Big Data for Business
Management
Module 1: Big Data in Context
1.3 – Big Data Value Creation
Our Journey
Module 1
Big Data in Context
> Module 2 Infrastructure > Module 3 Applications
■ Business Intelligence
■ Visual and Predictive Analytics
■ Social Media Analytics
■ Introduction to Big Data
■ Big Data in Business
■ Big Data Value Creation
This module introduces you to the concept of Big Data, its scope and enabling technologies. It
takes a closer look at the evolving Big Data industry and associated professional roles in relation
to business management.
■ Information Systems
■ Data Warehouse and Cloud
■ Processing Platforms for Data
Discovery
Bu 1 Dimensions of Big Data
2 Value Creation Process
3 Theoretical Propositions
In this topic, we look at the dimensions of Big Data and
the process of value creation. The Big Data maturity
model is considered and the complete value creation
model is presented.
Topic Overview
4 Value Creation Model
Dimensions of Big Data
2016 Gartner Hype Cycle
In Gartner’s model, “Big Data” as a
catch-all term disappears, but is instead
fragmented into various categories
including “advanced analytics”, hybrid
cloud, microdata centres and citizen
data science. Some of these have
been discussed in previous modules,
while others are coming up.
What is important here is the shift in
terms of how the industry has evolved:
the strategic value of data and analytics
is being increasingly recognised, and
how quickly and readily industry has
adopted aspects of Big Data and
business analytics into everyday
processes.
Dimensions of Big Data
The Five Dimensions of Big Data
We previously discussed the definitions and conceptualisation of Big Data – what it means
and how it is described (from a variety of perspectives).
Remember the 7Vs of Big Data – Volume, Velocity and Variety (and) Rijmenam’s Veracity,
Variability, Visualisation, and Value (2014). This helped us better conceptualise and
understand the impact and implications of Big Data on business strategy.
These 7Vs however are not perfect at encapsulating the technical complexity that stems
from the fact that complex predictive and prescriptive analytic methods need to be applied
to huge, heterogeneous data sets.
To extract the most value from these data sets, the challenges and opportunities of Big
Data can be viewed across five distinct dimensions: technology, application, economic,
legal and social.
Dimensions of Big Data
The Five Dimensions of Big Data
Big
Data
Economic
Dimension
Technologica
l Dimension
Application
Dimension
Legal
Dimension
Social
Dimension
Data Processing
Statistics
Visualisation
Linguistics
Data-driven decision making
Risk management
Strategic Intelligence
Quantified Self
Industry specific applications
Ownership and Copyright
Liability
Insolvency
Privacy and Security
Innovative business models
Open source
Benchmarking
Pricing models
User Behaviour
Societal Impact
Governance
Collaboration
Dimensions of Big Data
Application. Many emergent applications are arising in the information economy, such as information
marketplaces, which refine and sell enriched data. These information marketplaces are effectively building
the information economy from scratch. Other examples were discussed in the last module, such as the
quantified and digital self, medical and healthcare breakthroughs and Web 3.0 applications.
Technology. There is a need for scalable systems and platforms for data analysis (for
datasets of immense size) as well as insightful and original data analysis methods to
help crunch these effectively. Technologies are also needed to assist in overcoming a
distinct skills gap to enable data analysis methods to be accessible to much wider
audiences.
Key
Perspective
Dimensions of Big Data
Economic. Value creation in the economic dimension lies in new business models and enterprises,
and changes in how content is delivered, bought, and sold. For example, think about paradigm
shifts around the role of open-source software, as well as the marketplace for information as
commodity (especially in time-sensitive environments such as trading).
Legal. From a legal perspective, Big Data will present many challenges with respect to ownership,
liability, and insolvency, in addition to prevalent issues around privacy and security. Some of these
will be discussed later in the course.
Social. Lastly, Big Data and analytics will have a profound impact on society as a whole with respect
to social interaction, news, and democratic processes, among others. The creation of societal-value
should not and cannot be ignored, especially in the role of social enterprise.
