{"id":1524,"date":"2011-08-04T12:35:00","date_gmt":"2011-08-04T12:35:00","guid":{"rendered":"http:\/\/www.b.shuttle.de\/hayek\/Hayek\/Jochen\/wp\/blog-en\/2011\/08\/04\/business-intelligence-microsoft-press\/"},"modified":"2011-08-04T12:35:00","modified_gmt":"2011-08-04T12:35:00","slug":"business-intelligence-microsoft-press","status":"publish","type":"post","link":"https:\/\/wp.jochen.hayek.name\/blog-en\/2011\/08\/04\/business-intelligence-microsoft-press\/","title":{"rendered":"Business Intelligence – Microsoft Press"},"content":{"rendered":"

\t\t\t\tBusiness Intelligence – Microsoft Press<\/a>: “Business Intelligence”<\/p>\n

\n<\/a><\/div>\n

<\/p>\n

\n<\/div>\n
\nbought the paper book through Amazon as a used book, purchased the PDF at the o’Reilly’s web-site.<\/p>\n

Update 2011-08-08 : dead tree book arrived at my favourite\u00a0DHL Packstation 122<\/a> nearby.<\/p>\n<\/div>\n

\n
\n
\n
\n
\n
\nGlossary
\n<\/span>
80\/20 rule <\/span>A theory invented by Vilfredo Pareto in the late 1800s, also
\nknown as the Pareto principle, that describes the percentage imbalance
\nbetween input and output. The Pareto principle is not a law of science;
\nrather, it is a rule of thumb that can apply to many aspects of life. One of the
\nmost common business examples is when 80 percent of a company\u2019s revenue
\ncomes from 20 percent of its customers.
\n<\/span>
actionability <\/span>A criterion used to grade the importance of a BI opportunity
\narea based on its prospects of empowering people to take action in an organization. Actionability ratings are high, medium, and low.
\n<\/span>
ad hoc analysis <\/span>The impromptu and flexible examination of data without
\npredefined or fixed formats. Ad hoc analysis gives users the ability to ask and
\nget answers to an infinite variety of questions quickly.
\n<\/span>
affinity grouping <\/span>A descriptive data mining task that describes which items
\ngo together based on a set of characteristics.
\n<\/span>
alternate hierarchy <\/span>A different grouping of levels in a dimension. A dimension can have several alternate hierarchies to meet various analysis needs.
\n<\/span>
analysis gap <\/span>A gap between the information that decision makers require
\nand the mountains of data that businesses collect every day.
\n<\/span>
ancestor <\/span>Any member of a dimension at any higher level in relation to
\nanother member of the same dimension.
\n<\/span>
base measure <\/span>A measure that is captured at the transaction level in an
\noperational system.
\n<\/span>
benchmark <\/span>A measure used for making comparisons, for example, industry-specific ratios such as a price\/earnings ratio.
\n<\/span>
BI <\/span>See <\/span>business intelligence.
\n<\/span>
BI cycle <\/span>A performance management framework; an ongoing cycle by
\nwhich companies set their goals,\u00a0<\/span><\/p>\n
    \n
  • analyze<\/i> their progress,<\/span><\/li>\n
  • gain insight<\/i>,<\/span><\/li>\n
  • take
    \naction<\/i>,<\/span><\/li>\n
  • measure<\/i> their success,<\/span><\/li>\n
  • and start all over again.<\/span><\/li>\n<\/ul>\n

    BI solution <\/span>A mechanism that brings together people, technology, and data
    \nto deliver valuable information to business users.<\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n

