Business analytics

Business analytics (BA) is the
practice of
Iterative
methodical exploration of an
organization's data,
with an emphasis on statistical analysis.
 Business analytics is used by companies
committed to data-driven decision-
making.
BUSINESS ANALYTICS

BA techniques break down into two main areas
Business intelligence
 involves examining historical data to sense
how a business team performs over a
particular time
Statistical Analysis
 doing predictive analytics by applying
statistical algorithms to historical data to
make prediction about future performance
about a product, service. Eg: Cluster analytics
BUSINESS ANALYTICS
Contd.,

TYPES OF BUSINESS ANALYTICS
4 types
Descriptive Analytics
 tracks KPI (Key Performance Indicators) to
understand present state of business
Predictive Analytics
 analyze trend data to asses the likelihood of
future outcomes
Prespective Analytics
Uses past performance and generate
recommendations to handle current
situations
Contd.,

Diagnostic Analytics
 is a form of advance analytics that
examines data or content to answer the
question “Why did it happen”
Characterized by techniques such as drill
down data discovery, data mining and
correlations
Contd.,

Descriptive Analytics (BI) Predictive Analytics Prespective Analyticas
 What and when did it
happen
 What is likely to
happen next
 What is the best answer
 How much is the impact
and how often it happens
 What if these trends
continue
 What is the best outcome
given uncertainty
 What is the problem  What if  What are significantly
differing and better
choices
Statistics
Datamining
Predictive Modelling
Machine learning
Forecasting
Simulation
 Constraint-based
optimization.
 Multi objective
optimization
 Global Optimization
Information Management

Consumption Analytics is still an emerging
area, where companies are capturing lot of
clicks and data
That figures out the most valuable insights
The nature of consumption is different
among different technologies
Communication cycle provides a frame work
for making analytics consumable
CONSUMPTION ANALYTICS
Contd.,

Communication:
 core intention take analysis beyond your core team
(i.e., wide group of decision makers)
Implement:
Getting right ingredients in place to create the basic
human and technology infrastructures
Measure:
This is the true test of anlaytics
a succession decision is a healthy combination of
business experience and data analytics
Align Incentives:
Develop Cognitive Repairs:
COMMUNICATION CYCLE
Contd.,

Align Incentives:
 creates of more structured decision-making
processes
Puts a constraints on free-flowing, experience
driven decision making
Implementation will bring new incentives for a
single puzzle
Develop Cognitive Repairs:
Creation of counterintuitive business insights
based on data and proving it right
COMMUNICATION CYCLE

“ How to convert thought into action and
bridge the gap between analytics creation and
consumption”?
Do you have experience in creating a lot of
analytics but failing at consumption?
Does it make sense to ramp up/down
analytics creation to maintain balance with
consumption?
Human bias exists? Do you need to develop
structures that push people toward healthy
conflict and resolution?
CREATION TO CONSUMPTION

DATA VISUALIZATION
BY
ORGANIZATIONS

Def: “ It is form of visual communication of
information/data that has been abstracted in
some schematic form ”
 The primary goal of data visualization is to
communicate information clearly and effectively
using statistical graphs, plots and information
graphs.
Old methods of data visualization are charts &
graphs
These things were done using two things
Describing: explain a thing for basic meaning
Reporting: summarize finding from point-in-time
DATA VISUALIZATION
Contd.,

New Methods of data visualization are
 dynamic visualizations designed by data
artisans
 Data aritisans/data artists are individuals
with intersection of skills in science, design,
and art
Observing: Viewing data to identify significance or
patterns which unfold over a period of time.
Discovering: Interacting with data to explore,
interact, and understand relationship between data
DATA VISUALIZATIONS
Contd.,

 show the data
 induce the viewer to think about the substance rather than
about methodology, graphic design, the technology of graphic
production or something else
 avoid distorting what the data has to say
 present many numbers in a small space
 make large data sets coherent
 encourage the eye to compare different pieces of data
 reveal the data at several levels of detail, (bottom up
approach)
 serve a reasonably clear purpose: description, exploration,
tabulation or decoration
 be closely integrated with the statistical and verbal
descriptions of a data set.
CHARACTERISTICS OF EFFECTIVE
GRAPHICAL DISPLAYS
Contd.,

 Data artisans are using many different dimensions to
represent and evaluate data:
Spatial, geospatial: position, direction, velocity
Temporal, periodicity: state, cycle, phase
Scale, granularity: weight, size, count
Relativity, proximity
Value, priority
Resources: energy, temperature, matter
Constraints
DATA VISUALIZATIONS
Contd.,

BAR CHARTS
Visual Dimensions:
length/count
category
Usage: Comparison
HISTOGRAM
Visual Dimensions:
bin limits
Count/length
Usage: determine frequency and
observation of each bins
TYPES OF DATA VISUALIZATIONS
Contd.,

