Business Intelligence, Analytics, and Data Science A Managerial

Business Intelligence, Analytics, and Data Science: A
Managerial Perspective
Fourth Edition
Chapter 3
Descriptive Analytics II: Business Intelligence and Data
Warehousing
Copyright © 2018, 2014, 2011 Pearson Education, Inc. All
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Learning Objectives (1 of 2)
3.1 Understand the basic definitions and concepts of data
warehousing
3.2 Understand data warehousing architectures
3.3 Describe the processes used in developing and managing
data warehouses
3.4 Explain data warehousing operations
3.5 Explain the role of data warehouses in decision support
Slide 3-2
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Slide 2 is a list of textbook LO numbers and statements.
2
3.6 Explain data integration and the extraction, transformation,
and load (ETL) processes
3.7 Understand the essence of business performance

management (BPM)
3.8 Learn balanced scorecard and Six Sigma as performance
measurement systems
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3
OPENING VIGNETTE
Targeting Tax Fraud with Business Intelligence and Data
Warehousing
Why is it important for IRS and for U.S. state governments to
use data warehousing and business intelligence (BI) tools in
managing state revenues?
What were the challenges the state of Maryland was facing with
regard to tax fraud?
What was the solution they adopted? Do you agree with their
approach? Why?
What were the results that they obtained? Did the investment in
BI and data warehousing pay off?
What other problems and challenges do you think federal and
state governments are having that can benefit from BI and data
warehousing?
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Business Intelligence and Data Warehousing
BI used to be everything related to use of data for managerial
decision support
Now, it is a part of Business Analytics
BI = Descriptive Analytics

5
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What is a Data Warehouse?
A physical repository where relational data are specially
organized to provide enterprise-wide, cleansed data in a
standardized format
A relational database? (so what is the difference?)
“The data warehouse is a collection of integrated, subject-
oriented databases designed to support DSS functions, where
each unit of data is non-volatile and relevant to some moment in
time”
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A Historical Perspective to
Data Warehousing
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Characteristics of DWs
Subject oriented
Integrated
Time-variant (time series)
Nonvolatile
Summarized
Not normalized
Metadata

Web based, relational/multi-dimensional
Client/server, real-time/right-time/active...
Slide 3-8
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Data Mart
A departmental small-scale “DW” that stores only
limited/relevant data
Dependent data mart
A subset that is created directly from a data warehouse
Independent data mart
A small data warehouse designed for a strategic business unit or
a department
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Other DW Components
Operational data stores (ODS)
A type of database often used as an interim area for a data
warehouse
Oper marts
An operational data mart
Enterprise data warehouse (EDW)
A data warehouse for the enterprise
Metadata – “data about data”
In DW metadata describe the contents of a data warehouse and
its acquisition and use
Slide 3-10

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Application Case 3.1
A Better Data Plan: Well-Established TELCOs Leverage Data
Warehousing and Analytics to Stay on Top in a Competitive
Industry
Questions for Discussion
What are the main challenges for TELCOs?
How can data warehousing and data analytics help TELCOs in
overcoming their challenges?
Why do you think TELCOs are well suited to take full
advantage of data analytics?
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DW for Data-Driven Decision Making
An example of a DW supporting data-driven decision making in
automotive industry
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A Generic DW Framework
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DW Architecture
Three-tier architecture
Data acquisition software (back-end)
The data warehouse that contains the data & software
Client (front-end) software that allows users to access and
analyze data from the warehouse
Two-tier architecture
First two tiers in three-tier architecture are combined into one
… sometimes there is only one tier?
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DW Architectures
3-tier
architecture
2-tier
architecture
1-tier
Architecture
?
Slide 3-15
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Data Warehousing Architectures
Issues to consider when deciding which architecture to use:
Which database management system (DBMS) should be used?
Will parallel processing and/or partitioning be used?
Will data migration tools be used to load the data warehouse?
What tools will be used to support data retrieval and analysis?
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A Web-based DW Architecture
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Alternative DW Architectures
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Alternative DW Architectures

Each architecture has advantages and disadvantages!
Which architecture is the best?
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Ten Factors that Potentially Affect the Architecture Selection
Decision
Information interdependence between organizational units
Upper management’s information needs
Urgency of need for a data warehouse
Nature of end-user tasks
Constraints on resources
Strategic view of the data warehouse prior to implementation
Compatibility with existing systems
Perceived ability of the in-house IT staff
Technical issues
Social/political factors
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Data Integration and the Extraction, Transformation, and Load
Process
ETL = Extract Transform Load
Data integration
Integration that comprises three major processes: data access,
data federation, and change capture.

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.
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Data Integration and the Extraction, Transformation, and Load
Process
Slide 3-22
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ETL (Extract, Transform, Load)
Issues affecting the purchase of an ETL tool
Data transformation tools are expensive
Data transformation tools may have a long learning curve
Important criteria in selecting an ETL tool
Ability to read from and write to an unlimited number of data
sources/architectures
Automatic capturing and delivery of metadata
A history of conforming to open standards
An easy-to-use interface for the developer and the functional
user
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BP Lubricants Achieves BIGS Success
What is BIGS?
What were the challenges, the proposed solution, and the
obtained results with BIGS?
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Data Warehouse Development
Data warehouse development approaches
Inmon Model: EDW approach (top-down)
Kimball Model: Data mart approach (bottom-up)
Which model is best?
Table 3.3 provides a comparative analysis between EDW and
Data Mart approach
Another alternative is the hosted data warehouses
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Comparing EDW and Data Mart
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Use of Teradata Analytics for SAP
Solution
s Accelerates Big Data Delivery
What were the challenges faced by the large Dutch retailer?
What was the proposed multivendor solution? What were the
implementation challenges?
What were the lessons learned?
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Additional DW Considerations
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
… more in the book
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Representation of Data in DW
Dimensional Modeling
A retrieval-based system that supports high-volume query
access
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
Snowflakes schema
An extension of star schema where the diagram resembles a
snowflake in shape
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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)
Multidimensional presentation
Dimensions: products, salespeople, market segments, business
units, geographical locations, distribution channels, country, or
industry
Measures: money, sales volume, head count, inventory profit,
actual versus forecast
Time: daily, weekly, monthly, quarterly, or yearly
Multidimensionality
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Star Schema versus Snowflake Schema
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Analysis of Data in DW
OLTP vs. OLAP…
OLTP (Online Transaction Processing)
Capturing and storing data from ERP, CRM, POS, …
The main focus is on efficiency of routine tasks
OLAP (Online Analytical Processing)
Converting data into information for decision support
Data cubes, drill-down / rollup, slice & dice, …
Requesting ad hoc reports
Conducting statistical and other analyses
Developing multimedia-based applications
…more in the book
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OLAP vs. OLTP
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OLAP Operations

