Week 2 Lecture In Everyday Language And Terms Like Informati
Week 2 Lecturein Everyday Language Terms Like Information And Data Ar
In everyday language, terms like information and data are often used interchangeably. Researchers use these terms in specific ways that emphasize how useful each can be. Data are simply facts or recorded measures of certain phenomena (things or events). Information is data formatted (structured) to support decision making or to define the relationship between two facts. Business intelligence is the subset of data and information that actually has some explanatory power enabling effective managerial decisions to be made.
So, there is more data than information, and more information than intelligence. Relevance is a characteristic of data reflecting how pertinent these particular facts are to the situation at hand. Put another way, the facts are logically connected to the situation. Unfortunately, irrelevant data and information often creep into decision making. One particularly useful way to distinguish relevance from irrelevance is to think about how things change.
Data quality is the degree to which data represent the true situation. High-quality data are accurate, valid, and reliable, issues we discuss in detail in later chapters. High-quality data represent reality faithfully. If a consumer were to replace the product UPC from one drill at Home Depot with one from a different drill, not only would the consumer be acting unethically, but it would also mean that the data collected at the checkout counter would be inaccurate. Therefore, to the extent that the cash register is not actually recording the products that consumers take out of the stores, its quality is lowered.
Sometimes, researchers will try to obtain the same data from multiple data sources as one check on its quality. Data quality is a critical issue in business research, and it will be discussed throughout this text. Business is a dynamic field in which out-of-date information can lead to poor decisions. Business information must be timely—that is, provided at the right time. Computerized information systems can record events and dispense relevant information soon after the event.
A great deal of business information becomes available almost at the moment that a transaction occurs. Timeliness means that the data are current enough to still be relevant. Organizations can use knowledge in a similar way. Knowledge is accumulated not just from a single individual, however, but from many sources. Financial managers, human resource managers, sales managers, customer reports, economic forecasts, and custom-ordered research all contribute to an organization’s knowledge base.
All of this data forms the organization’s memory. From a company’s perspective, knowledge is a blend of previous experience, insight, and data that forms organizational memory. It provides a framework that can be thoughtfully applied when assessing a business problem. Business researchers and decision makers use this knowledge to help create solutions to strategic and tactical problems. Thus, knowledge is a key resource and a potential competitive advantage. Increased global competition and technological advances in interactive media have given rise to global information systems.
A global information system is an organized collection of computer hardware, software, data, and personnel designed to capture, store, update, manipulate, analyze, and immediately display information about worldwide business activities. It is a tool for providing past, present, and projected information on internal operations and external activity. Using satellite communications, high-speed microcomputers, electronic data interchanges, fiber optics, data storage devices, and other technological advances in interactive media, global information systems are changing the nature of business. A decision support system (DSS) is a system that helps decision makers confront problems through direct interaction with computerized databases and analytical software programs.
The purpose of a decision support system is to store data and transform them into organized information that is easily accessible to managers. Doing so saves managers countless hours so that decisions that might take days or even weeks otherwise can be made in minutes using a DSS. Modern decision support systems greatly facilitate customer relationship management (CRM). A CRM system is the part of the DSS that addresses exchanges between the firm and its customers. It brings together information about customers including sales data, market trends, marketing pro-motions and the way consumers respond to them, customer preferences, and more.
A CRM system describes customer relationships in sufficient detail so that financial directors, marketing managers, salespeople, customer service representatives, and perhaps the customers themselves can access information directly, match customer needs with satisfying product offerings, remind customers of service requirements, and know what other products a customer has purchased. A database is a collection of raw data arranged logically and organized in a form that can be stored and processed by a computer. A customer mailing list is one type of database. Population characteristics may be recorded by state, county, and city in another database. Production figures and costs can come from internal company records.
Modern computer technology makes both the storage and retrieval of this information easy and convenient. Twenty years ago, retrieving the population data needed to do a retail site analysis may have required days, possibly weeks, in a library. Today, the information is just a few clicks away. Data warehousing is the process allowing important day-to-day operational data to be stored and organized for simplified access. More specifically, a data warehouse is the multitiered computer storehouse of current and historical data.