BASIC SCATTER PLOT
Visual Dimensions:
• X position
• Y position
• (symbol/glyph)
• (colour)
• (size)
Usage: define relationship
3D-SCATTER PLOT
Usage:3D analysis
Visual Dimensions:
• Position X
• Position Y
• Position Z
• colour
Contd.,

NETWORK ANALYSIS
Visual Dimensions:
• Nodes size
• Nodes colour
• Ties thickness
• Ties colour
• spatilization
Usage: Finding clusters in
network( grouping)
TREE MAP
Visual Dimensions:
• Size
• colour
Usage: disk space by location /
file type
Contd.,

GNATT CHART
Visual Dimensions:
• colour
• Time flow
Usage: schedule / progress
(project planning )
HEAT MAP
Visual Dimensions:
• row
• colum
• Cluster
• colour
Usage: Analyzing risk
Contd.,

 Tableau, www.tableausoftware.com
 Qlikview, www.qlikview.com
 Microstrategy, www.microstrategy.com
 D3JS, Data Driven Documents java script library,
http://d3js.org.
 SAS, www.sas.com
 Gephi Org, open-source data visualization platform.,
https://gephi.org
 Arbor JS, a java-based graph library, http://arborjs.org
 Cubism, a plug-in for D3 for visualizaing time series,
http://square,github.com/cubism
 GeoCommons, a community building an open mapping
platform, http://geocommons.com
OPEN SOURCE TOOLS FOR
DATA VISUALIZATION

EXAMPLES OF
DATA VISUALIZATION
INTERACTIVE DATA VISULATIZATION
Contd.,

EXAMPLES OF
DATA VISUALIZATION
Global Temperature Trend Visualization
Contd.,

EXAMPLES OF
DATA VISUALIZATION
Global Site visitor Visualization
Contd.,

9/10 RULE
OF
CRITICAL THINKING

DATA SCIENTIST - CRITICAL THINKING

90% or 9 think about the people analysts,
analysis and intelligence
10% or 1 work on tools and professional services
for data with different patterns
Where should they go?
What data will be more useful to consumers?
What metrics should we think about and what kind of
psychological analysis should we think about next
9/10 RULE OR 90/10 RULE

DECISION SCIENCES
AND ANALYTICS

Def: “It is a mix of behavioral economics,
applied psychology, cognitive science, game
theory, statistics, risk analysis”
Or
‘Decision Sciences’ is a collaborative
approach involving mathematical formulae,
business tactics, technological applications
and behavioral sciences to help senior
management make data driven decisions
DECISION SCIENCE
Contd.,

Learning over knowing
Based on past domain must have ability to apply
principles and structured approaches for problem
solving
Agility
Update with continuous transformation
Scale and Convergence
Synergistic (co-operation of two or more
organizations)ecosystem of talent, capabilities,
processes, customers
Partners, domains, verticals
PROFESSIONAL TRAITS –
TO MAKE DECISION SCIENCE SUSTAINABLE
Contd.,

Multidisciplinary talent
Apply mathematics, business, technology, behaviour
together in business
Innovation
Increase breadth and depth of problem solving
Researching and deploying emerging trends,
technologies and applications
Cost Effectiveness
Ensure sustainability of problem solving across
organizations
PROFESSIONAL TRAITS –
TO MAKE DECISION SCIENCE SUSTAINABLE
Contd.,

Focus on nurturing of new employees
instead of labelelling
Be sure to give employees room to grow
Emphasize striving for a personal best
HOW CAN WE MAKE DECISION
SUSTAINABLE

When an organization expects the fruits
of analytics , the organizational structure
takes into account
the DNA of the organization
Culture
Overall goals
RIGHT ORGANIZATION STRUCTURE FOR
INSTITUTIONALIZING ANALYTICS

FORMS OF ORGANIZATION STRUCTURE
FOR ANALYTICAL NEEDS

RIGHT ORGANIZATION STRUCTURE FOR
INSTITUTIONALIZING ANALYTICS

PRIVACY
AND
SECURITY
IN BIG DATA

 7 Global principles
1. Notice (Transparency)-
- Informs individuals about the purpose for which
information is collected
2. Choice
- Offer an opportunity to users to choose how the
personal information can be disclosed
3. Consent
- disclose personal data information to third
parties with principles of notice and choice
4. Security
- Responsible to protect personal information from
- Loss, misuse, unauthorized access, disclosure,
alteration, destruction
GLOBAL PRIVACY PRINCIPLES

5. Data Integrity
• Assure reliability of personal information
• Ensure information accuracy, complete, updated
• Must use for intended use only
6. Access
• Provide individuals to access their personal information
only
7. Accountability
• An organization must be accountable for the above said
principles
• And must include mechanisms for assuring compliance
(assuring the action)
GLOBAL PRIVACY PRINCIPLES

Business analytics

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