Chapter 6- Data: Business Intelligence – Flashcards

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Value of information
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The ability to understand, digest, analyze, and filter information is key to growth and success for any professional in any industry
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Information granularity
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the extent of detail within the information (fine and detailed or coarse and abstract)
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two primary types of information
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transactional and analytical
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Transactional information
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encompasses all of the information contained within a single business process or unit of work, and its primary purpose is to support daily operational tasks (Organizations need to capture and store transactional information to perform operational tasks and repetitive decisions such as analyzing daily sales reports and production schedules to determine how much inventory to carry)
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Analytical information
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encompasses all organizational information, and its primary purpose is to support the performance of managerial analysis tasks (Analytical information is useful when making important decisions such as whether the organization should build a new manufacturing plant or hire additional sales personnel. Analytical information makes it possible to do many things that previously were difficult to accomplish, such as spot business trends, prevent diseases, and fight crime; identify many unusual trends)
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Real-time information
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immediate, up-to-date information
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Real-time systems
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provide real-time information in response to requests. Many organizations use real-time systems to uncover key corporate transactional information
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The growing demand for real-time information
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stems from organizations' need to make faster and more effective decisions, keep smaller inventories, operate more efficiently, and track performance more carefully
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biggest pitfalls associated with real-time information
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continual change
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Information inconsistency
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occurs when the same data element has different values
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Information integrity issues
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occur when a system produces incorrect, inconsistent, or duplicate data (can cause managers to consider the system reports invalid and will make decisions based on other sources)
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Example of Low-Quality Information
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Completeness. The customer's first name is missing. Another issue with completeness. The street address contains only a number and not a street name. Consistency. There may be a duplication of information since there is a slight difference between the two customers in the spelling of the last name. Similar street addresses and phone numbers make this likely. Accuracy. This may be inaccurate information because the customer's phone and fax numbers are the same. Some customers might have the same number for phone and fax, but the fact that the customer also has this number in the email address field is suspicious. Another issue with accuracy. There is inaccurate information because a phone number is located in the email address field. Another issue with completeness. The information is incomplete because there is not a valid area code for the phone and fax numbers.
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The four primary reasons for low-quality information
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1) Online customers intentionally enter inaccurate information to protect their privacy. 2) Different systems have different information entry standards and formats. 3) Data-entry personnel enter abbreviated information to save time or erroneous information by accident. 4) Third-party and external information contains inconsistencies, inaccuracies, and errors.
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Costs of Using Low-Quality Information
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1) Inability to track customers accurately. 2) Difficulty identifying the organization's most valuable customers. 3) Inability to identify selling opportunities. 4) Lost revenue opportunities from marketing to nonexistent customers. 5) The cost of sending undeliverable mail. 6) Difficulty tracking revenue because of inaccurate invoices. 7) Inability to build strong relationships with customers.