Key
Perspective
Exercise Creating Smarter Cities
Dimensions of Big Data
The following short Forbes article details
ways in which technological advancement
has lead to a proliferation of data and how
to make our cities operate more efficiently.
http://www.forbes.com/sites/bernardmarr/2
015/05/19/how-big-data-and-the-internet-ofthings-create-smarter-cities/#79e20fd963d
8
■ Explain and dot point each of the five dimensions you think are applicable to this
article. (Think closely about value creation from an economic and social
perspective.)
■ What is the value of real-time analytics and monitoring here to both private and
public interests? Explain why you think investment is so forthcoming.
■ Search for similar applications of the Internet of Things in an industry that interests
you. How is value being created from this technological advancement?
Dimensions of Big Data
The Five Dimensions of Big Data
As innovators and business decision makers, it is important to understand how value is – and can be
– created across and between these different dimensions.
Indeed, data management and value creation for technological advancement may look different
from the ways in which benefits are perceived from a societal perspective, and might come into
conflict with other dimensions (such as legal or economic).
One of the key challenges organisations face in building support for a Big Data initiative is to
ensure that the Big Data initiative is valued by, or of value to, as many stakeholders as possible.
And, rather unfortunately, stakeholders have become increasingly jaded with promises of the next
great technological advancement in terms of business and market growth potential. They are
hesitant to believe that another new technology is going to solve all of their data and analytic
problems.
You are now going to be introduced to a method that will ensure the business relevance of your
Big Data initiative to maximise value for involved stakeholders.
Dimensions of Big Data
What Types of Value? To Whom?
Both in businesses and in science, data use is handled in a fragmented way.
Actors along the Big Data Value chain should cooperate and form the basis of a strong and vibrant
data-driven ecosystem to maximise value creation of Big Data.
The following value chain extends this notion of benefits across all stakeholders, which will only
succeed when the individual links operate with needed and available capabilities.
Dimensions of Big Data
Value creation
■ Cooperation is needed across all aspects of the data and information management process.
■ All links are bound by overarching security, privacy and trust concerns.
■ The benefits here can be across all dimensions of the Big Data framework as discussed in the previous slides.
This can be the beginning of a healthy and vibrant approach to value creation from your data, with the maximum
benefits to most, if not all, actors and stakeholders involved.
Data Generation
and Acquisition
Data Analysis and
Processing Data Storage Data Visualisation
Social, Economic,
Legal,
Technological
Benefits
■ Device and sensor
networks
■ Structured and
unstructured data
■ Event recording
■ Multi-modality
■ Streams
■ Pre-processing
■ In-memory processing
■ Sentiment analysis
■ Correlations
■ Pattern recognition
■ Real-time analytics
■ Machine learning
■ In-memory storage
■ Data augmentation
and scaling
■ Validation and
elimination of
irrelevant data
■ Consistency
■ Revision and updating
■ Decision support and
reinforcement
■ Modelling
■ Simulation
■ Prediction
■ Exploration and
control
Security, data protection, privacy and trust
Value Creation Process
Big Data Business Model Maturity Index
When we talk about value creation, we also talk about business enablement and efficiency.
■ How far can Big Data take us from a business perspective? What is the end goal?
■ How far can I push Big Data to power, or even transform, my value-creation processes?
The previous value chain demonstrated the benefits of businesses moving from a retrospective
view of the business with partial chunks of data in batch to monitor their business performance, to
an environment that integrates predictive analytics with real-time data feeds that leverage all
available data in order to optimise the business function.
The following Big Data Business Model Maturity Index, developed by Schmarzo (2013) is useful in
helping us identify where organisations currently sit, and map out where they could be (their
desired state) with respect to leveraging Big Data to uncover new monetisation and business
development opportunities.
Value Creation Process
Big Data Business Model Maturity Index
The first three phases of the Index
are internally focused — optimising
an organisation’s internal business
processes. This part of the maturity
index leverages an organisation’s
data warehouse and business
intelligence investments, especially
the key performance indicators and
data models.
