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Data Warehousing

Data Warehouse Overview

In computing, a data warehouse or enterprise data warehouse (DW, DWH, or EDW) is a database used for reporting and data analysis. It is a central repository of data which is created by integrating data from one or more disparate sources. Data warehouses store current as well as historical data and are used for creating trending reports for senior management reporting such as annual and quarterly comparisons.

The data stored in the warehouse are uploaded from the operational systems (such as marketing, sales etc., shown in the figure to the right). The data may pass through an operational data store for additional operations before they are used in the DW for reporting.

The typical ETL-based data warehouse uses staging, data integration, and access layers to house its key functions. The staging layer or staging database stores raw data extracted from each of the disparate source data systems. The integration layer integrates the disparate data sets by transforming the data from the staging layer often storing this transformed data in an operational data store (ODS) database. The integrated data are then moved to yet another database, often called the data warehouse database, where the data is arranged into hierarchical groups often called dimensions and into facts and aggregate facts. The combination of facts and dimensions is sometimes called a star schema. The access layer helps users retrieve data.

A data warehouse constructed from an integrated data source systems does not require ETL, staging databases, or operational data store databases. The integrated data source systems may be considered to be a part of a distributed operational data store layer. Data federation methods or data virtualization methods may be used to access the distributed integrated source data systems to consolidate and aggregate data directly into the data warehouse database tables. Unlike the ETL-based data warehouse, the integrated source data systems and the data warehouse are all integrated since there is no transformation of dimensional or reference data. This integrated data warehouse architecture supports the drill down from the aggregate data of the data warehouse to the transactional data of the integrated source data systems.

A data mart is a small data warehouse focused on a specific area of interest. Data warehouses can be subdivided into data marts for improved performance and ease of use within that area. Alternatively, an organization can create one or more data marts as first steps towards a larger and more complex enterprise data warehouse.

This definition of the data warehouse focuses on data storage. The main source of the data is cleaned, transformed, cataloged and made available for use by managers and other business professionals for data mining, online analytical processing, market research and decision support (Marakas & O'Brien 2009). However, the means to retrieve and analyze data, to extract, transform and load data, and to manage the data dictionary are also considered essential components of a data warehousing system. Many references to data warehousing use this broader context. Thus, an expanded definition for data warehousing includes business intelligence tools, tools to extract, transform and load data into the repository, and tools to manage and retrieve metadata.

Benefits of a data warehouse

A data warehouse maintains a copy of information from the source transaction systems. This architectural complexity provides the opportunity to: Congregate data from multiple sources into a single database so a single query engine can be used to present data.

Mitigate the problem of database isolation level lock contention in transaction processing systems caused by attempts to run large, long running, analysis queries in transaction processing databases.

    • Maintain data history, even if the source transaction systems do not.
    • Integrate data from multiple source systems, enabling a central view across the enterprise. This benefit is always valuable, but particularly so when the organization has grown by merger.
    • Present the organization's information consistently.
    • Provide a single common data model for all data of interest regardless of the data's source.
    • Restructure the data so that it makes sense to the business users.
    • Add value to operational business applications, notably customer relationship management (CRM) systems.

    • The environment for data warehouses and marts includes the following:
    • Source systems that provide data to the warehouse or mart;
    • Data integration technology and processes that are needed to prepare the data for use;
    • Different architectures for storing data in an organization's data warehouse or data marts;
    • Different tools and applications for the variety of users;
    • Metadata, data quality, and governance processes must be in place to ensure that the warehouse or mart meets its purposes.
    • In regards to source systems listed above, Rainer states, 'A common source for the data in data warehouses is the company�s operational databases, which can be relational databases'.
    • Regarding data integration, Rainer states, It is necessary to extract data from source systems, transform them, and load them into a data mart or warehouse.
    • Rainer discusses storing data in an organization�s data warehouse or data marts. -There are a variety of possible architectures to store decision-support data-.
    • Metadata are data about data. -IT personnel need information about data sources; database, table, and column names; refresh schedules; and data usage measures.

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    Today, the most successful companies are those that can respond quickly and flexibly to market changes and opportunities. A key to this response is the effective and efficient use of data and information by analysts and managers (Rainer, 127). A -data warehouse- is a repository of historical data that are organized by subject to support decision makers in the organization (128). Once data are stored in a data mart or warehouse, they can be accessed.

    Rainer, R. Kelly (2012-05-01). Introduction to Information Systems: Enabling and Transforming Business, 4th Edition (Page 129). Wiley. Kindle Edition.v

    Data Warehouse >> History :

    The concept of data warehousing dates back to the late 1980s when IBM researchers Barry Devlin and Paul Murphy developed the "business data warehouse". In essence, the data warehousing concept was intended to provide an architectural model for the flow of data from operational systems to decision support environments. The concept attempted to address the various problems associated with this flow, mainly the high costs associated with it. In the absence of a data warehousing architecture, an enormous amount of redundancy was required to support multiple decision support environments. In larger corporations it was typical for multiple decision support environments to operate independently. Though each environment served different users, they often required much of the same stored data. The process of gathering, cleaning and integrating data from various sources, usually from long-term existing operational systems (usually referred to as legacy systems), was typically in part replicated for each environment. Moreover, the operational systems were frequently reexamined as new decision support requirements emerged. Often new requirements necessitated gathering, cleaning and integrating new data from "data marts" that were tailored for ready access by users.

    Key developments in early years of data warehousing were:
    • 1960s - General Mills and Dartmouth College, in a joint research project, develop the terms dimensions and facts.
    • 1970s - ACNielsen and IRI provide dimensional data marts for retail sales.
    • 1970s - Bill Inmon begins to define and discuss the term: Data Warehouse
    • 1975 - Sperry Univac Introduce MAPPER (MAintain, Prepare, and Produce Executive Reports) is a database management and reporting system that includes the world's first 4GL. It was the first platform specifically designed for building Information Centers (a forerunner of contemporary

    • Enterprise Data Warehousing platforms)
    • 1983 - Teradata introduces a database management system specifically designed for decision support.
    • 1983 - Sperry Corporation Martyn Richard Jones defines the Sperry Information Center approach, which while not being a true DW in the Inmon sense, did contain many of the characteristics of DW structures and process as defined previously by Inmon, and later by Devlin. First used at the TSB England & Wales
    • 1984 - Metaphor Computer Systems, founded by David Liddle and Don Massaro, releases Data Interpretation System (DIS). DIS was a hardware/software package and GUI for business users to create a database management and analytic system.
    • 1988 - Barry Devlin and Paul Murphy publish the article An architecture for a business and information system in IBM Systems Journal where they introduce the term "business data warehouse".
    • 1990 - Red Brick Systems, founded by Ralph Kimball, introduces Red Brick Warehouse, a database management system specifically for data warehousing.
    • 1991 - Prism Solutions, founded by Bill Inmon, introduces Prism Warehouse Manager, software for developing a data warehouse.
    • 1992 - Bill Inmon publishes the book Building the Data Warehouse.
    • 1995 - The Data Warehousing Institute, a for-profit organization that promotes data warehousing, is founded.
    • 1996 - Ralph Kimball publishes the book The Data Warehouse Toolkit.
    • 2000 - Daniel Linstedt releases the Data Vault, enabling real time auditable Data Warehouses warehouse.

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    Information storage

    A fact is a value or measurement, which represents a fact about the managed entity or system.
    Facts as reported by the reporting entity are said to be at raw level.

    E.g. if a BTS received 1,000 requests for traffic channel allocation, it allocates for 820 and rejects the remaining then it would report 3 facts or measurements to a management system:

    tch_req_total = 1000
    tch_req_success = 820
    tch_req_fail = 180
    Facts at raw level are further aggregated to higher levels in various dimensions to extract more service or business-relevant information out of it. These are called aggregates or summaries or aggregated facts.

    E.g. if there are 3 BTSs in a city, then facts above can be aggregated from BTS to city level in network dimension. E.g.
    tch\_req\_success\_city = tch\_req\_success\_bts1 + tch\_req\_success\_bts2 + tch\_req\_success\_bts3
    avg\_tch\_req\_success\_city = (tch\_req\_success\_bts1 + tch\_req\_success\_bts2 + tch\_req\_success\_bts3) / 3

    Dimensional vs. normalized approach for storage of data

    There are three or more leading approaches to storing data in a data warehouse - the most important approaches are the dimensional approach and the normalized approach.

    The dimensional approach, whose supporters are referred to as -Kimballites-, believe in Ralph Kimball�s approach in which it is stated that the data warehouse should be modeled using a Dimensional Model/star schema. The normalized approach, also called the 3NF model, whose supporters are referred to as -Inmonites-, believe in Bill Inmon's approach in which it is stated that the data warehouse should be modeled using an E-R model/normalized model.

    In a dimensional approach, transaction data are partitioned into "facts", which are generally numeric transaction data, and "dimensions", which are the reference information that gives context to the facts. For example, a sales transaction can be broken up into facts such as the number of products ordered and the price paid for the products, and into dimensions such as order date, customer name, product number, order ship-to and bill-to locations, and salesperson responsible for receiving the order.

    A key advantage of a dimensional approach is that the data warehouse is easier for the user to understand and to use. Also, the retrieval of data from the data warehouse tends to operate very quickly. Dimensional structures are easy to understand for business users, because the structure is divided into measurements/facts and context/dimensions. Facts are related to the organization�s business processes and operational system whereas the dimensions surrounding them contain context about the measurement (Kimball, Ralph 2008).

    The main disadvantages of the dimensional approach are:

    In order to maintain the integrity of facts and dimensions, loading the data warehouse with data from different operational systems is complicated, and

    It is difficult to modify the data warehouse structure if the organization adopting the dimensional approach changes the way in which it does business.

