Data Management Foundations Chapter 7 Terms

Multidimensional data analysis techniques are augmented by the following functions:

-Advanced data presentation functions. These functions include 3D graphics, pivot tables, crosstabs, data rotation, and three-dimensional cubes. -Advanced data aggregation, consolidation, and classification functions. These allow the data analyst to create multiple data aggregation levels, slice and dice data, and drill down and roll up data across different dimensions and aggregation levels. -Advanced computational functions. These include business-oriented variables such as market share, period comparisons, sales margins, product margins, and percentage changes; financial and accounting ratios, including profitability, overhead, cost allocations, and returns; and statistical and forecasting functions.

A modern BI system provides three distinctive reporting styles:

-Advanced reporting. A BI system presents insightful information about the organization in a variety of presentation formats. -Monitoring and alerting. After a decision has been made, the BI system offers ways to monitor the decision's outcome. -Advanced data analytics. A BI system provides tools to help the end user discover relationships, patterns, and trends hidden within the organization's data. These tools are used to create two types of data analysis: explanatory and predictive.

BI provides a framework for:

-Collecting and storing operational data -Aggregating the operational data into decision support data -Analyzing decision support data to generate information -Presenting such information to the end user to support business decisions -Making business decisions, which in turn generate more data that is collected, stored, and so on (restarting the process) -Monitoring results to evaluate outcomes of the business decisions, which again provides more data to be collected, stored, and so on -Predicting future behaviors and outcomes with a high degree of accuracy

technological trends in BI:

-Data storage improvements. Newer data storage technologies, such as solid state drives (SSD) and Serial Advanced Technology Attachment (SATA) drives, offer increased performance and larger capacity that make data storage faster and more affordable. -Business intelligence appliances. Vendors now offer plug-and-play appliances optimized for data warehouse and BI applications. -Business intelligence as a service. Vendors now offer data warehouses and BI as a service. These cloud-based services allow any corporation to rapidly develop a data warehouse store without the need for hardware, software, or extra personnel. -Big Data analytics. The Big Data phenomenon is creating a new market for data analytics. Organizations are turning to social media as the new source for information and knowledge to gain competitive advantages. Personal analytics. OLAP brought data analytics to the desktop of every end user in an organization. Mobile BI is extending business decision making outside the walls of the organization. BI can now be deployed to mobile users who are closer to customers. The main requirement is for the BI end user to have a key understanding of the business.

BI provides other benefits:

-Integrating architecture: Like any other IT project, BI has the potential of becoming the integrating umbrella for a disparate mix of IT systems within an organization. T -Common user interface for data reporting and analysis: BI front ends can provide up-to-the-minute consolidated information using a common interface for all company users. -Common data repository fosters single version of company data: BI provides a framework to integrate data under a common environment and present a single version of the data. -Improved organizational performance: BI can provide competitive advantages in many different areas, from customer support to manufacturing processes.

decision support system (DSS)

An arrangement of computerized tools used to assist managerial decision making within a business.

Multidimensional online analytical processing (MOLAP)

An extension of online analytical processing to multidimensional database management systems.

Relational online analytical processing (ROLAP)

Analytical processing functions that use relational databases and familiar relational query tools to store and analyze multidimensional data.

roll up

(1) To aggregate data into summarized components, that is, higher levels of aggregation. (2) In SQL, an OLAP extension used with the GROUP BY clause to aggregate data by different dimensions. Rolling up the data is the exact opposite of drilling down the data.

Data warehouses (DW)

The data warehouse is the foundation of a BI infrastructure. Data is captured from the production system and placed in the DW on a near real-time basis. BIprovides company-wide integration of data and the capability to respond to business issues in a timely manner.

True

The end-user analytical interface is one of the most critical OLAP components.

cube cache

In multidimensional OLAP, the shared, reserved memory area where data cubes are held. Using the ____________________ assists in speeding up data access.

Sparcity

In multidimensional data analysis, a measurement of the data density held in the data cube.

serialized items

Items for which each instance of the item must be tracked as an individually identifiable item.

non-serialized items

Items for which the attributes describe a generalized view of that kind of item, without identifying each individual instance of the item.

data cube

The multidimensional data structure used to store and manipulate data in a multidimensional DBMS. The location of each data value in the ______________ is based on its x-, y-, and z-axes. ________________ are static, meaning they must be created before they are used, so they cannot be created by an ad hoc query.

Data analysis and reporting tools

These advanced tools are used to query multiple and diverse data sources to create integrated reports.

Data-mining tools

These tools provide advanced statistical analysis to uncover problems and opportunities hidden within business data.

Data visualization

These tools provide advanced visual analysis and techniques to enhance understanding and create additional insight of business data and its true meaning.

Business intelligence (BI)

A comprehensive, cohesive, and integrated set of tools and processes used to capture, collect, integrate, store, and analyze data with the purpose of generating and presenting information to support business decision making.

multidimensional database management systems (MDBMSs)

A database management system that uses proprietary techniques to store data in matrix like arrays of n dimensions known as cubes.

data mart

A small, single-subject data warehouse subset that provides decision support to a small group of people.