Slice - a subset of a multidimensional array
Dice - a slice on more than two dimensions
Drill Down/Up - navigating among levels of data ranging from
the most summarized (up) to the most detailed (down)
Roll Up - computing all of the data relationships for one or
more dimensions
Pivot - used to change the dimensional orientation of a report or
an ad hoc query-page display
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OLAP
Slicing Operations on a Simple Tree-Dimensional
Data Cube
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Successful DW Implementation
Things to Avoid
Starting with the wrong sponsorship chain

Setting expectations that you cannot meet
Engaging in politically naive behavior
Loading the data warehouse with information just because it is
available
Believing that data warehousing database design is the same as
transactional database design
… more in the book
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Massive DW and Scalability
Scalability
The main issues pertaining to scalability:
The amount of data in the warehouse
How quickly the warehouse is expected to grow
The number of concurrent users
The complexity of user queries
Good scalability means that queries and other data-access
functions will grow linearly with the size of the warehouse
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EDW Helps Connect State Agencies in Michigan
Why would a state invest in a large and expensive IT
infrastructure (such as an EDW)?
What is the size and complexity of the EDW used by state
agencies in Michigan?
obtained results of the EDW?
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DW Administration and Security
Data warehouse administrator (DWA)
DWA should…
have the knowledge of high-performance software, hardware,
and networking technologies
possess solid business knowledge and insight
be familiar with the decision-making processes so as to suitably

design/maintain the data warehouse structure
possess excellent communications skills
Security and privacy is a pressing issue in DW
Safeguarding the most valuable assets
Government regulations (HIPAA, etc.)
Must be explicitly planned and executed
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The Future of DW
Sourcing…
Web, social media, and Big Data
Open source software
SaaS (software as a service)
Cloud computing
Data lakes
Infrastructure…
Columnar
Real-time DW
Data warehouse appliances
Data management practices/technologies
In-database & In-memory processing New DBMS

New DBMS, Advanced analytics, …
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Data Lakes
Unstructured data storage technology for Big Data
Data Lake versus Data Warehouse
Slide # of total
Slide 3-41
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Business Performance Management
Business Performance Management (BPM) is…
A real-time system that alerts managers to potential
opportunities, impending problems, and threats, and then
empowers them to react through models and collaboration
Also called corporate performance management (CPM by
Gartner Group), enterprise performanc e management (EPM by
Oracle), strategic enterprise management (SEM by SAP)

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Business Performance Management
BPM refers to the business processes, methodologies, metrics,
and technologies used by enterprises to measure, monitor, and
manage business performance.
BPM encompasses three key components
A set of integrated, closed-loop management and analytic
processes, supported by technology …
Tools for businesses to define strategic goals and then
measure/manage performance against them
Methods and tools for monitoring key performance indicators
(KPIs), linked to organizational strategy
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A Closed-Loop Process to Optimize Business Performance
Process Steps
Strategize
Plan
Monitor/analyze
Act/adjust
Each with its own sub-process steps
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1 - Strategize:
Where Do We Want to Go?
Strategic planning
Common tasks for the strategic planning process:
Conduct a current situation analysis
Determine the planning horizon

Conduct an environment scan
Identify critical success factors
Complete a gap analysis
Create a strategic vision
Develop a business strategy
Identify strategic objectives and goals
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2 - Plan:
How Do We Get There?
Operational planning
Operational plan: plan that translates an organization’s strategic
objectives and goals into a set of well-defined tactics and
initiatives, resource requirements, and expected results for some
future time period (usually a year).
Operational planning can be
Tactic-centric (operationally focused)
Budget-centric plan (financially focused)

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3 - Monitor/Analyze:
How Are We Doing?
A comprehensive framework for monitoring performance shoul d
address two key issues:
What to monitor?
Critical success factors
Strategic goals and targets
How to monitor?
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Success (or mere survival) depends on new projects: creating

new products, entering new markets, acquiring new customers
(or businesses), or streamlining some process.
Many new projects and ventures fail!
What is the chance of failure?
60% of Hollywood movies fail
70% of large IT projects fail, …
4 - Act and Adjust:
What Do We Need to Do Differently?
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AARP Transforms Its BI Infrastructure and Achieves a 347%
ROI in Three Years
What were the challenges AARP was facing?
What was the approach for a potential solution?
What were the results obtained in the short term, and what were
the future plans?

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Performance measurement system
A system that assists managers in tracking the implementations
of business strategy by comparing actual results against
strategic goals and objectives
Comprises systematic comparative methods that indicate
progress (or lack thereof) against goals
Performance Measurement
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Key performance indicator (KPI)
A KPI represents a strategic objective and metrics that measure
performance against a goal
Distinguishing features of KPIs
KPIs and Operational Metrics

Strategy
Targets
Ranges
Encodings
Time frames
Benchmarks
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Key performance indicator (KPI)
Outcome KPIs vs. Driver KPIs
(lagging indicators(leading indicators
e.g., revenues) e.g., sales leads)
Operational areas covered by driver KPIs
Customer performance
Service performance
Sales operations
Sales plan/forecast
Performance Measurement

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Balanced Scorecard (BSC)
A performance measurement and management methodology that
helps translate an organization’s financial, customer, internal
process, and learning and growth objectives and targets into a
set of actionable initiatives
“The Balanced Scorecard: Measures That Drive Performance”
(HBR, 1992)
Performance Measurement System
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Balanced Scorecard
The meaning of “balance” ?
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Six Sigma
A performance management methodology aimed at reducing the
number of defects in a business process to as close to zero
defects per million opportunities (DPMO) as possible
Six Sigma as a Performance Measurement System
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The DMAIC performance model
A closed-loop business improvement model that encompasses
the steps of defining, measuring, analyzing, improving, and
controlling a process
Lean Six Sigma
Lean manufacturing / lean production
Lean production versus six sigma?
Six Sigma as a Performance Measurement System
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Comparison of BSC and Six Sigma
Slide 1- 57
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Effective Performance Measurement Should
Measures should focus on key factors.
Measures should be a mix of past, present, and future.
Measures should balance the needs of shareholders, employees,
partners, suppliers, and other stakeholders.
Measures should start at the top and flow down to the bottom.
Measures need to have targets that are based on research and
reality rather than arbitrary.
Slide # of total
Slide 3-58
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Expedia.com’s Customer Satisfaction Scorecard
Who are the customers for Expedia.com? Why is customer
satisfaction a very important part of their business?
How did Expedia.com improve customer satisfaction with
scorecards?
obtained results?
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Plenty of Resources for DW @ TUN
Teradata University Network (TUN)
Slide 3-60
TeradataUniversityNetwork.com
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End of Chapter 3
Questions / Comments
Slide 3-61
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What happened?
What is happening?
What will happen?
Why will it happen?

