Unlocking the Value of Big Data (Innovation Summit 2014)Dun & Bradstreet
Big Data is central to the strategic thinking of today’s innovators and business executives as companies are scrambling to figure out the secret to transforming Big Data to Big Insight and that Insight into Action. As many companies struggle with the emerging technologies and nascent capabilities to discover and curate massive quantities of highly dynamic data, new problems are emerging in the form of how to ask meaningful questions that leverage the “V’s” of large amounts of data (e.g. volume, variety, velocity, veracity). In the Business-to-Business space, these challenges are creating both significant opportunity and ominous new types of risk. This presentation discusses how companies are reacting to these changes and provide valuable insight into new ways of thinking in a world with overwhelming quantities of data.
In this document, the five disruptive trends shaping the corporate IT landscape today are layed out. Out of the five, Big Data has the biggest potential to generate new sustainable competitive advantages. But the benefits will remain out of reach of many organizations as they struggle to adopt the technology, develop new capabilities, and manage the cultural change associated with the use of big data. This document offers a pragmatic approach to generating business value.
Unlocking the Value of Big Data (Innovation Summit 2014)Dun & Bradstreet
Big Data is central to the strategic thinking of today’s innovators and business executives as companies are scrambling to figure out the secret to transforming Big Data to Big Insight and that Insight into Action. As many companies struggle with the emerging technologies and nascent capabilities to discover and curate massive quantities of highly dynamic data, new problems are emerging in the form of how to ask meaningful questions that leverage the “V’s” of large amounts of data (e.g. volume, variety, velocity, veracity). In the Business-to-Business space, these challenges are creating both significant opportunity and ominous new types of risk. This presentation discusses how companies are reacting to these changes and provide valuable insight into new ways of thinking in a world with overwhelming quantities of data.
In this document, the five disruptive trends shaping the corporate IT landscape today are layed out. Out of the five, Big Data has the biggest potential to generate new sustainable competitive advantages. But the benefits will remain out of reach of many organizations as they struggle to adopt the technology, develop new capabilities, and manage the cultural change associated with the use of big data. This document offers a pragmatic approach to generating business value.
To be updated is not enough for companies today. Organizations must be constantly watching also to the trends in order to predict and forecast the next steps for their business. The following document is a Executive Summary of the current situation but also of the more notable trends that will help to understand the basics of the Analytics Market
There is little arguing the benefits and disruptive potential of Big Data. However, many organizations have not fully embedded Big Data in their operations. In fact, our research shows that only 13% have achieved full-scale production for their Big Data implementations. The most troubling development is that most organizations are failing to benefit from their investments. Only 27% of respondents described their Big Data initiatives as “successful” and only 8% described them as “very successful”.
Impact of Data Analytics in Changing the Future of Business and Challenges Fa...IJSRP Journal
Data Analytics refers to a comprehensive approach that makes use of both Qualitative and Quantitative Information in order to draw valuable insights and arriving at conclusions based on the extensive usage of statistical tools accompanied by explanatory and predictive models running over the data. It tries to understand the behavior and dynamics of businesses thereby leading to improved productivity and enhancing business gains by helping with appropriate decision making. Considering the intensified disruption caused by recent revolution in the field of Data Analytics, this articles aims to cover the potential impacts that Data Analytics could have over the already existing businesses and how new entrants, especially across the emerging economies, could make the best use of Data Analytics in gaining an edge over their competitors. It also aims to deep dive into the challenges faced by businesses while adopting or moving to Data Analytics and how they can overcome those challenging barriers for a successful future. .
Disruptive Data Science Series: Transforming Your Company into a Data Science...EMC
Big Data is the latest technology wave impacting C-Level executives across all areas of business, but amid the hype, there remains confusion about what it all means. The name emphasizes the exponential growth of data volumes worldwide (collectively, 2.5 Exabytes/ day in the latest estimate I saw from IDC), but more nuanced definitions of Big Data incorporate the following key tenets: diversification, low latency, and ubiquity. In the current developmental-phase of Big Data, CIOs are investing in platforms to “manage” Big Data.
