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Information systems and management in business
Chapter 8Business Intelligence (BI)
8.1 Introduction
Overview Businesses and organizations gather large volumes
of business data via their operational systems The data is typically kept in relational databases or
large data warehouses In practice this data is left in their relevant
databases, archived or discarded when it has no further operational value
Traditional management and executive information systems are not geared to analyzing the data in a manner which is capable of discovering business value that lies hidden within such large volume of data
BI systems are geared to fit this role
8.2 Business Intelligence (BI) Vs Knowledge Management
knowledge management (KM) The process and strategies with which an
organization creates, capture, store, use, distribute, and share its intellectual assets is a concept that is typically referred to as knowledge management (KM) [32 and 33]
BI definition The process of accessing and analyzing vast
volumes of business that is created by operational systems and stored in various relational databases, data warehouses or data marts using complex analytical tools or technologies in order to enhance the effectiveness of the business decision making process
8.2 Business Intelligence (BI) Vs Knowledge Management
The BI Triangle Three key areas (BI triangle) need to
carefully evaluated and managed in order to create an effective and beneficial BI environment
The business value of BI BI technologies Business intelligence issues of concern
8.2 Business Intelligence (BI) Vs Knowledge Management
The BI Triangle The business value of BI
Business intelligence has the potential to add value to businesses in a number of key business areas some of which include
Competitiveness Responsiveness Customer’s satisfaction & experience Creating business opportunities
8.2 Business Intelligence (BI) Vs Knowledge Management
The BI Triangle BI technologies Primarily there are two key business
intelligence technologies Data analysis
Data mining OLAP
Data technologies
8.2 Business Intelligence (BI) Vs Knowledge Management
The BI Triangle Business intelligence issues of concern
A number of issues need to be taken into consideration and appropriately evaluated prior to embarking on a business intelligence project
Direct Vs Indirect Data Feed Silo Vs Centralized BI Approach Context Empowerment
8.3 Business Intelligence Key Data Technologies
BI key data technologies Online Transaction processing (OLTP) Data warehouses Data marts
8.3 Business Intelligence Key Data Technologies
Online Transaction processing (OLTP) overview Operational data is gathered using various
operational systems FSIS, TPSs, ERP, CRM, SCM, etc..
The process of storing, retrieving and manipulating operational data using various operational information systems is known as online transaction processing (OLTP)
Accuracy and speed are critical factors for OLTP OLTP are typically designed with performance -
transactions speed in mind OLTP associated databases employ a process known
as normalization for structuring transactional data in order to deliver on the speed and accuracy goals
8.3 Business Intelligence Key Data Technologies
Data Warehouses What is a Data Warehouse?
A data warehouse is basically a centralized repository of a business’s or an enterprise’s various operational data such as finance, HR, inventory and so forth [40 and 44]
Data in a data warehouse is read only and none volatile (historical) where as in operational systems (OLTP systems), it is current and regularly changing [41 and 56]
8.3 Business Intelligence Key Data Technologies
Data warehouses advantages The ability to facilitate data analysis
and reporting a way from operational systems
Data centralization Unified and a comprehensive view of the
business or the organization The ability to employ data modeling
techniques and servers technologies Optimized for speeding up reporting and
data querying
8.3 Business Intelligence Key Data Technologies
What is ETL? Short for extraction, loading and
transformation A critical part of a data warehouse
architecture ELT is a process which involves
extracting data from operational systems and loading it into a data warehouse
8.3 Business Intelligence Key Data Technologies
Data Marts A data mart is typically a very small
type of data warehouse which is used to keep transactional data of a particular business function, operation or a geographic location as opposed to keeping an entire organizational data [36,
37 and 38]
8.4 Business Intelligence Categories
There are three categories of business intelligence [23] Strategic
Used by executive and senior managers Historical data sourced from operational systems Months – decision latency
Tactical Used by middle managers Historical data sourced from operational systems Days, weeks or months – decision latency
Operational Used by front line workers such as call center agents and