    In the normalized approach, the data in the data warehouse are stored following, to a degree, database normalization rules. Tables are grouped together by subject areas that reflect general data categories (e.g., data on customers, products, finance, etc.). The normalized structure divides data into entities, which creates several tables in a relational database. When applied in large enterprises the result is dozens of tables that are linked together by a web of joins. Furthermore, each of the created entities is converted into separate physical tables when the database is implemented (Kimball, Ralph 2008). The main advantage of this approach is that it is straightforward to add information into the database. A disadvantage of this approach is that, because of the number of tables involved, it can be difficult for users both to:

    join data from different sources into meaningful information and then
    access the information without a precise understanding of the sources of data and of the data structure of the data warehouse.
    It should be noted that both normalized and dimensional models can be represented in entity-relationship diagrams as both contain joined relational tables. The difference between the two models is the degree of normalization.

    These approaches are not mutually exclusive, and there are other approaches. Dimensional approaches can involve normalizing data to a degree (Kimball, Ralph 2008).

    In Information-Driven Business, Robert Hillard proposes an approach to comparing the two approaches based on the information needs of the business problem. The technique shows that normalized models hold far more information than their dimensional equivalents (even when the same fields are used in both models) but this extra information comes at the cost of usability. The technique measures information quantity in terms of Information Entropy and usability in terms of the Small Worlds data transformation measure.

    Top-down versus bottom-up design methodologies

    Bottom-up design

    Ralph Kimball, designed an approach to data warehouse design known as bottom-up.
    In the bottom-up approach, data marts are first created to provide reporting and analytical capabilities for specific business processes. Data marts contain, primarily, dimensions and facts. Facts can contain either atomic data and, if necessary, summarized data. The single data mart often models a specific business area such as "Sales" or "Production." These data marts can eventually be integrated to create a comprehensive data warehouse. The data warehouse bus architecture is primarily an implementation of "the bus", a collection of conformed dimensions and conformed facts, which are dimensions that are shared (in a specific way) between facts in two or more data marts.

    The integration of the data marts in the data warehouse is centered on the conformed dimensions (residing in "the bus") that define the possible integration "points" between data marts. The actual integration of two or more data marts is then done by a process known as "Drill across". A drill-across works by grouping (summarizing) the data along the keys of the (shared) conformed dimensions of each fact participating in the "drill across" followed by a join on the keys of these grouped (summarized) facts.

    Maintaining tight management over the data warehouse bus architecture is fundamental to maintaining the integrity of the data warehouse. The most important management task is making sure dimensions among data marts are consistent.

    Business value can be returned as quickly as the first data marts can be created, and the method gives itself well to an exploratory and iterative approach to building data warehouses. For example, the data warehousing effort might start in the "Sales" department, by building a Sales-data mart. Upon completion of the Sales-data mart, the business might then decide to expand the warehousing activities into the, say, "Production department" resulting in a Production data mart. The requirement for the Sales data mart and the Production data mart to be integrable, is that they share the same "Bus", that will be, that the data warehousing team has made the effort to identify and implement the conformed dimensions in the bus, and that the individual data marts links that information from the bus. The Sales-data mart is good as it is (assuming that the bus is complete) and the Production-data mart can be constructed virtually independent of the Sales-data mart (but not independent of the Bus).

    If integration via the bus is achieved, the data warehouse, through its two data marts, will not only be able to deliver the specific information that the individual data marts are designed to do, in this example either "Sales" or "Production" information, but can deliver integrated Sales-Production information, which, often, is of critical business value.

    Top-down design

    Bill Inmon, has defined a data warehouse as a centralized repository for the entire enterprise. The top-down approach is designed using a normalized enterprise data model. "Atomic" data, that is, data at the lowest level of detail, are stored in the data warehouse. Dimensional data marts containing data needed for specific business processes or specific departments are created from the data warehouse. In the Inmon vision, the data warehouse is at the center of the "Corporate Information Factory" (CIF), which provides a logical framework for delivering business intelligence (BI) and business management capabilities.

    The data warehouse is:

    The data in the data warehouse is organized so that all the data elements relating to the same real-world event or object are linked together.


    Data in the data warehouse are never over-written or deleted - once committed, the data are static, read-only, and retained for future reporting.


    The data warehouse contains data from most or all of an organization's operational systems and these data are made consistent.


    For An operational system, the stored data contains the current value. The data warehouse, however, contains the history of data values. The top-down design methodology generates highly consistent dimensional views of data across data marts since all data marts are loaded from the centralized repository. Top-down design has also proven to be robust against business changes. Generating new dimensional data marts against the data stored in the data warehouse is a relatively simple task. The main disadvantage to the top-down methodology is that it represents a very large project with a very broad scope. The up-front cost for implementing a data warehouse using the top-down methodology is significant, and the duration of time from the start of project to the point that end users experience initial benefits can be substantial. In addition, the top-down methodology can be inflexible and unresponsive to changing departmental needs during the implementation phases.

    Hybrid design

    Data warehouse (DW) solutions often resemble the hub and spokes architecture. Legacy systems feeding the DW/BI solution often include customer relationship management (CRM) and enterprise resource planning solutions (ERP), generating large amounts of data. To consolidate these various data models, and facilitate the extract transform load (ETL) process, DW solutions often make use of an operational data store (ODS). The information from the ODS is then parsed into the actual DW. To reduce data redundancy, larger systems will often store the data in a normalized way. Data marts for specific reports can then be built on top of the DW solution.

    It is important to note that the DW database in a hybrid solution is kept on third normal form to eliminate data redundancy. A normal relational database however, is not efficient for business intelligence reports where dimensional modelling is prevalent. Small data marts can shop for data from the consolidated warehouse and use the filtered, specific data for the fact tables and dimensions required. The DW effectively provides a single source of information from which the data marts can read, creating a highly flexible solution from a BI point of view. The hybrid architecture allows a DW to be replaced with a master data management solution where operational, not static information could reside.

    The Data Vault Modeling components follow hub and spokes architecture. This modeling style is a hybrid design, consisting of the best practices from both 3rd normal form and star schema. The Data Vault model is not a true 3rd normal form, and breaks some of the rules that 3NF dictates be followed. It is however, a top-down architecture with a bottom up design. The Data Vault model is geared to be strictly a data warehouse. It is not geared to be end-user accessible, which when built, still requires the use of a data mart or star schema based release area for business purposes.

    Data warehouses versus operational systems

    Operational systems are optimized for preservation of data integrity and speed of recording of business transactions through use of database normalization and an entity-relationship model. Operational system designers generally follow the Codd rules of database normalization in order to ensure data integrity. Codd defined five increasingly stringent rules of normalization. Fully normalized database designs (that is, those satisfying all five Codd rules) often result in information from a business transaction being stored in dozens to hundreds of tables. Relational databases are efficient at managing the relationships between these tables. The databases have very fast insert/update performance because only a small amount of data in those tables is affected each time a transaction is processed. Finally, in order to improve performance, older data are usually periodically purged from operational systems.

    Evolution in organization use

    These terms refer to the level of sophistication of a data warehouse:

    Offline operational data warehouse

    Data warehouses in this stage of evolution are updated on a regular time cycle (usually daily, weekly or monthly) from the operational systems and the data is stored in an integrated reporting-oriented data

    Offline data warehouse

    Data warehouses at this stage are updated from data in the operational systems on a regular basis and the data warehouse data are stored in a data structure designed to facilitate reporting.

    On time data warehouse

    Online Integrated Data Warehousing represent the real time Data warehouses stage data in the warehouse is updated for every transaction performed on the source data

    Integrated data warehouse

    These data warehouses assemble data from different areas of business, so users can look up the information they need across other systems.

    Data Warehouse Appliance :

    Data warehouse appliance

    In computing, a data warehouse appliance is a marketing term for an integrated set of servers, storage, operating system(s), DBMS and software specifically pre-installed and pre-optimized for data warehousing (DW). Alternatively, the term can also apply to similar software-only systems promoted as easy to install on specific recommended hardware configurations or preconfigured as a complete system.

    DW appliances are marketed to for midle-to-big data applications, most commonly on data volumes in the terabyte to petabyte range.


    Most DW appliances use massively parallel processing (MPP) architectures to provide high query performance and platform scalability. MPP architectures consist of independent processors or servers executing in parallel. Most MPP architectures implement a "shared-nothing architecture" where each server operates self-sufficiently and controls its own memory and disk. DW appliances distribute data onto dedicated disk storage units connected to each server in the appliance. This distribution allows DW appliances to resolve a relational query by scanning data on each server in parallel. The divide-and-conquer approach delivers high performance and scales linearly as new servers are added into the architecture.


    MPP database architectures have a long pedigree. Some consider Teradata's initial product as the first DW appliance - or Britton-Lee's. Teradata acquired Britton Lee - renamed ShareBase - in June, 1990. Others disagree, considering appliances as a "disruptive technology" for Teradata

    Additional vendors, including Tandem Computers, and Sequent Computer Systems also offered MPP architectures in the 1980s. Open source and commodity computing components aided a re-emergence of MPP data warehouse appliances. Advances in technology reduced costs and improved performance in storage devices, multi-core CPUs and networking components. Open-source RDBMS products, such as Ingres and PostgreSQL, reduce software-license costs and allow DW-appliance vendors to focus on optimization rather than providing basic database functionality. Open-source Linux became a common operating system for DW appliances.

    Other DW appliance vendors use specialized hardware and advanced software, instead of MPP architectures. Netezza announced a "data appliance" in 2003, and used specialized field-programmable gate array hardware. Kickfire followed in 2008 with what they called a dataflow "sql chip".