This project is about "Big Data Analytics," and it provides a comprehensive overview of topics related to Data and Analytics and a short note on Cognitive Analytics, Sentiment Analytics, Data Visualization, Artificial intelligence & Data-Driven Decision Making along with examples and diagrams.
Is Your Company Braced Up for handling Big Datahimanshu13jun
Has your company recently launched new product or company is concerned with the poor sales figure or want to reach new prospects and also reduce the existing customers' attrition, then this thought evoking short hand guide is available for you to explore.
Operationalizing the Buzz: Big Data 2013VMware Tanzu
The 2013 EMA/9sight Big Data research makes a clear case for the maturation of Big Data as a critical approach for innovative companies. This year’s survey went beyond simple questions of strategy, adoption and use to explore why and how companies are utilizing Big Data. This year’s findings show an increased level of Big Data sophistication between 2012 and 2013 respondents. An improved understanding of the “domains of data” drives this increased sophistication and maturity. Highly developed use of
Process-mediated, Machine-generated and Human-sourced information is prevalent throughout this year’s study.
How ‘Big Data’ Can Create Significant Impact on Enterprises? Part I: Findings...IJERA Editor
Big data is the latest buzz word in the BI domain, and is increasingly gaining traction amongst enterprises. The prospect of gaining highly targeted business and market insight from unmanageable and unstructured data sets is creating huge adoption potential for such solutions. The scope of big data moves beyond conventional enterprise databases to more open environments, covering new sources of information typically relating to various social networking sites, wikis and blogs. Moreover, advancements in communications and M2M technologies are also contributing to the massive availability of big data
This infographic is about how banks can maximize the value of their customer data using big data analytics. While the volume of data has been increasing in recent years, many banks have not been able to profit from this growth. Several challenges hold them back.
Banks Betting on Big Data Analytics and Real-Time Execution to Better Engage ...SAP Analytics
Winning new business and satisfying customers are top agenda items in bank boardrooms worldwide. Executives are bullish on new technologies to meet these objectives.
To be updated is not enough for companies today. Organizations must be constantly watching also to the trends in order to predict and forecast the next steps for their business. The following document is a Executive Summary of the current situation but also of the more notable trends that will help to understand the basics of the Analytics Market
There is little arguing the benefits and disruptive potential of Big Data. However, many organizations have not fully embedded Big Data in their operations. In fact, our research shows that only 13% have achieved full-scale production for their Big Data implementations. The most troubling development is that most organizations are failing to benefit from their investments. Only 27% of respondents described their Big Data initiatives as “successful” and only 8% described them as “very successful”.
Impact of Data Analytics in Changing the Future of Business and Challenges Fa...IJSRP Journal
Data Analytics refers to a comprehensive approach that makes use of both Qualitative and Quantitative Information in order to draw valuable insights and arriving at conclusions based on the extensive usage of statistical tools accompanied by explanatory and predictive models running over the data. It tries to understand the behavior and dynamics of businesses thereby leading to improved productivity and enhancing business gains by helping with appropriate decision making. Considering the intensified disruption caused by recent revolution in the field of Data Analytics, this articles aims to cover the potential impacts that Data Analytics could have over the already existing businesses and how new entrants, especially across the emerging economies, could make the best use of Data Analytics in gaining an edge over their competitors. It also aims to deep dive into the challenges faced by businesses while adopting or moving to Data Analytics and how they can overcome those challenging barriers for a successful future. .
Disruptive Data Science Series: Transforming Your Company into a Data Science...EMC
Big Data is the latest technology wave impacting C-Level executives across all areas of business, but amid the hype, there remains confusion about what it all means. The name emphasizes the exponential growth of data volumes worldwide (collectively, 2.5 Exabytes/ day in the latest estimate I saw from IDC), but more nuanced definitions of Big Data incorporate the following key tenets: diversification, low latency, and ubiquity. In the current developmental-phase of Big Data, CIOs are investing in platforms to “manage” Big Data.