    \n
    \n
    \n
    \n<\/div>\n<\/div>\n<\/div>\n
    \n<\/div>\n
    \n
    \n
    \nblueprint <\/span>A table that documents the measures and dimensions for answering business questions and reflects the most fundamental requirements for
    \nbuilding BI solutions.
    \n<\/span>
    business intelligence (BI) <\/span>An approach to management that allows an
    \norganization to define what information is useful and relevant to its corporate decision making. Business intelligence is a multifaceted concept that
    \nempowers organizations to make better decisions faster, convert data into
    \ninformation, and use a rational approach to management.
    \n<\/span>
    business reporting and analysis process <\/span>A subset of processes responsible
    \nfor taking data from a BI system, such as a data warehouse, assembling it
    \ninto a business-friendly format, and delivering data to business users.
    \n<\/span>
    business-to-business (B2B) <\/span>The exchange of products, services, or information between businesses.
    \n<\/span>
    business-to-consumer (B2C) <\/span>The exchange of products, services, or information between businesses and consumers.
    \n<\/span>
    business unit <\/span>An organizational structure in which a coherent set of functional activities rolls up into one line of business.
    \n<\/span>
    calculated measure <\/span>A measure that is calculated or derived from a combination of base measures.
    \n<\/span>
    child <\/span>A member that is directly subordinate to another member in a
    \nhierarchy.
    \n<\/span>
    classification <\/span>A predictive data mining task that assigns records to specific
    \ncategories according to the rules of a data mining model.
    \n<\/span>
    click-stream analysis <\/span>The analysis of a user\u2019s interaction with a Web site by
    \ninvestigating the data that is generated with each user\u2019s click in a Web
    \nbrowser. The goal of click-stream analysis is to understand the behavior of
    \nWeb site visitors, identify their likes and dislikes, and use this information to
    \nimprove the quality of the Web site.
    \n<\/span>
    closed-loop analysis <\/span>A process that allows end users to act on the outcomes of their analyses to automatically drive business processes.
    \n<\/span>
    clustering <\/span>A descriptive data mining task that divides data into small
    \ngroups based on similarity without predefinition of the data groups.
    \n<\/span>
    cube <\/span>A multidimensional data structure that represents the intersections of
    \neach unique combination of dimensions. At each intersection there is a cell
    \nthat contains a data value.
    \n<\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n
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    \n
    \n
    \nGlossary
    \n<\/span><\/div>\n<\/div>\n<\/div>\n
    \n
    \ncustom aggregation <\/span>A method of summarizing data from its lowest level of
    \ndetail to its highest level of detail in which measures are aggregated differently across different levels of a dimension.
    \n<\/span>
    database <\/span>A collection of related data that is organized in a useful manner
    \nfor easy retrieval. There are different applications of databases depending on
    \nthe type of data to be stored and how the data is to be used.
    \n<\/span>
    data modelers <\/span>Specialists who work with businesspeople and the technical
    \nexperts during the implementation of a BI solution. Data modelers are
    \nresponsible for gathering business requirements and translating these
    \nrequirements into a realistic design of dimensions and measures.
    \n<\/span>
    data mining <\/span>An automated process that uses a variety of analysis tools and
    \nstatistical techniques to reveal actionable patterns and relationships in large,
    \ncomplex data sets.
    \n<\/span>
    data mart <\/span>A collection of data that is structured in a way to facilitate analysis. Data marts support the study of a single subject area, with all relevant
    \ndata from all operational applications brought together into that data mart.
    \nData marts may be of the relational (RDBMS) variety or the OLAP variety
    \ndepending on the type of analysis to be performed.
    \n<\/span>
    data warehouse <\/span>A repository for data. Many experts define the data warehouse as a centralized data store that feeds data into a series of subject-specific data stores\u2014called data marts. Others accept a broader definition of
    \nthe data warehouse as a collection of integrated data marts.
    \n<\/span>
    descendant <\/span>Any member at any lower level in relation to another specific
    \nmember.
    \n<\/span>
    decision tree <\/span>A model for breaking data into groups. A decision tree uses a
    \nstatistical algorithm to split the set of data being mined into branches of\u00a0a tree.
    \n<\/span>
    descriptive data mining <\/span>A form of data mining that produces a model to
    \ndescribe patterns in historical data and requires human interaction to determine the significance and meaning of these patterns.
    \n<\/span>
    desktop online analytical processing (DOLAP) <\/span>An OLAP storage mode
    \nthat keeps data on a client\u2019s machine and provides local multidimensional
    \nanalysis.
    \n<\/span>
    dimension <\/span>A categorically consistent view of data. All members of a dimension belong together as a group.<\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
    \n
    \n
    \n
    \n<\/div>\n<\/div>\n<\/div>\n
    \n<\/div>\n
    \n
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    \ndirty data <\/span>Data that is uncleansed or invalid because it is missing, incorrect, or duplicated.
    \n<\/span>
    DOLAP <\/span>See <\/span>desktop online analytical processing (DOLAP).<\/span>
    EDI <\/span>See <\/span>electronic data interchange (EDI).
    \n<\/span>
    electronic data interchange (EDI) <\/span>A standard for the electronic exchange
    \nof business data.
    \n<\/span>
    enterprise resource planning (ERP) system <\/span>A business management system that integrates all facets of the business, including planning, manufacturing, sales, and marketing. ERP systems are most often implemented using
