root causes of quality or manufacturing problems.
Customer Retention and Acquisition: Helps prevent customer attrition and acquire new customers.
Cross-Selling: Identifies opportunities to cross-sell products to existing customers.
Customer Profiling: Enhances the accuracy of customer profiles for targeted marketing and personalized services.

The database management approach addresses issues inherent in the file processing approach by consolidating data into a unified database system. Here’s a summary of its key points:

Consolidation of Data: Unlike the file processing approach, which keeps data in separate files, the database management approach integrates data records into centralized databases. This enables multiple applications to access and share data efficiently.
Database Management System (DBMS): A DBMS acts as an intermediary between users and the database. It provides tools for creating, managing, and querying databases, thus simplifying data access and management.
Data Management: The DBMS controls how databases are constructed, queried, and maintained. This helps ensure data integrity, security, and accessibility.
Application Integration: By consolidating data into a common database, organizations can streamline their processes. For instance, a bank can use a single customer database for various applications like check processing, ATM systems, credit cards, and loan accounting, rather than maintaining separate files for each application.

Database Management System
– A database management system (DBMS) is the main software tool of the database management approach because it controls the creation, maintenance, and use of the databases of an organization and its end users.
– Examples of popular DBMS software are IBM’s DB2 Universal Database, Oracle 10g by Oracle Corp., and MySQL, a popular open-source DBMS.

Database development involves defining and organizing the content, relationships, and structure of the data needed to build a database.
– Database application development involves using a DBMS to develop prototypes of queries, forms, reports, and Web pages for a proposed business application.
– Database maintenance involves using transaction processing systems and other tools to add, delete, update, and correct the data in a database.
– The primary use of a database by end users involves employing the database interrogation capabilities of a DBMS to access the data in a database to selectively retrieve and display information and produce reports, forms, and other documents.

Database Interrogation

Database Management Systems (DBMS) Benefits:
- Query Feature: Allows end users to ask for information from a database.
- Report Generator: Provides formatted reports based on user specifications.
- Ease of Use: Users can get immediate responses without complex programming.
Query Language:
- Ad Hoc Requests: Users can make immediate data requests using simple queries.
- Natural Language: Some systems allow queries using common sentence structures.
Report Generation:
- Custom Formats: Users can specify how they want the information presented.

SQL Queries

Structured Query Language (SQL):
- International Standard: Commonly used across many DBMS packages.
- Basic Syntax:
```
SELECT [columns] 
FROM [tables] 
WHERE [conditions];
```
Components:
- SELECT: Specifies which data fields to retrieve.
- FROM: Indicates the tables from which to pull data.
- WHERE: Defines the conditions to filter the data.

Boolean Logic

Logical Operators:
- AND: Combines multiple conditions; all must be true.
- OR: At least one of the conditions must be true.
- NOT: Negates a condition; retrieves records where the condition is false.
Refining Searches:
- Example: To find students in the BIM program with no back papers:

SELECT * 
FROM Students 
WHERE program = 'BIM' AND BackPapers IS NULL;

Graphical and Natural Queries

Graphical Queries: Allow users to interact with a graphical interface to build queries.
Natural Language Queries: Users can type queries in everyday language, which are then converted into SQL or other query languages by the system.

Database Maintenance

Transaction Processing Systems and DBMS Support: Database maintenance is primarily managed by transaction processing systems (TPS) and other end-user applications, which are supported by the Database Management System (DBMS).
Utilities for Maintenance: End users and information specialists can use various utilities provided by the DBMS for tasks like updating and correcting data.
Continuous Updates: Databases need continuous updates to reflect new transactions and events (e.g., sales, production, shipping). Additionally, changes to data such as customer or employee information are necessary to ensure data accuracy.

Application Development

Role of DBMS in Application Development: DBMS packages are crucial for application development, offering tools and programming languages (like 4GL) that aid in creating custom applications.
Development Tools: DBMS tools help in developing various application components like data entry screens, forms, reports, and web pages. This integration streamlines the process of accessing and updating data in the database.
Simplifying Development: Developers can leverage data manipulation language (DML) statements and other DBMS features to handle data-related activities, reducing the need to manually code data-handling procedures.