Chapter 6: Business Benefits of High-Quality Information PDF

Chapter 6 The Business Benefits of High-Quality Information Successfully collecting, compiling, sorting, and analyzing information can provide tremendous insight into how an organization is performing. Information type: Transactional and Analytical Transactional information - Encompasses all of the...

Chapter 6 The Business Benefits of High-Quality Information Successfully collecting, compiling, sorting, and analyzing information can provide tremendous insight into how an organization is performing. Information type: Transactional and Analytical Transactional information - Encompasses all of the information contained within a single business process or unit of work, and its primary purpose is to support the performing of daily operational tasks. Analytical information - Encompasses all organizational information, and its primary purpose is to support the performing of managerial analysis tasks Transactional information Analytical information Sales receipt Product statistics Airlines ticket Sales projections Packing slip Future growth Trends The four primary traits of value of information 1. Information type 2. Information timeliness 3. Information quality 4. Information governance Information Timeliness Timeliness is an aspect of information that depends on the situation ○ Real-time information - immediate, up-to-date information ○ Real-time system - Provides real-time information response to requests. Information Quality Characteristics of high-quality information ○ Accurate ○ Complete ○ Consistent ○ Unique ○ Timely Understanding the Costs of Using Low-Quality Information The four primary sources of low quality information: 1. Customer intentionally enter inaccurate information to protect their privacy 2. Different entry standards and formats 3. Operators enter abbreviated or erroneous information by accident or to save time. 4. Thir-party and external information contains inconsistencies, inaccuracies, and errors. Potential business effects resulting form low quality information include: Inability to accurately track customers Diffciulty identifying valuable customers Inability to identify selling opportunities Marketing to nonexistent customers Difficulty tracking revenue Inability to build strong customer relationships Data gap analysis - examination of a company’s data to determine if it can meet business expectations while identifying possible data gaps or where missing data might exist. Understanding the Benefits of Good Information High-quality information can significantly improve the chances of making a good decision. Good decisions can directly impact an organisation's bottom line Information Governance Data governance - Refers to the overall management of the availability, usability, integrity, and security of company data Master data management (MDM) - The practice of gathering data and ensuring that it is uniform, accurate, consistent, and complete, including such entities as customers, suppliers, products, sales, employees, and other critical entities that are commonly integrated across organizational systems Data validation- includes the tests and evaluations used to determine compliance with data governance policies to ensure correctness of data. Storing information in a Relational Database Information is stored in databases Database - Maintains information about various types of objects (inventory), events (transactions), people (employees), and places (warehouses). Relationship of Database, DBMS, and User Database management system (DBMS) - Allows users to create, read, update, and delete data in a relational database. Database → DBMS → User Database DBMS Customers Enter new customers Orders Find Customer order Products Enter new products Distributors Business Advantages of a Relational Database Increased flexibility Increased scalability and performance Reduce information redundancy Increased information integrity Increased information security Increased flexibility A well-designed database should: Handle changes quickly and easily Provide users with different views Have one physical view - deals with the physical storage of information on a storage device. Have multiple logical views - Focuses on how individual users logically access information to meet their own particular business needs. Increased Scalability and Performance A database must scale to meet increased demand while maintaining acceptable performance levels. Scalability - Refers to how well a system can adapt to increased demands Performance - Measures how quickly a system performs a certain process or transaction. Reduced information redundancy Databases reduce information redundancy. Information redundancy - The duplication of data or storing the same information in multiple places. Inconsistency is one of the primary problems with redundant information. Increased Information Security Information is an organizational asset and must be protected. Databases offer several security features: ○ Password - provides authentication of the user. ○ Access level - Determines who has access to the different types of information. ○ Access control - Determines types of user access, such as read-only access. Data-driven websites with data Data-driven websites - An interactive website kept constantly updated and relevant to the needs of its customers using a database. ○ Content Creator ○ Content Editor ○ Static information ○ Dynamic information ○ Dynamic catalog Data-driven websites advantages: ○ Easy to manage content ○ Easy to store large amounts of data ○ Easy to eliminate human errors Section 6.2 - Business Intelligence Supporting Decisions with Business Intelligence Data warehouse - A logical collection of information gathered from many different operational databases that support business analysis activities and decision-making tasks. The primary purpose of a data warehouse is to aggregate information throughout an organization into a single repository for decision-making purposes. Data Marts Data aggregation - Data is collected from various sources for the purpose of data processing. Extraction, transformation, and loading (ETL) - A process that extracts information from internal and external databases, transforms the information using a common set of enterprise definitions, and loads the information into a data