    In 2009 more DW appliances emerged. IBM integrated its InfoSphere Warehouse (formerly DB2 Warehouse) with its own servers and storage to create the IBM InfoSphere Balanced Warehouse. Netezza introduced its TwinFin platform based on commodity IBM hardware. Other DW appliance vendors have also partnered with major hardware vendors to help bring their appliances to market. DATAllegro, prior to acquisition by Microsoft, partnered with EMC Corporation and Dell and implemented open-source Ingres on Linux. Greenplum has a partnership with Sun Microsystems and implements Greenplum Database (based on PostgreSQL) on Solaris using the ZFS file system. HP Neoview has a wholly owned solution and uses HP NonStop SQL. XtremeData offers a software stack that can be used to create a "virtual data-warehousing appliance" built on commodity hardware, on-premise or in the Cloud for "deep analytics" and data mining.

    The market has also seen the emergence of data-warehouse bundles where vendors combine their hardware and database software together as a data warehouse platform. The Oracle Optimized Warehouse Initiative combines the Oracle Database with hardware from various computer manufacturers (Dell, EMC, HP, IBM, SGI and Sun Microsystems). Oracle's Optimized Warehouses offer pre-validated configurations and the database software comes pre-installed. In September 2008 Oracle began offering a more classic appliance offering, the HP Oracle Database Machine, a jointly developed and co-branded platform that Oracle sold and supported and HP built in configurations specifically for Oracle. In September 2009, Oracle released a second-generation Exadata system, based on their newly acquired Sun Microsystems hardware.

    Business intelligence :

    Business intelligence

    Business intelligence (BI) is a set of theories, methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information for business purposes. BI can handle large amounts of information to help identify and develop new opportunities. Making use of new opportunities and implementing an effective strategy can provide a competitive market advantage and long-term stability.

    BI technologies provide historical, current and predictive views of business operations. Common functions of business intelligence technologies are reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics and prescriptive analytics.

    Though the term business intelligence is sometimes a synonym for competitive intelligence (because they both support decision making), BI uses technologies, processes, and applications to analyze mostly internal, structured data and business processes while competitive intelligence gathers, analyzes and disseminates information with a topical focus on company competitors. If understood broadly, business intelligence can include the subset of competitive intelligence.


    In a 1958 article, IBM researcher Hans Peter Luhn used the term business intelligence. He defined intelligence as: "the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal."

    Business intelligence as it is understood today is said to have evolved from the decision support systems that began in the 1960s and developed throughout the mid-1980s. DSS originated in the computer-aided models created to assist with decision making and planning. From DSS, data warehouses, Executive Information Systems, OLAP and business intelligence came into focus beginning in the late 80s.

    In 1989, Howard Dresner (later a Gartner Group analyst) proposed "business intelligence" as an umbrella term to describe "concepts and methods to improve business decision making by using fact-based support systems." It was not until the late 1990s that this usage was widespread.

    Business intelligence and data warehousing

    Often BI applications use data gathered from a data warehouse or a data mart. A data warehouse is a copy of transactional data that facillitates decision support. However, not all data warehouses are used for business intelligence, nor do all business intelligence applications require a data warehouse.

    To distinguish between the concepts of business intelligence and data warehouses, Forrester Research often defines business intelligence in one of two ways:
    Using a broad definition: "Business Intelligence is a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making." When using this definition, business intelligence also includes technologies such as data integration, data quality, data warehousing, master data management, text and content analytics, and many others that the market sometimes lumps into the Information Management segment. Therefore, Forrester refers to data preparation and data usage as two separate, but closely linked segments of the business intelligence architectural stack.

    Forrester defines the latter, narrower business intelligence market as, "...referring to just the top layers of the BI architectural stack such as reporting, analytics and dashboards."

    Business intelligence and business analytics

    Thomas Davenport argues that business intelligence should be divided into querying, reporting, OLAP, an "alerts" tool, and business analytics. In this definition, business analytics is the subset of BI based on statistics, prediction, and optimization.

    Applications in an Enterprise

    Business intelligence can be applied to the following business purposes, in order to drive business value.

    Measurement - program that creates a hierarchy of performance metrics (see also Metrics Reference Model) and benchmarking that informs business leaders about progress towards business goals (business process management).

    Analytics - program that builds quantitative processes for a business to arrive at optimal decisions and to perform business knowledge discovery. Frequently involves: data mining, process mining, statistical analysis, predictive analytics, predictive modeling, business process modeling, complex event processing and prescriptive analytics.

    Reporting/enterprise reporting - program that builds infrastructure for strategic reporting to serve the strategic management of a business, not operational reporting. Frequently involves data visualization, executive information system and OLAP.

    Collaboration/collaboration platform - program that gets different areas (both inside and outside the business) to work together through data sharing and electronic data interchange.

    Knowledge management - program to make the company data driven through strategies and practices to identify, create, represent, distribute, and enable adoption of insights and experiences that are true business knowledge. Knowledge management leads to learning management and regulatory compliance.

    In addition to above, business intelligence also can provide a pro-active approach, such as ALARM function to alert immediately to end-user. There are many types of alerts, for example if some business value exceeds the threshold value the color of that amount in the report will turn RED and the business analyst is alerted. Sometimes an alert mail will be sent to the user as well. This end to end process requires data governance, which should be handled by the expert.

    Prioritization of business intelligence projects

    It is often difficult to provide a positive business case for business intelligence initiatives and often the projects must be prioritized through strategic initiatives. Here are some hints to increase the benefits for a BI project.

    As described by Kimball you must determine the tangible benefits such as eliminated cost of producing legacy reports. Enforce access to data for the entire organization. In this way even a small benefit, such as a few minutes saved, makes a difference when multiplied by the number of employees in the entire organization.

    As described by Ross, Weil & Roberson for Enterprise Architecture, consider letting the BI project be driven by other business initiatives with excellent business cases. To support this approach, the organization must have enterprise architects who can identify suitable business projects.

    Use a structured and quantitative methodology to create defensible prioritization in line with the actual needs of the organization, such as a weighted decision matrix.

    Success factors of implementation
    • Before implementing a BI solution, it is worth taking different factors into consideration before proceeding. According to Kimball et al., these are the three critical areas that you need to assess within your organization before getting ready to do a BI project:
    • The level of commitment and sponsorship of the project from senior management
    • The level of business need for creating a BI implementation
    • The amount and quality of business data available.

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    Business sponsorship

    The commitment and sponsorship of senior management is according to Kimball et al., the most important criteria for assessment. This is because having strong management backing helps overcome shortcomings elsewhere in the project. However, as Kimball et al. state: 'even the most elegantly designed DW/BI system cannot overcome a lack of business sponsorship'.

    It is important that personnel who participate in the project have a vision and an idea of the benefits and drawbacks of implementing a BI system. The best business sponsor should have organizational clout and should be well connected within the organization. It is ideal that the business sponsor is demanding but also able to be realistic and supportive if the implementation runs into delays or drawbacks. The management sponsor also needs to be able to assume accountability and to take responsibility for failures and setbacks on the project. Support from multiple members of the management ensures the project does not fail if one person leaves the steering group. However, having many managers work together on the project can also mean that there are several different interests that attempt to pull the project in different directions, such as if different departments want to put more emphasis on their usage. This issue can be countered by an early and specific analysis of the business areas that benefit the most from the implementation. All stakeholders in project should participate in this analysis in order for them to feel ownership of the project and to find common ground.

    Another management problem that should be encountered before start of implementation is if the business sponsor is overly aggressive. If the management individual gets carried away by the possibilities of using BI and starts wanting the DW or BI implementation to include several different sets of data that were not included in the original planning phase. However, since extra implementations of extra data may add many months to the original plan, it's wise to make sure the person from management is aware of his actions.

    Business needs

    Because of the close relationship with senior management, another critical thing that must be assessed before the project begins is whether or not there is a business need and whether there is a clear business benefit by doing the implementation. The needs and benefits of the implementation are sometimes driven by competition and the need to gain an advantage in the market. Another reason for a business-driven approach to implementation of BI is the acquisition of other organizations that enlarge the original organization it can sometimes be beneficial to implement DW or BI in order to create more oversight.

    Companies that implement BI are often large, multinational organizations with diverse subsidiaries. A well-designed BI solution provides a consolidated view of key business data not available anywhere else in the organization, giving management visibility and control over measures that otherwise would not exist.

    Amount and quality of available data

    Without good data, it does not matter how good the management sponsorship or business-driven motivation is. Without proper data, or with too little quality data, any BI implementation fails. Before implementation it is a good idea to do data profiling. This analysis identifies the 'content, consistency and structure of the data. This should be done as early as possible in the process and if the analysis shows that data is lacking, put the project on the shelf temporarily while the IT department figures out how to properly collect data.

    When planning for business data and business intelligence requirements, it is always advisable to consider specific scenarios that apply to a particular organization, and then select the business intelligence features best suited for the scenario.

    Often, scenarios revolve around distinct business processes, each built on one or more data sources. These sources are used by features that present that data as information to knowledge workers, who subsequently act on that information. The business needs of the organization for each business process adopted correspond to the essential steps of business intelligence. These essential steps of business intelligence include but are not limited to:

    • Go through business data sources in order to collect needed data
    • Convert business data to information and present appropriately
    • Act on those data collected

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    User aspect

    Some considerations must be made in order to successfully integrate the usage of business intelligence systems in a company. Ultimately the BI system must be accepted and utilized by the users in order for it to add value to the organization. If the usability of the system is poor, the users may become frustrated and spend a considerable amount of time figuring out how to use the system or may not be able to really use the system. If the system does not add value to the users� mission, they simply don't use it.

    To increase user acceptance of a BI system, it can be advisable to consult business users at an early stage of the DW/BI lifecycle, for example at the requirements gathering phase. This can provide an insight into the business process and what the users need from the BI system. There are several methods for gathering this information, such as questionnaires and interview sessions. When gathering the requirements from the business users, the local IT department should also be consulted in order to determine to which degree it is possible to fulfill the business's needs based on the available data.

