BIA SUMMARY - PREMASTER
,Lecture 1: Setting the stage
Business intelligence = applications, technologies and processes aiming at gathering,
sorting, accessing, and analyzing data to make better business decisions (use of DSS)
-> becomes more complex, due to:
(a) Big Data (huge amounts of data are gathered)
(b) Cloud-based clusters and applications are needed to store and process data
(LaValle - the new path to value) Three levels of analytics:
1) Descriptive: what has occured (past)
2) Predictive: what will occur (future)
3) Prescriptive: what should occur (future/recommendation) -> is used to make
strategic decisions by managers
Key barriers:
- Lack of understanding how to use analytics
- Lack of management bandwidth due to competing priorities (management culture)
Five-point methodology/ recommendations for successfully implementing analytics-
driven management and for rapidly creating value:
1) Focus on biggest opportunities first (more value to more people)
2) Start with questions, not data -> narrowed scope = quicker value-creation
3) Embed insights to drive action (makes it more understandable, thus more
actionable)
4) Keep existing capabilities whilst adding new ones -> (repurposing) supplement older
tools -> more analytical value = quicker business benefits
5) Build analytics foundation according to information agenda (vision & high level
roadmap for information)
Three levels of analytical capabilities:
1) Aspirational: (focus still on efficiency to cut costs) -> low
2) Experienced: (focus: development of better ways to collect, understand, act on analytics to
optimize) -> mid
3) Transformed: focus: analytics as a competitive differentiator (automated operations
through effective use of insights) high
a) 3 times more likely to outperform peers-> high
,Big data: extending the business strategy toolbox (Woerner & Wixom)
- From supportive to a new source of value :
4 V’s: Volume, Velocity (speed), Variety,
Veracity (truthfulness)
Improve the business model
-> New insights and new actions can be gained through new data
most effective is strategy informed by data and then honed and shaped accordingly
Innovate the business model
A) Data monetization: ‘exchanging info-based prod/serv for legal tender/perceived
equa. value:
a) Selling: money in exchange for info
b) Bartering (wederdienst): e.g. discount on car insurance when you share data
from driving behavior
c) Wrapping: wrap data around existing products to make the product more
attractive e.g. nike run sensor that tracks running time/distance
B) Digital transformation: leveraging digitization to move to new industries/create new
ones
Required knowledge/capabilities for analytics
Business Domain (Business Analyst), : use of BI tools to understand and improve business
conditions/processes, business development, business model analysis
Data, or
Modelling (Data Scientist): data exploration, preparation, modelling, visualization and
representation
New patterns of innovation (Parmar et al.)
Creating value from data and analytics
(1, 2 and 3 = Core) (4 and 5 on top of core)
1. Augmented products to gather data: sensors/wireless communication
2. Digitizing physical assets: magazine to online magazine
, 3. Combining data with/across industries (IoT): transport & city agencies consolidate
data to overcome traffic congestion
4. Trading data: TeleCom firm identifies traffic jams and shares ( sells) info to Tom
Tom
5. Codifying a capability: Sell best-in-class process and sell it to other companies e.g.
AkzoNobel maritime data to ports
always highlight creating value with data / using data
Taxonomy of data-driven business models used by start-ups (Hartman et al.)
data as a key resource
- Analytics-as-a-Service -> e.g. fraud detection, improving customer services based
on analytics
- Free data:
- collector and aggregation -> collect & aggregate free data from various
sources
- knowledge discovery -> perform analytics on free data
- Data generation and analysis -> generate data themselves and perform analytics
(don’t rely on existing data)
- Data-aggregation-as-a-Service -> aggregate and provide data through interfaces
(B2B)
- Multi-source data mash-up and analysis-> aggregate and analyze data provided by
customers together with other data sources (B2B)
Lecture takeaways:
Data used to be supportive to make better decisions, but now data is the core source of
value that enables businesses to make better strategic decisions and allows businesses to
reinvent their business model (enter new markets/offer more extensive(or better)
products/services to existing markets) -> in order to gain a sustainable competitive
advantage.
