Big Data for Business
Management
Module 1: Big Data in Context
1.2 – Big Data in Business
Our Journey

> 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 Business and Data
2 Applications
3 Analytics
This topic explores some of the business environments
where Big Data is applied, in real time, through
examples and the evolution into business. It also looks
at current and emerging trends going into 2018.
Topic Overview
4 Business Trends
Business and Data
Origin of Business Data
The origin of data management coincides with the wide adoption of computers.
The discipline of statistics existed well before computer science, but computers empowered
statisticians to solve a wide variety of practical problems with real life implications, since heavy
number crunching and massive storage of data became feasible due to the emergence of modern
computing technologies.
The invention of database management systems in the 1960s, and relational database management
systems in the 1970s, accelerated the pace of this marriage between statistics and computer science.
■ In the late 1980s, terms such as “knowledge discovery” and “data mining” emerged.
■ In the early 1990s, database industry practitioners started noticing the explosion of business data in
the form of Big Data. The official use of the phrase Big Data can be traced back to an article
published in the ACM Digital Library in 1997.
■ In the late 1990s, the phrase “data science” first appeared to inspire researchers and professionals.
LinkedIn Learning, 2016
Business and Data
The Role of Data for Commerce
In the previous topic, we addressed Minelli, Chambers and Dhiraj’s (2012) three-pronged perfect storm
that allowed Big Data to enact its seismic revolution upon businesses:
■ A computing perfect storm
○ The rise and prevalence of social networks, cloud computing and mobile technology that is
cheaper and more accessible than ever for business use.
■ A data perfect storm
○ The sheer volume, speed and variety of data that is now able to be transferred and shared by
businesses.
■ A convergence perfect storm
○ The merging of suitable technologies, such as traditional software technologies, to open source
and commodity and peripheral hardware, to provide new IT alternatives for business practices.
This is especially relevant in our framing of the Big Data industry, as it nicely conceptualises the various
key influences on its rise in both relevance and function to businesses.
○ Think closely here about how IT and software, as well as technological and societal acceptance,
has allowed for the rise and ubiquity of information and data in the marketplace more widely.
Business and Data
Big Data Still Unappreciated?
We also explored McKinsey’s key definitions and conceptualisation
of Big Data, which focused on the volume, rate and pace of data
management and collection. This watershed report written in 2011
by the firm also hailed Big Data as the next “frontier” for innovation,
competition and productivity.
However, managers and business leaders are still in some ways
frustrated because their organisations do not give data and
information the attention they deserve; most data sits unused, with
opportunities to capitalise on this data (think R&D potential,
customer insights and transactional or market information)
sometimes completely ignored.
Business and Data
Not Just Business Assets
What is most illuminating, and perhaps striking, is that bad data management and interpretation has been at the
heart of more recent mismanagement and improper business functions, and has even had global ramifications:
think about the subprime mortgage meltdown and following market volatility.
There are also the more socially relevant effects: hospital and medical diagnosis errors, identity theft and fraud,
as well as the effect of contested elections in democracies.
Business and Data
Out of The Office and Into The…
When organisations fail to recognise the importance of data and
information, chances are there is a trickle-down effect on consumer
awareness (or lack of it!). What impacts the market and economy is also
likely to impact society, and the benefits of this relationship have led to
suitable and relevant products, improved services, lower prices for
necessities, and accountability and transparency practices.
We must also make note here of the consumer’s own willingness to harness
and create data. Previously, we looked at the impact of Web 2.0
technologies for networking and sharing of data. The “quantified self”
revolution presents itself as a pertinent example of the information age and
societal adoption.
This relationship between society driving technological change, and the
market and organisational impacts, is best explained by the
Technology-Impact cycle.
Quantified Self
The use of personal data to
improve one’s health and
well-being. The concept of
Quantified Self entails tracking
exercise, sleep patterns, diet
and other factors and making
lifestyle changes based on the
results. The term “Quantified
Self” also refers to an informal
group of manufacturers and
users of self-tracking
instruments that connect
through live conferences and
online forums.