    Taking on a user-centered approach throughout the design and development stage may further increase the chance of rapid user adoption of the BI system. Besides focusing on the user experience offered by the BI applications, it may also possibly motivate the users to utilize the system by adding an element of competition. Kimball suggests implementing a function on the Business Intelligence portal website where reports on system usage can be found. By doing so, managers can see how well their departments are doing and compare themselves to others and this may spur them to encourage their staff to utilize the BI system even more.

    In a 2007 article, H. J. Watson gives an example of how the competitive element can act as an incentive. Watson describes how a large call centre implemented performance dashboards for all call agents, with monthly incentive bonuses tied to performance metrics. Also, agents could compare their performance to other team members. The implementation of this type of performance measurement and competition significantly improved agent performance. BI chances of success can be improved by involving senior management to help make BI a part of the organizational culture, and by providing the users with necessary tools, training, and support. Training encourages more people to use the BI application.

    Providing user support is necessary to maintain the BI system and resolve user problems. User support can be incorporated in many ways, for example by creating a website. The website should contain great content and tools for finding the necessary information. Furthermore, helpdesk support can be used. The help desk can be manned by power users or the DW/BI project team.

    BI Portals

    A Business Intelligence portal (BI portal) is the primary access interface for Data Warehouse (DW) and Business Intelligence (BI) applications. The BI portal is the users first impression of the DW/BI system. It is typically a browser application, from which the user has access to all the individual services of the DW/BI system, reports and other analytical functionality. The BI portal must be implemented in such a way that it is easy for the users of the DW/BI application to call on the functionality of the application.

    The BI portal's main functionality is to provide a navigation system of the DW/BI application. This means that the portal has to be implemented in a way that the user has access to all the functions of the DW/BI application.

    The most common way to design the portal is to custom fit it to the business processes of the organization for which the DW/BI application is designed, in that way the portal can best fit the needs and requirements of its users.

    The BI portal needs to be easy to use and understand, and if possible have a look and feel similar to other applications or web content of the organization the DW/BI application is designed for (consistency).

    The following is a list of desirable features for web portals in general and BI portals in particular:


    User should easily find what they need in the BI tool.

    Content Rich

    The portal is not just a report printing tool, it should contain more functionality such as advice, help, support information and documentation.


    The portal should be designed so it is easily understandable and not over complex as to confuse the users


    The portal should be updated regularly.


    The portal should be implemented in a way that makes it easy for the user to use its functionality and encourage them to use the portal. Scalability and customization give the user the means to fit the portal to each user.

    Value Oriented

    It is important that the user has the feeling that the DW/BI application is a valuable resource that is worth working on.


    There are a number of business intelligence vendors, often categorized into the remaining independent "pure-play" vendors and consolidated "megavendors" that have entered the market through a recent trend of acquisitions in the BI industry. Some companies adopting BI software decide to pick and choose from different product offerings (best-of-breed) rather than purchase one comprehensive integrated solution (full-service).


    Specific considerations for business intelligence systems have to be taken in some sectors such as governmental banking regulations. The information collected by banking institutions and analyzed with BI software must be protected from some groups or individuals, while being fully available to other groups or individuals. Therefore BI solutions must be sensitive to those needs and be flexible enough to adapt to new regulations and changes to existing law.

    Semi-structured or unstructured data

    Businesses create a huge amount of valuable information in the form of e-mails, memos, notes from call-centers, news, user groups, chats, reports, web-pages, presentations, image-files, video-files, and marketing material and news. According to Merrill Lynch, more than 85% of all business information exists in these forms. These information types are called either semi-structured or unstructured data. However, organizations often only use these documents once.

    The management of semi-structured data is recognized as a major unsolved problem in the information technology industry. According to projections from Gartner (2003), white collar workers spend anywhere from 30 to 40 percent of their time searching, finding and assessing unstructured data. BI uses both structured and unstructured data, but the former is easy to search, and the latter contains a large quantity of the information needed for analysis and decision making. Because of the difficulty of properly searching, finding and assessing unstructured or semi-structured data, organizations may not draw upon these vast reservoirs of information, which could influence a particular decision, task or project. This can ultimately lead to poorly informed decision making.

    Therefore, when designing a business intelligence/DW-solution, the specific problems associated with semi-structured and unstructured data must be accommodated for as well as those for the structured data.

    Unstructured data vs. semi-structured data

    Unstructured and semi-structured data have different meanings depending on their context. In the context of relational database systems, unstructured data cannot be stored in predictably ordered columns and rows. One type of unstructured data is typically stored in a BLOB (binary large object), a catch-all data type available in most relational database management systems. Unstructured data may also refer to irregularly or randomly repeated column patterns that vary from row to row within each file or document.

    Many of these data types, however, like e-mails, word processing text files, PPTs, image-files, and video-files conform to a standard that offers the possibility of metadata. Metadata can include information such as author and time of creation, and this can be stored in a relational database. Therefore it may be more accurate to talk about this as semi-structured documents or data, but no specific consensus seems to have been reached.

    Unstructured data can also simply be the knowledge that business users have about future business trends. Business forecasting naturally aligns with the BI system because business users think of their business in aggregate terms. Capturing the business knowledge that may only exist in the minds of business users provides some of the most important data points for a complete BI solution.

    Problems with semi-structured or unstructured data

    There are several challenges to developing BI with semi-structured data. According to Inmon & Nesavich, some of those are:
    Physically accessing unstructured textual data - unstructured data is stored in a huge variety of formats.
    Terminology - Among researchers and analysts, there is a need to develop a standardized terminology.
    Volume of data - As stated earlier, up to 85% of all data exists as semi-structured data. Couple that with the need for word-to-word and semantic analysis.

    Searchability of unstructured textual data - A simple search on some data, e.g. apple, results in links where there is a reference to that precise search term. (Inmon & Nesavich, 2008) gives an example: -a search is made on the term felony. In a simple search, the term felony is used, and everywhere there is a reference to felony, a hit to an unstructured document is made. But a simple search is crude. It does not find references to crime, arson, murder, embezzlement, vehicular homicide, and such, even though these crimes are types of felonies.-

    The use of metadata

    To solve problems with searchability and assessment of data, it is necessary to know something about the content. This can be done by adding context through the use of metadata. Many systems already capture some metadata (e.g. filename, author, size, etc.), but more useful would be metadata about the actual content - e.g. summaries, topics, people or companies mentioned. Two technologies designed for generating metadata about content are automatic categorization and information extraction.

    • A 2009 Gartner paper predicted these developments in the business intelligence market:
    • Because of lack of information, processes, and tools, through 2012, more than 35 percent of the top 5,000 global companies regularly fail to make insightful decisions about significant changes in their business and markets.
    • By 2012, business units will control at least 40 percent of the total budget for business intelligence.
    • By 2012, one-third of analytic applications applied to business processes will be delivered through coarse-grained application mashups.
    • A 2009 Information Management special report predicted the top BI trends: "green computing, social networking, data visualization, mobile BI, predictive analytics, composite applications, cloud computing and multitouch."

    • Other business intelligence trends include the following:
    • Third party SOA-BI products increasingly address ETL issues of volume and throughput.
    • Cloud computing and Software-as-a-Service (SaaS) are ubiquitous.
    • Companies embrace in-memory processing, 64-bit processing, and pre-packaged analytic BI applications.
    • Operational applications have callable BI components, with improvements in response time, scaling, and concurrency.
    • Near or real time BI analytics is a baseline expectation.
    • Open source BI software replaces vendor offerings.

  • .

    Other lines of research include the combined study of business intelligence and uncertain data.In this context, the data used is not assumed to be precise, accurate and complete. Instead, data is considered uncertain and therefore this uncertainty is propagated to the results produced by BI.

    According to a study by the Aberdeen Group, there has been increasing interest in Software-as-a-Service (SaaS) business intelligence over the past years, with twice as many organizations using this deployment approach as one year ago - 15% in 2009 compared to 7% in 2008.

    An article by InfoWorld�s Chris Kanaracus points out similar growth data from research firm IDC, which predicts the SaaS BI market will grow 22 percent each year through 2013 thanks to increased product sophistication, strained IT budgets, and other factors.

    Data Mart :

    Data mart

    A data mart is the access layer of the data warehouse environment that is used to get data out to the users. The data mart is a subset of the data warehouse that is usually oriented to a specific business line or team. Data marts are small slices of the data warehouse. Whereas data warehouses have an enterprise-wide depth, the information in data marts pertains to a single department. In some deployments, each department or business unit is considered the owner of its data mart including all the hardware, software and data. This enables each department to use, manipulate and develop their data any way they see fit; without altering information inside other data marts or the data warehouse. In other deployments where conformed dimensions are used, this business unit ownership will not hold true for shared dimensions like customer, product, etc.

    The related term spreadmart is a derogatory label describing the situation that occurs when one or more business analysts develop a system of linked spreadsheets to perform a business analysis, then grow it to a size and degree of complexity that makes it nearly impossible to maintain.

    The primary use for a data mart is business intelligence (BI) applications. BI is used to gather, store, access and analyze data. The data mart can be used by smaller businesses to utilize the data they have accumulated. A data mart can be less expensive than implementing a data warehouse, thus making it more practical for the small business. A data mart can also be set up in much less time than a data warehouse, being able to be set up in less than 90 days. Since most small businesses only have use for a small number of BI applications, the low cost and quick set up of the data mart makes it a suitable method for storing data.