,Lecture 1: Setting the stage
Business intelligence = applications, technologies and processes aiming at gathering,
sorting, accessing, and analyzing data to make better business decisions (use of DSS)
-> becomes more complex, due to:
(a) Big Data (huge amounts of data are gathered)
(b) Cloud-based clusters and applications are needed to store and process data
(LaValle - the new path to value) Three levels of analytics:
1) Descriptive: what has occured (past)
2) Predictive: what will occur (future)
3) Prescriptive: what should occur (future/recommendation) -> is used to make
strategic decisions by managers
Key barriers:
- Lack of understanding how to use analytics
- Lack of management bandwidth due to competing priorities (management culture)
Five-point methodology/ recommendations for successfully implementing analytics-
driven management and for rapidly creating value:
1) Focus on biggest opportunities first (more value to more people)
2) Start with questions, not data -> narrowed scope = quicker value-creation
3) Embed insights to drive action (makes it more understandable, thus more
actionable)
4) Keep existing capabilities whilst adding new ones -> (repurposing) supplement older
tools -> more analytical value = quicker business benefits
5) Build analytics foundation according to information agenda (vision & high level
roadmap for information)
Three levels of analytical capabilities:
1) Aspirational: (focus still on efficiency to cut costs) -> low
2) Experienced: (focus: development of better ways to collect, understand, act on analytics to
optimize) -> mid
3) Transformed: focus: analytics as a competitive differentiator (automated operations
through effective use of insights) high
a) 3 times more likely to outperform peers-> high
,Big data: extending the business strategy toolbox (Woerner & Wixom)
- From supportive to a new source of value :
4 V’s: Volume, Velocity (speed), Variety,
Veracity (truthfulness)
Improve the business model
-> New insights and new actions can be gained through new data
most effective is strategy informed by data and then honed and shaped accordingly
Innovate the business model
A) Data monetization: ‘exchanging info-based prod/serv for legal tender/perceived
equa. value:
a) Selling: money in exchange for info
b) Bartering (wederdienst): e.g. discount on car insurance when you share data
from driving behavior
c) Wrapping: wrap data around existing products to make the product more
attractive e.g. nike run sensor that tracks running time/distance
B) Digital transformation: leveraging digitization to move to new industries/create new
ones
Required knowledge/capabilities for analytics
Business Domain (Business Analyst), : use of BI tools to understand and improve business
conditions/processes, business development, business model analysis
Data, or
Modelling (Data Scientist): data exploration, preparation, modelling, visualization and
representation
New patterns of innovation (Parmar et al.)
Creating value from data and analytics
(1, 2 and 3 = Core) (4 and 5 on top of core)
1. Augmented products to gather data: sensors/wireless communication
2. Digitizing physical assets: magazine to online magazine
, 3. Combining data with/across industries (IoT): transport & city agencies consolidate
data to overcome traffic congestion
4. Trading data: TeleCom firm identifies traffic jams and shares ( sells) info to Tom
Tom
5. Codifying a capability: Sell best-in-class process and sell it to other companies e.g.
AkzoNobel maritime data to ports
always highlight creating value with data / using data
Taxonomy of data-driven business models used by start-ups (Hartman et al.)
data as a key resource
- Analytics-as-a-Service -> e.g. fraud detection, improving customer services based
on analytics
- Free data:
- collector and aggregation -> collect & aggregate free data from various
sources
- knowledge discovery -> perform analytics on free data
- Data generation and analysis -> generate data themselves and perform analytics
(don’t rely on existing data)
- Data-aggregation-as-a-Service -> aggregate and provide data through interfaces
(B2B)
- Multi-source data mash-up and analysis-> aggregate and analyze data provided by
customers together with other data sources (B2B)
Lecture takeaways:
Data used to be supportive to make better decisions, but now data is the core source of
value that enables businesses to make better strategic decisions and allows businesses to
reinvent their business model (enter new markets/offer more extensive(or better)
products/services to existing markets) -> in order to gain a sustainable competitive
advantage.