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Data governance
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refers to the overall management of the availability, usability, integrity, and security of company data
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Master data management (MDM)
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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
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database
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maintains information about various types of objects (inventory), events (transactions), people (employees), and places (warehouses) (store information) (core component of any system, regardless of size, is a database and a database management system)
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database management system (DBMS)
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creates, reads, updates, and deletes data in a database while controlling access and security. Managers send requests to the DBMS, and the DBMS performs the actual manipulation of the data in the database
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query-by-example (QBE)
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tool that helps users graphically design the answer to a question against a database
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Two primary tools are available for retrieving information from a DBMS
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query-by-example (QBE) tool and a structured query language (SQL)
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structured query language (SQL)
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that asks users to write lines of code to answer questions against a database
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data element (or data field)
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the smallest or basic unit of information (can include a customer's name, address, email, discount rate, preferred shipping method, product name, quantity ordered, and so on)
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Data models
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logical data structures that detail the relationships among data elements by using graphics or pictures
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Metadata
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provides details about data. F(an image could include its size, resolution, and date created. Metadata about a text document could contain document length, data created, author's name, and summary)
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data dictionary
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compiles all of the metadata about the data elements in the data model
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DBMS use three primary data models for organizing information
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hierarchical, network, and the relational database, the most prevalent
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relational database model
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stores information in the form of logically related two-dimensional tables
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A relational database management system
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allows users to create, read, update, and delete data in a relational database. Although the hierarchical and network models are important, this text focuses only on the relational database model
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primary concepts of the relational database model
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entities, attributes, keys, and relationships
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entity (also referred to as a table)
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stores information about a person, place, thing, transaction, or event (ex. TRACKS, RECORDINGS, MUSICIANS, and CATEGORIES) -columns, attributes, fields-> (supplier, inventory, materials, distribution)
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Attributes (also called columns or fields)
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the data elements associated with an entity (the entity TRACKS are TrackNumber, TrackTitle, TrackLength, and RecordingID. Attributes for the entity MUSICIANS are MusicianID, MusicianName, MusicianPhoto, and MusicianNotes)
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record
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a collection of related data elements (in the MUSICIANS table, these include "3, Lady Gaga, gag.tiff, Do not bring young kids to live shows")
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primary key
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a field (or group of fields) that uniquely identifies a given record in a table. In the table RECORDINGS, the primary key is the field RecordingID that uniquely identifies each record in the table. Primary keys are a critical piece of a relational database because they provide a way of distinguishing each record in a table; for instance, imagine you need to find information on a customer named Steve Smith. Simply searching the customer name would not be an ideal way to find the information because there might be 20 customers with the name Steve Smith
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foreign key
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a primary key of one table that appears as an attribute in another table and acts to provide a logical relationship between the two tables
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Business Advantages of a Relational Database 1) Increased Flexibility
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distinction between logical and physical views is important in understanding flexible database user views
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physical view of information
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deals with the physical storage of information on a storage device
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logical view of information
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focuses on how individual users logically access information to meet their own particular business needs
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Business Advantages of a Relational Database 2) Increased Scalability and Performance
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scalable to handle the massive volumes of information, the large numbers of users expected for the launch of the website, and need to perform quickly under heavy use
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Information redundancy Business Advantages of a Relational Database 3) Reduced Information Redundancy
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the duplication of data, or the storage of the same data in multiple places (can cause storage issues along with data integrity issues, making it difficult to determine which values are the most current or most accurate. Employees become confused and frustrated when faced with incorrect information causing disruptions to business processes and procedures. One primary goal of a database is to eliminate information redundancy by recording each piece of information in only one place in the database)
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Business Advantages of a Relational Database 4) Increased Information Integrity (Quality)
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database design needs to consider integrity constraints
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Information integrity
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measure of the quality of information
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Integrity constraints
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rules that help ensure the quality of information
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two types of integrity constraints
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1) relational 2) business critical
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Relational integrity constraints
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rules that enforce basic and fundamental information-based constraints. For example, a relational integrity constraint would not allow someone to create an order for a nonexistent customer, provide a markup percentage that was negative, or order zero pounds of raw materials from a supplier
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business rule
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defines how a company performs certain aspects of its business and typically results in either a yes/no or true/false answer Stating that merchandise returns are allowed within 10 days of purchase is an example of a business rule
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Business-critical integrity
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constraints enforce business rules vital to an organization's success and often require more insight and knowledge than relational integrity constraints no product returns are accepted after 15 days past delivery (makes sense because of spoilage of produce)
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Business Advantages of a Relational Database 5) Increased Information Security
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Managers must protect information, like any asset, from unauthorized users or misuse Security risks are increasing as more and more databases and DBMS systems are moving to data centers run in the cloud
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content creator
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the person responsible for creating the original website content
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content editor
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the person responsible for updating and maintaining website content
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Static information
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includes fixed data incapable of change in the event of a user action
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Dynamic information
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includes data that change based on user actions. For example, static websites supply only information that will not change until the content editor changes the information. Dynamic information changes when a user requests information. A dynamic website changes information based on user requests such as movie ticket availability, airline prices, or restaurant reservations
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dynamic catalog
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an area of a website that stores information about products in a database (dynamic website information)
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data-driven website
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an interactive website kept constantly updated and relevant to the needs of its customers using a database (especially useful when a firm needs to offer large amounts of information, products, or services. Can help limit the amount of information displayed to customers based on unique search requirements)
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advantages to using the web to access company databases
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1) web browsers are much easier to use than directly accessing the database by using a custom-query tool 2) the web interface requires few or no changes to the database model 3) it costs less to add a web interface in front of a DBMS than to redesign and rebuild the system to support changes. Additional data-driven website advantages include: -Easy to manage content: Website owners can make changes without relying on MIS professionals; users can update a data-driven website with little or no training. -Easy to store large amounts of data: Data-driven websites can keep large volumes of information organized. Website owners can use templates to implement changes for layouts, navigation, or website structure. This improves website reliability, scalability, and performance. -Easy to eliminate human errors: Data-driven websites trap data-entry errors, eliminating inconsistencies while ensuring that all information is entered correctly.
