Business analytics presentation - unit 1 in Anna University BA4206 Evolution of Business analytics terminologies, process, importance, relationship with organisational decision making, competitive advantage etc

1. INTRODUCTION TO
BUSINESS ANALYTICS
(BA)
Rhema Joy Sharath
AP, MBA

2. BUSINESS ANALYTICS
TERMINOLOGIES
PROCESS
IMPORTANCE
RELATIONSHIP WITH ORG. DM
BA FOR COMPETITIVE ADVANTAGE
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3. • Business Analytics is the practice of iterative, methodical exploration of
an organisation’s data, with an emphasis on statistical analysis. Business
analytics is used by companies committed to data-driven decision making.
• Business analytics provides the models and procedures to BI.
• Data  Information  useful insights
• “Business analyst” – makes reports and advices org.
• Business Analytics requires quantitative methods and evidence-based data
for business modelling and decision making; as such, Business Analytics
requires the use of Big D ata.
• 2 main areas: Business Intelligence & Statistical Analysis
INTRODUCTIONTO BA

4. • Data-driven companies treat their data as a business asset and actively look for ways
to turn it into a competitive advantage. Success with business analytics depends on
data quality, skilled analysts who understand the technologies and the business, and a
commitment to using data to gain insights that inform business decisions.

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TYPES AND TECHNIQUES OF BA

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DESCRIPTIVE ANALYTICS: The application of simple statistical
techniques that describes what is contained in a data set or database.
Example: An age bar chart is used to depict retail shoppers for a
department store that wants to target advertising to customers by age.
PREDICTIVE ANALYTICS: An application of advanced statistical,
information software, or operations research methods to identify
predictive variables and build predictive models to identify trends and
relationships not readily observed in a descriptive analysis. Example:
Multiple regression is used to show the relationship between age,
weight, and exercise on diet food sales. Knowing that relationships
exist helps explain why one set of independent variables influences
dependent variables such as business performance.
PRESCRIPTIVE ANALYTICS: An application of decision science,
management science, and operations research methodologies
(applied mathematical techniques) to make best use of allocable
resources. Example: A department store has a limited advertising
budget to target customers. Linear programming models can be used
to optimally allocate the budget to various advertising media.

7. EVOLUTION OF BUSINESS
ANALYTICS
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8. SCOPE OF BA
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1. Helping organizations know their customers
2. Reputation management
3. Improve Operational Efficiency
4. Financial Management
5. Manage waste, fraud and abuse
6. Proactive Risk Management
7. Analytics in Education

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10. NEED OF BA
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• To make strong commercial
decisions – to enhance
business profit, increase
market share and revenue,
provide better returns to
shareholders.
• Easy understanding of
primary and secondary
data.
• Competitive Benefits
• Information can be
produced in any format.

11. COMPONENTS OF BA
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Data Mining Text Mining
Forecasting
Predictive
Analysis
Optimization Visualization

12. DATA MINING
• Data Mining derives its name from the similarities between
searching for valuable business information in a large database, and
mining a mountain for a vein of valuable ore. Both processes require
either an immense amount of material or intelligently probing it to
find exactly where the value resides.
• Uncovering previously unknown trends and patterns in vast amount
of data
• Data Mining can be achieved through
1. Classification
2. Regression
3. Clustering
4. Associations & Sequencing models
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13. TEXT MINING
• Text Mining is the application of data mining to non-structured or
less structured text files.
• A---B,C
• It is used to extract entities and objects for frequency analysis,
identify files with certain attributes.
• Eg: Understand sentiments of customers on Social Media
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14. FORECASTING
1. It is used to analyse and forecast processes that take place
over the period of time. Eg: Predict seasonal energy
demand using historical trends, ice creams on summer, etc.
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15. PREDICTIVE ANALYTICS
• It is used to create, manage and deploy predictive scoring
models.
• Eg: Customer churn & retention, Credit scoring, predicting
failure in shop floor machinery.
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16. OPTIMIZATION
• Using simulation techniques to identify scenarios which will
produce best results.
• Sales price optimization, identifying optimal inventory for
maximum fulfilment, avoid stock outs.
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17. VISUALIZATION
• Visual Analysis: non-technical users, VizQL
(Visual Query Language used by Tableau) –
Hyperion’s Visual Explorer, Analytica,
Endeca.
• Dashboards & Scorecards  EIS
(Executive Information Systems) – charts,
graphs and tables.
• Virtual Reality
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19. TYPES OF BA
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DESCRIPTIVE
ANALYSIS
•Data query,
Data
dashboards
PREDICTIVE
ANALYSIS
Data Mining,
Simulation
PRESCRIPTIVE
ANALYSIS
Simulation
Optimization,
Decision
Analysis