What should I do?
Why should I do it?
üBusiness reporting
üDashboards
üScorecards
üData warehousing
üData mining
üText mining
üWeb/media mining
üForecasting
üOptimization
üSimulation
üDecision modeling
üExpert systems
Well defined
business problems
and opportunities
Accurate projections
of future events and
outcomes
Best possible
business decisions
and actions
Q
u

e
s
t
i
o
n
s
E
n
a
b
l
e
r
s
O
u
t
c
o
m
e
s
DescriptivePredictivePrescriptive
Business Analytics

Business Intelligence
Advanced Analytics
1970s1980s1990s2000s2010s
üMainframe computers
üSimple data entry
üRoutine reporting
üPrimitive database structures
üTeradata incorporated
üMini/personal computers (PCs)
üBusiness applications for PCs
üDistributer DBMS
üRelational DBMS
üTeradata ships commercial DBs
üBusiness Data Warehousecoined
üCentralized data storage
üData warehousing was born
üInmon, Building the Data Warehouse
üKimball, The Data Warehouse Toolkit
üEDW architecture design
üExponentially growing data Web data
üConsolidation of DW/BI industry
üData warehouse appliances emerged
üBusiness intelligence popularized
üData mining and predictive modeling
üOpen source software

üSaaS, PaaS, Cloud Computing
üBig Data analytics
üSocial media analytics
üText and Web Analytics
üHadoop, MapReduce, NoSQL
üIn-memory, in-database
Data Warehouse
One management and analytics platform
for product configuration, warranty, and
diagnostic readout data
Reduced
Infrastructure
Expenses
2/3 cost reduction through
data mart consolidation
Produced Warranty
Expenses
Improved reimbursement
accuracy through improved
claim data quality
Improved Cost of
Quality
Faster identification,
prioritization, and resolution
of quality issues

Accurate
Environmental
Performance
Reporting
IT Architecture
Standardization
One strategic platform for
business intelligence and
compliance reporting
Data
Sources
ERP
Legacy
POS
Other
OLTP/Web
External
Data
Select
Transform
Extract
Integrate
Load
ETL
Process

Enterprise
Data warehouse
Metadata
Replication
A
P
I
/
M
i
d
d
l
e
w
a

r
e
Data/text
mining
Custom built
applications
OLAP,
Dashboard,
Web
Routine
Business
Reporting
Applications
(Visualization)
Data mart
(Operations)
Data mart
(Marketing)
Data mart
(Finance)
Data mart
(...)
Data
Marts
No data marts option

Tier 2:
Application server
Tier 1:
Client workstation
Tier 3:
Database server
Tier 1:
Client workstation
Tier 2:
Application & database server
Web
Server
Client
(Web browser)
Application
Server
Data
warehouse
Web pages
Internet/
Intranet/
Extranet
Source
Systems
Staging

Area
Independent data marts
(atomic/summarized data)
End user
access and
applications
ETL
(a) Independent Data Marts Architecture
Source
Systems
Staging
Area
End user
access and
applications
ETL
Dimensionalized data marts
linked by conformed dimentions
(atomic/summarized data)
(b) Data Mart Bus Architecture with Linked Dimensional
Datamarts
Source
Systems
Staging
Area

End user
access and
applications
ETL
Normalized relational
warehouse (atomic data)
Dependent data marts
(summarized/some atomic data)
(c) Hub and Spoke Architecture (Corporate Information
Factory)
Source
Systems
Staging
Area
Normalized relational
warehouse (atomic/some
summarized data)
End user
access and
applications
ETL
(d) Centralized Data Warehouse Architecture
End user
access and
applications

Logical/physical integration of
common data elements
Existing data warehouses
Data marts and legacy systmes
Data mapping / metadata
(e) Federated Architecture
Packaged
application
Legacy
system
Other internal
applications
Transient
data source
Data
warehouse
Data
marts
ExtractExtractExtractExtract
Product
T
i
m
e
G

e
o
g
r
a
p
h
y
Sales volumes of
a specific Product
on variable Time
and Region
Sales volumes of
a specific Region
on variable Time
and Products
Sales volumes of
a specific Time on
variable Region
and Products
Cells are filled
with numbers
representing
sales volumes
A 3-dimensional

OLAP cube with
slicing
operations
VISION &
STRATEGY
Internal
Business
Process
Perspective
Customer
Perspective
Financial
Perspective
Learning and
Growth
Perspective
Fourth Edition
Chapter 2
Descriptive Analytics I: Nature of Data, Statistical
Modeling, and Visualization

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1
2.1 Understand the nature of data as it relates to business
intelligence (BI) and analytics
2.2 Learn the methods used to make real-world data analytics
ready
2.3 Describe statistical modeling and its relationship to business
analytics
2.4 Learn about descriptive and inferential statistics
2.5 Define business reporting, and understand its historical
evolution
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2
2.6 Understand the importance of data/information visualization
2.7 Learn different types of visualization techniques
2.8 Appreciate the value that visual analytics brings to business
analytics
2.9 Know the capabilities and limitations of dashboards
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3
OPENING VIGNETTE
Attracts and Engages a New Generation of Radio Consumers
with Data-Driven Marketing
What does SiriusXM do? In what type of market does it conduct
its business?
What were the challenges? Comment on both technology and
data-related challenges.
What were the proposed solutions?
How did they implement the proposed solutions? Did they face

any implementation challenges?
What were the results and benefits? Were they worth the
effort/investment?
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The Nature of Data
Data: a collection of facts
usually obtained as the result of experiences, observations, or
experiments
Data may consist of numbers, words, images, …
Data is the lowest level of abstraction (from which information
and knowledge are derived)
Data is the source for information and knowledge
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The Nature of Data
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Metrics for Analytics Ready Data
Data source reliability
Data content accuracy
Data accessibility
Data security and data privacy
Data richness
Data consistency
Data currency/data timeliness
Data granularity
Data validity and data relevancy
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A Simple Taxonomy of Data
Data (datum—singular form of data): facts
Structured data
Targeted for computers to process
Numeric versus nominal

Unstructured/textual data
Targeted for humans to process/digest
Semi-structured data?
XML, HTML, Log files, etc.
Data taxonomy…
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A Simple Taxonomy of Data
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Medical Device Company Ensures Product Quality While
Saving Money
What were the main challenges for the medical device
company? Were they market or technology driven?
What was the proposed solution?
What were the results? What do you think was the real return on

investment (ROI)?
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The Art and Science of Data Preprocessing
The real-world data is dirty, misaligned, overly complex, and
inaccurate
Not ready for analytics!
Readying the data for analytics is needed
Data preprocessing
Data consolidation
Data cleaning
Data transformation
Data reduction
Art – it develops and improves with experience
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The Art and Science of Data Preprocessing
Data reduction
Variables

Dimensional reduction
Variable selection
Cases/samples
Sampling
Balancing / stratification
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Data Preprocessing Tasks and Methods
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Application Case 2.2 (1 of 4)
Improving Student Retention with Data-Driven Analytics
What is student attrition, and why is it an important problem in
higher education?

What were the traditional methods to deal with the attrition
problem?
List and discuss the data-related challenges within context of
this case study.
What was the proposed solution? And, what were the results?
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Improving Student Retention with Data-Driven Analytics (2
of 4)
Student retention
Freshmen class
Why it is important?
What are the common techniques to deal with student attrition?
Analytics versus theoretical approaches to student retention
problem
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Data imbalance problem
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Results…
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Statistical Modeling for Business Analytics
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Statistical Modeling for Business Analytics
Statistics
A collection of mathematical techniques to characterize and
interpret data
Descriptive Statistics
Describing the data (as it is)
Inferential statistics
Drawing inferences about the population based on sample data
Descriptive statistics for descriptive analytics
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Measures of Centrality Tendency
Arithmetic mean
Median

The number in the middle
Mode
The most frequent observation
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Measures of Dispersion
Dispersion
Degree of variation in a given variable
Range
Max - Min
Variance Standard Deviation
Mean Absolute Deviation (MAD)
Average absolute deviation from the mean
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Measures of Dispersion
Quartiles
Box-and-Whiskers Plot
a.k.a. box-plot
Versatile / informative
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Shape of a Distribution
Histogram – frequency chart
Skewness
Measure of asymmetry
Kurtosis
Peak/tall/skinny nature of the distribution
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Relationship Between Dispersion and Shape Properties
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Technology Insights 2.1 – Descriptive Statistics in Excel
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Technology Insights 2.1 – Descriptive Statistics in Excel
Creating box-plot in Microsoft Excel
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Town of Cary Uses Analytics to Analyze Data from Sensors,
Assess Demand, and Detect Problems
What were the challenges the Town of Cary was facing?
What was the proposed solution?
What were the results?
What other problems and data analytics solutions do you foresee
for towns like Cary?
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Regression Modeling for Inferential Statistics
Regression
A part of inferential statistics
The most widely known and used analytics technique in
statistics
Used to characterize relationship between explanatory (input)
and response (output) variable

It can be used for
Hypothesis testing (explanation)
Forecasting (prediction)
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Regression Modeling
Correlation versus Regression
What is the difference (or relationship)?
Simple Regression versus Multiple Regression
Base on number of input variables
How do we develop linear regression models?
Scatter plots (visualization—for simple regression)
Ordinary least squares method
A line that minimizes squared of the errors
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Regression Modeling
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Regression Modeling
x: input, y: output
Simple Linear Regression
Multiple Linear Regression
Sign (+ or -) and magnitude
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Process of Developing a Regression Model
How do we know if the model is good enough?
R2 (R-Square)
p Values

Error measures (for prediction problems)
MSE, MAD, RMSE
Slide 2-32
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Regression Modeling Assumptions
Linearity
Independence
Normality (Normal Distribution)
Constant Variance
Multicollinearity
What happens if the assumptions do NOT hold?
What do we do then?
Slide 2-33
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Logistic Regression Modeling
A very popular statistics-based classification algorithm

Employs supervised learning
Developed in 1940s
The difference between Linear Regression and Logistic
Regression
In Logistic Regression Output/Target variable is a binomial
(binary classification) variable (as opposed to numeric variable)
Slide 2-34
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Logistic Regression Modeling
Slide 2-35
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Predicting NCAA Bowl Game Outcomes
Slide 2-36

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The analytics process to develop prediction models (both
regression and classification type) for NCAA Bowl Game
outcomes
Slide 2-37
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Slide 2-38
Prediction Results
Classification
Regression
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What are the foreseeable challenges in predicting sporting event
outcomes (e.g., college bowl games)?
How did the researchers formulate/design the prediction
problem (i.e., what were the inputs and output, and what was
the representation of a single sample—row of data)?
How successful were the prediction results? What else can they
do to improve the accuracy?
Slide 2-39
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Time Series Forecasting
Is it different than Simple Linear Regression? How?
Slide 2-40
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Business Reporting

Definitions and Concepts
Report?
Any communication artifact prepared to convey specific
information
A report can fulfill many functions
To ensure proper departmental functioning
To provide information
To provide the results of an analysis
To persuade others to act
To create an organizational memory…
Slide 2-41
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41
What is a Business Report?
A written document that contains information regarding
business matters.
Purpose: to improve managerial decisions
Source: data from inside and outside the organization (via the
use of ETL)

Format: text + tables + graphs/charts
Distribution: in-print, email, portal/intranet
Data
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Business Reporting
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Slide 2-43
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Types of Business Reports
Metric Management Reports
Help manage business performance through metrics (SLAs for
externals; KPIs for internals)
Can be used as part of Six Sigma and/or TQM

Dashboard-Type Reports
Graphical presentation of several performance indicators in a
single page using dials/gauges
Balanced Scorecard–Type Reports
Include financial, customer, business process, and learning &
growth indicators
Slide 1- 44
Slide 2-44
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Flood of Paper Ends at FEMA
What is FEMA, and what does it do?
What are the main challenges that FEMA faces?
How did FEMA improve its inefficient reporting practices?
Slide 2-45
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Data Visualization
“The use of visual representations to explore, make sense of,

and communicate data.”
Data visualization vs. Information visualization
Information = aggregation, summarization, and
contextualization of data
Related to information graphics, scientific visualization, and
statistical graphics
Often includes charts, graphs, illustrations, …
Slide 1- 46
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46
A Brief History of Data Visualization
Data visualization can date back to the second century AD
Most developments have occurred in the last two and a half
centuries
Until recently it was not recognized as a discipline
Today’s most popular visual forms date back a few centuries
Slide 1- 47
Slide 2-47

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The First Pie Chart
Created by William Playfair in 1801
Slide 1- 48
William Playfair is widely credited as the inventor of the
modern chart, having created the first line and pie charts.
Slide 2-48
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Decimation of Napoleon’s Army During the 1812 Russian
Campaign
Slide 1- 49
Arguably the most popular multi-dimensional chart
By Charles Joseph Minard
Slide 2-49
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Macfarlan Smith Improves Operational Performance Insight
with Tableau Online
What were the data and reporting related challenges Macfarlan
Smith facing?
What was the solution and the obtained results and/or benefits?
Slide 2-50
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Which Chart or Graph Should You Use?
Slide 1- 51
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An Example Gapminder Chart
Wealth and Health of Nations
Slide 1- 52

See gapminder.org for
Interesting animated examples
Slide 2-52
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The Emergence of Data Visualization and Visual Analytics
Slide 1- 53
Magic Quadrant for Business Intelligence and Analytics
Platforms (Source: Gartner.com)
Many data visualization companies are in the 4th quadrant
There is a move towards visualization
Slide 2-53
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The Emergence of Data Visualization and Visual Analytics
Emergence of new companies
Tableau, Spotfire, QlikView, …
Increased focus by the big players