    Design schemas

    Star schema - fairly popular design choice; enables a relational database to emulate the analytical functionality of a multidimensional database

    Reasons for creating a data mart
    • Easy access to frequently needed data
    • Creates collective view by a group of users
    • Improves end-user response time
    • Ease of creation
    • Lower cost than implementing a full data warehouse
    • Potential users are more clearly defined than in a full data warehouse
    • Contains only business essential data and is less cluttered.

  • .

    Dependent data mart

    According to the Inmon school of data warehousing, a dependent data mart is a logical subset (view) or a physical subset (extract) of a larger data warehouse, isolated for one of the following reasons:

    A need refreshment for a special data model or schema: e.g., to restructure for OLAP

    Performance: to offload the data mart to a separate computer for greater efficiency or to obviate the need to manage that workload on the centralized data warehouse.

    Security: to separate an authorized data subset selectively

    Expediency: to bypass the data governance and authorizations required to incorporate a new application on the Enterprise Data Warehouse

    Proving Ground: to demonstrate the viability and ROI (return on investment) potential of an application prior to migrating it to the Enterprise Data Warehouse

    Politics: a coping strategy for IT (Information Technology) in situations where a user group has more influence than funding or is not a good citizen on the centralized data warehouse.

    Politics: a coping strategy for consumers of data in situations where a data warehouse team is unable to create a usable data warehouse. According to the Inmon school of data warehousing, tradeoffs inherent with data marts include limited scalability, duplication of data, data inconsistency with other silos of information, and inability to leverage enterprise sources of data.

    The alternative school of data warehousing is that of Ralph Kimball. In his view, a data warehouse is nothing more than the union of all the data marts. This view helps to reduce costs and provides fast development, but can create an inconsistent data warehouse, especially in large organizations. Therefore, Kimball's approach is more suitable for small-to-medium corporations.

    Data Mining :

    Data mining

    Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), an interdisciplinary subfield of computer science, is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating.

    The term is a buzzword, and is frequently misused to mean any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) but is also generalized to any kind of computer decision support system, including artificial intelligence, machine learning, and business intelligence. In the proper use of the word, the key term is discovery, commonly defined as "detecting something new". Even the popular book "Data mining: Practical machine learning tools and techniques with Java" (which covers mostly machine learning material) was originally to be named just "Practical machine learning", and the term "data mining" was only added for marketing reasons. Often the more general terms "(large scale) data analysis", or "analytics" - or when referring to actual methods, artificial intelligence and machine learning - are more appropriate.

    The actual data mining task is the automatic or semi-automatic analysis of large quantities of data to extract previously unknown interesting patterns such as groups of data records (cluster analysis), unusual records (anomaly detection) and dependencies (association rule mining). This usually involves using database techniques such as spatial indices. These patterns can then be seen as a kind of summary of the input data, and may be used in further analysis or, for example, in machine learning and predictive analytics. For example, the data mining step might identify multiple groups in the data, which can then be used to obtain more accurate prediction results by a decision support system. Neither the data collection, data preparation, nor result interpretation and reporting are part of the data mining step, but do belong to the overall KDD process as additional steps.

    The related terms data dredging, data fishing, and data snooping refer to the use of data mining methods to sample parts of a larger population data set that are (or may be) too small for reliable statistical inferences to be made about the validity of any patterns discovered. These methods can, however, be used in creating new hypotheses to test against the larger data populations.

    Data mining uses information from past data to analyze the outcome of a particular problem or situation that may arise. Data mining works to analyze data stored in data warehouses that are used to store that data that is being analyzed. That particular data may come from all parts of business, from the production to the management. Managers also use data mining to decide upon marketing strategies for their product. They can use data to compare and contrast among competitors. Data mining interprets its data into real time analysis that can be used to increase sales, promote new product, or delete product that is not value-added to the company.


    The Knowledge Discovery in Databases (KDD) process is commonly defined with the stages:

    • (1) Selection
    • (2) Pre-processing
    • (3) Transformation
    • (4) Data Mining
    • (5) Interpretation/Evaluation.

  • .

    It exists, however, in many variations on this theme, such as the Cross Industry Standard Process for Data Mining (CRISP-DM) which defines six phases:

    • (1) Business Understanding
    • (2) Data Understanding
    • (3) Data Preparation
    • (4) Modeling
    • (5) Evaluation
    • (6) Deployment

  • .

    or a simplified process such as (1) pre-processing, (2) data mining, and (3) results validation.
    Polls conducted in 2002, 2004, and 2007 show that the CRISP-DM methodology is the leading methodology used by data miners. The only other data mining standard named in these polls was SEMMA. However, 3-4 times as many people reported using CRISP-DM. Several teams of researchers have published reviews of data mining process models, and Azevedo and Santos conducted a comparison of CRISP-DM and SEMMA in 2008.


    Before data mining algorithms can be used, a target data set must be assembled. As data mining can only uncover patterns actually present in the data, the target data set must be large enough to contain these patterns while remaining concise enough to be mined within an acceptable time limit. A common source for data is a data mart or data warehouse. Pre-processing is essential to analyze the multivariate data sets before data mining. The target set is then cleaned. Data cleaning removes the observations containing noise and those with missing data.

    Data mining

    Data mining involves six common classes of tasks:
    Anomaly detection (Outlier/change/deviation detection) - The identification of unusual data records, that might be interesting or data errors that require further investigation.

    Association rule learning (Dependency modeling) - Searches for relationships between variables. For example a supermarket might gather data on customer purchasing habits. Using association rule learning, the supermarket can determine which products are frequently bought together and use this information for marketing purposes. This is sometimes referred to as market basket analysis.

    Clustering - is the task of discovering groups and structures in the data that are in some way or another "similar", without using known structures in the data.

    Classification - is the task of generalizing known structure to apply to new data. For example, an e-mail program might attempt to classify an e-mail as "legitimate" or as "spam".

    Regression - Attempts to find a function which models the data with the least error.

    Summarization - providing a more compact representation of the data set, including visualization and report generation.

    Sequential pattern mining - Sequential pattern mining finds sets of data items that occur together frequently in some sequences. Sequential pattern mining, which extracts frequent subsequences from a sequence database, has attracted a great deal of interest during the recent data mining research because it is the basis of many applications, such as: web user analysis, stock trend prediction, DNA sequence analysis, finding language or linguistic patterns from natural language texts, and using the history of symptoms to predict certain kind of disease.

    Results validation

    The final step of knowledge discovery from data is to verify that the patterns produced by the data mining algorithms occur in the wider data set. Not all patterns found by the data mining algorithms are necessarily valid. It is common for the data mining algorithms to find patterns in the training set which are not present in the general data set. This is called overfitting. To overcome this, the evaluation uses a test set of data on which the data mining algorithm was not trained. The learned patterns are applied to this test set and the resulting output is compared to the desired output. For example, a data mining algorithm trying to distinguish "spam" from "legitimate" emails would be trained on a training set of sample e-mails. Once trained, the learned patterns would be applied to the test set of e-mails on which it had not been trained. The accuracy of the patterns can then be measured from how many e-mails they correctly classify. A number of statistical methods may be used to evaluate the algorithm, such as ROC curves.

    If the learned patterns do not meet the desired standards, then it is necessary to re-evaluate and change the pre-processing and data mining steps. If the learned patterns do meet the desired standards, then the final step is to interpret the learned patterns and turn them into knowledge.


    There have been some efforts to define standards for the data mining process, for example the 1999 European Cross Industry Standard Process for Data Mining (CRISP-DM 1.0) and the 2004 Java Data Mining standard (JDM 1.0). Development on successors to these processes (CRISP-DM 2.0 and JDM 2.0) was active in 2006, but has stalled since. JDM 2.0 was withdrawn without reaching a final draft.

    For exchanging the extracted models � in particular for use in predictive analytics � the key standard is the Predictive Model Markup Language (PMML), which is an XML-based language developed by the Data Mining Group (DMG) and supported as exchange format by many data mining applications. As the name suggests, it only covers prediction models, a particular data mining task of high importance to business applications. However, extensions to cover (for example) subspace clustering have been proposed independently of the DMG.

    Notable uses


    Since the early 1960s, with the availability of oracles for certain combinatorial games, also called tablebases (e.g. for 3x3-chess) with any beginning configuration, small-board dots-and-boxes, small-board-hex, and certain endgames in chess, dots-and-boxes, and hex; a new area for data mining has been opened. This is the extraction of human-usable strategies from these oracles. Current pattern recognition approaches do not seem to fully acquire the high level of abstraction required to be applied successfully. Instead, extensive experimentation with the tablebases ' combined with an intensive study of tablebase-answers to well designed problems, and with knowledge of prior art (i.e. pre-tablebase knowledge) ' is used to yield insightful patterns. Berlekamp (in dots-and-boxes, etc.) and John Nunn (in chess endgames) are notable examples of researchers doing this work, though they were not ' and are not ' involved in tablebase generation.


    Data mining is the analysis of historical business activities, stored as static data in data warehouse databases, to reveal hidden patterns and trends. Data mining software uses advanced pattern recognition algorithms to sift through large amounts of data to assist in discovering previously unknown strategic business information. Examples of what businesses use data mining for include performing market analysis to identify new product bundles, finding the root cause of manufacturing problems, to prevent customer attrition and acquire new customers, cross-sell to existing customers, and profile customers with more accuracy. In today�s world raw data is being collected by companies at an exploding rate. For example, Walmart processes over 20 million point-of-sale transactions every day. This information is stored in a centralized database, but would be useless without some type of data mining software to analyse it. If Walmart analyzed their point-of-sale data with data mining techniques they would be able to determine sales trends, develop marketing campaigns, and more accurately predict customer loyalty. Every time we use our credit card, a store loyalty card, or fill out a warranty card data is being collected about our purchasing behavior. Many people find the amount of information stored about us from companies, such as Google, Facebook, and Amazon, disturbing and are concerned about privacy. Although there is the potential for our personal data to be used in harmful, or unwanted, ways it is also being used to make our lives better. For example, Ford and Audi hope to one day collect information about customer driving patterns so they can recommend safer routes and warn drivers about dangerous road conditions.

