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TRANSCRIPT
What Business Analysts Need to Know aboutData Science
Presentation to IIBA Vancouver
Derek Belyea
Analytix Studio Inc.
Analytix Studio
§ Business analytics and data science for business
§ Boutique consulting practice
§ Supporting mid-tier BC businesses (100 to 500)
§ Employing data science graduates from SFU, UBC, BCIT
§ Associated with GNA Consulting Group
§ Founded July 2016
The future is here,
it's just not evenly
distributed yet.
Agenda
§ What exactly is Data Science?
§ Why has Data Science become a big deal?
§ Business Analytics versus Data Science
§ Business Analysts versus Data Scientists
§ Managing the Data Science Function
§ The Future of Data Science
§ A Software Demonstration
The purpose of DATA SCIENCE is to
find
patterns
with meaning
Data Science encompasses many industries
and fields……
including but not limited to digital analytics,
search technology, marketing, fraud detection, astronomy, energy, healthcare,
bioinformatics, social networks, finance,
forensics, security, mobile, human resources, telecommunications, transportation, weather
forecasts, and fraud detection.
Data Science
Core to scientific research in many fields:• Health sciences• Biology• Advanced materials• Genetics• Chemistry• Engineering• Physics• Meteorology
Objectives of Data Science
•Support the advancement of knowledge though the disciplined application of mathematics, statistics and computational programming to data collected from the real world
•Can be applied to pure science, applied science, social science and the world of commerce.
Data Science projects include § text mining
§clustering applied to big data sets
§recommendation engines
§simulations
§rule systems for statistical scoring engines
§root cause analysis
§automated bidding
§ forensics
§early detection of unusual trends or change of direction
Data Science Questions
• What is the best location for my next retail store?
• What is the optimal product mix for my current manufacturing facility?
• What changes can I make to my trucking fleet to reduce fuel costs and improve on-time deliveries?
• Will more special promotions increase or decrease my bottom line?
• What changes to my hiring criteria will reduce staff turnover?
All the Data Ever Created
2012 and 2013
Up to
2011
All the Data Ever Created
2014 and 2015
Up to
2013
All the Data Ever Created
2016 and 2017
Up to
2015
Board of Directors
Decision Models for the 19th Century
GUT INSTINCT HIPPOMAJORITY OPINION
Known to Organization Unknown to Organization
Known to
Customers
PUBLIC
Business Intelligence
BLIND
Customer Analytics
Unknown to
Customers
PRIVATE
Proprietary / Confidential
UNKNOWN
Big Data Analytics
Known to Organization Unknown to Organization
Known to
Customers
PUBLIC
Business Intelligence
BLIND
Customer Analytics
Unknown to
Customers
PRIVATE
Proprietary / Confidential
UNKNOWN
Big Data Analytics
How Data Scientists can help you
• Reveal new and valuable insights about your business
• Inform solutions and improve product and service designs
• Solve mysteries (fraud, poor performance, failures)
• Enhance customer / client experiences
• Discover and integrate new data from disparate sources
• Provide statistical validation : assumptions about business dynamics
• Be a strong ally in delivering on strategy, creating long term value
• Support data driven decision making
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Serv
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& S
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Op
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Fin
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Hu
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eso
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Pro
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Inve
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Ship
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istr
ibu
tio
nCustomer
Relationship
Management
Operations
Management
Supply Chain
Management
Integrated Insights and Analytics
CUSTOMERS
EMPLOYEES
SUPPLIERS
OrderCredit Check
Fulfill Package Ship Invoice Payment
Where are analytics
used in your business?
ORDER TO CASH CYCLE
Difficulty
Value
Descriptive
Analytics
What
happened?
Diagnostic
Analytics
Why did it
happen?
Predictive
Analytics
What will
happen?
Prescriptive
Analytics
How can we
make it happen?
e
Agenda
§ What exactly is Data Science?
§ Why has Data Science become a big deal?
