Indiya Bruinsma
Premaster DBI 20-21
Business Intelligence & Analytics
Lecture 1
Business intelligence: broad category of applications, technologies and processes.
That aims at gathering, sorting, accessing and analyzing data. With the purpose of
helping business users make better decisions.
Information is a processed, organized data presented in a given context and is useful
to humans. Data is an individual unit that contains raw material which does not carry
any specific meaning.
Analytics:
o Descriptive: what has occurred?
o Predictive: what will occur?
o Prescriptive: what should occur?
The analytics pyramid:
9 Maturity concept
, Indiya Bruinsma
Premaster DBI 20-21
Knowledge required for advanced analytics:
o Role of data scientist: uses advanced algorithms and interactive exploration
tools to uncover non-obvious patterns in data. Usually has a multidisciplinary
background. modeling
Involved in:
Data exploration and preparation
Data representation and transformation
Computing with data
Data modelling
Data visualization and representation
Science about data science
o Role of business analysts: uses business intelligence tools and applications to
understand and improve business conditions and business processes. Business
analysts can have various degrees of technical knowhow. Business domain
Involved in:
Business development
Identification of business needs and opportunities
Business model analysis
Process design
Systems analysis
Interpretation of business rules and developing system
requirements
o Where to put analytics team:
Spread throughout organization
Standalone unit
Some form of cross-functional competence centre
Tableau:
o Measure (x): data item of interest that we want to sum, average, or process
otherwise (e.g. price quantity, age, grade) always a number
o Dimension (y): attribute or characteristic of a measure (x by y), can only be
counted (e.g. date, location, region, gender).
o Not set in stone: dimensions can be measures and numeric values can be
dimensions.
Big data: extending the business strategy toolbox, Woener and Wixom
Companies are using big data to resolve previously unanswerable burning questions
in order to refine and optimize business processes and decision making. Big data
offers:
o New data
o New insight
o New action
, Indiya Bruinsma
Premaster DBI 20-21
Business Model Innovation:
o Digital transformation: when companies leverage digitization to move into
completely new industries or to create new ones.
9 Big data can enable companies to transform in ways that propel them into
new industries or ecosystems and alters traditional competitive landscapes.
o Data monetization: the act of exchanging information-based products and
services for legal tender or something of perceived equivalent value.
Wrapping: wrapping information around other core products and
services.
Selling: when companies receive money or some form of legal tender in
exchange for information offerings.
Bartering: when companies choose to trade information in return for
new tools, services, or special deals.
Analytics: the new path to value by Lavalle et al.
Analytics capability levels:
o Aspirational farthest from achieving analytical goals. Often focus on
efficiency of automation of existing processes, searching for ways to cut costs.
o Experienced some analytical experience, developing better ways to
effectively collect, incorporate and act on analytics so they can begin to
optimize their organizations.
o Transformed substantial experience using analytics across a broad range of
functions. Analytics as a competitive differentiator. Most focused on driving
customer profitability and targeted investments in niche analytics.
9 be able to categorize a company to a capability level and identify steps to take to
get to a higher level.
, Indiya Bruinsma
Premaster DBI 20-21
Recommendations for implementing analytics-driven management:
1) Focus on the biggest and highest value opportunities:
o Potential big reward creates better support. Link incentives and compensation
to desired behaviors.
2) Within each opportunity, start with questions, not with data:
o Identify a clear business need and collect data needed for answers to safe
time in data management.
3) Embed insights to drive actions and deliver value:
o New methods and tools to embed information into business processes are
making insights more understandable and actionable (data visualizations,
simulations and scenario development, analytics applies within business
processes, advanced statistical techniques).
o Alignment between business and IT strategy
o Ask to see what analytics went into decisions
o Strong data-infrastructure
o The right analytical tools
4) Keep existing capabilities while adding new ones:
o Recognize that some people can’t or won’t adjust
o Stress that outdated methods must be discontinued
o Strong analytical people in an appropriate organizational structure
o New tools should supplement earlier ones
o Reusability creates a snowball effect as models from one function are
repurposed into another with minimal modifications.
5) Use an information agenda to plan for the future:
o All data must be molded into an information foundation that is integrated,
consistent and trustworthy. Every phase of implementation needs to align the
data foundation to an overall information agenda.
o Alignment of IT and (changing) business goals
o Enables to keep pace with advances in mathematical sciences and technology
Recommendations per phase:
Premaster DBI 20-21
Business Intelligence & Analytics
Lecture 1
Business intelligence: broad category of applications, technologies and processes.