    Data mining in customer relationship management applications can contribute significantly to the bottom line.[citation needed] Rather than randomly contacting a prospect or customer through a call center or sending mail, a company can concentrate its efforts on prospects that are predicted to have a high likelihood of responding to an offer. More sophisticated methods may be used to optimize resources across campaigns so that one may predict to which channel and to which offer an individual is most likely to respond (across all potential offers). Additionally, sophisticated applications could be used to automate mailing. Once the results from data mining (potential prospect/customer and channel/offer) are determined, this "sophisticated application" can either automatically send an e-mail or a regular mail. Finally, in cases where many people will take an action without an offer, "uplift modeling" can be used to determine which people have the greatest increase in response if given an offer. Uplift modeling thereby enables marketers to focus mailings and offers on persuadable people, and not to send offers to people who will buy the product without an offer. Data clustering can also be used to automatically discover the segments or groups within a customer data set.

    Businesses employing data mining may see a return on investment, but also they recognize that the number of predictive models can quickly become very large. Rather than using one model to predict how many customers will churn, a business could build a separate model for each region and customer type. Then, instead of sending an offer to all people that are likely to churn, it may only want to send offers to loyal customers. Finally, the business may want to determine which customers are going to be profitable over a certain window in time, and only send the offers to those that are likely to be profitable. In order to maintain this quantity of models, they need to manage model versions and move on to automated data mining.

    Data mining can also be helpful to human resources (HR) departments in identifying the characteristics of their most successful employees. Information obtained � such as universities attended by highly successful employees � can help HR focus recruiting efforts accordingly. Additionally, Strategic Enterprise Management applications help a company translate corporate-level goals, such as profit and margin share targets, into operational decisions, such as production plans and workforce levels.

    Another example of data mining, often called the market basket analysis, relates to its use in retail sales. If a clothing store records the purchases of customers, a data mining system could identify those customers who favor silk shirts over cotton ones. Although some explanations of relationships may be difficult, taking advantage of it is easier. The example deals with association rules within transaction-based data. Not all data are transaction based and logical, or inexact rules may also be present within a database.

    Market basket analysis has also been used to identify the purchase patterns of the Alpha Consumer. Alpha Consumers are people that play a key role in connecting with the concept behind a product, then adopting that product, and finally validating it for the rest of society. Analyzing the data collected on this type of user has allowed companies to predict future buying trends and forecast supply demands. Data mining is a highly effective tool in the catalog marketing industry.[citation needed] Catalogers have a rich database of history of their customer transactions for millions of customers dating back a number of years. Data mining tools can identify patterns among customers and help identify the most likely customers to respond to upcoming mailing campaigns.

    Data mining for business applications is a component that needs to be integrated into a complex modeling and decision making process. Reactive business intelligence (RBI) advocates a "holistic" approach that integrates data mining, modeling, and interactive visualization into an end-to-end discovery and continuous innovation process powered by human and automated learning.

    In the area of decision making, the RBI approach has been used to mine knowledge that is progressively acquired from the decision maker, and then self-tune the decision method accordingly.

    An example of data mining related to an integrated-circuit (IC) production line is described in the paper "Mining IC Test Data to Optimize VLSI Testing." In this paper, the application of data mining and decision analysis to the problem of die-level functional testing is described. Experiments mentioned demonstrate the ability to apply a system of mining historical die-test data to create a probabilistic model of patterns of die failure. These patterns are then utilized to decide, in real time, which die to test next and when to stop testing. This system has been shown, based on experiments with historical test data, to have the potential to improve profits on mature IC products.

    Science and engineering

    In recent years, data mining has been used widely in the areas of science and engineering, such as bioinformatics, genetics, medicine, education and electrical power engineering.

    In the study of human genetics, sequence mining helps address the important goal of understanding the mapping relationship between the inter-individual variations in human DNA sequence and the variability in disease susceptibility. In simple terms, it aims to find out how the changes in an individual's DNA sequence affects the risks of developing common diseases such as cancer, which is of great importance to improving methods of diagnosing, preventing, and treating these diseases. The data mining method that is used to perform this task is known as multifactor dimensionality reduction.

    In the area of electrical power engineering, data mining methods have been widely used for condition monitoring of high voltage electrical equipment. The purpose of condition monitoring is to obtain valuable information on, for example, the status of the insulation (or other important safety-related parameters). Data clustering techniques - such as the self-organizing map (SOM), have been applied to vibration monitoring and analysis of transformer on-load tap-changers (OLTCS). Using vibration monitoring, it can be observed that each tap change operation generates a signal that contains information about the condition of the tap changer contacts and the drive mechanisms. Obviously, different tap positions will generate different signals. However, there was considerable variability amongst normal condition signals for exactly the same tap position. SOM has been applied to detect abnormal conditions and to hypothesize about the nature of the abnormalities.

    Data mining methods have also been applied to dissolved gas analysis (DGA) in power transformers. DGA, as a diagnostics for power transformers, has been available for many years. Methods such as SOM has been applied to analyze generated data and to determine trends which are not obvious to the standard DGA ratio methods (such as Duval Triangle).

    Another example of data mining in science and engineering is found in educational research, where data mining has been used to study the factors leading students to choose to engage in behaviors which reduce their learning, and to understand factors influencing university student retention. A similar example of social application of data mining is its use in expertise finding systems, whereby descriptors of human expertise are extracted, normalized, and classified so as to facilitate the finding of experts, particularly in scientific and technical fields. In this way, data mining can facilitate institutional memory.

    Other examples of application of data mining methods are biomedical data facilitated by domain ontologies, mining clinical trial data, and traffic analysis using SOM.

    In adverse drug reaction surveillance, the Uppsala Monitoring Centre has, since 1998, used data mining methods to routinely screen for reporting patterns indicative of emerging drug safety issues in the WHO global database of 4.6 million suspected adverse drug reaction incidents. Recently, similar methodology has been developed to mine large collections of electronic health records for temporal patterns associating drug prescriptions to medical diagnoses.

    Data mining has been applied software artifacts within the realm of software engineering: Mining Software Repositories.

    Human rights

    Data mining of government records - particularly records of the justice system (i.e. courts, prisons) - enables the discovery of systemic human rights violations in connection to generation and publication of invalid or fraudulent legal records by various government agencies.

    Medical data mining

    In 2011, the case of Sorrell v. IMS Health, Inc., decided by the Supreme Court of the United States, ruled that pharmacies may share information with outside companies. This practice was authorized under the 1st Amendment of the Constitution, protecting the "freedom of speech."

    Spatial data mining

    Spatial data mining is the application of data mining methods to spatial data. The end objective of spatial data mining is to find patterns in data with respect to geography. So far, data mining and Geographic Information Systems (GIS) have existed as two separate technologies, each with its own methods, traditions, and approaches to visualization and data analysis. Particularly, most contemporary GIS have only very basic spatial analysis functionality. The immense explosion in geographically referenced data occasioned by developments in IT, digital mapping, remote sensing, and the global diffusion of GIS emphasizes the importance of developing data-driven inductive approaches to geographical analysis and modeling.

    Data mining offers great potential benefits for GIS-based applied decision-making. Recently, the task of integrating these two technologies has become of critical importance, especially as various public and private sector organizations possessing huge databases with thematic and geographically referenced data begin to realize the huge potential of the information contained therein. Among those organizations are:

      • offices requiring analysis or dissemination of geo-referenced statistical data
      • public health services searching for explanations of disease clustering
      • environmental agencies assessing the impact of changing land-use patterns on climate change
      • geo-marketing companies doing customer segmentation based on spatial location.

    • .

      Challenges in Spatial mining: Geospatial data repositories tend to be very large. Moreover, existing GIS datasets are often splintered into feature and attribute components that are conventionally archived in hybrid data management systems. Algorithmic requirements differ substantially for relational (attribute) data management and for topological (feature) data management. Related to this is the range and diversity of geographic data formats, which present unique challenges. The digital geographic data revolution is creating new types of data formats beyond the traditional "vector" and "raster" formats. Geographic data repositories increasingly include ill-structured data, such as imagery and geo-referenced multi-media.

      There are several critical research challenges in geographic knowledge discovery and data mining. Miller and Han offer the following list of emerging research topics in the field:

      Developing and supporting geographic data warehouses (GDW's): Spatial properties are often reduced to simple aspatial attributes in mainstream data warehouses. Creating an integrated GDW requires solving issues of spatial and temporal data interoperability - including differences in semantics, referencing systems, geometry, accuracy, and position.

      Better spatio-temporal representations in geographic knowledge discovery: Current geographic knowledge discovery (GKD) methods generally use very simple representations of geographic objects and spatial relationships. Geographic data mining methods should recognize more complex geographic objects (i.e. lines and polygons) and relationships (i.e. non-Euclidean distances, direction, connectivity, and interaction through attributed geographic space such as terrain). Furthermore, the time dimension needs to be more fully integrated into these geographic representations and relationships.

      Geographic knowledge discovery using diverse data types: GKD methods should be developed that can handle diverse data types beyond the traditional raster and vector models, including imagery and geo-referenced multimedia, as well as dynamic data types (video streams, animation).

      Sensor data mining

      Wireless sensor networks can be used for facilitating the collection of data for spatial data mining for a variety of applications such as air pollution monitoring. A characteristic of such networks is that nearby sensor nodes monitoring an environmental feature typically register similar values. This kind of data redundancy due to the spatial correlation between sensor observations inspires the techniques for in-network data aggregation and mining. By measuring the spatial correlation between data sampled by different sensors, a wide class of specialized algorithms can be developed to develop more efficient spatial data mining algorithms.