Data warehouse management requires that the detailed data from operational systems be extracted, transformed, placed into logical partitions (for example, daily data, weekly data, etc.), and stored in a consistent manner. Organizations with data warehouses may integrate databases from both inside and outside the company. Managing a data warehouse effectively requires considerable computing power and expertise.
Paper For Above instruction
Understanding the fundamental distinctions and interrelations between data, information, and business intelligence is essential for effective decision-making in contemporary organizations. Data, the raw facts recorded about phenomena or events, serve as the foundational building blocks of knowledge. However, data alone lack contextual relevance unless they are processed, structured, or formatted into meaningful information that can support managerial decisions. For example, raw sales figures are merely data, but when organized to reveal sales trends over time, they become valuable information that informs strategy.
The transformation from data to information involves organizing data in a way that highlights relationships and relevance. Business intelligence (BI) extends this further by providing insights and explanatory power through analysis, which helps managers understand the implications of data within specific business contexts. Effective BI enables organizations to anticipate changes, detect patterns, and make informed strategic decisions. Given the vast amounts of data generated daily, the challenge lies in filtering relevant data, ensuring data quality, and maintaining timeliness.
Data quality is pivotal because inaccurate, invalid, or unreliable data can lead to flawed decision-making. High-quality data accurately represent the real-world phenomena they intend to capture. For instance, correct inventory data ensure proper stock levels, preventing overstocking or stockouts. Conversely, incorrect data, such as an inaccurate UPC code, can distort analytical outcomes, leading to poor business decisions. Multiple data sources are often used for validation to enhance data quality, but this requires rigorous data management processes.
Timeliness, the attribute that ensures data is current and relevant, becomes especially critical in fast-paced business environments. Real-time or near-real-time data allow managers to respond promptly to market trends, operational issues, or customer needs. Modern computerized information systems facilitate this by enabling automatic recording and immediate access to data as transactions occur. This swift flow of information enhances operational efficiency and strategic agility.
Beyond data and information, organizations accumulate knowledge—comprising data, insights, and experiential learning—forming organizational memory. Knowledge is a crucial resource that supports strategic planning, problem-solving, and competitive advantage. Organizations leverage this accumulated knowledge through global information systems that span geographic boundaries, integrating data from various sources worldwide.
Global information systems consist of hardware, software, data, and personnel working collaboratively to monitor, analyze, and display international business activities. These systems utilize advanced technologies such as satellite communications, fiber optics, and electronic data interchanges to capture and process data instantly, transforming how global organizations operate. A key component of these systems is decision support systems (DSS), which allow managers to interact directly with data and analytical tools to solve complex, often unstructured problems efficiently.
Particularly relevant in contemporary business is customer relationship management (CRM), a module within DSS that centralizes customer data—sales, preferences, responses to promotions—and details interactions across channels. Effective CRM improves customer satisfaction through personalized service, targeted marketing, and timely engagement, providing a competitive edge.
Databases constitute the backbone of organizational data management, storing raw data in logical formats accessible for processing and analysis. Modern technology enables quick retrieval, storage, and integration of data from internal and external sources. Data warehousing consolidates operational data from multiple systems into a central repository, facilitating strategic analysis and reporting. This process involves extraction, transformation, and loading (ETL), ensuring data consistency and quality across diverse datasets.
In summary, understanding the distinctions between data, information, and intelligence—and how they are managed and transformed—is crucial for navigating today’s data-driven business environment. High-quality, timely data supports effective decision-making, and advanced information systems enable organizations to stay competitive and responsive to changing global dynamics.
References
- Turban, E., Sharda, R., & Delen, D. (2018). Decision Support and Business Intelligence Systems. Pearson.
Management Information Systems: Managing the Digital Firm. Pearson. Introduction to Information Technology. Cengage Learning. Data Warehousing Fundamentals. Wiley. International Journal of Electronic Commerce, 6(2), 35-60. International Journal of Information Management, 58, 102326. Business Information Systems (pp. 351-378). Springer.