20. TERMINOLOGIES IN BA
• AI – Artificial Intelligence
• Big data
• BI – Business Intelligence
• Behavioural Analytics
• Balanced scorecard
• Contextual Data
• Data Analytics
• Descriptive Analytics
• Dashboard
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21. TERMINOLOGIES IN BA
• Data cleansing
• Data Processing
• Data Translation
• Data Visualization
• Database
• Gap Analysis
• Geospatial Analytics
• KPI – Key Performance Indicator
• Metadata
• Metrics
• Multipolar analytics
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22. TERMINOLOGIES IN BA
• Predictive analysis
• Prescriptive analysis
• Pivot table
• Statistical Analysis
• Time-Series forecasting
• Variety, Velocity, Volume.
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23. AI – ARTIFICIAL INTELLIGENCE
• It is a broad term for using vast data sets to
provide a high level of understanding and
sometimes a higher level of consciousness.
• Used along with Machine learning
• Specific applications of AI include expert
systems, natural language processing, speech
recognition and machine vision.
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24. EXAMPLES OF AI
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25. BIG DATA
• It is a widely used term referring to the
derivation of insights from large data sets.
• Eg: GPS, IOT
• Big data refers to extremely large and diverse
collections of structured, unstructured, and
semi-structured data that continues to grow
exponentially over time.
• used by Ola, Amazon Prime, Spotify
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26. EXAMPLES OF BIG DATA
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27. BI – BUSINESS INTELLIGENCE
• The act of informing an organization using data.
• Business intelligence (BI) can be defined as a
set of processes and technologies that convert
data into meaningful and useful information for
business purposes.
• It is a software that ingests business data and
presents it in user-friendly views such as
reports, dashboards, charts and graphs.
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28. BEHAVIOURAL ANALYTICS
• Using data about people’s behaviour to
understand intent and predict future actions.
• focuses on providing insight into the actions of
people, usually regarding online purchasing.
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30. BALANCED SCORECARD
(BSC)
A performance management tool that holistically
captures an organization’s performance from
several vantage points on a single page.
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32. CONTEXTUAL DATA
A structuring of big data that attaches situational
contexts to single elements of big data to enrich
them with business meaning. The result is a more
complete understanding of the costumer and their
lifestyle.
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33. DATA ANALYTICS
The process of cleansing, transforming and
modelling data with the goal of discovering useful
information, informing conclusions and supporting
decision-making.
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34. DESCRIPTIVE ANALYTICS
Descriptive analytics is a statistical interpretation
used to analyze historical data to identify patterns
and relationships. It explains the current state of
the data.
Eg: Website traffic, number of followers, Financial
Statements
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35. DASHBOARD
It is a general software application user design
construct used in many software applications to
provide the user with a bird’s eye view of the
software application.
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36. DATA CLEANSING
It reviews all business data to ensure it is
formatted correctly and consistently, corrects as
needed, or notifies end-user to address “dirty”
data if it does not meet the business standards.
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38. DATA PROCESSING (ETL)
It is a software designed to extract business data
from all data sources and conduct data cleansing
and transformation.
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39. DATA TRANSLATION
It involves mapping and applying custom
business logic to cleansed business data with the
goal of organizing and normalizing business
information for storage and consumption.
(Converting data from one format to another.)
Eg: Word file to PDF
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40. DATA VISUALIZATION
It is the science of deriving meaning from data
sets by using graphical and other non-tabular
presentations.
Eg: Line charts, pie charts, column charts.
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42. DATABASE
It is a computer system (cloud or on-premises)
that stores data in a persistent state, typically to
be retrieved and modified by other software.
Eg: Cloud database, Grocery stores
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43. GAP ANALYSIS
A study of whether the data that a company has
can meet the business expectations that the
company has set for its reporting and BI, and
where possible data gaps or missing data might
exist.
Market Gap, Strategic gap, Profit Gap, Skills gap
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44. GEOSPATIAL ANALYTICS
It associates data with a location. It is a type of
data visualization that is often used to overlay
data onto digital maps.
Eg: GIS, Railways, NHAI, Weather forecasting,
Agriculture.
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45. KPI – KEY PERFORMANCE
INDICATOR
A metric a business uses to measure its progress
to determine whether it is meeting its goals.
Eg: Customer Satisfaction, Sales, Monthly
website traffic etc.
“If you can’t measure it, you can’t manage it” -
Peter Drucker.
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46. METADATA
Data about data.
Data that gives information about what the
primary data is about.
Eg: Photo – resolution, time taken, place etc.
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47. METRICS
Measures of performance that observe progress
and evaluate trends within an organization.
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48. MULTIPOLAR ANALYTICS
A distributed big data model where data is
collected, stored, and analysed in different areas
of the company instead of being centrally located
and analysed.
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49. PREDICTIVE ANALYSIS
Predictive analysis applies statistical analysis to
predict future behaviour. It explains the current
state of the data.
Eg: Forecasting Future Cash Flow, Determining
Staffing Needs, Behavioral Targeting, Preventing
Malfunction
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50. PRESCRIPTIVE ANALYSIS
Prescriptive analysis is finding the best course of
action for a given situation.
Examples: “Recommended for you” in Youtube,
email automation, Investment decisions
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51. PIVOT TABLE
A tool used in MS Excel.
It summarizes information extracted from large ,
detailed datasets.
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52. STATISTICAL ANALYSIS
It is the general application of math and statistics
to data.
Eg: Mean, Median, Mode, Standard Deviation,
Variance
SW: SAS, R, SPSS
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53. TIME-SERIES FORECASTING
It applies statistical modeling to a time-series and
forecasts that into future time periods.
Eg: Stocks, Birth rate, Corn production
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54. VARIETY, VELOCITY, VOLUME
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55. PROCESS OF BUSINESS ANALYTICS
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57. DEFINING THE BUSINESS NEEDS
• Area of improvement
• Goal – smaller goals
• Decide Relevant data
• Example: COVID CASES
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Key Questions
• What data is available?
• How can we use it?
• Do we have sufficient data?