    \npackaged software applications that support each facet of the business.
    \n<\/span>
    ERP system <\/span>See <\/span>enterprise resource planning (ERP) system.
    <\/span>estimation <\/span>A predictive data mining task used to assign a new record with\u00a0<\/span>a predicted value according to the rules of a data mining model.<\/span>
    ETL <\/span>See <\/span>extract, transform, and load (ETL) processes.
    \n<\/span>
    Extensible Markup Language for Analysis (XML\/A) <\/span>A standard protocol
    \nthat OLAP clients can use to talk to OLAP servers. XML\/A is based on the
    \nwidely adopted XML (Extensible Markup Language) standard and uses the
    \nprogramming language Multidimensional Expressions (MDX).
    \n<\/span>
    extract, transform, and load (ETL) processes <\/span>Processes that are responsible for transporting and integrating data from one or more source systems
    \ninto one or more destination systems.
    \n<\/span>
    front-end tool <\/span>A category of software that harvests the data stored in a data
    \nwarehouse and presents the data to users in the form of reports and interactive reviews.
    \n<\/span>
    functional area <\/span>A department of a business unit that is focused on a specific function.
    \n<\/span>
    hierarchy <\/span>The organization of levels within a dimension that (1) reflects
    \nhow data is aggregated from detailed levels to summarized levels and<\/span>
    (2) serves as the drill-down path for top-down business analysis.
    \n<\/span>
    HOLAP <\/span>See <\/span>hybrid online analytical processing (HOLAP).
    \n<\/span>
    hybrid online analytical processing (HOLAP) <\/span>An OLAP tool that can store
    \ndata in both multidimensional databases and relational databases.
    \n<\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n
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    \ninformation consumer <\/span>A community of business users that requires the
    \nability to dynamically query the database via a \u201cguided\u201d user experience that
    \nallows drill down and pivoting when desired, while eliminating options that
    \nmay create undesirable results.<\/span><\/div>\n<\/div>\n<\/div>\n
    \n
    \ninformation user <\/span>A community of business users that generally requires
    \nstandard reports without needing to analyze the data on an ad hoc basis.
    \n<\/span>
    interoperability <\/span>A product\u2019s ability to work together and interact with other
    \nproducts.
    \n<\/span>
    key performance indicator (KPI) <\/span>A measure that ranks as one of the most
    \nimportant metrics in an organization. KPIs guide businesses in making decisions that affect particular business units as well as the company at large.
    \n<\/span>Key performance indicator <\/span>is used interchangeably with <\/span>metric.
    \n<\/span>
    KPI <\/span>See <\/span>key performance indicator (KPI).
    \n<\/span>
    leaf member <\/span>A bottom-level member in a dimension.
    \n<\/span>
    materiality <\/span>A criterion used to grade the importance of an BI opportunity
    \narea based on how financially significant the opportunity is to the organization. Materiality ratings are high, medium, and low.
    \n<\/span>
    measure <\/span>A numeric value that is of interest to business analysis.
    <\/span>member <\/span>An item in a dimension that represents one or more occurrences\u00a0<\/span>of data.<\/span>
    mental model <\/span>A collection of everything that we think we know about how
    \nsomething works (in this case our business). This labeling of our understanding applies to not only people but also organizations. Some people refer
    \nto the company\u2019s mental model as \u201ctribal wisdom.\u201d
    \n<\/span>
    metadata <\/span>Information about the properties of data, such as business logic
    \nthat describes the structure and content of dimensions and measures.
    \n<\/span>
    metric <\/span>A measure that guides businesses in making decisions that affect
    \nparticular business units as well as the company at large. <\/span>Metric <\/span>is used
    \ninterchangeably with <\/span>key performance indicator.
    \n<\/span>
    MOLAP <\/span>See <\/span>multidimensional online analytical processing (MOLAP).
    \n<\/span>
    multidimensional analysis <\/span>A way of analyzing data in a top-down
    \nfashion by examining measures simultaneously broken out by multiple
    \ndimensions.<\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
    \n
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    \n
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    \nmultidimensional online analytical processing (MOLAP) <\/span>An OLAP storage
    \nmode in which data is placed into special structures that are stored on a central server(s).
    \n<\/span>
    OLAP <\/span>See <\/span>online analytical processing (OLAP).
    \n<\/span>
    OLE DB <\/span>An application programming interface (API) for accessing data.
    \nOLE DB supports accessing data stored in any format (databases, spreadsheets, text files, and so on) for which an OLE DB provider is available.
    \n<\/span>
    OLE DB for OLAP <\/span>Formerly the separate specification that addressed
    \nOLAP extensions to OLE DB. Beginning with OLE DB 2.0, OLAP extensions
    \nare incorporated into the OLE DB specification.
    \n<\/span>
    OLTP <\/span>See <\/span>online transaction processing (OLTP).
    \n<\/span>
    online analytical processing (OLAP) <\/span>Multidimensional analysis that is supported by interface tools and database structures that allow instantaneous
    \naccess and easy user manipulation. Online analytical processing got its name
    \nbecause this name contrasts well with OLTP, a term that was already in widespread use when the term OLAP was created. There are fundamental differences between transaction processing and analytical processing. OLAP systems
    \nsupport multidimensional analysis at the speed of thought. OLAP typically
    \nfollows the client\/server paradigm, where an OLAP database <\/span>server <\/span>is accessed
    \nby many users who use multidimensional <\/span>client <\/span>tools to analyze data.
    \n<\/span>
    online transaction processing (OLTP) <\/span>A data processing system designed
