Loading presentation...

Present Remotely

Send the link below via email or IM


Present to your audience

Start remote presentation

  • Invited audience members will follow you as you navigate and present
  • People invited to a presentation do not need a Prezi account
  • This link expires 10 minutes after you close the presentation
  • A maximum of 30 users can follow your presentation
  • Learn more about this feature in our knowledge base article

Do you really want to delete this prezi?

Neither you, nor the coeditors you shared it with will be able to recover it again.


The Architecture of Business Intelligence

No description

Anabel Fernandez

on 25 April 2015

Comments (0)

Please log in to add your comment.

Report abuse

Transcript of The Architecture of Business Intelligence

Data Management
Internal Sources
Enterprise Resource Management (“ERM”) Systems
Customer Relationship Management (“CRM”) Systems
Point-of-sale (“POS”) Systems

External Sources
Syndicated Data Providers

Data Management
The greatest data challenge facing companies is “dirty” data: information that is inconsistent, fragmented, and out of context.

There’s a significant payoff for those who invest the effort to master data management. IT and business experts must tackle their data issues by addressing five areas:
Data relevance
Data sourcing
Data quantity
Data quality
Data governance

Data Relevance
What data is needed to compete on analytics?

Types of Data
Credit Scores
SAT / GMAT Scores

Non-Numerical Data
Performance Reviews

Considerable business expertise is required to help IT understand the potential relationships in the data for optimum organization.

Data Sourcing
Where Can Data Be Obtained?

Internal Information
Enterprise Systems
Workstations and Servers

External Information
Specialized Firms
Customer Websites

The Architecture of Business Intelligence
Data Quantity
How Much Data Is Needed?
In addition to gathering the right data, companies need to collect a lot of it in order to distill trends and predict behavior.

Two pitfalls
Collecting data “Just in case”
Collecting data that not important but easy to capture

This results in
Negative impact of irrelevant information
Costs outweigh the benefits

Data Quality
How can we make data more valuable?

Quantity vs. Quality

Characteristics of Valuable Data
In Context
Data Governance
What Rules and Processes are
needed to manage the data from
its acquisition through its retirement?

Data Management Life Cycle

Data acquisition
Determine what data is needed, and how to integrate systems with business processes.
Data cleansing
Detect and remove data that is out of date, incorrect, incomplete, or redundant.
Data organization and storage
Data must then be put into the right repository and format so that it is ready to use.
Data Maintenance
Create procedures to ensure data privacy, security, and integrity of data gathered
how data that is no longer needed will be saved, archived, or retired.

Transformation Tools and Processes
Extract, Transform, and load (ETL) data
Transformation process define the business logic that maps data from its destination to its source
Clean and validate the data using business rules
Standardize data definitions to make that business concepts have consistent, comparable definitions across the organization

1. Data Warehouses

2. Data Mart

3. Metadata Repository

Data Warehouses

Databases that contain integrated data from different sources and are regularly updated

Contains historical information

May be a module of an enterprise system or an independent database
Metadata Repository

Contains technical information and data definitions

It may include information about data reliability, accuracy, and instructions on how the data should be applied.

It significantly reduces the time needed for maintenance

Data Mart
A separate repository or a partitioned section of the Data Warehouse

Used to support a single business function

Contains some predetermined analysis

Should only be used if the designers are confident that no broader set of data will ever be used for analysis

Today's Goals
Analytical Tools and Applications
What is Business Intelligence?
How is Business Intelligence used in companies?
How does the architecture of business intelligence relate to the five course anchor ideas?
what software tools or applications are right for your business needs?
how involved is decision making in the business process?
third party application or create one?
Which system meets your business goals?
IT governance
Build, Acquire, and Implement
Analytical Technologies
easy to use
used right before report is presented
prone to human error
Online Analytical Processors
used for semistructured decision and analysis
not efficient to analyze array-based data
designed for multidimensional array-based problems
Analytical Technologies
Statistical Algorithms
used by sophisticated managers to analyze data
encompass predictive modeling applications, optimization, and simulations
used for business rules that use conditional statements
Example) Insurance agency
Rule Engines
Business Process
Analytical Technologies
Accounting Systems
Data Mining Tools
IT Governance
Objective is to identify patterns in complex data
Build, Acquire,
and Implement
Example) Which AT&T user will switch wireless carriers and take existing phone number
Simulation Tools
uses mathematical, scientific, engineering, and financial functions
can be used as a training device for users to understand a change of business process
help health care employees decide where to send donated organs based on criteria
Future Analytical Technologies
Business Goals
Just like data mining, but can look for patterns in audio or video
Information extraction
extracts concepts such as names and geographical entities from VERY unstructured data
5 Course
Anchor Ideas

Presentation Tools and Applications
Analytic competitors must empower their people to put their insights

Reporting tools

Presentation Tools and Applications
Presentation tools should allow users to:
Create ad hoc reports
Interactively visualize complex data
Be alerted to exceptions through communication tools
Collaboratively share data

Presentation Tools and Applications
New generation of visual analytical tools allow for manipulation of data

Explore data without modifying underlying model

Operational Processes
Data & Applications

Audio and Video Mining
Alligned with:
business strategy
corporate culture
management style

Senior Managers must work with IT
Establish and enforce data management policies
Committed to creation and use of high quality data

Well documented

Flexibility to adapt to changing business needs
Identify technology, data, and governance processes for analytics
Components of core business Intelligence
Close Collaboration with IT and Business Managers to determine:
Technical Capabilities
Corporate Priorities
Objective Linked
IT architect is responsible
Analytics used in making decisions
SOX requires:
decisions made based on "trustworthy, meaningful, authoritative, and accurate date"
data provides true picture of company by showing risks, trends, opportunities
Gaining a competitive advantage
by using business intelligence
Use data to:

Provides analytics
Helps make decisions
Allows data, content, and
analyses to go to the
right person when
they need it
Information Workers
Functional Heads
Top Management
What is Provided?
Reliable, accurate
information for
Traditional Reports
Adhoc Analysis Tools
Corp. Dashboards
Use of software and applications to :
Organize data
Store the data
Analyze the data
Presents data in different types of data analysis:
Online analytical processors
Data and text mining tools
Simulation tools
Achieve the 6 core elements of business intelligence architecture:
1. Data Management: right data is acquired and managed
2. Establish transformation tools to properly populate databases
3. Repositories created to organize and store data
4. Applications are used to create analysis
5. Fully document how to access, display. visulaize, and manipulate data
6. Plans addressed for security, error, auditability, and privacy of data

Standards, processes, and policies must be defined and enforced across the entire organization
Top management can help IT team plan and establish guiding principles
Full transcript