MicroStrategy improved Visual Insight
SAP launched Visual Intelligence
SAS launched Visual Analytics
Microsoft bolstered PowerPivot with Power View
IBM launched Cognos Insight
Oracle acquired Endeca
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Slide 2-54
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Visual Analytics
A recently coined term
Information visualization + predictive analytics
Information visualization
Descriptive, backward focused
“what happened” “what is happening”
Predictive analytics
Predictive, future focused
“what will happen” “why will it happen”
There is a strong move toward visual analytics
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Visual Analytics by SAS Institute
Slide 1- 56
SAS Visual Analytics Architecture
Big data + In memory + Massively parallel processing + ..
Slide 2-56
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Visual Analytics by SAS Institute
At teradatauniversitynetwork.com, you can learn more about
SAS VA, experiment with the tool
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Slide 2-57
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Technology Insight 2.3
Telling Great Stories with Data and Visualization

58
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Performance Dashboards
Performance dashboards are commonly used in BPM software
suites and BI platforms
Dashboards provide visual displays of important information
that is consolidated and arranged on a single screen so that
information can be digested at a single glance and easily drilled
in and further explored
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59
Slide 2-60

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60
Dallas Cowboys Score Big with Tableau and Teknion
How did the Dallas Cowboys use information visualization?
What were the challenge, the proposed solution, and the
obtained results?
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Dashboard design
The fundamental challenge of dashboard design is to display all
the required information on a single screen, clearly and without
distraction, in a manner that can be assimilated quickly
Three layer of information
Monitoring
Analysis

Management
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62
What to look for in a dashboard
Use of visual components to highlight data and exceptions that
require action
Transparent to the user, meaning that they require minimal
training and are extremely easy to use
Combine data from a variety of systems into a single,
summarized, unified view of the business
Enable drill-down or drill-through to underlying data sources or
reports
Present a dynamic, real-world view with timely data
Require little coding to implement, deploy, and maintain
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63
Best Practices in Dashboard Design
Benchmark KPIs with Industry Standards
Wrap the Metrics with Contextual Metadata
Validate the Design by a Usability Specialist
Prioritize and Rank Alerts and Exceptions
Enrich Dashboard with Business-User Comments
Present Information in Three Different Levels
Pick the Right Visual Constructs
Provide for Guided Analytics
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Visual Analytics Helps Energy Supplier Make Better
Connections
Why do you think energy supply companies are among the
prime users of information visualization tools?

How did Electrabel use information visualization for the single
version of the truth?
What were their challenges, the proposed solution, and the
obtained results?
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End of Chapter 2
Slide 2-66
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Data
Repositories
Business Functions
UOB 1.0X
UOB 2.2
UOB 2.1XUOB 3.0
1

Machine
Failure
SymbolCountDescription
Exception Event
Transactional Records
PHASE 5
DEPT 4
DEPT 3
DEPT 2
DEPT 1
PHASE 4PHASE 3PHASE 2PHASE 1
DEPLOYMENT CHART
1
2
3
4
5
Information
(reporting)
Decision
Maker
Action
(decision)
Data

Fourth Edition
Chapter 1
An Overview of Business Intelligence, Analytics, and Data
Science
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Learning Objectives
1.1 Understand the need for computerized support of managerial
decision making
1.2 Recognize the evolution of such computerized support to the
current state—analytics/data science
1.3 Describe the business intelligence (BI) methodology and
concepts
1.4 Understand the various types of analytics, and see selected
applications
1.5 Understand the analytics ecosystem to identify various key
players and career opportunities

2
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2
OPENING VIGNETTE Sports Analytics—An Exciting Frontier
for Learning and Understanding Applications of Analytics (1 of
5)
Sports analytics is becoming a specialty within analytics
Sports is a big business
Generating $145B in revenues annually
Additional $100B in legal and $300B in illegal gambling
Analytic in sports popularized by the Moneyball book by
Michael Lewis in 2003
About Oakland A’s
And the follow-on movie in 2011
Nowadays, analytics is used in many facets of sports
Slide 1-3
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5)
Example 1: The Business Office
FIGURE 1.1 Season Ticket Renewals—Survey Scores
Slide 1-4
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4
5)
Example 2: The Coach
FIGURE 1.4 Heat Map Zone Analysis for Passing Plays
Slide 1-5
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5)
Example 3: The Trainer
FIGURE 1.7 Single Leg Squat Hold Test – Core Body Strength
Test
(Source: WILKERSON and GUPTA).
Slide 1-6
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5)
Discussion Questions
What are three factors that might be part of a PM for season
ticket renewals?
What are two techniques that football teams can use to do
opponent analysis?
How can wearables improve player health and safety? What
kinds of new analytics can trainers use?
What other analytics applications can you envision in sports?
Slide 1-7

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Changing Business Environments and Evolving Needs for
Decision Support and Analytics
Increased hardware, software, and network capabilities
Group communication and collaboration
Improved data management
Managing giant data warehouses and Big Data
Analytical support
Overcoming cognitive limits in processing and storing
information
Knowledge management
Anywhere, anytime support
Slide 1-8
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Evolution of Computerized Decision Support to Analytics/Data

Science
FIGURE 1.8 Evolution of Decision Support, Business
Intelligence, and Analytics
Slide 1-9
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A Framework for Business Intelligence
Slide 1-10
Definition of Business Intelligence
[Broad Definition] An umbrella term that combines
architectures, tools, databases, analytical tools, applications,
and methodologies
[Narrow Definition] Descriptive analytics tools and techniques
(i.e., reporting tools)
A Brief History of BI –
The Origins and Drivers of BI (See Figure 1.9)
The Architecture of BI (See Figure 1.10)

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FIGURE 1.9 Evolution of Business
Slide 1-11
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The Architecture of BI
FIGURE 1.10 A High-Level Architecture of BI
Slide 1-12
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Sabre Helps Its Clients through Dashboards and Analytics

What is traditional reporting? How is it used in the
organization?
How can analytics be used to transform the traditional
reporting?
How can interactive reporting assist organizations in decision
making?
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A Multimedia Exercise in Business Intelligence
Slide 1-14
TUN (TeradataUniversityNetwork.com)
BSI Videos (Business Scenario Investigations)
Analogues to CSI (Crime Scene Investigation)
Go To
www.youtube.com/watch?v=NXEL5F4_aKA
See the
www.slideshare.net/teradata/bsi-how-we-did-it-the-case-of-the-
misconnecting-passengers.slides
Discuss the case presented in the video and in the slides

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Transaction Processing versus Analytic Processing
Online Transaction Processing (OLTP)
Operational databases
ERP, SCM, CRM, …
Goal: data capture
Online Analytical Processing (OLAP)
Data warehouses
Goal: decision support
What is the relationship between OLTP and OLAP?
Slide 1-15
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Appropriate Planning and Alignment with the Business Strategy
Functionality, and Infrastructure
Functions served by BI Competency Center
How BI is linked to strategy and execution of strategy
Encourage interaction between the potential business user
communities and the IS organization

Serve as a repository and disseminator of best BI practices
between and among the different lines of business.
Standards of excellence in BI practices can be advocated and
encouraged throughout the company
…
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Real-Time, On-Demand BI Is Attainable
Emergence of real-time BI applications
Justifying the need
Is there a need for real-time [is it worth the additional
expense]?
Leveraging the enablers
RFID
Web services
Intelligent agents
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Critical BI System Considerations