Investopedia (2015)
Business and Data
The Technology Impact Cycle
This is considered an ongoing cycle which
takes many years for a full transference. If we
apply this to our previous definitions of Big
Data and see that it was first explored as a
concept in 1997, and then again in 2005, we
can see that organisations and the economy
are only now addressing these in business
and commercial terms.
Exercise The Technology Impact Cycle
Think about the following societal and technological revolutions.
Have they followed the Technology Impact (TI) cycle here?
Have they progressed into other sectors? Why or why not?
Where do you think they originated?
1. Societal: A shift to handmade, artisan, crafted products and bespoke services
2. Societal: Recycling and eco-awareness
3. Technological: Wi-Fi technology
4. Technological: Fingerprint and biometric scanning
Business and Data
Data Applications
A Short History
Minelli et. al (2012)
ERP:
Enterprise
Resource Planning
CRM:
Customer
Relationship
Management
The 1980s was all about ERP and applications designed to make an organisation’s processes
more effective and efficient. By better managing all of the key functions of a company –
accounting, warehouse, inventory and supply chain – ERP allowed the company to focus on what
really matters: the information and raw data.
Data Applications
A Short History
Customer Relationship Management (CRM) systems focus on exactly that: customers. They aim to
manage all customer information and capture relevant details for the company which can ultimately
build their customer relationships in order to increase customer loyalty and therefore revenue.
During the CRM era of the 1990s, many companies invested heavily in customer-facing technologies
which were not overly effective. Management did not fully comprehend that big projects require a
coordinated and business-wide transformation of people, process, and technology. All three must be
in harmony or the desired transformation is negligible.
The arrival of eCommerce and analytics in the 2000s saw this convergence of several trends as
discussed earlier — more data and less-expensive, faster hardware. Currently, in this age of Big Data
business analytics, we have faster processing at an affordable price that is within reach and available
to most businesses.
Minelli et. al (2012)
Data Applications
Reflect and think about how insurance companies, in particular, have evolved with these data
system changes.
Think about what they are now capable of in terms of not just managing their customers, but also
projection and risk assessment, all the way to complex prediction and forecasting of multiple
variables.
Key
Perspective
Data Applications
Data in Many Guises
■ Online and web data (clickstream analysis- this is coming up!, social media sites)
■ Location-based and geospatial data (from mobile devices)
■ Data from devices (RFID sensors, LIMs and medical devices, telemetry, NFC)
■ Imaging data (online and mobile video, satellite images, surveillance and
observation)
■ Supply chain information (ordering, vendor catalogs and pricing information)
■ Primary research methods (surveys and experiments)
■ Secondary research (industry and marketplace data)
The variety of data sources continues to increase as applications evolve. Internally focused
operational systems, such as ERP and CRM applications, were once the major sources of data used in
analytic processing. However, in order to increase knowledge and awareness, the complexity of data
sources that feed into analytic processes is rapidly growing to include a wider variety of data sources
such as:
Data Applications
Data and Healthcare
The changing health-care landscape is an excellent example of where Big Data advances and
analytics can have an immediate beneficial impact. Revolution of the health-care system will come
through Big Data-driven decisions and improved insights from a variety of information sources.
Important advancements from the management of chronic disease to the delivery of personalised
medicine are now possible due to the insights able to be extracted from large and complex data sets
being collected in this industry. In addition to saving and improving lives, Big Data has the potential to
transform the entire health-care system with objective, data-driven insights. Data here can range from:
■ Biological data, including gene expression and large-scale epigenetics
■ Special Needs Plans (SNPs) for those with chronic disease or disability and impairment
■ Proteomics and the study of proteins and genomes
■ Metabolomics and small-molecule profiling
■ Gene-sequencing data.
Data Applications
Data and Healthcare
This exponential growth in data is further fuelled by the digitisation of patient data. This is now more
commonly stored in Electronic Health Records (EHRs) and Health Information Exchanges (HIEs) and
supplemented with data from imaging and test results, medical and prescription information, and,
increasingly, personal health devices and wearable technology.
https://hitconsultant.net/2015/04/10/big-data
-analytics-in-healthcare/
An interesting infographic on the sheer numbers
of this industry data revolution….
