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This summary is about all papers you need to read for the course business intelligence and analytics.
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Business Intelligence & Analyti csLecture 1
Hartmann, et al. (2014). Big data for big business? A taxonomy of data-driven
business models used by start-up firms.
Big data: high-volume, high-velocity, high-variety and high-veracity (uncertainty)
information assets that demand cost-effective, innovative forms of information
processing for enhanced insight and decision making
Creation of value with the help of Big Data: On the one hand, (big) data is used for the
incremental improvement and optimization of current business practices and services.
On the other hand, new products and business models can be innovated based on the
use of data.
Data-driven business model: a business model that relies on data as a key resource.
o A data-driven business model is not limited to companies conducting analytics,
but also includes companies that are ‘merely’ aggregating or collecting data.
o 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 The focus lies on companies that are using data as a key resource for their
business model.
Research question article: What types of business model are present among companies
relying on data as a resource of major importance for their business (key resource)?
The objective of this paper is to build a taxonomy of business models relying on data as a
key resource in the start-up world. A taxonomy
is an empirically derived classification scheme
used in various scientific disciplines
Dimensions used to classify various business models:
Key resources: resources are needed to make
products/ services and to create value. By
definition, a DDBM has data as a key resource.
Key activities: activities that are performed to
create value
Value proposition: the value created for
customers through the offering
Customer segment: using classification, e.g.,
divide into business and individual costumers
Revenue model
, Cost structure: a firm could have a specific cost advantage if the data used in its product
or service were created independently of the specific offering
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.
Analytics-as-a-service: These companies are characterized by conducting analytics on
data provided by their customers. Further noteworthy activities include data
distribution, mainly through providing access to the analytics results via an API and
visualization of the analytics results.
Data generation and analysis: generation of data 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.
Free data knowledge discovery: characterized 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.
Data-aggregation-as-a-service: Companies in this cluster create value neither by
analyzing nor creating data but through aggregating data from multiple internal sources
for their customers. After aggregating the data, the companies provide the data through
various interfaces and/or visualize it.
Multi-source data mash-up and analysis: 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 characterized by using other external
data sources to enrich or benchmark customer data.
LaValle, et al. (2010). Analytics: The New Path to Value
Top performers view analytics as a differentiator: top-performing companies are more
likely to use analytics and are more likely to say that analytics provide a competitive
advantage.
The biggest obstacle is not the data: getting the data right is not the key barrier, but lack
of understanding how to use analytics and lack of management bandwidth are.
Leaders are headed toward making information come alive: convert information into
scenarios and simulation that make insights easier to understand and to act on.
Five-point methodology for successfully implementing analytics-driven management and
for rapidly creating value:
o Focus on the biggest opportunities first: than everyone can immediately see the
value of it. With a potential big reward in sight, a significant effort is easier to
justify, and people across functions and levels are better able to support it.
o Start with questions, not data: organizations should implement analytics by first
defining the insights and questions needed to meet the big business objective
and then identify those pieces of data needed for answers.
o Embed insights to drive action and to deliver value: (even nonexperts need to be
able to understand and
act): embedding
information into
business processes (use
, cases, analytics solutions, optimization, workflows and simulations) are making
insights more understandable and actionable.
o Keep existing capabilities while adding new ones. Outdated methods should be
discontinued, some people will not adjust.
o Build the analytics foundation according to an information agenda: the
information agenda identifies foundational information practices and tools while
aligning IT and business goals through enterprise information plans and
financially justifies deployment road maps.
All these five recommendations meet three critical management needs:
o Reduced time to value: early in the process to analytics sophistication, value can
be achieved. It doesn’t require perfect data or a full-scale transformation.
o Increased likelihood of transformation that’s both significant and enduring
o Greater focus on achievable steps
Parmar, et al (2014). The New Patterns of Innovation
5 distinct patterns in creating value from data and analytics:
Augmenting Products to Generate Data: using data that physical objects now generate
(or could generate) to improve a product or service or create new business value.
o Rolls Royce monitoring performance of engines leading to a new business model
Digitizing assets: digitizing physical assets (think about music, books, etc)
Combining data within and across industries: coordination of data across industries /
sectors through enhanced data integration
o Monitoring elderly people to detect unusual conditions and behaviours such that
they could live on their own longer and there is enough space in elderly homes.
, Trading data: selling the data and developed capabilities under patterns (1) to (3) to
others (Vodafone selling information to TomTom)
Codifying a Distinctive Service Capability: A practical way to take the processes they’ve
perfected, standardize them, and sell them to other parties. Any process that is best-in-
class—but not central to a company’s competitive advantage—can thus be turned into a
profitable business. Cloud computing has put such opportunities within even closer
reach, because it allows companies to easily distribute software, simplify version control,
and offer customers “pay as you go” pricing.
The five patterns are a helpful way to structure a conversation about new business ideas—
and, as we’ve shown, there are good examples of all five—but actual initiatives often
encompass two or three of the patterns. In addition, what begins as a relatively simple
extension of an existing business often grows into a whole new business.
4 success factors: strong technology presence, input from external parties, motivated
leadership, emotional commitment
Woerner & Wixom, et al. (2015). Big Data: Extending the Business Strategy Toolbox
Digitization creates challenges because for most companies it is unevenly distributed
throughout the organization > this makes it more difficult to simplify data > more
difficult to derive insight.
Big data is here to stay and every enterprise will have to accommodate the
problematic nature of big data as it decides on a course of action.
This commentary is an effort to show how big data is being used in practice to craft
strategy and the company business model.
Companies are not replacing their business strategy toolboxes, but rather are using
existing toolboxes more effectively – they now have access to essential data needed
to solve problems or gain insights that was not possible to collect before. The results
are quite exciting.
Improving the business model:
o New data: Armed with new data, these companies can advance to generate
insight.
o New insight: New big data approaches and techniques that ranged from high-
end statistics and models to colorful visualizations of the output.
o New action: As companies become well-armed with big data and proficient at
making insights based on that data, they act differently – often faster and
more wisely.
Big data facilitates improvements to business models across industries. The most
effective improvements result from creating well-articulated strategies that are
informed by data and then honed and shaped accordingly.
Innovate the business model:
o Data monetization: refers to the process of using data to increase revenue.
Wrapping: refers to wrapping information around other core products
and services and thus adding value to products
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