Developing or Acquiring BI Systems
Make versus buy
BI shells
Justification and Cost–Benefit Analysis
A challenging endeavor, why?
Security
Protection of Privacy
Integration to Other Systems and Applications
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Analytics Overview
Analytics…a relatively new term/buzz-word
Analytics…the process of developing actionable decisions or
recommendations for actions based on insights generated from
historical data
According to the Institute for Operations Research and
Management Science (INFORMS)
Analytics represents the combination of computer technology,
management science techniques, and statistics to solve real
problems.
Slide 1-19

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Business Analytics
FIGURE 1.11 Three Types of Analytics
Slide 1-20
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Descriptive Analytics
Descriptive or reporting analytics
Answering the question of what happened
Retrospective analysis of historic data
Enablers
OLAP / DW
Data visualization
Dashboards and Scorecards
Descriptive statistics
Slide 1-21
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Silvaris Increases Business with Visual Analysis and Real -Time
Reporting Capabilities
What was the challenge faced by Silvaris?
How did Silvaris solve its problem using data visualization with
Tableau?
Slide 1-22
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Siemens Reduces Cost with the Use of Data Visualization
What challenges were faced by Siemens’ visual analytics group?
How did the data visualization tool Dundas BI help Siemens in
reducing cost?
Slide 1-23
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Predictive Analytics

Aims to determine what is likely to happen in the future
(foreseeing the future events)
Looking at the past data to predict the future
Enablers
Data mining
Text mining / Web mining
Forecasting (i.e., time series)
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Analyzing Athletic Injuries
What types of analytics are applied in the injury analysis?
How do visualizations aid in understanding the data and
delivering insights into the data?
What is a classification problem?
What can be derived by performing sequence analysis?
Slide 1-25
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Prescriptive Analytics
Aims to determine the best possible decision
Uses both descriptive and predictive to create the alternatives,
and then determines the best one
Enablers
Optimization
Simulation
Multi-Criteria Decision Modeling
Heuristic Programming
Analytics Applied to Many Domains
Analytics or Data Science?
Slide 1-26
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A Specialty Steel Bar Company Uses Analytics to Determine
Available-to-Promise Dates
Why would reallocation of inventory from one customer to
another be a major issue for discussion?
How could a DSS help make these decisions?
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Analytics Examples in Selected Domains
Analytics Application in HealthCare—Humana Examples
Example 1: Preventing Falls in a Senior Population—An
Analytic Approach
Example 2 : Humana’s Bold Goal—Application of Analytics to
Define the Right Metrics
Example 3: Predictive Models to Identify the Highest Risk
Membership in a Health Insurer
Slide 1-28
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Analytics Examples in Selected Domains
Slide 1-29
Analytics in Retail Value Chain
FIGURE 1.12 Example of Analytics Applications in a Retail
Value Chain

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Analytics Examples in Retail Value Chain
Slide 1-30
For the complete table, refer to your textbook
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A Brief Introduction to Big Data Analytics
What Is Big Data? (Is it just “big”?)
Big Data is data that cannot be stored or processed easily using
traditional tools/means
Big Data typically refers to data that comes in many different
forms: large, structured, unstructured, continuous
3Vs – Volume, Variety, Velocity
Data (Big Data or otherwise) is worthless if it does not provide
business value (and for it to provide business value, it has to be
analyzed)
More on Big Data Analytics is in Chapter 7
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CenterPoint Energy Uses Real-Time Big Data Analytics to
Improve Customer Service
How can electric companies predict possible outage at a
location?
What is customer sentiment analysis?
How does customer sentiment analysis help provide a
personalized service to their customers?
Slide 1-32
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An Overview of the Analytics Ecosystem
What are the key players in analytics industry?
What do they do?
Is there a place for you to be a part of it?
There is a need to classify different industry participants in the
broader view of analytics to
Identify providers (as an analytics consumer)
Identify roles to play (as a potential provider)

Identify job opportunities
Identify investment/entrepreneurial opportunities
Understand the landscape and the future of computerized
decision sport systems
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FIGURE 1.13 Analytics Ecosystem
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Data Generation Infrastructure Providers
Data Management Infrastructure Providers
Data Warehouse Providers

Middleware Providers
Data Service Providers
Analytics Focused Software Developers
Descriptive, Predictive, Prescriptive
Application Developers: Industry Specific or General
Analytics Industry Analysts and Influencers
Slide 1-35
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Academic Institutions and Certification Agencies
Certificates
Masters programs
Undergraduate programs
Offered by
MIS, Engineering
Marketing, Statistics
Computer Science
…
Regulators and Policy Makers
Analytics User Organizations

Slide 1-36
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Plan of the Book
FIGURE 1.15 Plan of the Book
Slide 1-37
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Resources
Teradata University Network (TUN)
Slide 1-38
TeradataUniversityNetwork.com
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End of Chapter 1

Slide 1-39
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Artwork tamar Cohen, The Big Quick
2010, silk screen collage on vintage book
pages, 40" x 50"
Spotlight
Spotlight on Big Data
Dominic Barton is
McKinsey’s global managing
director, based in London.
David Court leads the
fi rm’s advanced analytics
practice, based in Dallas.

Making
Advanced
Analytics
Work
For You
A practical guide to
capitalizing on big data
by Dominic Barton and
David Court
B
ig data and analytic s
have rocketed to the top
of the corporate agenda.
Executives look with ad-
miration at how Google,
Amazon, and others have
eclipsed competitors
with powerful new busi-
ness models that derive
from an ability to exploit
data. They also see that big data is attracting serious
investment from technology leaders such as IBM and

Hewlett-Packard. Meanwhile, the tide of private-
equity and venture-capital investments in big data
continues to swell.
The trend is generating plenty of hype, but we be-
lieve that senior leaders are right to pay attention. Big
data could transform the way companies do business,
delivering the kind of performance gains last seen in
the 1990s, when organizations redesigned their core
processes. As data-driven strategies take hold, they
will become an increasingly important point of com-
petitive differentiation. According to research by
Andrew McAfee and Erik Brynjolfsson, of MIT, com-
panies that inject big data and analytics into their
operations show productivity rates and pro� tability
that are 5% to 6% higher than those of their peers (see
“Big Data: The Management Revolution” in this issue).
Even so, our experience reveals that most compa-
nies are unsure how to proceed. Leaders are under-
standably leery of making substantial investments in
big data and advanced analytics. They’re convinced
that their organizations simply aren’t ready. After all,
companies may not fully understand the data they al-

ready have, or perhaps they’ve lost piles of money on
data-warehousing programs that never meshed with
business processes, or maybe their current analytics
programs are too complicated or don’t yield insights
that can be put to use. Or all of the above. No wonder
skepticism abounds.
Many CEOs, too, recall their experiences with
customer relationship management in the mid-1990s,
when new CRM software products often prompted
great enthusiasm. Experts descended on boardrooms
promising impressive results if new IT systems were
built to collect massive amounts of customer data.
HBR.ORG
October 2012 Harvard Business Review 79
SPOTLIGHT ON BIG DATA
It didn’t turn out that way. Too many C-suites were
blind to the practical implications of new CRM tech-
nologies—namely, that to capitalize on them, or-