That aims at gathering, sorting, accessing and analyzing data. With the purpose of
helping business users make better decisions.
Information is a processed, organized data presented in a given context and is useful
to humans. Data is an individual unit that contains raw material which does not carry
any specific meaning.
Analytics:
o Descriptive: what has occurred?
o Predictive: what will occur?
o Prescriptive: what should occur?
The analytics pyramid:
9 Maturity concept
, Indiya Bruinsma
Premaster DBI 20-21
Knowledge required for advanced analytics:
o Role of data scientist: uses advanced algorithms and interactive exploration
tools to uncover non-obvious patterns in data. Usually has a multidisciplinary
background. modeling
Involved in:
Data exploration and preparation
Data representation and transformation
Computing with data
Data modelling
Data visualization and representation
Science about data science
o Role of business analysts: uses business intelligence tools and applications to
understand and improve business conditions and business processes. Business
analysts can have various degrees of technical knowhow. Business domain
Involved in:
Business development
Identification of business needs and opportunities
Business model analysis
Process design
Systems analysis
Interpretation of business rules and developing system
requirements
o Where to put analytics team:
Spread throughout organization
Standalone unit
Some form of cross-functional competence centre
Tableau:
o Measure (x): data item of interest that we want to sum, average, or process
otherwise (e.g. price quantity, age, grade) always a number
o Dimension (y): attribute or characteristic of a measure (x by y), can only be
counted (e.g. date, location, region, gender).
o Not set in stone: dimensions can be measures and numeric values can be
dimensions.
Big data: extending the business strategy toolbox, Woener and Wixom
Companies are using big data to resolve previously unanswerable burning questions
in order to refine and optimize business processes and decision making. Big data
offers:
o New data
o New insight
o New action
, Indiya Bruinsma
Premaster DBI 20-21
Business Model Innovation:
o Digital transformation: when companies leverage digitization to move into
completely new industries or to create new ones.
9 Big data can enable companies to transform in ways that propel them into
new industries or ecosystems and alters traditional competitive landscapes.
o Data monetization: the act of exchanging information-based products and
services for legal tender or something of perceived equivalent value.
Wrapping: wrapping information around other core products and
services.
Selling: when companies receive money or some form of legal tender in
exchange for information offerings.
Bartering: when companies choose to trade information in return for
new tools, services, or special deals.
Analytics: the new path to value by Lavalle et al.
Analytics capability levels:
o Aspirational farthest from achieving analytical goals. Often focus on
efficiency of automation of existing processes, searching for ways to cut costs.
o Experienced some analytical experience, developing better ways to
effectively collect, incorporate and act on analytics so they can begin to
optimize their organizations.
o Transformed substantial experience using analytics across a broad range of
functions. Analytics as a competitive differentiator. Most focused on driving
customer profitability and targeted investments in niche analytics.
9 be able to categorize a company to a capability level and identify steps to take to
get to a higher level.
, Indiya Bruinsma
Premaster DBI 20-21
Recommendations for implementing analytics-driven management:
1) Focus on the biggest and highest value opportunities:
o Potential big reward creates better support. Link incentives and compensation
to desired behaviors.
2) Within each opportunity, start with questions, not with data:
o Identify a clear business need and collect data needed for answers to safe
time in data management.
3) Embed insights to drive actions and deliver value:
o New methods and tools to embed information into business processes are
making insights more understandable and actionable (data visualizations,
simulations and scenario development, analytics applies within business
processes, advanced statistical techniques).
o Alignment between business and IT strategy
o Ask to see what analytics went into decisions
o Strong data-infrastructure
o The right analytical tools
4) Keep existing capabilities while adding new ones:
o Recognize that some people can’t or won’t adjust
o Stress that outdated methods must be discontinued
o Strong analytical people in an appropriate organizational structure
o New tools should supplement earlier ones
o Reusability creates a snowball effect as models from one function are
repurposed into another with minimal modifications.
5) Use an information agenda to plan for the future:
o All data must be molded into an information foundation that is integrated,
consistent and trustworthy. Every phase of implementation needs to align the
data foundation to an overall information agenda.
o Alignment of IT and (changing) business goals
o Enables to keep pace with advances in mathematical sciences and technology
Recommendations per phase:
