4. Data Analytics Architecture
Business
Information
Technology
Partnership and
Stewardship
Wisdom
Information &
Knowledge
Data
Data
Rules
Tools
Action
Action
“The greatest value of a picture is when
it forces to notice what we never
expected to see.” ~John Tukey
11
Analysis is on top –
discovery and decision
making
5. Current state – Reliability Data
12
(Looking back
What happened?)
(Looking forward
What will happen?)
Information
Manual
Data
6. Current state – Reliability Data
13
(Looking back) (Looking forward)
Uses variety of data, techniques to predict
future trends and behavior patterns
7. Design Differences
14
High volume of transactions
Data In
Historical reporting & analysis
Information Out
Key:
Organize the
data for
analytics
8. Know What
Data
Know Why
Wisdom
Know How
Information &
Knowledge
Analysi
s
Translation
Data
Turn Data Into Information
Turn Information into Knowledge
ODS
Repository
Automate the manual process
18
**New
9.
10. Businesses Need Support for
Decision Making
• Uncertain economics
• Rapidly changing environments
• Global competition
• Demanding customers
• Taking advantage of information acquired by companies is a
Critical Success Factor.
11. Changing Business Environment
• Companies are moving aggressively to computerized support of their operations
=>The environment in which organizations operate today is becoming more and
more complex, creating:
• opportunities, and
• problems.
• Example: globalization.
12. Business Pressures–Responses–Support Model
– Business pressures /factors are the results of today’s business climate that
facilitate change in an attempt to handle challenges and, thus, create
opportunities.
– Responses to counter the pressures
– Support to better facilitate the process
– Decisions and support facilitate the monitoring of the environment and enhances
the response actions taken by organizations; by computer support in the form of
data warehousing, software tools for data analysis and manipulation, monitoring
conditions through the use of dashboards, etc
–
14. Business Environment Factors
markets, consumer demands, technology, and societal.
FACTOR DESCRIPTION
Markets Strong competition
Expanding global markets
Blooming electronic markets on the Internet
Innovative marketing methods
Opportunities for outsourcing with IT support
Need for real-time, on-demand transactions
Consumer Desire for customization
demand Desire for quality, diversity of products, and speed of delivery
Customers getting powerful and less loyal
Technology More innovations, new products, and new services
Increasing obsolescence rate
Increasing information overload
Social networking, Web 2.0 and beyond
Societal Growing government regulations and deregulation
Workforce more diversified, older, and composed of more women
Prime concerns of homeland security and terrorist attacks
Necessity of Sarbanes-Oxley Act and other reporting-related legislation
Increasing social responsibility of companies
Greater emphasis on sustainability
15. Organizational Responses
• Business Responses are the actions taken by a company to respond to the pressures and survive in the business
environment; these responses must be reactive, anticipative, adaptive, and proactive.
• Managers may take actions, such as
– Employ strategic planning
– Use new and innovative business models
– Restructure business processes
– Participate in business alliances
– Improve corporate information systems
– Improve partnership relationships
– Encourage innovation and creativity …cont…>
16. Managers actions, continued
– Improving customer service and relationships.
– Moving to electronic commerce (e-commerce).
– Moving to make-to-order production and on-demand manufacturing and
services.
– Using new IT to improve communication, data access (discovery of information),
and collaboration.
– Responding quickly to competitors' actions (e.g., in pricing, promotions, new
products and services).
– Automating many tasks of white-collar employees.
– Automating certain decision processes.
– Improving decision making by employing analytics.
18. The Information Gap
• The shortfall between gathering information and using it for
decision making.
– Firms have inadequate data warehouses.
– Business Analysts spend 2 days a week gathering and formatting
data, instead of performing analysis. (Data Warehousing Institute).
– Business Intelligence (BI) seeks to bridge the information gap.
19. Closing the Strategy Gap
• One of the major objectives of computerized decision support is to facilitate 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
20. Data Mining
• “Data mining is an interdisciplinary subfield of computer
science. It 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.” - Wikipedia
• Examining large databases to produce new information.
– Uses statistical methods and artificial intelligence to analyze data.
– Finds hidden features of the data that were not yet known.
21. BI
• Tools and techniques to turn data into meaningful information.
– Process: Methods used by the organization to turn data into
knowledge.
– Product: Information that allows businesses to make decisions.
22. What is Business Intelligence?
• Collecting and refining information from many sources
(internal and external)
• Analyzing and presenting the information in useful ways
(dashboards, visualizations)
• So that people can make better decisions
• That help build and retain competitive advantage.
25. BI Applications
• Customer Analytics
• Human Capital Productivity Analysis
• Business Productivity Analytics
• Sales Channel Analytics
• Supply Chain Analytics
• Behavior Analytics
26. BI Initiatives
• 70% of senior executives report that analytics will be important
for competitive advantage. Only 2% feel that they’ve achieved
competitive advantage. (zassociates report)
• 70-80% of BI projects fail because of poor communication and
not understanding what to ask. (Goodwin, 2010)
• 60-70% of BI projects fail because of technology, culture and
lack of infrastructure (Lapu, 2007)
29. Data Warehouse
• Collection of data
from multiple
sources (internal
and external)
• Summary, historical and raw data from operations.
• Data “cleaning” before use.
• Stored independently from
operational data.
• Broken down into DataMarts for
use.
Chapter 4 of ISBB Text
30. Data Warehouses
• Data warehouse
– Collection of data used to support decision-making applications and
generate business intelligence
• Multidimensional data
• Characteristics
– Subject oriented
– Integrated
– Time variant
– Type of data
– Purpose
31. Input
• Variety of sources
– External
– Databases
– Transaction files
– ERP systems
– CRM systems
32. ETL
• Extraction, transformation, and loading (ETL)
• Extraction
– Collecting data from a variety of sources
– Converting data into a format that can be used in transformation
processing
• Transformation processing
– Make sure data meets the data warehouse’s needs
• Loading
– Process of transferring data to the data warehouse
35. Output
• Data warehouse supports different types of analysis
– Generates reports for decision making
• Online analytical processing (OLAP)
– Generates business intelligence
– Uses multiple sources of information and provides multidimensional
analysis
– Hypercube
– Drill down and drill up
37. Output (cont’d.)
• Data-mining analysis
– Discover patterns and relationships
• Reports
– Cross-reference segments of an organization’s operations for
comparison purposes
– Find patterns and trends that can’t be found with databases
– Analyze large amounts of historical data quickly
38. Data Warehouse Applications at InterContinental
Hotels Group (IHG)
• The new system has increased the company’s query response
time from hours to minutes
• It has generated valuable BI on both its customers and the
competition
• Future plans include the migration of financial data, which will
enable IHG to perform side-by-side analyses of operations,
marketing, sales, and financial data
39. Data Marts
• Data mart
– Smaller version of data warehouse
– Used by single department or function
• Advantages over data warehouses
• More limited scope than data warehouses
41. 5 Tasks of Data Mining in Business
• Classification – Categorizing data into actionable groups. (ex.
loan applicants)
• Estimation – Response rates, probabilities of responses.
• Prediction – Predicting customer behavior.
• Affinity Grouping – What items or services are customers likely
to purchase together?
• Description – Finding interesting patterns.
42. Data Mining Techniques
• Market Basket Analysis
• Cluster Analysis
• Decision Trees and Rule Induction
• Neural Networks
43. Market Basket Analysis
• Finding patterns or sequences in the way that people purchase
products and services.
• Walmart Analytics
– Obvious: People who buy Gin also buy tonic.
– Non-obvious: Men who bought diapers would also purchase beer.
44. Cluster Analysis
• Grouping data into like clusters based on specific attributes.
• Examples
– Crime map clusters to better deploy police.
– Where to build a cellular tower.
– Outbreaks of Zika virus.
45. Summary
• Explained BI, Analytics, Data Marts and Big
Data.
• Defined the characteristics of data for good
decision making.
• Described data mining in detail.
• Explained and gave examples of
market basket and cluster analysis.
Paul
Pillars and Foundation
With these pillars and foundation, you can have successful data analytics.
We do this manually and it takes time
Analysis is on top – discovery and decision making
Example for Human Resource (HR) Analytics:
Google has analyzed substantial data on its own employees to determine the characteristics of great leaders, to assess factors that contribute to productivity, and to evaluate potential new hires.
Google also uses predictive analytics to continually update its forecast of future employee turnover and retention.
Example of high-impact marketing analytics:
Automobile manufacturer Chrysler teamed with J.D. Power and Associates to develop an innovate set of predictive models to support its pricing decisions for automobiles.
These models help Chrysler to better understand the ramifications of proposed pricing structures (a combination of manufacturer’s suggested retail price, interest rate offers, and rebates) and, as a result, to improve its pricing decisions.
The models have generated an estimated annual savings of $500 million.
Example for use of prescriptive analytics for diagnosis and treatment:
A group of scientists in Georgia used predictive models and optimization to develop personalized treatment for diabetes.
They developed a predictive model that uses fluid dynamics and patient monitoring data to establish the relationship between drug dosage and drug effect at the individual level.
Alleviates the need for more invasive procedures to monitor drug concentration.
Example for supply chain analytics:
ConAgra Foods uses predictive and prescriptive analytics to better plan capacity utilization by incorporating the inherent uncertainty in commodities pricing.
ConAgra realized a 100% return on its investment in analytics in under three months—an unheard of result for a major technology investment.
Example of analytics for government agencies:
The New York State Department has worked with IBM to use prescriptive analytics in the development of a more effective approach to tax collection. The result was an increase in collections from delinquent payers of $83 million over two years.
Example of analytics for nonprofit agencies:
Catholic Relief Services (CRS) is the official international humanitarian agency of the U.S. Catholic community. The CRS mission is to provide relief for the victims of both natural and human-made disasters and to help people in need around the world through its health, educational, and agricultural programs.
CRS uses an analytical spreadsheet model to assist in the allocation of its annual budget based on the impact that its various relief efforts and programs will have in different countries.
Online experimentation involves exposing various subgroups to different versions of a web site and tracking the results.
Because of the massive pool of Internet users, experiments can be conducted without risking the disruption of the overall business of the company.
Such experiments are proving to be invaluable because they enable the company to use trial-and-error in determining statistically what makes a difference in their web site traffic and sales.
This slide introduces the concept of business intelligence and analytics. It is important to understand that business intelligence and business analytics are products defined by hardware and software vendors. This is also one of the fastest growing segments in the U.S. software environment. Ask students why this might be so.