This project is about "Big Data Analytics," and it provides a comprehensive overview of topics related to Data and Analytics and a short note on Cognitive Analytics, Sentiment Analytics, Data Visualization, Artificial intelligence & Data-Driven Decision Making along with examples and diagrams.
Is Your Company Braced Up for handling Big Datahimanshu13jun
Has your company recently launched new product or company is concerned with the poor sales figure or want to reach new prospects and also reduce the existing customers' attrition, then this thought evoking short hand guide is available for you to explore.
Operationalizing the Buzz: Big Data 2013VMware Tanzu
The 2013 EMA/9sight Big Data research makes a clear case for the maturation of Big Data as a critical approach for innovative companies. This year’s survey went beyond simple questions of strategy, adoption and use to explore why and how companies are utilizing Big Data. This year’s findings show an increased level of Big Data sophistication between 2012 and 2013 respondents. An improved understanding of the “domains of data” drives this increased sophistication and maturity. Highly developed use of
Process-mediated, Machine-generated and Human-sourced information is prevalent throughout this year’s study.
How ‘Big Data’ Can Create Significant Impact on Enterprises? Part I: Findings...IJERA Editor
Big data is the latest buzz word in the BI domain, and is increasingly gaining traction amongst enterprises. The prospect of gaining highly targeted business and market insight from unmanageable and unstructured data sets is creating huge adoption potential for such solutions. The scope of big data moves beyond conventional enterprise databases to more open environments, covering new sources of information typically relating to various social networking sites, wikis and blogs. Moreover, advancements in communications and M2M technologies are also contributing to the massive availability of big data
This infographic is about how banks can maximize the value of their customer data using big data analytics. While the volume of data has been increasing in recent years, many banks have not been able to profit from this growth. Several challenges hold them back.
Banks Betting on Big Data Analytics and Real-Time Execution to Better Engage ...SAP Analytics
Winning new business and satisfying customers are top agenda items in bank boardrooms worldwide. Executives are bullish on new technologies to meet these objectives.
This presentation was delivered on invitation, for the UK Trade & Investments wing of British Deputy High Commission at Chennai. This ppt talks about how big data is currently leveraged in India, future trends, challenges and areas of opportunity.
Convergence Partners has released its latest research report on big data and its meaning for Africa. The report argues that big data poses a threat to those it overlooks, namely a large percentage of Africa’s populace, who remain on big data’s periphery.
Dan Sullivan - Data Analytics and Text Mining with MongoDB - NoSQL matters Du...NoSQLmatters
Data analysis is an exploratory process that requires a variety of tools and a flexible data store. Data analysis projects are easy to start but quickly become difficult to manage and error prone when depending on file-based data storage. Relational databases are poorly equipped to accommodate the dynamic demands complex analysis. This talk describes best practices for using MongoDB for analytics projects. Examples will be drawn from a large scale text mining project (approximately 25 million documents) that applies machine learning (neural networks and support vector machines) and statistical analysis. Tools discussed include R, Spark, Python scientific stack, and custom pre-processing scripts but the focus is on using these with the document database.
Everyone is awash in the new buzzword, Big Data, and it seems as if you can’t escape it wherever you go. But there are real companies with real use cases creating real value for their businesses by using big data. This talk will discuss some of the more compelling current or recent projects, their architecture & systems used, and successful outcomes.