      Visual data mining

      In the process of turning from analogical into digital, large data sets have been generated, collected, and stored discovering statistical patterns, trends and information which is hidden in data, in order to build predictive patterns. Studies suggest visual data mining is faster and much more intuitive than is traditional data mining. See also Computer vision.

      Music data mining

      Data mining techniques, and in particular co-occurrence analysis, has been used to discover relevant similarities among music corpora (radio lists, CD databases) for the purpose of classifying music into genres in a more objective manner.


      Data mining has been used to fight terrorism by the U.S. government. Programs include the Total Information Awareness (TIA) program, Secure Flight (formerly known as Computer-Assisted Passenger Prescreening System (CAPPS II)), Analysis, Dissemination, Visualization, Insight, Semantic Enhancement (ADVISE), and the Multi-state Anti-Terrorism Information Exchange (MATRIX). These programs have been discontinued due to controversy over whether they violate the 4th Amendment to the United States Constitution, although many programs that were formed under them continue to be funded by different organizations or under different names.

      In the context of combating terrorism, two particularly plausible methods of data mining are "pattern mining" and "subject-based data mining".

      Pattern mining

      "Pattern mining" is a data mining method that involves finding existing patterns in data. In this context patterns often means association rules. The original motivation for searching association rules came from the desire to analyze supermarket transaction data, that is, to examine customer behavior in terms of the purchased products. For example, an association rule "beer ? potato chips (80%)" states that four out of five customers that bought beer also bought potato chips.

      In the context of pattern mining as a tool to identify terrorist activity, the National Research Council provides the following definition: "Pattern-based data mining looks for patterns (including anomalous data patterns) that might be associated with terrorist activity - these patterns might be regarded as small signals in a large ocean of noise." Pattern Mining includes new areas such a Music Information Retrieval (MIR) where patterns seen both in the temporal and non temporal domains are imported to classical knowledge discovery search methods.

      Subject-based data mining

      "Subject-based data mining" is a data mining method involving the search for associations between individuals in data. In the context of combating terrorism, the National Research Council provides the following definition: "Subject-based data mining uses an initiating individual or other datum that is considered, based on other information, to be of high interest, and the goal is to determine what other persons or financial transactions or movements, etc., are related to that initiating datum."

      Knowledge grid

      Knowledge discovery "On the Grid" generally refers to conducting knowledge discovery in an open environment using grid computing concepts, allowing users to integrate data from various online data sources, as well make use of remote resources, for executing their data mining tasks. The earliest example was the Discovery Net, developed at Imperial College London, which won the "Most Innovative Data-Intensive Application Award" at the ACM SC02 (Supercomputing 2002) conference and exhibition, based on a demonstration of a fully interactive distributed knowledge discovery application for a bioinformatics application. Other examples include work conducted by researchers at the University of Calabria, who developed a Knowledge Grid architecture for distributed knowledge discovery, based on grid computing.

      Reliability / Validity

      Data mining can be misused, and can also unintentionally produce results which appear significant but which do not actually predict future behavior and cannot be reproduced on a new sample of data. See Data dredging.

      Privacy concerns and ethics

      Some people believe that data mining itself is ethically neutral. While the term "data mining" has no ethical implications, it is often associated with the mining of information in relation to peoples' behavior (ethical and otherwise). To be precise, data mining is a statistical method that is applied to a set of information (i.e. a data set). Associating these data sets with people is an extreme narrowing of the types of data that are available. Examples could range from a set of crash test data for passenger vehicles, to the performance of a group of stocks. These types of data sets make up a great proportion of the information available to be acted on by data mining methods, and rarely have ethical concerns associated with them. However, the ways in which data mining can be used can in some cases and contexts raise questions regarding privacy, legality, and ethics. In particular, data mining government or commercial data sets for national security or law enforcement purposes, such as in the Total Information Awareness Program or in ADVISE, has raised privacy concerns.

      Data mining requires data preparation which can uncover information or patterns which may compromise confidentiality and privacy obligations. A common way for this to occur is through data aggregation. Data aggregation involves combining data together (possibly from various sources) in a way that facilitates analysis (but that also might make identification of private, individual-level data deducible or otherwise apparent). This is not data mining per se, but a result of the preparation of data before - and for the purposes of - the analysis. The threat to an individual's privacy comes into play when the data, once compiled, cause the data miner, or anyone who has access to the newly compiled data set, to be able to identify specific individuals, especially when the data were originally anonymous.

      It is recommended that an individual is made aware of the following before data are collected:
      the purpose of the data collection and any (known) data mining projects
      how the data will be used
      who will be able to mine the data and use the data and their derivatives
      the status of security surrounding access to the data
      how collected data can be updated.

      In America, privacy concerns have been addressed to some extent by the US Congress via the passage of regulatory controls such as the Health Insurance Portability and Accountability Act (HIPAA). The HIPAA requires individuals to give their "informed consent" regarding information they provide and its intended present and future uses. According to an article in Biotech Business Week', "'in practice, HIPAA may not offer any greater protection than the longstanding regulations in the research arena,' says the AAHC. More importantly, the rule's goal of protection through informed consent is undermined by the complexity of consent forms that are required of patients and participants, which approach a level of incomprehensibility to average individuals." This underscores the necessity for data anonymity in data aggregation and mining practices.

      Data may also be modified so as to become anonymous, so that individuals may not readily be identified. However, even "de-identified"/"anonymized" data sets can potentially contain enough information to allow identification of individuals, as occurred when journalists were able to find several individuals based on a set of search histories that were inadvertently released by AOL.

      Free open-source data mining software and applications
      • Carrot2: Text and search results clustering framework.
      • A chemical structure miner and web search engine.
      • ELKI: A university research project with advanced cluster analysis and outlier detection methods written in the Java language.
      • GATE: a natural language processing and language engineering tool.
      • SCaViS: Java cross-platform data analysis framework developed at Argonne National Laboratory.
      • KNIME: The Konstanz Information Miner, a user friendly and comprehensive data analytics framework.
      • ML-Flex: A software package that enables users to integrate with third-party machine-learning packages written in any programming language,
      • execute classification analyses in parallel across multiple computing nodes, and produce HTML reports of classification results.
      • NLTK (Natural Language Toolkit): A suite of libraries and programs for symbolic and statistical natural language processing (NLP) for the Python language.
      • SenticNet API: A semantic and affective resource for opinion mining and sentiment analysis.
      • Orange: A component-based data mining and machine learning software suite written in the Python language.
      • R: A programming language and software environment for statistical computing, data mining, and graphics. It is part of the GNU Project.
      • RapidMiner: An environment for machine learning and data mining experiments.
      • UIMA: The UIMA (Unstructured Information Management Architecture) is a component framework for analyzing unstructured content such as text, audio and video � originally developed by IBM.
      • Weka: A suite of machine learning software applications written in the Java programming language.

    • .

      Commercial data-mining software and applications
      • Angoss KnowledgeSTUDIO: data mining tool provided by Angoss.
      • BIRT Analytics: visual data mining and predictive analytics tool provided by Actuate Corporation.
      • Clarabridge: enterprise class text analytics solution.
      • IBM DB2 Intelligent Miner: in-database data mining platform provided by IBM, with modeling, scoring and visualization services based on the SQL/MM - PMML framework.
      • IBM SPSS Modeler: data mining software provided by IBM.
      • KXEN Modeler: data mining tool provided by KXEN.
      • LIONsolver: an integrated software application for data mining, business intelligence, and modeling that implements the Learning and Intelligent
      • OptimizatioN (LION) approach.
      • Oracle Data Mining: data mining software by Oracle.
      • Predixion Insight: data mining software by Predixion Software.
      • SAS Enterprise Miner: data mining software provided by the SAS Institute.
      • STATISTICA Data Miner: data mining software provided by StatSoft.

    • .

      Marketplace surveys

      Several researchers and organizations have conducted reviews of data mining tools and surveys of data miners. These identify some of the strengths and weaknesses of the software packages. They also provide an overview of the behaviors, preferences and views of data miners. Some of these reports include:

      • 2011 Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
      • Annual Rexer Analytics Data Miner Surveys (2007-2011)
      • Forrester Research 2010 Predictive Analytics and Data Mining Solutions report
      • Gartner 2008 "Magic Quadrant" report
      • Robert A. Nisbet's 2006 Three Part Series of articles "Data Mining Tools: Which One is Best For CRM?"
      • Haughton et al.'s 2003 Review of Data Mining Software Packages in The American Statistician

    • .

  • Business Performance Management :

    Business Performance Management

    Business performance management is a set of management and analytic processes that enables the management of an organization's performance to achieve one or more pre-selected goals. Synonyms for "business performance management" include "corporate performance management (CPM)" and "enterprise performance management".

    Business performance management is contained within approaches to business process management. Business performance management has three main activities:

    selection of goals,

    consolidation of measurement information relevant to an organization�s progress against these goals, and interventions made by managers in light of this information with a view to improving future performance against these goals. Although presented here sequentially, typically all three activities will run concurrently, with interventions by managers affecting the choice of goals, the measurement information monitored, and the activities being undertaken by the organization.

    Because business performance management activities in large organizations often involve the collation and reporting of large volumes of data, many software vendors, particularly those offering business intelligence tools, market products intended to assist in this process. As a result of this marketing effort, business performance management is often incorrectly understood as an activity that necessarily relies on software systems to work, and many definitions of business performance management explicitly suggest software as being a definitive component of the approach.

    This interest in business performance management from the software community is sales-driven - "The biggest growth area in operational BI analysis is in the area of business performance management."