    \nto record all the business transactions of an organization as they occur.
    \nOLTP systems are structured for the purposes of running the day-to-day raw
    \ndata of business, which requires efficiency and minute processing of transactions at the lowest level of detail. An OLTP system processes a transaction,
    \nperforms all the elements of the transaction in real time, and processes
    \nmany transactions on a continuous basis. OLTP systems usually offer little or
    \nno analytical capabilities.
    \n<\/span>
    open database connectivity (ODBC) <\/span>A data access application programming interface (API) that supports access to any data source for
    \nwhich an ODBC driver is available. ODBC is aligned with the American
    \nNational Standards Institute (ANSI) and International Organization for
    \nStandardization (ISO) standards for a database call level interface (CLI).
    \n<\/span>
    operational database <\/span>A database that supports the day-to-day operations of
    \nan organization. Operational databases host the systems that organizations
    \nuse to run their business day to day. Most operational databases are OLTP
    \nsystems and store the data in a relational database management system.
    \n<\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n
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    \nopportunity area <\/span>In BI technical terms, the logical grouping of measure
    \nrequirements, where data can be obtained consistently across all the dimensions at the same lowest level of detail. In business terms, similar to a project where a consistent set of requirements for a group of users can be
    \naccommodated more or less from the same end-to-end system structures or
    \nsolution.<\/span><\/div>\n<\/div>\n<\/div>\n
    \n
    \nparent <\/span>A member that is directly above another member in a hierarchy.<\/span>
    pilot project <\/span>A short-term BI project that tests the feasibility of pursuing a\u00a0<\/span>specific opportunity area.<\/span>
    pivot and nest <\/span>Point-and-click manipulations that facilitate multidimensional analysis. Pivoting means rotating rows to columns, and columns to
    \nrows, in a cross-tabular data browser. Nesting is layering multiple dimensions on the rows or columns of a browser.
    \n<\/span>
    power analyst <\/span>A community of business users that requires the full analytical power of the data mart. These users are willing to learn the details of
    \ndatabase design and the query tool in order to obtain the necessary results.
    \n<\/span>
    predictive data mining <\/span>Data mining that produces a model for use with
    \nnew data to forecast a value or predict a probable outcome based on patterns
    \ndiscovered in historical data.
    \n<\/span>
    proof-of-concept project <\/span>A BI project that evaluates and selects technologies that can be used to host a data mart.
    \n<\/span>
    ragged hierarchy <\/span>A hierarchy that has an inconsistent number of drill-down
    \nlevels.
    \n<\/span>
    ratio <\/span>A measure where the result is calculated specifically from dividing
    \none measure by another.
    \n<\/span>
    RDBMS <\/span>See <\/span>relational database management system (RDBMS).
    \n<\/span>
    refresh rate <\/span>The frequency by which data is updated. Typically the refresh
    \nrate corresponds to the lowest level of detail of a time dimension required
    \nfor a group of measures.
    \n<\/span>
    relational database management system (RDBMS) <\/span>A set of programs that
    \nallows users to create, update, and administer data that is stored in a database of related tables.
    \n<\/span>
    relational online analytical processing (ROLAP) <\/span>An OLAP storage mode
    \nwhere data is stored in relational databases.<\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n
    \n
    \n
    \n
    \n<\/div>\n<\/div>\n<\/div>\n
    \n
    \n
    \nROLAP <\/span>See <\/span>relational online analytical processing (ROLAP).
    <\/span>roll-up <\/span>The hierarchical aggregations of data typical in multidimensional\u00a0<\/span>structures.<\/span>
    segmentation <\/span>A data mining technique that analyzes data to discover
    \nmutually exclusive collections of records that share similar attribute sets. A
    \nsegmentation algorithm can use unsupervised learning techniques such as
    \nclustering or supervised learning for a specific prediction field.
    \n<\/span>
    semiadditive aggregation <\/span>A method of summarizing data from its lowest
    \nlevel of detail to its highest level of detail in which measures are not aggregated uniformly across all dimensions.
    \n<\/span>
    sibling <\/span>A member that is at the same level as one or more other members
    \nsharing the same parent.
    \n<\/span>
    slice and dice <\/span>Two complementary methods for interacting with data.
    \nSlicing means isolating a specific member of a dimension for analysis. Dicing
    \nmeans breaking a data set into smaller pieces by examining how measures
    \nintersect with multiple dimensions.
    \n<\/span>
    slowly changing dimension <\/span>A term that describes how dimensions reflect
    \ndata changes over time.
    \n<\/span>
    SQL <\/span>See <\/span>structured query language (SQL).
    \n<\/span>
    structured query language <\/span>(SQL; pronounced <\/span>sequel) <\/span>An industry
    \nstandard language for accessing data (also called querying) in a relational
    \ndatabase management system (RDBMS).
    \n<\/span>
    uniform aggregation <\/span>A method of summarizing data from its lowest level
    \nof detail to its highest level of detail, where data can be aggregated the same
    \nway across all dimensions.
    \n<\/span>
    visualization <\/span>A graphical representation of data that sometimes reveals
    \npatterns that are more apparent to the human eye.
    \n<\/span>
    write back <\/span>The ability for users to update data in an underlying data mart.
    \n<\/span>
    zero client footprint <\/span>A tool that does not require software to be installed
    \non a business user\u2019s desktop, thus making the client application easier to
    \ndeploy to more users.\u00a0<\/span><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