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business intelligence examples
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Airlines: Analyze popular vacation locations with current flight listings. Banking: Understand customer credit card usage and nonpayment rates. Health care: Compare the demographics of patients with critical illnesses. Insurance: Predict claim amounts and medical coverage costs. Law enforcement: Track crime patterns, locations, and criminal behavior. Marketing: Analyze customer demographics. Retail: Predict sales, inventory levels, and distribution. Technology: Predict hardware failures.
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How BI Can Answer Tough Customer Questions 2
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Where has the business been? Historical perspective offers important variables for determining trends and patterns. Where is the business now? Looking at the current business situation allows managers to take effective action to solve issues before they grow out of control. Where is the business going? Setting strategic direction is critical for planning and creating solid business strategies
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data warehouse
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a logical collection of information, gathered from many operational databases, that supports business analysis activities and decision-making tasks primary purpose is to combine information, more specifically, strategic information, throughout an organization into a single repository in such a way that the people who need that information can make decisions and undertake business analysis (collect information from multiple systems in a common location that uses a universal querying tool)
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data warehouse enables business users, typically managers, to be more effective in many ways, including:
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Developing customer profiles. Identifying new-product opportunities. Improving business operations. Identifying financial issues. Analyzing trends. Understanding competitors. Understanding product performance
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Extraction, transformation, and loading (ETL)
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a process that extracts information from internal and external databases, transforms it using a common set of enterprise definitions, and loads it into a data warehouse. The data warehouse then sends portions (or subsets) of the information to data marts
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data mart (1 of 3 core concepts of data warehousing)
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data mart contains a subset of data warehouse information. To distinguish between data warehouses and data marts, think of data warehouses as having a more organizational focus and data marts as having a functional focus
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information cube
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the common term for the representation of multidimensional information
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Dirty data
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erroneous or flawed data (complete removal of dirty data from a source is impractical or virtually impossible) dirty data is a business problem, not an MIS problem
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Information cleansing or scrubbing (2 of 3 core concepts of data warehousing)
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a process that weeds out and fixes or discards inconsistent, incorrect, or incomplete information
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Specialized software tools
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exist that use sophisticated procedures to analyze, standardize, correct, match, and consolidate data warehouse information
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complete but inaccurate information
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2/31/10 is an example of complete but inaccurate information (February 31 does not exist)
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data quality audits
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determine the accuracy and completeness of its data. Most organizations determine a percentage of accuracy and completeness high enough to make good decisions at a reasonable cost, such as 85 percent accurate and 65 percent complete.