    Since 1992, business performance management has been strongly influenced by the rise of the balanced scorecard framework. It is common for managers to use the balanced scorecard framework to clarify the goals of an organization, to identify how to track them, and to structure the mechanisms by which interventions will be triggered. These steps are the same as those that are found in BPM, and as a result balanced scorecard is often used as the basis for business performance management activity with organizations.

    In the past, owners have sought to drive strategy down and across their organizations, transform these strategies into actionable metrics and use analytics to expose the cause-and-effect relationships that, if understood, could give insight into decision-making.


    Reference to non-business performance management occurs in Sun Tzu's The Art of War. Sun Tzu claims that to succeed in war, one should have full knowledge of one's own strengths and weaknesses as well as those of one's enemies. Lack of either set of knowledge might result in defeat. Parallels between the challenges in business and those of war include:

    collecting data - both internal and external discerning patterns and meaning in the data (analyzing) responding to the resultant information Prior to the start of the Information Age in the late 20th century, businesses sometimes took the trouble to laboriously collect data from non-automated sources. As they lacked computing resources to properly analyze the data, they often made commercial decisions primarily on the basis of intuition.

    As businesses started automating more and more systems, more and more data became available. However, collection often remained a challenge due to a lack of infrastructure for data exchange or due to incompatibilities between systems. Reports on the data gathered sometimes took months to generate. Such reports allowed informed long-term strategic decision-making. However, short-term tactical decision-making often continued to rely on intuition.

    In 1989 Howard Dresner, a research analyst at Gartner, popularized "business intelligence" (BI) as an umbrella term to describe a set of concepts and methods to improve business decision-making by using fact-based support systems. Performance management builds on a foundation of BI, but marries it to the planning-and-control cycle of the enterprise - with enterprise planning, consolidation and modeling capabilities.

    Increasing standards, automation, and technologies have led to vast amounts of data becoming available. Data warehouse technologies have allowed the building of repositories to store this data. Improved ETL and enterprise application integration tools have increased the timely collecting of data. OLAP reporting technologies have allowed faster generation of new reports which analyze the data. As of 2010, business intelligence has become the art of sieving through large amounts of data, extracting useful information and turning that information into actionable knowledge.

    Definition and scope

    Business performance management consists of a set of management and analytic processes, supported by technology, that enable businesses to define strategic goals and then measure and manage performance against those goals. Core business performance management processes include financial planning, operational planning, business modeling, consolidation and reporting, analysis, and monitoring of key performance indicators linked to strategy.

    Business performance management involves consolidation of data from various sources, querying, and analysis of the data, and putting the results into practice.


    Various methodologies for implementing business performance management exist. The discipline gives companies a top-down framework by which to align planning and execution, strategy and tactics, and business-unit and enterprise objectives. Reactions may include the Six Sigma strategy, balanced scorecard, activity-based costing (ABC), Total Quality Management, economic value-add, integrated strategic measurement and Theory of Constraints.

    The balanced scorecard is the most widely adopted performance management methodology.

    Methodologies on their own cannot deliver a full solution to an enterprise's CPM needs. Many pure-methodology implementations fail to deliver the anticipated benefits due to lack of integration with fundamental CPM processes.

    Metrics and key performance indicators

    Some of the areas from which bank management may gain knowledge by using business performance management include:

    • customer-related numbers:
    • new customers acquired
    • status of existing customers
    • attrition of customers (including breakup by reason for attrition)
    • turnover generated by segments of the customers - possibly using demographic filters
    • outstanding balances held by segments of customers and terms of payment - possibly using demographic filters
    • collection of bad debts within customer relationships
    • demographic analysis of individuals (potential customers) applying to become customers, and the levels of approval, rejections and pending numbers
    • delinquency analysis of customers behind on payments
    • profitability of customers by demographic segments and segmentation of customers by profitability
    • campaign management
    • real-time dashboard on key operational metrics
    • overall equipment effectiveness
    • clickstream analysis on a website
    • key product portfolio trackers
    • marketing-channel analysis
    • sales-data analysis by product segments
    • callcenter metrics
    • Though the above list describes what a bank might monitor, it could refer to a telephone company or to a similar service-sector company.
    • Items of generic importance include:
    • consistent and correct KPI-related data providing insights into operational aspects of a company
    • timely availability of KPI-related data
    • KPIs designed to directly reflect the efficiency and effectiveness of a business
    • information presented in a format which aids decision-making for management and decision-makers
    • ability to discern patterns or trends from organized information
    • Business performance management integrates the company's processes with CRM or ERP. Companies should become better able to gauge customer
    • satisfaction, control customer trends and influence shareholder value.

  • .

    Application software types

    People working in business intelligence have developed tools that ease the work of business performance management, especially when the business-intelligence task involves gathering and analyzing large amounts of unstructured data.

    Tool categories commonly used for business performance management include:

    OLAP - online analytical processing, sometimes simply called "analytics" (based on dimensional analysis and the so-called "hypercube" or "cube") scorecarding, dashboarding and data visualization

    • data warehouses
    • document warehouses
    • text mining
    • BPO - business performance optimization
    • EPM - enterprise performance management
    • EIS - executive information systems
    • DSS - decision support systems
    • MIS - management information systems
    • SEMS - strategic enterprise management software

  • .

    Design and implementation

    Questions asked when implementing a business performance management program include:

    Goal-alignment queries

    Determine the short- and medium-term purpose of the program. What strategic goal(s) of the organization will the program address? What organizational mission/vision does it relate to? A hypothesis needs to be crafted that details how this initiative will eventually improve results / performance (i.e. a strategy map).

    Baseline queries

    Assess current information-gathering competency. Does the organization have the capability to monitor important sources of information? What data is being collected and how is it being stored? What are the statistical parameters of this data, e.g., how much random variation does it contain? Is this being measured?

    Cost and risk queries

    Estimate the financial consequences of a new BI initiative. Assess the cost of the present operations and the increase in costs associated with the BPM initiative. What is the risk that the initiative will fail? This risk assessment should be converted into a financial metric and included in the planning.

    Customer and stakeholder queries

    Determine who will benefit from the initiative and who will pay. Who has a stake in the current procedure? What kinds of customers / stakeholders will benefit directly from this initiative? Who will benefit indirectly? What quantitative / qualitative benefits follow? Is the specified initiative the best or only way to increase satisfaction for all kinds of customers? How will customer benefits be monitored? What about employees, shareholders, and distribution channel members?

    Metrics-related queries

    Information requirements need operationalization into clearly defined metrics. Decide which metrics to use for each piece of information being gathered. Are these the best metrics and why? How many metrics need to be tracked? If this is a large number (it usually is), what kind of system can track them? Are the metrics standardized, so they can be benchmarked against performance in other organizations? What are the industry standard metrics available?

    Measurement methodology-related queries

    Establish a methodology or a procedure to determine the best (or acceptable) way of measuring the required metrics. How frequently will data be collected? Are there any industry standards for this? Is this the best way to do the measurements? How do we know that?

    Results-related queries

    Monitor the BPM program to ensure that it meets objectives. The program itself may require adjusting. The program should be tested for accuracy, reliability, and validity. How can it be demonstrated that the BI initiative, and not something else, contributed to a change in results? How much of the change was probably random?

    Quality Service

    Quality in a service or product is not what you put into it. It is what the client or customer gets out of it.
    -Peter Drucker

    Intelligent Quotes

    A solid working knowledge of productivity software and other IT tools has become a basic foundation for success in virtually any career. Beyond that, however, I don't think you can overemphasise the importance of having a good background in maths and science.....
    "Every software system needs to have a simple yet powerful organizational philosophy (think of it as the software equivalent of a sound bite that describes the system's architecture)... A step in thr development process is to articulate this architectural framework, so that we might have a stable foundation upon which to evolve the system's function points. "
    "All architecture is design but not all design is architecture. Architecture represents the significant design decisions that shape a system, where significant is measured by cost of change"
    "The ultimate measurement is effectiveness, not efficiency "
    "It is argued that software architecture is an effective tool to cut development cost and time and to increase the quality of a system. "Architecture-centric methods and agile approaches." Agile Processes in Software Engineering and Extreme Programming.
    "Java is C++ without the guns, knives, and clubs "
    "When done well, software is invisible"
    "Our words are built on the objects of our experience. They have acquired their effectiveness by adapting themselves to the occurrences of our everyday world."
    "I always knew that one day Smalltalk would replace Java. I just didn't know it would be called Ruby. "
    "The best way to predict the future is to invent it."
    "In 30 years Lisp will likely be ahead of C++/Java (but behind something else)"
    "Possibly the only real object-oriented system in working order. (About Internet)"
    "Simple things should be simple, complex things should be possible. "
    "Software engineering is the establishment and use of sound engineering principles in order to obtain economically software that is reliable and works efficiently on real machines."
    "Model Driven Architecture is a style of enterprise application development and integration, based on using automated tools to build system independent models and transform them into efficient implementations. "
    "The Internet was done so well that most people think of it as a natural resource like the Pacific Ocean, rather than something that was man-made. When was the last time a technology with a scale like that was so error-free? The Web, in comparison, is a joke. The Web was done by amateurs. "
    "Software Engineering Economics is an invaluable guide to determining software costs, applying the fundamental concepts of microeconomics to software engineering, and utilizing economic analysis in software engineering decision making. "
    "Ultimately, discovery and invention are both problems of classification, and classification is fundamentally a problem of finding sameness. When we classify, we seek to group things that have a common structure or exhibit a common behavior. "
    "Perhaps the greatest strength of an object-oriented approach to development is that it offers a mechanism that captures a model of the real world. "
    "The entire history of software engineering is that of the rise in levels of abstraction. "
    "The amateur software engineer is always in search of magic, some sensational method or tool whose application promises to render software development trivial. It is the mark of the professional software engineer to know that no such panacea exist "

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