58. EXPLORE THE DATA
• Cleaning the data – messy & unwanted data is
removed
• Make computations for missing data
• Remove outliers
• Transform combinations of variables to form new
variables
• Time series graphs
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59. ANALYSE THE DATA
• Use statistical analysis methods – correlation
analysis, hypothesis testing
• Simple regression analysis – how many ventilators,
age, gender
• In this stage data is cut, sliced and diced and
different comparisons are made.
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60. PREDICT WHAT IS LIKELY TO HAPPEN
• Proactive in DM
• Data modelling using Predictive techniques.
(Decision trees, Neural networks, logical regression)
• Compare predictive values and actual values
• Predictive errors
• Best model is selected based on accuracy.
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61. OPTIMISE (FIND THE BEST
SOLUTION)
• ‘what if’ scenarios
• Best solution is given considering the limitations.
(less no. of kits in gov – private, quarantine
centers.)
• What action is required from your side?
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62. MAKE A DECISION AND MEASURE
THE OUTCOME
• Take action based on the derived insights
• Outcome of the action is measured after a time
period.
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63. UPDATE THE SYSTEM WITH THE
RESULTS OF THE DECISION
• Update database
• No. of deaths, recoveries, etc
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• Was the decision and action
effective?
• How did the treatment group
compare with the control
group?
• What was the ROI?

64. IMPORTANCE / ADVANTAGES OF
BUSINESS ANALYTICS
• Enhance customer experience
• Make informed decisions
• Improve efficiency
• Identify frauds
• Cut manufacturing costs
• Make the most of your investment
• Improved Advertising
• Better product management
• Tackle problems
• Accelerate through uncertainty
• Conduct a competitor Analysis
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65. TOOLS OF BA
• MS Excel
• SAS
• SPSS Modeler (Clementine)
• Salford Systems
• KXEN
• MATLAB
• R
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Excel is an excellent reporting and dash boarding
tool.

67. • “Statistical Analysis System”
• It is the most commonly used software in the Indian analytics market despite its
monopolistic pricing.
• SAS software has wide ranging capabilities from data management to advanced
analytics.
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69. • “Statistical Package for the Social Sciences”
• SPSS Modeler is a data mining software tool by SPSS Inc., an IBM company.
• It was originally named Clementine.
• This tool has an intuitive GUI and its point and click modelling technique capabilities are
very comprehensive.
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71. • “Knowledge Extraction Engines”
• Automated analytics.
• easy to use, fast and can work with large amounts of data.
• ‘Black box’
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72. • MATrix LABoratory
• It is a statistical computing software developed by MathWorks.
• MATLAB allows matrix manipulations, plotting of functions and data, implementation of algoriths
and creation of many UI.
• MATLAB is not free
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74. • R is a programming language and s/w environment for statistical computing and graphics.
• Open source tool
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76. CHALLENGES OF BA
• Lack of technical skills in employees
• Fuss over acceptance of BA by staff
• Data security and maintenance
• Integrity of data
• Delivering relevant information in the given time
• Inability to address complex issues
• Costs involved in implementing BA
• Investment of staff time in implementation of BA
• Lack of proper strategy to implement BA
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77. APPLICATION AREAS OF BA
• Finance
• Marketing
• HR
• CRM
• Manufacturing
• Credit card companies
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78. BUSINESS ANALYSIS IN DECISION
MAKING
1. Perception of disequilibrium
2. Diagnostic Process
3. Problem statement
4. Solution Strategy Selection
5. Implementation
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80. BUSINESS ANALYSIS AND
COMPETITIVE ADVANTAGE
• Competitive advantage can be defined as the
superiority that is enjoyed by a firm over its
competitors in an industry.
• Price leadership
• Sustainability
• Operation Efficiency
• Service Effectiveness
• Innovation
• Product differentiation
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81. THANK
YOU