And also Google’s pioneering and now
finalised efforts on flu-mapping
https://www.google.org/flutrends/about/
Data Applications
Medicine
Concurrently, medicine is undergoing a seismic shift with the increasing adoption and application of data
and analytics into both laboratory and practicing fields of industry.
Now, datasets are available that contain thousands of genes. Typically, medical datasets now contain
somewhere between 100,000 to 500,000 genetic variations.
Algorithms are developed and uncovered that identify the interactions (say, between environmental factors
and diseases) which are assisted by rapid built-in search techniques. Statistical analysis is also available on
these datasets: permutation analysis which may require incremental change is now assisted using
large-scale computing mechanisms. This mean that it is not impossible to achieve 500,000 genetic
variations in a single patient sample for diagnosis or investigation.
Pharmacology is also undergoing a transformation through similar Big Data applications:
innovation, improving research efficiency, increasing time to clinical trial, while building new tools for
physicians, consumers, insurers, and regulators to assist in more specialised drugs and medicine.
One of your readings for this module is the McKinsey article on this sector: not to be missed!
Data Applications
Business Operation Applications
We should also not forget the many various applications within businesses and the function of data and
information in regards to internal operations. Such as:
■ Procurement: identifying which suppliers are most cost-effective in delivering products on-time.
■ Product development: uncovering product usage insights to speed product development processes and
improve new product launch effectiveness.
■ Manufacturing: highlighting machinery and process variances that might be indicators of quality problems.
■ Distribution: quantifying optimal inventory levels and optimising supply-chain activities based on external
variables such as weather, holidays, and economic conditions.
■ Marketing: identifying which promotions and campaigns are most effective in driving customer traffic,
engagement, and sales.
Minelli et. Al (2012) From Marr (2015)
Data Applications
Business Operation Applications
■ Pricing and yield management: optimising prices for “perishable” goods such as groceries, dynamic
pricing such as airline seats, concert tickets and fashion merchandise.
■ Merchandising: optimising merchandise markdowns based on current buying patterns, inventory levels,
and product interest insights gleaned from social media data for example.
■ Sales: optimising sales resource assignments, product mix, commissions modeling, and account
assignments.
■ Store operations: optimising stock levels, given buying patterns, coupled with local and real-time
demographic, weather, and events data.
■ Human resources: identifying the characteristics and behaviours of your most successful and effective
employees and how to manage this resource efficiently with variable buying patterns.
Minelli et. Al (2012) From Marr (2015)
Data Applications
Business Operation Applications
In 2013, pharmaceutical giants, Merck, used analysis to dramatically cut the amount of waste
caused by variance in manufacturing environment conditions.
It took three months and involved 15 billion calculations on individual production data from 5.5
million vaccine batches.
This allowed them to discover the optimum conditions during the fermentation process and should
greatly increase their yield, once the FDA has approved the proposed changes to the
manufacturing process.
Minelli et. Al (2012) From Marr (2015)
Data Applications
The Advertising Sector
Traditionally, the advertising industry had quite powerful and useful channels and tools
to reach their audiences: media such as billboards, newspapers, radio stations and
television.
Clients to this industry were overly focused on dominating these channels: trying to
reach as many consumers as possible and aiming for volume over targeted selection or
distribution of message. As the industry matured and as advertising space became
cluttered and at a premium, advertisers were forced to learn more about their
audiences. Media research firms stepped into the void to statistically measure channels
and record and survey who watched or read broadcasts and at which times. In this way
research firms developed segments and profiles which in essence could be targeted.
This enabled advertisers to best place their ads and target and position their content to
the most relevant audience, making “media spend” more data-informed and efficient.
Data Applications
The Advertising Sector
Analytics in this sector also helped advertisers answer the following questions:
■ How much do I need to spend?
■ How do I allocate that spend across all the marketing communication touch points?
■ How do I optimise my advertising effectiveness against my brand equity and ROI in
real-time?
Today, agencies are devoted to metrics and real-time analytics to track audiences and
convey this information back to clients for more precise indicators of audience
engagement.
