AI for business intelligence
Week 1
Big Data for Big Business? A taxonomy of business models
What is data
“Data exists prior to argument or interpretation that converts them to facts,
evidence and information” (Rosenberg, 2013)
Big data def:
big data as ‘high-volume, high-velocity and high-variety information assets that demand
cost-effective, innovative forms of information processing for enhanced insight and decision
making’
The four V
- big data not only by volume of data but also by variety of different data types, which
poses challenges to integrating the different formats, as well as velocity of the data,
referring to the speed at which the data is created, processed and analysed
- Second, it refers to big data as an ‘information asset’, implying that it lends value to
the organisation.
- Third, it places emphasis on the demand for new solutions to process this data cost-
effectively
Synthesising the main ideas the way big data creates value for companies:
- On the one hand, (big) data is used for the incremental improvement and
optimisation of current business practices and services;
o for example, through the optimisation of existing processes, the customer
relationship, the innovation process and the collaboration of employees.
- On the other hand, new products and business models can be innovated based on
the use of data.
So we talk about data-driven business model as a business model that relies on data as a key
resource.
- First, a data-driven business model is not limited to companies conducting analytics,
but also includes companies that are ‘merely’ aggregating or collecting data.
, - Second, a company may sell not just data or information, but also any other product
or service that relies on data as a key resource.
o An example is a company called kinsa, which sells thermometers for the
iPhone and provides a service to constantly monitor the body temperature.
- Third, it is obvious that any company uses data in some way to conduct business –
even a small restaurant relies on the contact details of its suppliers and uses a
reservation book. However, the focus lies on companies that are using data as a key
resource for their business model.
Types of data
- Operational data comes from transaction systems, the monitoring of streaming data
and sensor data;
- Dark data is data that you already own but don't use: emails, contracts, written
reports and so forth;
- Commercial data may be structured or unstructured, and is purchased from industry
organisations, social media providers and so on;
- Social data comes from Twitter, Facebook and other interfaces;
- Public data: information that is freely available. can have numerous formats and
topics, such as economic data, socio-demographic data and even weather data.’
Cluster types:
Type A: ‘Free data collector and aggregator’
Companies of this cluster create value by collecting and aggregating data from a vast
number of different, mostly free, available data sources. Subsequently, the other distinctive
key activity is data distribution, for example, through an API or Web-based dashboard.
Type B: ‘Analytics-as-a-service’
These companies are characterised by conducting analytics on data provided by their
customers. In addition to the data provided by customers, some companies in this cluster
also include other data sources, mainly to improve the analytics.
Type C: ‘Data generation and analysis’
Companies in this cluster all share the common characteristic that they generate data
themselves rather than relying on existing data. Subsequently, all companies in this cluster
share the key activity ‘data generation’. Besides generating data, most of the companies
also perform analytics on this data
Type D: ‘Free data knowledge discovery’
The companies in this cluster are characterised by the use of free available data and
analytics performed on this data. Furthermore, as not all free data sources are available in
a machine-readable format, some such companies crawl data from the Web → generation
of data
Type E: ‘Data-aggregation-as-a-service‘
Companies in this cluster create value through aggregating data from multiple internal
sources for their customers.
,Type F: ‘Multi-source data mash-up and analysis’
Cluster F contains companies that aggregate data provided by their customers with other
external, mostly free, available data sources, and perform analytics on this data. The
offering of companies in this cluster is characterised by using other external data sources
to enrich or benchmark customer data
Analytics: the new path to value
Top performers put analytics to use in the widest possible range of decisions.
→ they are twice as likely to guide future strategies as use insights to guide day-to-day
operations
Analyzing the data because we have the tools doesn’t make sense, we always have a
question. Also, we need to get the organization ready. This is about organizational change,
you have to convince people in the organization that the system is useful and can be
actionable. Otherwise, it is not embraced by other people in the organization.
Recommendations for implementing BI&AI (Lavalle et al., 2010)
- Within each opportunity, start with questions, not data
• A clear business need
- Focus on the biggest and highest value opportunities
• Be a vocal supporter
• Strong, committed sponsorship
• Link incentives and compensation to desired behaviours
- Embed insights to drive actions and deliver value
, • Alignment between the business and IT Strategy
• Ask to see what analytics went into decisions
• A strong data-infrastructure
• The right analytical tools
- Keep existing capabilities while adding new ones
• Recognise that some people can’t or won’t adjust
• Stress that outdated methods must be discontinued
• Strong analytical people in an appropriate organizational structure
- Use an information agenda to plan future
Looking ahead: capabilities, patterns and business models
Role of the data scientist
Modelling – uses advanced algorithms and interactive exploration tools to uncover non-
obvious patterns in data. Involved in (Donoho, 2017)
- Data exploration and preparation
- Data representation and transformation
- Computing with data
- Data modelling
- Data visualization and representation
- Science about data science
- Usually has a multidisciplinary background
Business domain
uses business intelligence tools and applications to understand and improve business
conditions and business practices
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
- Business analysts can have various degrees of technical know how
- Where to put the analytics team
- Spread throughout the organization
- In a standalone unit
- In some form of cross-functional competence centre
Where to put the analytics team
- Spread throughout the organization
- In a standalone unit
- In some form of cross-functional competence centre
Week 1
Big Data for Big Business? A taxonomy of business models
What is data
“Data exists prior to argument or interpretation that converts them to facts,
evidence and information” (Rosenberg, 2013)
Big data def:
big data as ‘high-volume, high-velocity and high-variety information assets that demand
cost-effective, innovative forms of information processing for enhanced insight and decision
making’
The four V
- big data not only by volume of data but also by variety of different data types, which
poses challenges to integrating the different formats, as well as velocity of the data,
referring to the speed at which the data is created, processed and analysed
- Second, it refers to big data as an ‘information asset’, implying that it lends value to
the organisation.
