IT445 - Decision Support Systems ch 1-3

Multidimensionality

The ability to organize, present, and analyze data by several dimensions, such as sales by region, by product, by salesperson, and by time (four dimensions)

dimensional approach

star schema, snowflake schema shows multi-dimentiality, dimensions, different time intervals

active data warehouse

strategic and tactical decisions. more robust, more users, more power, more expense more dollars

traditional data warehouse

strategic decisions only

Characteristics of Data Warehouses

subject oriented integrated nonvolatile summarized not normalized metadata

transactional data

summarized for use. Transactions that have occured

performance management system

system that assits managers in tacking the implementations of business strategy by comparing actual results against strategic goals and objectives

change capture

the identification, capture and delivery of the changes made to enterprise data stores

relational OLAP

the implementation of an OLAP database on top of an existing relational database

data federation

the integration of business views across multiple datastores

intelligence

the modern companies ethically and legally organize themselves to glean as much information from their customers, stakeholders, processes, to extract valuable information

Relational OLAP (ROLAP)

...

Ten factors that potentially affect the architecture selection decision

1) Information interdependence between organizational units 2) Upper management's information needs 3) Urgency of need for a data warehouse 4) Nature of end-user tasks 5) Constraints on resources 6) Strategic view of the data warehouse prior to implementation 7) Compatibility with existing systems 8) Perceived ability of the in-house IT staff 9)Technical issues 10) Social/political factors

A BI system has four major components

1) a data warehouse, with its source data 2) business analytics, a collection of tools for manipulating, mining, and analyzing the data in the data warehouse 3) business performance management (BPM) for monitoring and analyzing performance 4) a user interface (e.g., dashboard)

BPM 3 key components

1) a set of integrated, closed-loop management and analytical processes, 2) tools for businesses to define strategic goals and then measure/manage performance against them 3) methods and tools for monitoring key performance indicators linked to organizational strategy

Important criteria in selecting an ETL tool

1)Ability to read from and write to an unlimited number of data sources/architectures 2) Automatic capturing and delivery of metadata 3) A history of conforming to open standards 4) An easy-to-use interface for the developer and the functional user

Three-tier architecture

1 Data acquisition software (back-end) 2 The data warehouse that contains the data & software 3 Client (front-end) software that allows users to access and analyze data from the warehouse

Data warehouse definition

A physical repository where relational data are specially organized to provide enterprise-wide, cleansed data in a standardized format originally only comprised of historical data but can now be realtime

Enterprise application integration (EAI)

A technology that provides a vehicle for pushing data from source systems into a data warehouse

Enterprise information integration (EII)

An evolving tool space that promises real-time data integration from a variety of sources, such as relational or multidimensional databases, Web services, etc.

One Definition of Business Intelligence

BI is an umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies

ETL =

Extract Transform Load

multidimensional OLAP

MOLAP pre-aggregated

Successful BI

BI must be aligned with the company's business strategy

Hosted Data Warehouses

Benefits: Requires minimal investment in infrastructure Frees up capacity on in-house systems Frees up cash flow Makes powerful solutions affordable Enables solutions that provide for growth Offers better quality equipment and software Provides faster connections

Data warehouse techniques

Data warehouse and BI initiatives typically follow a process similar to that used in military intelligence initiatives

DW Implementation Issues

Identification of data sources and governance Data quality planning, data model design ETL tool selection Establishment of service-level agreements Data transport, data conversion Reconciliation process End-user support Political issues

Data integration

Integration that comprises three major processes: data access, data federation, and change capture.

Concerns about real-time BI

Not all data should be updated continuously Mismatch of reports generated minutes apart May be cost prohibitive May also be infeasible

OLAP extracts from OTAP

OLAP from OTAP

Data warehouses are designed to work with what systems

OLAP online analytical processing

Online analytic processing (OLAP) systems are involved in extracting information from data stored by OLTP systems

Routine sales reports by product, by region, by sales person, by ... Often built on top of a data warehouse where the data is not transactional Main goal is the effectiveness (and then, efficiency) - provide correct information in a timely manner More on OLAP will be covered in Chapter 2

closed-loop

The loop implies that optimum performance is achieved by setting goals and objectives (i.e., strategize), establishing initiatives and plans to achieve those goals (i.e., plan), monitoring actual performance against the goals and objectives (i.e., monitor), and taking corrective action (i.e., act and adjust). Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 126). Prentice Hall. Kindle Edition.

Star schema

The most commonly used and the simplest style of dimensional modeling Contain a fact table surrounded by and connected to several dimension tables

operational data store (ODS)

This type of database is often used as an interim staging area for a data warehouse. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 43). Prentice Hall. Kindle Edition.

balanced scorecard

a performance measurement and management methodology that helps translate an organization's financial, customer, growth and learning objectives and targets into a set of actionable initiatives.

enterprise application integration

a technology that provides a vehicle for pushing data from source systems into a data warehouse

ETL

also has implied cleansing of data

enterprise information integration

an evolving tool space that promises real-time data integration from a variety of sources, such as relational or multidimensional databases, web services, etc.

Transaction processing systems (OLTP)

are constantly involved in handling updates (add/edit/delete) to what we might call operational databases ATM withdrawal transaction, sales order entry via an ecommerce site - updates DBs OLTP - handles routine on-going business ERP, SCM, CRM systems generate and store data in OLTP systems The main goal is to have high efficiency

Inmon top down model

beggining with enterprise data warehouse

Kimball

beginning bottom-up, data mart to start then federateq

major objective of decision support

closing the gap between the current performance of an organization and its desired performance, as expressed in its mission, objectives, and goals, and the strategy to achieve them

Normilization

data warehouses normally not normalized

data warehouse administrator

excellent communication skills, business knowledge, software knowledge

ETL tools

expensive tough long learning curve

online transaction processing systems OLTP

handle a company's routine ongoing business. In contrast, a data warehouse is typically a distinct system that provides storage for data that will be made use of in analysis. Sharda, Ramesh; Delen, Dursun; Turban, Efraim; King, David (2013-12-23). Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) (Page 15). Prentice Hall. Kindle Edition.

data integration

integration that comprises three major processes: data access, data federation and change capture

two-tier

less flexible

Business Intelligence Major objective

major objective is to enable easy access to data (and models) to provide business managers with the ability to conduct analysis

federated architecture

mapped metadata to pull existing information from existing data warehouses

Hub and Spoke and Centralized data ware houses

most popular

information

process data that is aggregated, summarized and contextual.

bpm business performance managemeng

real time system that alerts managers to potential opportunities, impending problems and threats and empowers them to react through models and collaboration

relational approach

represented in form of tables projections, process thousands or millions of records to satisfy a query.

Client

requests services

OLAP vs. OLTP

see image on desktop

data warehouse hardware

separate from online processing system to ensure that the transactions are not affected by analytics on data warehouse

etl and metadata

should extract and capture meta data slide 67

data mart

small scale data warehouse

oper mart

staging area for a data mart

Datamart

A departmental small-scale "DW" that stores only limited/relevant data Could be a subset of a data warehouse or independently created