Data Applications
Towards Market Automation
Historically, database marketing was mostly direct mail. As companies grew and systems
proliferated, we ended up with a landscape where you had one system for one product, another
system for another product, and then potentially another system for another product, leading to a
very silo-ed and disconnected approach to CRM.
Database marketing is concerned with building databases containing information about
individuals, using that information to better understand those individuals, and communicating
effectively with some of those individuals to drive business value.
Data Applications
Towards Market Automation
In this age of analytics, we now have the ability and systems available to use database marketing
for customer acquisition.
Companies began to develop technologies to manage and duplicate data from multiple source
systems. Primarily, companies started collecting information from customer care centers. The data
became richer, creating a better picture of the individual and the mix of products and services
purchased. We had much better sets of data around who’s contacting whom, and could proactively
seek and target these groups.
Now, proactive communication is commonplace, and the arrival of newer and more effective
marketing automation solutions accelerates the trend. Marketing automation refers to software
platforms designed to automate repetitive tasks in critical areas such as campaign management and
allows agencies and in-house marketing teams to reduce lead times, and maximise the efficiency of a
targeted campaign.
Analytics
Social Media Analytics
More and more people are using social media. This generates an enormous amount of data. Data scientists
are naturally attracted to these emerging types of data sets. Social media refers to websites where users
can post their own content and share it with friends and others. Depending on their focus, social media
sites promote different types of interests.
■ For example, Facebook offers a forum for building informal and personal relationships, compared with a
professional networking tool like LinkedIn.
In addition to its size qualifying as big data, another unique value of social media data lies in the data about
data, or metadata, it carries.
■ For example, a post on Facebook can accompany location information as well as timestamps. With these
kinds of unstructured but very rich data sets, a lot of useful insights can be derived about a person who
is posting and consuming information.
■ Another example – IBM has a product called Personality Insights which offers a profiling service for
companies.
(Linkedin Learning, 2016)
Analytics
Disease Control
One area of social media analytics applications is disease control.
■ University of Pennsylvania researchers conducted a study on a predictive relationship between Twitter
post content and heart disease. Emotional factors are linked to heart disease. The study identified
indicators of emotional distress expressed in words and correlated them to the occurrences of heart
disease. Their study used linguistic analysis techniques as well as various Big Data analytics techniques
to reveal key words of emotion such as hate to be strongly correlated to the incidence of heart disease.
■ Conversely, positive words such as wonderful showed the opposite correlation. The Twitter data they
collected consisted of tweets posted by 88 percent of the people from countries in 2009 and 2010. This
ample data set provided much stronger evidence of correlation than what they could provide through
conventional surveys of subjects.
■ https://news.upenn.edu/news/twitter-can-predict-rates-coronary-heart-disease-according-penn-research
Exercise SAP Hana
Please read the following extract from an interview with Steve Lucas, SAP Hana Executive and VP
on Big Data Technologies.
“You don’t have to guess. You can look at actual data, from real customers.
You can extract and analyse every policy they’ve ever held. The answers to your questions are
buried in this kind of massive mound of data — potentially petabytes worth of data if you consider
all of your insurance customers across the lifespan of their policies. It’s unbelievable how much
information exists.
But now you’ve got to go from the level of petabytes and terabytes down to the level of a byte.
That’s a very complex process. But today you can do it — you can compare one individual to all the
other people in an age bracket and perform an analysis, in real time. That’s pretty powerful stuff.
Imagine if a customer service rep had access to that kind of information in real time. Think of all
the opportunities and advantages there would be, for the company and for the customer.”
Analytics
Exercise SAP Hana
“Here’s another example: You go into a store to buy a pair of pants. You take the pants up to the cash
register and the clerk asks you if you would like to save 10 percent off your purchase by signing up for the
store’s credit card.
99.9 percent of the time, you’re going to say “no”. But now let’s imagine if the store could automatically
look at all of my past purchases and see what other items I bought when I came in to buy a pair of pants —
and then offer me 50 percent off a similar purchase? Now that would be relevant to me. The store isn’t
offering me another lame credit card — it’s offering me something that I probably want, at an attractive
price.”
