Understanding Data vs. Information

• Data constitutes raw numbers, facts, or observations, often meaningless without context. Data is typically unprocessed and exists in various forms.

Characteristics of Data

• Unprocessed data is not yet organized or analyzed.
• Discrete data consists of individual units or facts.
• Raw data alone may not convey any meaning or insight.
• Data is often available in large quantities today.
• Information is data processed, organized, or structured to provide meaning or context, answering "what happened?" or "what does this mean?"

Characteristics of Information

• Processed information has been analyzed and given meaning.
• Contextual information is relevant to a specific situation.
• Organized information is presented in a structured format for easier use.
• Actionable information guides decisions, actions, or strategies.
• Data becomes information with processing and context.

Types of Data

• Structured data is organized in a predefined format like tables in databases.
• Unstructured data lacks defined structure and includes text, images, and social media.
• Semi-structured data has some organizational structure but does not conform to a rigid format; XML and JSON files are examples.

Types of Information

• Descriptive information details what happened or is happening and answers “What happened?”.
• Predictive information uses historical data to forecast trends and answers “What might happen next?”.
• Prescriptive information suggests actions based on data analysis and answers “What should we do?”.

Data Transformation

• Data Transformation refers to converting data from its raw form into a more suitable format for analysis, storage, or presentation.
• The transformation involves changing data's structure, format, or values to enhance consistency and usefulness for business intelligence is very important.

Key Characteristics of Data Transformation

• Standardization ensures data from various sources conforms to a unified format or structure.
• Cleaning and Enrichment involves correcting errors, removing duplicates, and enhancing data.
• Preparation for Analysis provides transformed data in a ready to be processed stage which allows for analytical tools, enabling better insights and decision-making.

The 7 Steps in the Data Transformation Process

• Data Collection: starts with collecting raw data from multiple sources.
• Data Cleansing: involves removing errors, inconsistencies, and duplicates.
• Data Mapping: includes matching fields from different datasets to ensure consistency.
• Data Normalization: consists of organizing data into a standard format.
• Data Aggregation: summarizes or consolidates data for a broader view.
• Data Enrichment: consists of adding external data to enhance.
• Data Formatting and Structuring: ensures transformed data aligns with its intended use.

Importance of Data Transformation

• Improves data quality by eliminating errors and redundancies.
• Enables data integration from multiple sources into a unified view.
• Facilitates data analysis for better insights and decision-making.
• Supports compliance with data regulations like GDPR and HIPAA.
• Enhances data usability for analytics, reporting, and machine learning.

Challenges in Data Transformation

• Data complexity arises in the act of handling large datasets from various sources.
• Data quality issues can lead to inaccurate outcomes.
• Time-consuming data transformation can be, particularly when dealing with large datasets.
• Security and compliance risks when handling sensitive data.

Business Processes

• Business Process refers to a collection of activities required to produce a product or service.
• Information Technology enhances business processes by increasing efficiency, enabling new transformative processes; change flow of information.

Transaction Processing Systems

• Transaction Processing Systems perform and record daily transactions necessary to conduct business, such as sales order entry, payroll, and shipping.
• Allows managers to monitor operations and relations and serve predefined goals.

Business Intelligence Systems

• Business Intelligence Systems are software applications that analyze historical data to find patterns and trends to aid decision-making.
• The software is Used in systems that support middle and senior management.

Management Information Systems

• Management Information Systems serve middle management, utilizing data from TPS.
• Provide answers to routine questions with a predefined procedure.

Decision Support Systems

• Decision Support Systems serve middle management and support non-routine decision-making.
• Example: the impact on production schedule if December sales doubled.

Executive Support Systems

• Executive Support Systems support senior management and address non-routine decisions.
• This requires judgment, evaluation, and insight.

Enterprise Applications

• Enterprise Applications are systems for linking the enterprise that span functional areas.
• Also executes business processes across an entire corporation to include all management levels.
• Enterprise systems, supply chain, customer relationship, and knowledge management systems are 4 major categories.

Supply Chain Management Systems

• Supply Chain Management Systems manage relationships with suppliers and share information about orders and production.

Customer Relationship Management Systems

• Customer Relationship Management Systems coordinate business processes that deal with customers to optimize revenue and customer satisfaction.

Knowledge Management Systems

• Knowledge Management Systems support processes for creating, storing, and distributing applying knowledge.

Information Systems Department Roles

• Programmers: Highly trained specialists who write the software instructions for computers.
• Systems analysts: Job is to translate business problems and requirements into information requirements and systems.
• Information systems managers: Leaders of teams of programmers and analysts, project managers, physical facility managers, telecommunications managers, or database specialists.
• Chief information officer (CIO): Oversees I.T. use in the firm.
• Chief security officer (CSO): manages in charge of information systems security for the firm.
• Chief privacy officer (CPO): Ensures company complies with data privacy laws.
• Chief knowledge officer (CKO): Responsible for the firm's knowledge management program.
• Chief data officer (CDO): is responsible for enterprise-wide governance and utilization of information.
• End users: Representatives of departments for whom applications are developed.

Organization Definitions

• Technical Definiton: Formal social structure that processes resources from the environment to produce outputs.
• Behavioral Definition: Collection of rights, privileges, obligations, and responsibilities balanced through conflict and resolution.

Google and Amazon as Organizations

• Technical: Formal social structures processing resources via defined rules and processes.
• Behavioral: Dynamic social systems with rights and power structures shaped by conflict and resolution.

Organizational Culture

• Organizational Culture encompasses a set of assumptions that define goal and product Routines and Business Processes
• Routines are precise rules and procedures developed to cope with expected situations.
• Business processes are collections of routines.

Organizational Environment

• Organizations rely on the social and physical environment and, also, influence the environment.
• Information systems are used for environmental scanning.

Organizational Structure

• The information systems structure reflects the organizational one.
• Mintzberg's five basic kinds of organizational structure: Entrepreneurial, machine bureaucracy, divisionalized bureaucracy, professional bureaucracy, and adhocracy.

Economic Impacts

• IT changes relative costs of capital and information.
• IT affects the cost and quality of information, which changes information economics.
• Information technology helps firms in contract size, which reduces transaction costs (the cost of participating in markets) outsourcing

Organizations and Organizational Behavior

• IT flattens organizations, which is, decision-making is pushed to lower levels.
• Fewer managers are now needed (IT enables/speeds up decision-making while increasing span of control)
• Postindustrial orgs flatten, which means authority now relies on knowledge/competence rather than formal positions.