Schmarzo (2013)
Internally
focused
Externally
focused
The last two phases of the Index are externally focused — creating new monetisation opportunities
based upon the customer, product, and market insights gleaned from the first three phases of the
maturity index. This is the part of the Big Data journey that catches most organisations’ attention: the
opportunity to leverage the insights gathered through the coordination of their internal business
processes to create new business and value opportunities.
Exercise Amazon
Value Creation Process
Watch this short video on this history of Amazon.
http://www.telegraph.co.uk/technology/amazon/11790823/The-history-of-Amazon.html
Then, read about Amazon’s latest efforts in North America.
http://www.thestar.com/business/2015/11/03/amazon-opening-physical-bookstore-stocking-based-ondata-with-heart.html
Think closely about the Big Data Business Model Maturity Index, and Amazon’s current position in
terms of integration of analytics and monetisation of this data. Where would you currently place them
on the index? Why?
Afterwards, perhaps plot Amazon’s trajectory over the past 20 years in terms of internal and external
focus and how this has led to successful value creation.
Consider what is next for Amazon. How do they capitalise on this information? Does it require
internal optimisation, or capitalisation on external market insights?
Theoretical Propositions
How Can Data Be Turned Into Value
How can data be turned into value?
The previous Business Model Maturity Index explains one such pathway, but what are the components
of these stages? What does “business optimisation” mean when we are dealing with analytics, data and
information? The first and most common proposition for successful value creation is acceptance of the
following: An organisation’s business analytics capability mediates between its data and the creation of
value. (see above graphic). Here, organisations extract and promote value (across many dimensions, see
previous slides) because of their capabilities and some of their systems in place.
BIG DATA BUSINESS ANALYTICAL
CAPABILITIES
VALUE CREATION
Adapted from Vidgen, 2014
Theoretical Propositions
More Specifically
With regards to what organisations should do if they want to create value from their data, the model above
proposes an analytics “eco-system” that places the business analytics function in a much broader
organisational context. Three key dimensions here, which we will explore further, are data, management and
technology.
This theoretical position helps us explore the following pertinent questions:
1. What dimensions do organisations need to address when building a business analytics capability?
2. What are the organisational change implications of building and exploiting a business analytics capability?
BIG DATA BUSINESS ANALYTICAL
CAPABILITIES
VALUE CREATION
Data Processes
Technology
Strategy
Management
Theoretical Propositions
Data
Vidgen, 2014
What dimensions do organisations need to address in building a business analytics capability?
Dimension
Key Consideration
Ensure good quality data
All data is ready and fit for task purpose.
Appropriate permissions and
security
Transparency is needed not just for accountability, but for customer assurance.
Trust is maintained and confidence established through these safeguards for
all stakeholders.
Sharing mindset
All outcomes, both positive benefits and negative externalities (i.e. cost) of data
extraction and analytical process are shared across all appropriate parties.
Building data partnerships
Value comes from this shared approach: partnerships because of mutual
benefits, rather than viewing of data as a saleable commodity.
Creation of both public and private
benefits
Data is used for both public and societal benefit, as well as commercial
benefits (i.e. operations optimisation, versus public weather alerts and
warnings systems).
Appropriate regulation and control
Changes in legislation may result in fundamental shifts in what can be done
with customer data (consumer rights to collection/contact etc).
Theoretical Propositions
Management
Vidgen, 2014
What dimensions do organisations need to address in building a business analytics capability?
Dimension
Key Consideration
Robust corporate analytics strategy
A strategy that is clear and articulated about where value will be created
Embracing organisational change
Agility across both hardware and software organisational units, uniting the
business in a data-driven cohesive cultural shift.
Dynamic sector knowledge
Deep understanding of the organisation and its place in the industry (as well
as key competitors) in terms of analytics advancement and technological
progress.
Organisational structure
Teams and units are cross-functional and contain a mix of data scientists,
business thinkers and IT specialists. Teams should have resources available
to explore new analytics opportunities.
Innovative Partnerships
Proactive partnerships with key think-tanks and institutions, including
informatics faculties in universities and data laboratories.