■ Explain and dot point the benefits to both business and consumer for this transaction.
■ What is the value of real-time analytics to this industry in particular?
■ What benefit is there to customers? Why would they choose to shop at this store?
Analytics
Analytics
The Emergent Big Data Universe
One of the most significant benefits of the emergence of Big Data analytics for businesses is that they are
not constrained by predefined sets of questions or queries. With traditional analytics, the scope of
questions they could ask the database is (relatively) extremely small. With Big Data analytics, this universe
is vast. Businesses and organisations can define new variables in the moment, dynamically, or “on the fly”.
This is a very different scenario from the previous approach, in which our ability to ask questions was
limited and constrained by programming and initial planning.
Why is the ability to define new variables so critically important?
The answer is straightforward. In the marketplace, you don’t always know what you are looking for.
You can’t possibly know in advance which questions you’ll need to ask to find a solution.
The following covers some emergent applications of Big Data that are opening up due to this very premise.
The questions have not been asked in advance, yet we are seeing results and uses of analytics that are
causing disruption and upheaval in certain industries.
Analytics
WEB 3.0 and Beyond
As a result of the growing popularity and use of social media around
the world and across nearly every demographic, the amount of
user-generated content — or Big Data — created is immense, and
continues growing exponentially.
Millions of status updates, blog posts, photographs, and videos are
shared every second.
Successful organisations will not only need to identify the
information relevant to their company and products but also be able
to dissect it, make sense of it, and respond to it, in real time and on a
continuous basis, drawing business intelligence, or insights, that help
predict likely future customer behaviour.
Web 2.0, as we know it, is on its way out the
door. For many, Web 2.0 is characterised
mainly by the ability of users to share
information quickly with others, which
developed into the phenomenon that we
call “social media”.
Web 3.0 is being referred to by experts as
the semantic web; semantic meaning data
driven. The data will come from the user
and the web will essentially adjust to meet
the needs of the user.
For example, if you do a lot of searching for
“design blogs”, you’ll receive more
advertisements related to design. Also,
when you search for other things, for
example, “computers”, the web will keep in
mind that you often search for design and
may pull up search queries that combine
“design” and “computers”.
1stWebdesiner(2015)
Analytics
Digital Marketing
Clickstream Analysis: collection and analysis of the “online fingerprint”: sophisticated tracking on web
navigation, often in close to real-time, and embedded in many “pushed” marketing content items.
Affiliate marketing: word-of-mouth marketing has been the most powerful form, long before the Internet
existed. This complex digital architecture is what can link consumers via referrals to likes and comments.
Multi-platform management: coordination of campaigns and content across multiple-platforms, with
monitoring and support able to be strategically aligned.
Big Data on the web will completely transform a company’s ability to understand the effectiveness of its
marketing and hold its people accountable for the millions of dollars that they spend. It will also transform a
company’s ability to understand how its competitors are behaving. Data-driven innovations have led to the
emergence, and importance, of:
Analytics
Digital Marketing
Social media and influencer identification: smart software is emerging that crunches large social datasets to
define things like the sentiment of a post and likelihood of response to a piece of online content. To some
brands, influence is measured purely by reach, and to others, true influence is more of a function of quality and
thoughtfulness of posts showing a real understanding of a given topic and yet others gauge influence via
social engagement or conversations.
Cross-channel lifecycle marketing: this is the increasingly prevalent capturing of customer permissions,
contact information, and preferences across multiple channels. It also requires marketers to have the right
integrated marketing and customer information systems for recording and automation for optimisation
throughout the customer lifecycle.
Data-driven innovations continued:
Analytics
Watch
The curly fry conundrum: why social media “likes” say more than you might think.
https://www.ted.com/talks/jennifer_golbeck_the_curly_fry_conundrum_why_social_media_likes_say_
more_than_you_might_think?language=en
Watch the TED talk and reflect on the following questions:
■ Did anything shock you about the pervasiveness of the data collection tools and methods?
■ Can you personally remember a time on social media where you felt as if your information had been
adopted for commercial reasons?
■ Do you think there are any risks with these increasingly commonplace approaches?