To deliver efficient decision support, OLAP tools must have the following advanced data access features:

Access to many different kinds of DBMSs, flat files, and internal and external data sources Access to aggregated data warehouse data as well as to the detail data found in operational databases Advanced data navigation features such as drill-down and roll-up Rapid and consistent query response times The ability to map end-user requests, expressed in either business or model terms, to the appropriate data source and then to the proper data access language (usually SQL). The query code must be optimized to match the data source, regardless of whether the source is operational or data warehouse data. Support for very large databases. As explained earlier, the data warehouse could easily and quickly grow to multiple terabytes in size.

True

BI covers a range of technologies and applications to manage the entire data life cycle from acquisition to storage, transformation, integration, presentation, analysis, monitoring, and archiving.

False

Data cubes can be created by ad hoc queries.

Online analytical processing (OLAP)

Decision support system (DSS) tools that use multidimensional data analysis techniques. _________________________ creates an advanced data analysis environment that supports decision making, business modeling, and operations research.

An OLAP system has three main architectural components:

Graphical user interface (GUI) Analytical processing logic Data-processing logic

dimension tables

In a data warehouse, tables used to search, filter, or classify facts within a star schema.

ETL - extraction, transformation, and loading

In a data warehousing environment, the integrated processes of getting data from original sources into the data warehouse. ETL includes retrieving data from original data sources (extraction), manipulating the data into an appropriate form (transformation), and storing the data in the data warehouse (loading).

master data management(MDM)

In business intelligence, a collection of concepts, techniques, and processes for the proper identification, definition, and management of data elements within an organization.

Key performance indicators (KPIs)

In business intelligence, quantifiable numeric or scale-based measurements that assess a company's effectiveness or success in reaching strategic and operational goals. Examples are product turnovers, sales by promotion, sales by employee, and earnings per share.

Governance

In business intelligence, the methods for controlling and monitoring business health and promoting consistent decision making.

Online analytical processing (OLAP) is a BI style whose systems share three main characteristics:

Multidimensional data analysis techniques Advanced database support Easy-to-use end-user interfaces

ROLAP adds the following extensions to traditional RDBMS technology:

Multidimensional data schema support within the RDBMS Data access language and query performance optimized for multidimensional data Support for very large databases (VLDBs)

OLAP tools

Online analytical processing provides multidimensional data analysis.

predictive analysis

Provides the end user with ways to create models that predict future outcomes.

Explanatory analysis

Provides ways to discover relationships, trends, and patterns among data.

False

Support for VLDBs is not a requirement for decision support databases.

True

Tactical and strategic decisions are also shaped by constant pressure from external and internal forces, including globalization, the cultural and legal environment, and technology.

Data store

The ______________ is optimized for decision support and is generally represented by a data warehouse or a data mart. The data is stored in structures that are optimized for data analysis and query speed.

slice and dice

The ability to focus on slices of a data cube (drill down or roll up) to perform a more detailed analysis.

Data monitoring and alerting

This component allows real-time monitoring of business activities. The BI system will present the concise information in a single integrated view for the data analyst. This integrated view could include specific metrics about the system performance or activities, such as number of orders placed in the last four hours, number of customer complaints by product by month, and total revenue by region. Alerts can be placed on a given metric; once the value of a metric goes below or above a certain baseline, the system will perform a given action, such as emailing shop floor managers, presenting visual alerts, or starting an application.

Data analytics

This component performs data analysis and data-mining tasks using the data in the data store. This tool advises the user as to which data analysis tool to select and how to build a reliable business data model. Business models are generated by special algorithms that identify and enhance the understanding of business situations and problems. Data analysis can be either explanatory or predictive. Explanatory analysis uses the existing data in the data store to discover relationships and their types, and predictive analysis creates statistical models of the data that allow predictions of future values and events.

Query and reporting

This component performs data selection and retrieval, and it is used by the data analyst to create queries that access the database and create the required reports. Depending on the implementation, the query and reporting tool accesses the operational database, or more commonly, the data store.

Data visualization

This component presents data to the end user in a variety of meaningful and innovative ways. This tool helps the end user select the most appropriate presentation format, such as summary reports, maps, pie or bar graphs, mixed graphs, and static or interactive dashboards.

drill down

To decompose data into more atomic components—that is, data at lower levels of aggregation. This approach is used primarily in a decision support system to focus on specific geographic areas, business types, and so on.

Business Intelligence (BI)

______________ is not a product by itself, but a framework of concepts, practices, tools, and technologies that help a business better understand its core capabilities, provide snapshots of the company situation, and identify key opportunities to create competitive advantage.

Portals

______________ provide a unified, single point of entry for information distribution. ________________ are a web-based technology that use a web browser to integrate data from multiple sources into a single webpage. Many different types of BI functionality can be accessed through a portal.

Dashboards

_______________ use web-based technologies to present key business performance indicators or information in a single integrated view, generally using graphics that are clear, concise, and easy to understand.