Ethics and process diligence
Review committees should be consistently convened to provide oversight
about how data is used to protect the reputation of organisations
Theoretical Propositions
Technology
Vidgen, 2014
What dimensions do organisations need to address in building a business analytics capability?
Dimension
Key Consideration
Visualisation Tactics Across
Functions
Using visualisation to assist in explaining key business plans and strategic
direction: making the data accessible for all business units.
Embracing Technological
Dynamism
Understanding the dynamic pace of technological change and ensuring
protection from over-capitalisation in risky IT investments, while promoting a
culture of innovation and excitement in advances.
Capable and Curious Data
Scientists
The people working with your data must be able to work independently with
large sets and tools to manage these, as well as able to process this from data to
actionable insights.
Assemblage Mindset
Teams and units are able to find solutions using a variety of analysis and
interpretation methods. Organisations must encourage and facilitate many
different types of tools to bring these different types of data together.
Acquisition and Retention of Key
Staff
Data analytics teams are retained and managed closely and have solid career
progression and paths of advancement to encourage personnel retention and
commitment to current projects.
Theoretical Propositions
Case Study – City Trans
CityTrans is a governmental, not-for-profit provider of an
integrated public transport system for a major city, dealing with
every aspect of how people move across the city using
different modes of public and private transport. The
organisation works with many datasets, such as network
operations, travel data, traffic data, loadweigh data, infra-red
data, and customer data. Some of these data assets have been
linked for operational analysis and planning but there are still
some unexplored opportunities in joining these numerous and
diverse datasets.
The following case study details not only a
sophisticated integration of Big Data and
analytics processes, but also a unique
emphasis on end-user value creation.
Theoretical Propositions
Case Study – City Trans
CityTrans collects the bulk of its public transport travel data through a smart travel card (STC), which
can be used anonymously or as a registered (and therefore identifiable) customer. Any one
passenger may have a number of anonymous, pay as you go or low-value season ticket STCs,
making identification of an individual difficult. To further protect individuals’ identities CityTrans’ data
scientists and research partners use pseudonymised (“hashed”) IDs for their analyses of customer
behaviour.
The STC generates millions of “taps” a day as customers use it on different modes of transport
throughout the city. This data typically tells CityTrans where a passenger entered the system and
where they left it but it is more difficult to determine what route they took through the network.
CityTrans also accepts contactless payments from credit and debit cards on its bus network and has
plans to roll out contactless payment to the rest of the public transport network. CityTrans has been
active in data analysis and modelling of passenger flow for many years, with analysis grounded in
traditional operational research methods. Making use of data has become more prominent since the
introduction of the STC and as storage capacity has increased and new software tools have become
available.
Theoretical Propositions
Case Study – City Trans
Customer Value Creation
The data collected from users of the transport system and the system operations are used to
measure the reliability of the service and to gain insight into the customer experience. Rather than
focus on average journey times as a proxy for customer satisfaction, CityTrans is now able to use
the STC data to get closer to the customer experience and to use the data to minimise travel
delays.
When there is disruption to the network, a measure of central tendency, such as the average or
the median, is a poor representation of the impact on passengers. CityTrans is also investigating
the use of data for behavioural change, to shift travel patterns so as to spread the load across the
network. CityTrans envisages changing traveller behaviour using “recommender” systems that will
assist the traveller in planning their journeys.
Vidgen, 2014
Theoretical Propositions
Case Study – City Trans
Vidgen, 2014
Partnerships
In order to enable even better use of STC data, CityTrans has established research partnerships
with several universities, to explore their data and apply new analytic techniques, such as
visualisation. A relationship with MIT has proved particularly effective.
Processes
The decision to develop a new data warehouse has led CityTrans to look at its processes for
extracting, transforming and loading data, and in report development to align them with best
practice for business intelligence systems development. A modified Agile Software approach is
used for development.
Value Creation Model
Coordination of Big Data for VC
The previous three slides illustrate three areas on which businesses must focus when building their
analytics and data capability. From a value creation perspective, while all need to be in alignment, we
can see key dimensions here that are distinct but intertwined with reciprocal relationships.