■ Why would online businesses (think e-tailers) be highly interested in advanced digital marketing
practices such as this?
■ How do you think a start-up company or enterprise might find these tactics appealing? What
information would they be most interested in?
Analytics
Mobile
There is no need to explain the prevalence and ubiquity of mobile
devices but this ties in nicely to our discussions around Web 3.0
and the semantic web. The number of mobile users is expected to
increase in the coming years, with a greater dependency on mobile
in general. As of 2016, 61% of web traffic came from wireless
devices as opposed to desktops, contributing heavily to the growth
of Big Data.
Companies are attempting to create software solutions presently
but while analytics might be handling the problem on the surface,
this model is not going to last with the vast amounts of data soon to
be heading our way.
Analytics
Mobile
Mobile is not a trend…
What is important about mobile is how consumers
are using their mobile phones not just for personal
utility, but also for commercial reasons. Businesses
are starting to plough money into it as a platform,
understanding that there is ample opportunity in
this ever-evolving channel. To remain competitive,
there is a need to focus on mobile analytics,
allowing for improved user experience.
Analytics
Fraud Detection and Security
Fraud is intentional deception made for personal gain or to damage another individual.
Anyone who has had their credit-card “swiped” / “skimmed” or details stolen would appreciate the role that Big
Data has played in the banking industry in terms of security. Real-time business analytics are assisting here in
monitoring credit card transactions, monitoring and checking in near-real time. If the checks identify pattern
inconsistencies and suspicious activity, the transaction is identified for review and escalation, often with
sophisticated (and fast!) methods of processing.
Big Data technologies provide an optimal technology solution due to the reliance on large dataset figures, as well
as cross-referencing of information. The precision that is required to detect (often globally in terms of real-time
data collection and processing) is also incredibly important in this age of integrated markets and e-commerce.
Another emergent approach to solving fraud with Big Data is social network analysis (SNA). SNA is the precise
analysis of social networks which view social relationships and make assumptions. SNA can effectively reveal all
individuals involved in fraudulent activity, from perpetrators to their associates, and attempt to understand their
relationships and behaviours to identify an in-progress fraud case.
Example
Analytics
Fraud Detection and Security
Javelin’s “2012 Identity Fraud Report: Social Media and Mobile Forming the New Fraud
Frontier” found that:
68% of people with public social media profiles shared their birthday information (with 45%
sharing month, date, and year);
63% shared their high-school name; 18% shared their phone number;
12% shared their pet’s name — all are prime examples of personal information a company
would use to verify your identity.
Analytics
Fraud Detection and Security
It is not uncommon to hear top analytics professionals state that they work
in risk management (RM). Almost unequivocally, risk management is now
data-driven. The two most common types of risk management are credit
risk management and market risk management. A third type of risk,
operational risk management, is not as common, but is becoming
increasing visible in the RM space.
The tactics for risk professionals typically include avoiding risk, reducing
the negative effect or probability of risk, or accepting some or all of the
potential consequences in exchange for a potential upside gain. The vast
amount of both qualitative and quantitative information available to credit
risk professionals can be overwhelming to digest and can slow down a
process with potential sales at risk. With advanced analytical tools, these
abundant and complex data sources can be distilled into simple solutions
that provide actionable insights and are relatively easy to implement.
Credit risk analytics focus on past
credit behaviours to predict the
likelihood that a borrower will default
on any type of debt by failing to make
payments which they obligated to
make. For example, “Is this person
likely to default on their $300,000
mortgage?”
Market risk analytics focus on
understanding the likelihood that the
value of a portfolio will decrease due
to a change in stock prices, interest
rates, foreign exchange rates, and
commodity prices. For example,
“Should we sell this holding if the
price drops another 10 percent?”
Analytics
Commerce and Trading
There has been a significant shift in the use of data in the financial services industry over the past
decade, with financial institutions at the forefront of applying these analytics for a variety of commercial
and trading functions: from risk management, proprietary trading to portfolio management. Investment
banks, in particular, are adept at using highly sophisticated algorithmic trading systems.