ganizations would have to make complex process
changes and build employees’ skills. The promised
gains in performance were often slow in coming,
because the systems remained stubbornly discon-
nected from how companies and frontline managers
actually made decisions, and new demands for data
management added complexity to operations. To
be fair, most companies eventually managed to get
their CRM programs on track, but not before some
had su� ered sizable losses and several CRM champi-
ons had lost career momentum.
Given this history, we empathize with executives
who are cautious about big data. Nevertheless, we
believe that the time has come to de� ne a pragmatic
approach to big data and advanced analytics—one
tightly focused on how to use the data to make bet-
ter decisions.
In our work with dozens of companies in six data-
rich industries, we have found that fully exploiting
data and analytics requires three mutually support-
ive capabilities. (See the exhibit “How to Benefit
from Big Data.”) First, companies must be able to
identify, combine, and manage multiple sources of

data. Second, they need the capability to build ad-
vanced analytics models for predicting and optimiz-
ing outcomes. Third, and most critical, management
must possess the muscle to transform the organiza-
tion so that the data and models actually yield better
decisions. Two important features underpin those
activities: a clear strategy for how to use data and
analytics to compete, and deployment of the right
technology architecture and capabilities.
Equally important, the desired business impact
must drive an integrated approach to data sourcing,
model building, and organizational transformation.
That’s how you avoid the common trap of starting
with the data and simply asking what it can do for
you. Leaders should invest sufficient time and en-
ergy in aligning managers across the organization in
support of the mission.
1. Choose the Right Data
The universe of data and modeling has changed
vastly over the past few years. The sheer volume of
information, particularly from new sources such as
social media and machine sensors, is growing rap-
idly. The opportunity to expand insights by combin-

ing data is also accelerating, as more-powerful, less
costly software abounds and information can be ac-
cessed from almost anywhere at any time. Bigger and
better data give companies both more-panoramic
and more-granular views of their business environ-
ment. The ability to see what was previously invis-
ible improves operations, customer experiences, and
strategy. But mastering that environment means up-
ping your game, � nding deliberate and creative ways
to identify usable data you already have, and explor-
ing surprising sources of information.
Source data creatively. Often companies al-
ready have the data they need to tackle business
problems, but managers simply don’t know how
the information can be used for key decisions. Op-
erations executives, for instance, might not grasp
the potential value of the daily or hourly factory and
customer-service data they possess. Companies can
impel a more comprehensive look at information
sources by being speci� c about business problems
they want to solve or opportunities they hope to
exploit. For example, a banking team that needed
to improve the efficiency of its customer-service
operations created a 360-degree view by combining

information from ATM transactions, online queries,
customer complaints, and so on. That allowed dupli-
cative interactions to be identi� ed, thereby reducing
costs and streamlining the customer experience.
Managers also need to get creative about the po-
tential of external and new sources of data. Social
media are generating terabytes of nontraditional,
unstructured data in the form of conversations,
photos, and video. Add to that the streams of data
� owing in from sensors, monitoring processes, and
external sources that range from local demograph-
ics to weather forecasts. One way to prompt broader
thinking about potential data is to ask, “What deci-
A shipping � rm improved on-time performance
of its � eet by tapping data on weather and port
availability that it hadn’t realized were available.
A shipping � rm improved on-time performance
of its � eet by tapping data on weather and port
availability that it hadn’t realized were available.
80 Harvard Business Review October 2012

Making advanced analytics Work for you
sions could we make if we had all the information we
need?” Using that logic, one shipping company im-
proved the on-time performance of its fleet by tap-
ping specialized weather forecast data and live infor-
mation about port availability that it hadn’t realized
were available.
Senior executives can take the lead here. The CEO
of one major packaged-goods company told us that
he views data as a strategic asset whose value he
takes into account when assessing potential acquisi-
tions. But leaders at all levels must also be attuned
to novel approaches to gathering and husbanding
information. As business practices in the internet era
continue to evolve, inspiration can often arise from
a scan of the external environment. A corporate fi-
nance executive, for instance, might look to a com-
pany such as Kabbage, a start-up that supplies work-
ing capital to online businesses. To slash the time
required to underwrite loans, Kabbage asks mer-
chants to opt in to sharing their customer-feedback
ratings, Facebook interactions, and electronic ship-

ping records. Those with the strongest feedback and
highest business volume receive greater financing.
Get the necessary IT support. Legacy IT struc-
tures may hinder new types of data sourcing, storage,
and analysis. Existing IT architecture may prevent
the integration of siloed information, and managing
unstructured data often remains beyond traditional
IT capabilities. Many legacy systems were built to de-
liver data in batches, so they can’t furnish continuous
flows of information for real-time decisions.
Fully resolving these issues often takes years.
However, business leaders can address short-term
big data needs by working with CIOs to prioritize
requirements. This means quickly identifying and
connecting the most important data for use in ana-
lytics, followed by a cleanup operation to synchro-
nize and merge overlapping data and then to work
around missing information. Such short-term tactics
may lead companies to vendors that focus on analyt-
ics services or emerging software. New cloud-based
technologies may also offer ways to scale computing
power up or down to meet big data demands cost-

effectively. Together those approaches establish an IT
infrastructure that propels innovation by facilitating
collaboration, rapid analysis, and experimentation.
2. Build Models that Predict and
optimize Business outcomes
Data are essential, but performance improvements
and competitive advantage arise from analytics
models that allow managers to predict and opti-
mize outcomes. More important, the most effective
approach to building a model rarely starts with the
data; instead it originates with identifying the busi-
ness opportunity and determining how the model
can improve performance.
Unfortunately, not all model building follows this
course. One approach that gets inconsistent results,
for instance, is simple data mining. Corralling huge
data sets allows companies to run dozens of statisti-
cal tests to identify submerged patterns, but that pro-
vides little benefit if managers can’t effectively use
the correlations to enhance business performance. A
pure data-mining approach often leads to an endless
search for what the data really say.

One company followed a more targeted strategy
to optimize complex product pricing. At its core
was a model based on the historical price elasticity
of its products, sales data, competitors’ responses,
and other variables. To improve its chances of suc-
cess, the company began the modeling process by
positing which factors affected sales volumes (for
instance, competitors’ pricing and promotions) and
then asked what data and which model would best
deliver insights that were useful for making business
decisions. We have found that such hypothesis-led
modeling generates faster outcomes and also roots
idea in Brief
The trend toward big data is
growing rapidly, and senior
leaders can’t afford to dismiss
it as hype.
Advanced analytics is likely to
become a decisive competitive
asset in many industries and
a core element in companies’
efforts to improve performance.