§ Business Analytics versus Data Science
§ Business Analysts versus Data Scientists
Define
Context and
Needs
Align Needs to
Solutions
Business Analysts
…discovering, synthesizing and analyzing information
from a variety of sources within an enterprise
…eliciting the actual needs of stakeholders …to
determining underlying issues and causes
BABOK 3 – 1.3
“…aligning the designed and delivered
solutions with the needs of stakeholders”
BABOK 3 - 1.2
Raw Data
Is Collected
Data is
Processed
Clean
Data
Exploratory
Data
Analysis
Machine
Learning
Algorithms
Statistical
ModelsCommunicate
Visualizations
Report
Findings
Build Data
Product
Data Scientists
Exploratory
Data
Analysis
Raw Data
Is Collected
Data is
Processed
Clean
Data
Machine
Learning
Algorithms
Statistical
ModelsCommunicate
Visualizations
Report
Findings
Build Data
Product
Data Scientists
60 % to 80% of the work
What BAs and Data Scientists have in common
§ Search for patterns of behavior that enhance value
§ Use a variety of tools and models
§ Process orientation
§ Recent arrivals in many organizations
§ Heavy users of computers and data
§ Use visualizations to communicate
§ Need to partner with business leaders
§ Overlapping concerns about content and issues
Shared Tools and Techniques (BABOK3)
10.3 Balanced Scorecard
10.11 Concept Modeling
10.12 Data Dictionary
10.14 Data Mining
10.15 Data Modeling
10.16 Decision Analysis
10.17 Decision Modeling
10.25 Metrics and KPIs
10.36 Prototyping
10.40 Root Cause Analysis
Differences between BAs and Data Scientists
BAs
§ Strong on domain knowledge
§ Focus on discovering needs
§ Focus on explanation and design
§ Search for solutions
§ Limited reliance on statistics
§ Standardized presentation
§ Longer history, widely accepted
Data Scientists
§ Need domain knowledge
§ Focus on data analysis
§ Focus on scientific method
§ Search for explanation / proof
§ Statistics a core competency
§ Creative visualization
§ Shorter history, less understood
What each can give the other
Business Analysts give
§ Business knowledge
§ Organizational context
§ Advice on setting priorities
§ Validation of relevance and feasibility
Data Scientists give
§ Insightful discoveries
§ Scientific support
§ Statistical rigor
§ Creative thinking
Pitfalls in Business Analytics, Data Science
ü No clear objectives for the work
ü Working on the wrong problem(s)
ü Failing to recognize real world constraints
ü Testing without control groups
ü Not communicating results clearly
Programming
Skills
Mathematics
Statistics
Business &
Strategy
Scientific Method
Linear Programming
Time Series Analysis
Regression Analysis
Data Mining
Visualization
Simulation
Python
R
Excel / PowerBI
SQL
NOSQL
Visualization Tools
Communication Skills
Industry Knowledge
Functional Domain Knowledge
Product DesignConsulting Skills
Ethics - Governance
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DATA SCIENCE TAKES TEAMWORK
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Vacoover
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Vanncouver
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Question 1: Is this A or B?
Question 2: Is this weird?
Question 3: How much? or How many?
Question 4: How is this organized?
Question 5: What should I do now?
5 Questions data science answers
How you can help your data scientists
• Collaborate on defining critical business problems
• Invite them early to the party
• Give them meaty problems
• Point them to the data
• Share insights about the dynamics of the business
• Be a sounding board for their work
Agenda
§ What exactly is Data Science?
§ Why has Data Science become a big deal?
§ Business Analytics versus Data Science
§ Business Analysts versus Data Scientists
§ Managing the Data Science Function
§ The Future of Data Science
5 Stages of Analytics Maturity
Stage Description
Stage 1: Analytically Impaired Not data-driven, reliant on gut instincts to make decisions
Stage 2: Localized Analytics Use of analytics or reporting within business silos
Stage 3: Analytical Aspirations Some sense of value for analytics but limited support
Stage 4: Analytical Companies Data-oriented companies with a wide array of tools
Stage 5: Analytical Competitors Broad use of analytics to compete and drive strategy
4 Challenges for the future
§ Quality Data
§ Data Science Skills
§ Real Time Learning Systems
§ Absorbing Change
6 things to take away….
§ Data science and analytics are driving innovation and competition
§ Your business will accumulate exponentially increasing volumes of data
§ Businesses that can harness this data will win
§ Data scientists work better in teams
§ Data scientists need your business leadership
§ Excel is not enough
Challenges of Spreadsheets
§ Undocumented Worksheets
§ Hidden logic errors
§ Overwritten formulas
§ Lack of support for complex workflows
§ Version control
§ Vulnerable to fraud or human errors
§ Limited support for collaboration
§ No audit trail
Data Science Resources for Business Analysts Prepared by Derek Belyea, Analytix Studio Inc. Vancouver BC Canada
For business analysts who are curious to learn more about data science here are
some sources to get you started.
Web Sites Topic Search term
Data Science Central http://www.datasciencecentral.com
Data Science Roundup http://roundup.fishtownanalytics.com
KDnuggets http://www.kdnuggets.com
Videos Topic Search term
Data Science: Where are We Going? https://www.youtube.com/watch?v=3_1reLdh5xw
How data will transform business https://www.youtube.com/watch?v=EHTmxmuhZ10
Big Data and the Rise of Augmented
Intelligence
https://www.youtube.com/watch?v=mKZCa_ejbfg
How to Monetize Big Data https://www.youtube.com/watch?v=hsoKlE67rTw
Books
Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
by Foster Provost and Tom Fawcett
Storytelling with Data: A Data Visualization Guide for Business Professionals
by Cole Nussbaumer Knaflic
Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
by Eric Siegel
For more information, contact Derek at Analytix Studio
http://www.analytixstudio.com/contact.html