These processes are enabled by machines built on complex algorithms and mathematics to determine
buy and sell orders for equities, commodities, interest rate and foreign exchange rates, derivatives, and
fixed-income instruments at as close to instant as possible.
A key component of algorithmic trading is determining return and the risk of each potential trade, and
then making a decision to buy or sell.
Quantitative risk analysts help banks develop trading rules and implement these rules using modern
technology. Speed is obviously a priority in an era of digitised commodity and stock trading, and
increasingly, decisions are “automated” via systems of intense calculation.
Analytics
Application Evolution
The multiple applications we have canvassed here also require and demand multiple skills in
the decision-making team, multiple tools, and multiple types of data (clickstream data,
consumer data, competitive intelligence data, etc.).
Historically, businesses have failed to embrace this variety of data, instead focusing on “good”
data, that is clean, manageable and complete.
Analytics
Application Evolution
These emergent applications of Big Data show the
opposite can be true: dynamic, real-time data that is large
in set and complex in its format is sometimes the richest in
terms of insights available.
It is imperative that if you want to make sound decisions
with increasingly digital and online information,
organisations must learn the many different types of tools
to bring this different type of data together. The table
demonstrates the multiple business functions and
outcomes versus the evolution of data applications we
have canvassed in this module.
We can see here what existing and what emergent
analytic capabilities can provide, not only for businesses,
but for society.
Business Trends
Data Trends
■ Governance and Self-Service Analytics become best pals!
Businesses have realised that data governance, when done right, can help nurture a culture of analytics and
meet the needs of the business. People are likely to dig into their data, when they have centralised, clean
and fast data sources and when they are confident about an IT person looking after security and
performance.
Watch the Video: Citrix Story from Tableau, a case on how to scale the culture of analytics in business.
https://vimeo.com/189506655
■ Data becomes democratised
Self-Service Analytics tools have changed expectations of people for good. As millennials enter the
workforce, employees will seek empowerment. Data in businesses have to be shaped on the go. Therefore,
the demand for self-service data preparation tools and self-service warehouses will grow. Democratisation of
data allows people to respond quickly to shifting priorities in business!
Tableau, 2016
Business Trends
Data Trends
■ Data Integration is Exciting
Today, business want to connect the right data to the right people, quickly. Data sources are varied and
there is an increasing rise of agile tools that enable data explorers to connect to any dataset, wherever it
lives. It is predicted that the growth of agile tools will increase over the next 10 years.
■ Data is no longer an analyst’s domain
All business people are expected to become data savvy, to obtain meaningful experiences. Platforms that
enable users to apply statistics, ask a series of questions and stay in the flow of analysis, will become
mainstream.
■ Cloud takes off!
Cloud storage and retrieval has now become commonplace. Cloud analytics helps businesses become agile
and they have become more reliant on it – similar to any other enterprise systems. (Module 2 will cover more
on Cloud platforms).
Tableau, 2016
Business Trends
Data Trends
■ COE becomes excellent!
Many business are establishing Centre of Excellence (COE) to foster adoption of self-service analytics.
There are also enablement programs such as online training forums and one-to-one training. These
centers enable data to inform workflows across the organisation in the long term.
■ Mobile analytics gains recognition
Working with mobile devices and data is no longer an extension of computing. As part of dynamic
analytics processes, the world is becoming mobile. Data visualisation using iPads is an example.
■ Internet of Things (IoT)
Everything today is poised to have sensors that send information back and forth. Location tracking using
sensors is now becoming mainstream and, as it grows, companies will look for tools that allow users to
explore data and share findings, securely, but in an interactive manner.
Tableau, 2016
Business Trends
Try to answer these review questions briefly:
■ Having looked at the evolution of business analytics and its various applications, what do
you think are the three most important functions of these applications for industry? Why?
■ Why is there so much conjecture around the conceptualisations of Web 2.0 and Web 3.0.
What are the key differences? Moreover, what new opportunities might be presented to
new businesses or enterprises with these advances?
■ What do you think are some of the operational (and perhaps commercial) reasons behind
organisations not employing complete marketing automation? What are some of the key
benefits?