It’s a mistake to assume that
acquiring the right kind of big
data is all that matters. Also
essential is developing analyt-
ics tools that focus on business
outcomes and that are relevant
and easy to use for everyone
from the C-suite to the front lines.
That requires transforming your
organization’s culture and capa-
bilities, not in a rush to action but
in a deliberative effort to weave
big data into the fabric of daily
operations.
hbr.org
october 2012 harvard business review 81
SPOTLIGHT ON BIG DATA
models in practical data relationships that are more

broadly understood by managers.
Remember, too, that any modeling exercise has
inherent risk. Although advanced statistical methods
indisputably make for better models, statistics ex-
perts sometimes design models that are too complex
to be practical. For example, a predictive model with
30 variables may explain historical data with high ac-
curacy, but managing so many variables will exhaust
most organizations’ capabilities. Companies should
repeatedly ask, “What’s the least complex model that
would improve our performance?”
3. Transform Your Company’s
Capabilities
The lead concern expressed to us by senior execu-
tives is that their managers don’t understand or trust
big data–based models. One large retailer intended
its model to optimize returns on advertising spend-
ing, but despite considerable investment, it wasn’t
being used. The reason soon became evident: The
frontline marketers who made key decisions on ad
spending didn’t believe the model’s results and had
little familiarity with how it worked.

Many companies grapple with such problems,
often because of a mismatch between the organi-
zation’s existing culture and capabilities and the
emerging tactics to exploit analytics successfully.
In short, the new approaches don’t align with how
companies actually arrive at decisions, or they fail
to provide a clear blueprint for realizing business
goals. Tools seem to be designed for experts in mod-
eling rather than for people on the front lines, and
few managers � nd the models engaging enough to
champion their use—a key failing if companies want
the new methods to permeate the organization. Bot-
tom line: Using big data requires thoughtful organi-
zational change, and three areas of action can get
you there.
Develop business-relevant analytics that
can be put to use. Like early CRM misadventures,
many initial implementations of big data and ana-
lytics fail simply because they aren’t in sync with
the company’s day-to-day processes and decision-
making norms. The aforementioned case of a com-
pany that aimed to optimize prices illustrates how
to avoid those common pain points. The company
started with an analytics task force that convened

a series of meetings with pricing and promotions
managers to better understand the types of deci-
sions they made when setting prices—and how
How to Benefi t from Big Data
1 2 3
To improve performance with advanced analytics,
companies need to develop strengths in three areas.
Multiple Data
Sources
Prediction and
Optimization
Models
Organizational
Transformation
Creatively source
internal and
external data.
Focus on the

biggest drivers of
performance.
Create simple,
understandable tools
for people on the
front lines.
Upgrade IT architecture
and infrastructure for
easy merging of data.
Build models that
balance complexity
with ease of use.
Update processes and
develop capabilities to
enable tool use.
those choices ultimately a� ected revenue and cus-
tomer retention. Model designers also inquired
about the types of business judgments that manag-
ers make to align their actions with broader com-
pany goals. These conversations ensured that both

pricing analytics and resulting scenario tools would
complement existing decision processes. The mod-
eling allowed the company to reach its ultimate goal:
more-effective management of price and volume
trade-o� s as product launches proliferated.
Embed analytics into simple tools for the
front lines. Managers need transparent methods for
using the new models and algorithms on a daily ba-
sis. By necessity, terabytes of data and sophisticated
modeling are required to sharpen marketing, risk
management, and operations. The key is to separate
the statistics experts and software developers from
the managers who use the data-driven insights. One
large industrial company, for instance, sought to bet-
ter forecast workforce needs to re� ect local market
variations. Historically, as the company had tried to
keep labor costs low, it had often found itself short-
sta� ed in some markets, leading to signi� cant over-
time costs and service snafus.
To remedy the problem, the company convened a
small working group of analysts and IT programmers
who developed a series of predictive models that

82 Harvard Business Review October 2012
Making advanced analytics Work for you
literacy. Managers must come to view analytics as
central to solving problems and identifying oppor-
tunities—to make it part of the fabric of daily opera-
tions. Efforts will vary depending on a company’s
goals and desired time line. Adult learners often
benefit from a “field and forum” approach, whereby
they participate in real-world, analytics-based work-
place decisions that allow them to learn by doing.
At one industrial services company, the mission
was to get basic analytics tools into the hands of its
roughly 200 sales managers. Training began with an
in-field assignment to read a brief document and col-
lect basic facts about the market. Next managers met
in centralized, collaborative training sessions during
which they figured out how to use the tools and mar-
ket facts to improve sales performance. They then
returned to the field to apply what they had learned
and, several weeks later, reconvened to review

progress, receive coaching, and learn about second-
order analysis of their data. This process enabled a
four-person team to eventually build capabilities
across the entire sales management organization.
Adjusting culture and mind-sets typically re-
quires a multifaceted approach that includes train-
ing, role modeling by leaders, and incentives and
metrics to reinforce behavior. One large consumer-
products company applied such an approach suc-
cessfully. It created a sophisticated program to
improve the profitability of promotional spending
with its retailers. The launch included training—led
by company management—and a new promotions-
analysis tool for sales representatives. However, af-
ter an initial whirlwind of activity, the program and
use of the tool fizzled. The obstacle was that com-
pany incentives and reporting protocols for sales
managers tracked sales and sales growth, not prof-
its. As a result, the managers considered the profit-
focused program to be bureaucratic overhead that
was unrelated to their key sales goals. After a series
Using a simple tool to deliver complex

analytics substantially improved
workforce planning and reduced the
need for new hires and overtime.
forecast workforce availability on the basis of factors
such as vacation time, absenteeism, and work rules
in labor contracts. The models incorporated millions
of new data points on thousands of employees across
dozens of locations. But rather than providing man-
agers with reams of data and complex models, they
created a simple visual interface that highlighted
projected workforce needs and necessary actions.
Ultimately, that approach of using a simple tool to
deliver complex analytics substantially improved
workforce planning and reduced the need for new
hires and overtime.
Develop capabilities to exploit big data.
Even with simple and usable models, most organiza-
tions will need to upgrade their analytical skills and
of discussions with the managers, the company re-
launched the program, offered new incentives for
improving profits, and tailored reports to profit-
related data. Although ongoing training and coach-

ing was necessary, the efforts gradually produced a
shift in mind-set such that the power of promotions
analytics is now used to further the common goal of
increasing profitability.
the era of big data is evolving rapidly, and our ex-
perience suggests that most companies should act
now. But rather than undertaking massive over-
hauls of their companies, executives should con-
centrate on targeted efforts to source data, build
models, and transform the organizational culture.
Such efforts will play a part in maintaining flexibility.
That nimbleness is essential, given that the informa-
tion itself—along with the technology for managing
and analyzing it—will continue to grow and change,
yielding a constant stream of opportunities. As more
companies learn the core skills of using big data,
building superior capabilities may soon become a
decisive competitive asset.
hbr reprint R1210E
hbR.oRg
october 2012 harvard business Review 83

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