sales executive Fresh and real or near real time data Few seconds, minutes or hours– decision latency
8.6 Key Business Intelligence Technologies
What is Data Mining? Generally data mining is defined as
searching and analyzing large volumes of data in order to identify patterns and relationships and to find useful information [48, 49, 50 and 51]
8.6 Key Business Intelligence Technologies
Data Mining Scope Generally, the data mining analysis process falls into
a number of categories [21, 26, 27] Examples
Classification Analyzing the data in order to identify predictive
information Regression
Similar to classification except that it is limited to working with continuous quantitative data [21]
Association Analyze the data in order to discover hidden patterns or
correlation that exists in the data Clustering
Entities that have similar characteristics are grouped together
8.6 Key Business Intelligence Technologies
The Data Mining Process Four steps process
Analysis request Request processing
Data mining application Typically involve some data modeling
based on statistical or machine learning techniques
Data retrieval OLTP, data warehouses, data marts
Analysis presentation
8.6 Key Business Intelligence Technologies
Data Mining Techniques (Algorithms) overview When we talk about data mining algorithms we are
basically referring to the statistical and machine learning techniques that are used to perform the data analysis which discover information in the data or make prediction from the data
There are a number of techniques which data mining employ for its predictive (classification or regression) or descriptive analysis (clustering or association) of the data
Artificial neural networks, decision trees, nearest neighbor method and rule induction [3, 9 and 26]
8.6 Key Business Intelligence Technologies
Data Mining In Practice The data mining vendors provide solutions
(products) that often incorporate a number of different analytical techniques
A single product may have the capability to perform classification, regression, association as well as clustering using various algorithms such as neural networks, CART and nearest neighbors [21, 26]
This feature is essential for building users confidence with using the generated data model
8.6 Key Business Intelligence Technologies
Online Analytical Processing Concept overview What is OLAP?
Generally, OLAP may be simply defined as a category of software applications or technologies which are designed to support the decision making process through providing a visual, speedy, interactive and a multi perspective (dimensions) view of the dataxx
8.6 Key Business Intelligence Technologies
OLAP Process Architecture Multidimensional data modelling and storage is
a key component of the OLAP process architecture
An OLAP server is at the centre of architecture Performs all the data manipulation,
computation and analysis required in order to satisfy all analysis queries received from its clients
OLAP Clients – the third component of the architecture
Typically present the analysis output in a multidimensional dimensional highly visual presentational formats
8.6 Key Business Intelligence Technologies
OLAP Activities There are a number of activities which an OLAP
client could deploy in order to analyze a multidimensional data structure with OLAP [57]
Slice and dice Slicing and dicing is basically about the ability to
break up large data into slices that could then be broken further into smaller chunks (dicing) in order to get a further insight into it
Drill down Analyzing the data from a hierarchal
perspective
8.7 Customer Relationship Management (CRM)
Customer relationship management definition A business philosophy or a strategy
that is focused in understanding and anticipating customer’s needs in order to create a strong and a profitable relationship
8.7 Customer Relationship Management (CRM)
The Business Value of CRM CRM places the customer at the centre of its
architecture Having a business strategy which puts the
customer at the center of this is likely to positively affect the profitability and the competitive position of the business
Providing a service that understands, anticipates and satisfy customer’s needs is an enabler to the process of retaining existing customers and potentially attracting new ones
8.7 Customer Relationship Management (CRM)
How to realize the CRM benefits A great deal of planning and careful
budgeting Appropriate training Enterprise wide early involvement Choosing the appropriate implementation
process A through understanding of need for
customization and the potential problems that may be associated with it.
A high degree of commitment and support from the top of the business management hierarchy
Chapter 8 Problems Solving Skills Development
Visit the book’s Web site www.halaeducation.com & select module 8
Perform Chapter 8 associated skills development through their respective skills development exercises link
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