■ Explain what you think the relationship between CRM and Big Data looks like for the
modern-day organisation. Do you think these processes and systems are essential?
■ Explain why mobile should not be considered a “trend” for industry. Think closely about the
current “information era” and what has changed in the way business is done on this
platform.
Quiz
Summary
What society is doing with Big Data – our thinking about the technology-impact cycle; the order
by which technology passes through society and perhaps into industry. We also noted
consumers’ own willingness to harness and create data and touched on the quantified self
revolution which presents itself as a pertinent example of the information age and societal
adoption.
Big Data applications – an overview of the various types of data sources, a history of application
from ERP to CRM and now into the age of analytics. We also touched upon many industries and
the various ways in which Big Data is used, from medicine to advertising and marketing.
Emerging analytic applications – the emerging Big Data universe was explored with the many
possibilities around mobile, technological innovation and online web 3.0 development. We took a
deeper dive into the mobile/web and network research areas around analytics usage, and some
of the major impacts on the commercial, trading and finance sector of these new innovations
around informatics and analytics. The evolution and maturity over time of business analytics
applications was used to conclude this exploration of contemporary methods.
Trends – here we touched upon some trends such as mobile analytics, IoT, data democratisation,
integration and the creation of Centres of Excellence for Data. We explore these much more in
detail in Modules 2 and 3.
■ Investopedia (2015) http://www.investopedia.com/terms/q/quantified-self.asp#ixzz3yIFK3s8H
■ Marr, B. (2015). Big Data: Using SMART Big Data. Analytics and Metrics to Make Better Decisions and
Improve Performance.
■ Minelli, M., Chambers, M., & Dhiraj, A. (2012). Big data, big analytics: emerging business intelligence and
analytic trends for today’s businesses. John Wiley & Sons.
■ Redman, T. C. (2008). Data driven: profiting from your most important business asset. Harvard Business
Press.
■ 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.
■ 1stwebdesigner (n.d.). A brief introduction to Web 3.0, http://www.1stwebdesigner.com/web-3-introduction/
■ LinkedIn Learning (2016) Data Science and Analytics Career Paths and Certifications
■ Tableau (2016) Top 10 trends for 2016 – Business Intelligence.
References
Extra Resources
•Barns (2015) “Big Data’s Big Impact on the Future of Advertising” in re/code:
http://recode.net/2015/06/22/big-datas-big-impact-on-the-future-of-advertising/ (accessed Jan 2016)
•Barns, who is the CEO of Nielsen (the large market survey and insights firm), gives a succinct
defence of why it is not about ‘big data’, but the ‘right data’.
•Brown, B., Chui, M., & Manyika, J. (2011). Are you ready for the era of ‘big data’. McKinsey Quarterly, 4,
24-35.
https://www.mckinsey.com/business-functions/strategy-and-corporate-finance/our-insights/are-you-read
y-for-the-era-of-big-data
•A slightly outdated (2011) article on Big Data with some interesting predictions. Read this in it’s
historical context and think about what had changed or not gone to plan. Page 6 has a nice
graphic on the various industries and their prospective impact.
Light, easy and fun
Extra Resources
For the academically-curious
Issa, N. T., Byers, S. W., & Dakshanamurthy, S. (2014). Big data: the next frontier for innovation in
therapeutics and healthcare. Expert review of clinical pharmacology, 7(3), 293-298. accessed online at:
http://www.salzburgglobal.org/fileadmin/user_upload/Documents/2010-2019/2015/Session_548/Issa.Big
_Data_2014.pdf
A top-line and straightforward overview of the impact of Big Data and informatics on pharmacology. An
excellent, if short, overview of the enabling potential of data in this industry.
Arnold, M., & Kiker, D. R. (2015). The Big Data Collection Problem of Little Mobile Devices. Richmond
Journal of Law & Technology, 21(3). Can be read online at: http://jolt.richmond.edu/v21i3/article10.pdf
Slightly off-track but incredibly interesting if you are into the legality of data collection. This looks at
mobile and is fascinating to see how information can legally and commercially be collected, and
uncovers a few ways in which applications are used on mobile devices that you may not be aware of!