For example, the technological dimension may constrain what can be done with available data. At the
same time, the data held and the analytics strategy will influence the management approach. The
analytics eco-system illustrates the mutual co-dependencies that are of particular interest when defining
an analytics strategy.
This goes some way to looking at how we enable organisations and businesses to adopt this focus
on data systems and analytics, however, the second question we needed to ask is:
What are the organisational-change implications of building and exploiting a business analytics
capability?
Theoretical Propositions
Coordination of Big Data for VC
There are three fundamental questions businesses
have for decades had to answer:
● Who are the most valuable and important
customers?
● How do I reach them?
● With which products?
Schmarzo’s illustration highlights how Big Data drives
value creation.
Big data analytics has changed how these new
sources of data can be leveraged to answer these key
business questions. For example, we can better direct
R&D thanks to shifting our focus from the most
“profitable” customers to the most “influential”.
Big Data enables you to answer these questions not
only with a higher level of detail but at faster speeds,
greater precision, and further insight.
Theoretical Propositions
A Complete Model of VC
The transition to a data-driven organisation in which business decisions are routinely made on the basis
of data and hypothesis testing will require deep changes to processes, management, structure, and
ultimately to the culture of the organisation. Verhoef et al’s model here of value creation nicely
encapsulates these various dimensions and influencing factors.
Big Data
Assets
Big Data Capabilities
Big Data Analytics
Big Data Value
Data
Data
Data
People
Systems
Processes
Organisations
Insights
Decision / Support
Value to Firm
Value to
Customer
Actions /
Campaigns
Models
Information Based
Products/ solutions
Theoretical Propositions
Not To Be Used in Isolation
Throughout your studies you have been exposed to many other models of Value Creation, notably,
Michael Porter’s Valuation Creation Models.
These cannot be discounted for the insights they provide especially in regards to resources and
capabilities, when looking at process models such as:
• Five Forces Analysis
• Value Chain Analysis
These illustrate impacting factors of the competitor space, as well as some of the internal activities of
a business. Think closely here about what these models aim to illuminate, as many of these insights
can be combined with the extra benefits and solutions that a comprehensive Big Data and analytics
strategy can provide for even greater outcomes.
Value creation should be seen as a process: inherently “value” as a concept is dictated by market
exchanges. This process can, and should be, supported and supplemented through rigorous
understanding of how these exchanges operate, and interact. Ultimately, the focus here is on how
value can be added from these very exchanges, and the impact on the organisation, its customers,
and society more widely.
Theoretical Propositions
So What?
For businesses to maximise the benefits of their analytics programs, data and value should be
considered and managed jointly. To create value data has to be managed (remember: quality of the
data, permissions and regulation, etc.) and that value may have to be shared with those who provide the
data.
Secondly, business analytics is not a technical project to be given only to the IT department – it is a total
organisation-wide transformation that requires a strategy, management support, and active and careful
change management. This is not to say that IT is unimportant but we must recognise the role of data and
analysis teams as enablers of the business analytics process and essentially embedded in the
organisation’s wider processes and practices.
Theoretical Propositions
So What?
Thirdly, the organisation needs a healthy respect for change to implement a robust business analytics
strategy. This will be expanded later in the course when we look at the organisation and decision making
regarding learnings and strategy from Big Data strategy.
Fourth, data scientists need a strong sense of independence and problem-solving ability, as well as the
ability to combine tools and techniques towards novel and actionable insights.
And finally, business analytics and data science needs a process model, which acknowledges the
organisational context to define and structure these efforts described, to extract (or “capture”) the most
value from its activities.
Summary
In this topic we looked at the following:
The Five Dimensions of Big Data – Introduction to the five dimensions of Big Data and the
various challenges and opportunities each can present. To extract the most value from data sets,
the challenges and opportunities of Big Data can be viewed across: technology, application,
economic, legal and social dimensions.