- Third, it places emphasis on the demand for new solutions to process this data cost-
effectively
Synthesising the main ideas the way big data creates value for companies:
- On the one hand, (big) data is used for the incremental improvement and
optimisation of current business practices and services;
o for example, through the optimisation of existing processes, the customer
relationship, the innovation process and the collaboration of employees.
- On the other hand, new products and business models can be innovated based on
the use of data.
So we talk about data-driven business model as a business model that relies on data as a key
resource.
- First, a data-driven business model is not limited to companies conducting analytics,
but also includes companies that are ‘merely’ aggregating or collecting data.
, - Second, a company may sell not just data or information, but also any other product
or service that relies on data as a key resource.
o An example is a company called kinsa, which sells thermometers for the
iPhone and provides a service to constantly monitor the body temperature.
- Third, it is obvious that any company uses data in some way to conduct business –
even a small restaurant relies on the contact details of its suppliers and uses a
reservation book. However, the focus lies on companies that are using data as a key
resource for their business model.
Types of data
- Operational data comes from transaction systems, the monitoring of streaming data
and sensor data;
- Dark data is data that you already own but don't use: emails, contracts, written
reports and so forth;
- Commercial data may be structured or unstructured, and is purchased from industry
organisations, social media providers and so on;
- Social data comes from Twitter, Facebook and other interfaces;
- Public data: information that is freely available. can have numerous formats and
topics, such as economic data, socio-demographic data and even weather data.’
Cluster types:
Type A: ‘Free data collector and aggregator’
Companies of this cluster create value by collecting and aggregating data from a vast
number of different, mostly free, available data sources. Subsequently, the other distinctive
key activity is data distribution, for example, through an API or Web-based dashboard.
Type B: ‘Analytics-as-a-service’
These companies are characterised by conducting analytics on data provided by their
customers. In addition to the data provided by customers, some companies in this cluster
also include other data sources, mainly to improve the analytics.
Type C: ‘Data generation and analysis’
Companies in this cluster all share the common characteristic that they generate data
themselves rather than relying on existing data. Subsequently, all companies in this cluster
share the key activity ‘data generation’. Besides generating data, most of the companies
also perform analytics on this data
Type D: ‘Free data knowledge discovery’
The companies in this cluster are characterised by the use of free available data and
analytics performed on this data. Furthermore, as not all free data sources are available in
a machine-readable format, some such companies crawl data from the Web → generation
of data
Type E: ‘Data-aggregation-as-a-service‘
Companies in this cluster create value through aggregating data from multiple internal
sources for their customers.
,Type F: ‘Multi-source data mash-up and analysis’
Cluster F contains companies that aggregate data provided by their customers with other
external, mostly free, available data sources, and perform analytics on this data. The
offering of companies in this cluster is characterised by using other external data sources
to enrich or benchmark customer data
Analytics: the new path to value
Top performers put analytics to use in the widest possible range of decisions.
→ they are twice as likely to guide future strategies as use insights to guide day-to-day
operations
Analyzing the data because we have the tools doesn’t make sense, we always have a
question. Also, we need to get the organization ready. This is about organizational change,
you have to convince people in the organization that the system is useful and can be
actionable. Otherwise, it is not embraced by other people in the organization.
Recommendations for implementing BI&AI (Lavalle et al., 2010)
- Within each opportunity, start with questions, not data
• A clear business need
- Focus on the biggest and highest value opportunities
• Be a vocal supporter
• Strong, committed sponsorship
• Link incentives and compensation to desired behaviours
- Embed insights to drive actions and deliver value
, • Alignment between the business and IT Strategy
• Ask to see what analytics went into decisions
• A strong data-infrastructure
• The right analytical tools
- Keep existing capabilities while adding new ones
• Recognise that some people can’t or won’t adjust
• Stress that outdated methods must be discontinued
• Strong analytical people in an appropriate organizational structure
- Use an information agenda to plan future
Looking ahead: capabilities, patterns and business models
Role of the data scientist
Modelling – uses advanced algorithms and interactive exploration tools to uncover non-
obvious patterns in data. Involved in (Donoho, 2017)
- Data exploration and preparation
- Data representation and transformation
- Computing with data
- Data modelling
- Data visualization and representation
- Science about data science
- Usually has a multidisciplinary background
Business domain
uses business intelligence tools and applications to understand and improve business
conditions and business practices
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
- Business analysts can have various degrees of technical know how
- Where to put the analytics team
- Spread throughout the organization
- In a standalone unit
- In some form of cross-functional competence centre
Where to put the analytics team
- Spread throughout the organization
- In a standalone unit
- In some form of cross-functional competence centre