warehouse. Data mart - Contains a subset of data warehouse information. Internal Databases External Databases Data warehouse Data Mart Marketing Competitor Marketing Marketing data mart information Information Sales Industry information Inventory information Inventory data mart Inventory Mailing Lists Sales information Billing Stock market analysis Billing information Sales data mart Competitor information Industry information Mailing Lists Stock market analysis Information Cleansing or Scrubbing An organization must maintain high-quality data in the data warehouse. ○ Dirty data - Erroneous or flawed data ○ Information cleansing or scrubbing - A process that weeds out and fixes or discords inconsistent, incorrect, or incomplete information Dirty Data Problems Duplicate Data Inaccurate data Misleading data Non-integrated Data Incorrect data Non-formatted data Violates Business Rules Data Business Intelligence Data point - An individual item on a graph or a chart Data broker - A business that collects personal information about consumers and sells that information to other organizations Data lake - A storage repository that holds a vast amount of raw data in its original format until the business needs it The problem: Data Rich, Information Poor Many organizations find themselves in the position of being data-rich and information-poor Even in today’s electronic world, managers struggle with the challenge of turning their business data into business intelligence. The Solution: Business Intelligence Improving the quality of business decisions has a direct impact on casts and revenue BI enables business users to receive data for analysis that is: ○ Reliable ○ Consistent ○ Understandable ○ Easily Manipulated How can BI can Answer tough customer questions? Question Answer Why are sales below target? Because we sold less in the Western region. Why did we sell less in the West? Because sales of product X drop. Why did X sales drop? Because customer complaints increased. What did customer customers complaints Because late deliveries went up 60% increase? The Solution: Business Intelligence Competitive monitoring - A company keeps tabs of its competitor’s activities on the web using software that automatically tracks all competitor website activities such as discounts and new products. Data map - A technique for establishing a match, or balance, between, between the source fata and the target data warehouse Data-driven decision management - An approach to business governance that values decisions that can backed up with verifiable data. The Powerr of Big Data Analytics Big data - A collection of large, complex data sets, including structured and unstructured data, which cannot be analyzed using traditional database methods and tools and includes the following common characteristics: ○ Variety - Different forms of structured and unstructured data. ○ Veracity - The uncertainty of data, including biases, noise, and abnormalities. ○ Volume - The scale of data ○ Velocity - The analysis of streaming data as it travels around in the internet. The two primary computing models - that have shaped teh collection of big data include: ○ Distributed computing - processes and manages algorithms across many machines in a computing environment. ○ Virtualization - The creation of a virtual (rather than actual) version of computing resources, such as an operating system, a server, a storage device or network resources. Business Focus Areas of Big Data Data Mining Data Analysis Data visualization Data Mining Data mining - The process of analyzing data to extract information not offered by the raw data alone. The three elements of data mining include: ○ Data - Foundation for data-directed decision-making ○ Discovery - Process of identifying new patterns, trends, and insights. ○ Deployment - Process of implementing discoveries drives success. Data mining analysis techniques Data profiling - The process of collecting statistics and information about data in an existing source. Data replication - The process of sharing information to ensure consistency between multiple data sources Recommendation engine - A data mining algorithm that analyzes a customer’s purchases and actions on a website and then uses the data to recommend complementary products Data mining techniques Estimation analysis - determines for an unknown continuous variable behavior or estimated future value. Affinity Grouping Analysis - Reveals the relationship between variables along with the nature and frequency of the relationships. Cluster Analysis - A technique used to divide an information set into mutually exclusive groups such that the members of each group are as close together as possible to one another and the different groups are as far apart as possible. Classification Analysis - The process of organizing data into categories or groups for its most effective and efficient use. Data mining modeling techniques for prediction Data mining tools - Use a variety of techniques to find patterns and relationships in a large volumes of information that predict fure behavior and guide decision making. Prediction modeling techniques include: ○ Optimization modeling: finds the way to make a design, system, or decision as effective as possible. ○ Forecasting modeling - are predictions based on time-series information ○ Regression modeling - A statistical process for estimating the relationships among variables. Advanced-Data Analytics Algorithms - Mathematical formulas placed in software that performs an analysis on a data set. Algorithms help uncover: ○ Anomaly detection - The process of identifying rare or unexpected items or events in a data set that do not conform to other items in the data set. ○ Outliers - A data value is numerically distant from most of the other data. ○ Analysis paralysis - The user goes into an emotional state of over-analysis (or over-thinking) a situation so that a decision or action is never taken, in effect paralyzing the outcome. Fast data - The application of big data analysis to smaller data sets in near-real or real-time in order to solve a problem or create business value. Data scientists perform big data analytics using: ○ Behavioral analysis ○ Correlation analysis ○ Exploratory data analysis ○ Pattern recognition analysis ○ Social media analysis ○ Speech analysis ○ Text analysis ○ Web analysis