The Big Data Business Model maturity index – We looked at this index to get a sense of growth
and trajectory of analytics incorporation. It is useful in helping us identify where our organisations
currently sit, and map out where they could be (their desired state) with respect to leveraging big
data to uncover new monetisation and business development opportunities.
Summary
Models of Value Creation – Here we adopt a theoretical perspective, exploring some of the
models of data system incorporation, and discuss the methods by which these insights are
processed by businesses.
Coordination of Big Data for VC – We finish the module with a complete process model, taking
into account the above module components. This roadmap will provide you with a template for
effective implementation of data and business analytics plans, and can be used in your
assignments, and for your own entrepreneurial ideas and planning. It is important that as business
leaders we build an analytics strategy that clearly articulates data sources, emphasises data
quality, and enables cultural change. Analytics projects do not simply concern themselves with IT
departments. Entrepreneurs should learn and incorporate these Big Data and analytics processes
quickly, cheaply, and with agility in response to technological and market shifts.
References
Gartner (2015) “Gartner’s 2015 Hype Cycle for Emerging Technologies Identifies the Computing Innovations That
Organizations Should Monitor”, accessed online at: http://www.gartner.com/newsroom/id/3114217 [January, 2016]
GE’s Global Innovation Barometer, 2016 , accessed online
at:http://www.forbes.com/sites/louiscolumbus/2016/01/20/ge-global-innovation-barometer-2016-61-of-execs-using-big-d
ata-analytics-to-improve-decisions/#4b4233f35f89 [January, 2016]
Minelli, M., Chambers, M., & Dhiraj, A. (2012). Big data, big analytics: emerging business intelligence and analytic trends
for today’s businesses. John Wiley & Sons.
Schmarzo, B. (2013). Big Data: Understanding how data powers big business. John Wiley & Sons.
Van Rijmenam, M. (2014). Think Bigger: Developing a Successful Big Data Strategy for Your Business. AMACOM Div
American Mgmt Assn.
Verhoef, P. C., Kooge, E., & Walk, N. (2016). Creating Value with Big Data Analytics: Making Smarter Marketing
Decisions. Routledge.
Vidgen, R., (2014). Creating business value from Big Data and business analytics: organisational, managerial and human
resource implications. Hull University Business School Research Memorandum, no. 94
Readings
• Bain (2015) “Creating Value Through Advanced Analytics” at
http://www.bain.com/publications/articles/creating-value-through-advanced-analytics.aspx
[January, 2016]
– Bain & Co. always have a unique spin on things, and this page and report is very
good at stressing the business case for value creation. I dare you to read this and
not consider analytics the primary tool for strategic advantage! A perfect pitch, this
one.
• Taylor (2014) “What’s the social value of the arts? Big Data has some answers (even if
artists don’t want to hear it)”, accessed online at:
http://www.theglobeandmail.com/arts/music/whats-the-social-value-of-the-arts-big-data-hassome-answers-even-if-artists-dont-want-to-hear-it/article21697303/ [January, 2016]
– The article by Taylor presents an alternate view to Bain and Co…how can we
measure the social benefit or value of sectors like the Arts. An interesting footnote in how
data and analytics may help answer these questions.
Light, easy and fun
Readings
Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of Google Flu: traps in big data
analysis. Science, 343(14 March). accessed online at:
http://gking.harvard.edu/files/gking/files/0314policyforumff.pdf?m=1394735706 [January, 2016]
An excellent article looking at the role businesses (here, Google) can play in solving social ills, through
technology. This is a well-cited article and nicely explores the role of hubris, transparency, as well as
expectations in regards to technological determinism.
Big Data Alchemy: How Can Banks Maximise the Value of their Customer Data. Can be read online at:
https://www.capgemini.com/wp-content/uploads/2017/07/bigdatainbanking_2705_v5_1.pdf [January,
2016]
A solid report put out by the Capgemini group which looks at the role of Big Data analytics in the banking
sector and nicely demonstrates the role data in aiding the customer-bank relationship. What I love here
is the roadmap to analytics maturity they include, perhaps consider it a more detailed version of the
index we canvassed in this module!