    Business Intelligence – Microsoft Press: “Business Intelligence” bought the paper book through Amazon as a used book, purchased the PDF at the o’Reilly’s web-site. Update 2011-08-08 : dead tree book arrived at my favourite\u00a0DHL Packstation 122 nearby. Glossary 80\/20 rule A theory invented by Vilfredo Pareto in the late 1800s, also known as the Pareto […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_feature_clip_id":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":false,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"jetpack_post_was_ever_published":false,"_share_on_mastodon":"0"},"categories":[55,91,341,822],"tags":[],"class_list":["post-1524","post","type-post","status-publish","format-standard","hentry","category-business-intelligence","category-data-warehouses","category-microsoft","category-oreilly-publishers"],"share_on_mastodon":{"url":"","error":""},"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/paO0kP-oA","jetpack_likes_enabled":true,"amp_enabled":true,"_links":{"self":[{"href":"https:\/\/wp.jochen.hayek.name\/blog-en\/wp-json\/wp\/v2\/posts\/1524","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wp.jochen.hayek.name\/blog-en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wp.jochen.hayek.name\/blog-en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wp.jochen.hayek.name\/blog-en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/wp.jochen.hayek.name\/blog-en\/wp-json\/wp\/v2\/comments?post=1524"}],"version-history":[{"count":0,"href":"https:\/\/wp.jochen.hayek.name\/blog-en\/wp-json\/wp\/v2\/posts\/1524\/revisions"}],"wp:attachment":[{"href":"https:\/\/wp.jochen.hayek.name\/blog-en\/wp-json\/wp\/v2\/media?parent=1524"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wp.jochen.hayek.name\/blog-en\/wp-json\/wp\/v2\/categories?post=1524"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wp.jochen.hayek.name\/blog-en\/wp-json\/wp\/v2\/tags?post=1524"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}