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Data mining
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the process of analyzing data to extract information not offered by the raw data alone (can also begin at a summary information level (coarse granularity) and progress through increasing levels of detail (drilling down) or the reverse (drilling up))
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Data-mining tools
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Data-mining tools use a variety of techniques to find patterns and relationships in large volumes of information that predict future behavior and guide decision making. help users uncover business intelligence in their data
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Data Mining Analysis Methods
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-Analyzing customer buying patterns to predict future marketing and promotion campaigns. -Building budgets and other financial information. -Detecting fraud by identifying deceptive spending patterns. -Finding the best customers who spend the most money. -Keeping customers from leaving or migrating to competitors. -Promoting and hiring employees to ensure success for both the company and the individual.
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Structured data
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has a defined length, type, and format and includes numbers, dates, or strings such as Customer Address. (typically stored in a traditional system such as a relational database or spreadsheet and accounts for about 20 percent of the data that surrounds us)
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The sources of structured data include:
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Machine-generated data ; Human-generated data (structured)
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Machine-generated data
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created by a machine without human intervention Machine-generated structured data includes sensor data, point-of-sale data, and web log (blog) data
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Human-generated data
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Human-generated data is data that humans, in interaction with computers, generate Human-generated structured data includes input data, click-stream data, or gaming data
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Unstructured data
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is not defined, does not follow a specified format, and is typically free-form text such as emails, Twitter tweets, and text messages (Unstructured data accounts for about 80 percent of the data that surrounds us)
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The sources of unstructured data include:
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Machine-generated unstructured data ; Human-generated unstructured data
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Machine-generated unstructured data
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satellite images, scientific atmosphere data, and radar data
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Human-generated unstructured data
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text messages, social media data, and emails
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Big data
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a collection of large, complex data sets, including structured and unstructured data, which cannot be analyzed using traditional database methods and tools
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The four common characteristics of big data
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Big data requires sophisticated tools to analyze all the unstructured information from millions of customers, devices, and machine interactions. Big data are analyzed for marketing trends in business as well as in the fields of manufacturing, medicine, and science
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Distributed computing
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processes and manages algorithms across many machines in a computing environment
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Advanced analytics
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focuses on forecasting future trends and producing insights using sophisticated quantitative methods, including statistics, descriptive and predictive data mining, simulation, and optimization (uses data patterns to make forward-looking predictions to explain to the organization where it is headed)
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data scientist
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extracts knowledge from data by performing statistical analysis, data mining, and advanced analytics on big data to identify trends, market changes, and other relevant information
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Infographics
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present the results of data analysis, displaying the patterns, relationships, and trends in a graphical format (exciting and quickly convey a story users can understand without having to analyze numbers, tables, and boring charts)
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Analysis paralysis
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occurs when 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 In the time of big data, analysis paralysis is a growing problem. One solution is to use data visualizations to help people make decisions faster
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Data visualization
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technologies that allow users to see or visualize data to transform information into a business perspective Data visualization is a powerful way to simplify complex data sets by placing data in a format that is easily grasped and understood far quicker than the raw data alone
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Data visualization tools
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move beyond Excel graphs and charts into sophisticated analysis techniques such as controls, instruments, maps, time-series graphs, and more Data visualization tools can help uncover correlations and trends in data that would otherwise go unrecognized
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Business intelligence dashboards
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track corporate metrics such as critical success factors and key performance indicators and include advanced capabilities such as interactive controls, allowing users to manipulate data for analysis. The majority of business intelligence software vendors offer a number of data visualization tools and business intelligence dashboards
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data artist
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a business analytics specialist who uses visual tools to help people understand complex data
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Info
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Big data is one of the most promising technology trends occurring today. Of course, notable companies such as Facebook, Google, and Netflix are gaining the most business insights from big data currently, but many smaller markets are entering the scene, including retail, insurance, and health care.
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Info 2
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Over the next decade, as big data starts to improve your everyday life by providing insights into your social relationships, habits, and careers, you can expect to see the need for data scientists and data artists dramatically increase.
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Time-series information
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timestamped information collected at a particular frequency
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association detection
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reveals the relationship between variables along with the nature and frequency of the relationships
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market basket analysis
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analyzes such items as websites and checkout scanner information to detect customers' buying behavior and predict future behavior by identifying affinities among customers' choices of products and services
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