Norbert Kraft, Referent Research & Technology, Nokia Siemens Networks
Durch die weltweite Verfügbarkeit, Abdeckung und Nutzung sind Mobile Telekommunikationsnetze heute ein typisches Anwendungsgebiet für 'Big Data' und insbesondere für komplexe Datenanalyseverfahren. Norbert Kraft beschreibt in dieser Session Einsatzszenarien dieser Technologien in der Telekommunikationsindustrie anhand konkreter Beispiele, die im Rahmen eines Forschungsprojektes des Zentralbereiches 'Technologie und Innovation' von Nokia entstanden sind. In einen Entwicklungsprototypen wurden hier Möglichkeiten der Netzausfallvorhersage sowie der Ursachenanalyse für solche Ereignisse untersucht und entwickelt. Hierbei kommen unterschiedliche Data Mining und Machine Learning Verfahren zum Einsatz, z.B. (Un-)supervised Learning, Clustering Verfahren für die Klassifizierung von Kundenprofilen oder Radiozellen sowie eine Zeitreihenanalyse zur Vorhersage von Netzausfällen. Eine wichtige Rolle neben der Erkennung von Fehlerszenarien ist hierbei immer die Ermittlung einer möglichen Fehlerursache, wobei der erkannte Netzfehler mit einer Vielzahl möglicher Einflussgrößen (z.B. SW Konfiguration, Lastprofil) korreliert wird.
Big Data - The 5 Vs Everyone Must KnowBernard Marr
This slide deck, by Big Data guru Bernard Marr, outlines the 5 Vs of big data. It describes in simple language what big data is, in terms of Volume, Velocity, Variety, Veracity and Value.
The need, applications, challenges, new trends and
a consulting perspective
(Why is Big Data a strategic need for optimization of organizational processes especially in the business domains and what is the consultant’s role?)
With every transaction and activity, organizations churn out data. This process happens even in the case of idle operation. Hence, data needs to be effectively analyzed to manage all processes better. Data can be used to make sense of the current situation and predict outcomes. It also can be used to optimize business processes and operations. This is easier said than done as data is being produced at an unprecedented rate, huge volumes and a high degree of variety. For the outcome of the data analysis to be relevant, all the data sets must be factored in to the analysis and predictions. This is where big data analysis comes in with its sophisticated tools that are also now easy on the pocket if one prefers the open source.
The future of high potential marketing lead generation would be based on big data. Virtually every business vertical can benefit from big data initiatives. Even those without deep pockets can use the cloud model for business analytics/big data analysis.
Some challenges remain to be addressed to engender large scale adoption but the current benefits outweigh the concerns.
India has seen a massive growth in big data adoption and the trend will grow though it is generally amongst the bigger players. As quality of data improves and customer reluctance to being honest when they volunteer data reduces, the forecasts will become more accurate and Big Data will have come to its rightful place as a key enabler.
Whether you believe into the hype around Big Data's affirmation to transform business, it is true that learning how to use the present deluge of data can help you make better decisions. Thanks to big data technologies, everything can now be used as data, giving you unparalleled access to market determinants. Contact V2Soft's Big Data Solutions if you wish to implement big data technology in your business and need help getting started. https://bit.ly/2kmiYFp
Data Mining: The Top 3 Things You Need to Know to Achieve Business Improvemen...Dr. Cedric Alford
While companies have been using various CRM and automation technologies for many years to capture and retain traditional business data, these existing technologies were not built to handle the massive explosion in data that is occurring today. The shift started nearly 10 years ago with expanding usage of the internet and the introduction of social media. But the pace has accelerated in the past five years following the introduction of smart phones and digital devices such as tablets and GPS devices. The continued rise in these technologies is creating a constant increase in complex data on a daily basis.
The result? Many companies don't know how to get value and insights from the massive amounts of data they have today. Worse yet, many more are uncertain how to leverage this data glut for business advantage tomorrow. In this white paper, we will explore three important things to know about big data and how companies can achieve major business benefits and improvements through effective data mining of their own big data.
Dr. Cedric Alford provides a roadmap for organizations seeking to understand how to make Big Data actionable.
Running title TRENDS IN COMPUTER INFORMATION SYSTEMS1TRENDS I.docxanhlodge
Running title: TRENDS IN COMPUTER INFORMATION SYSTEMS 1
TRENDS IN COMPUTER INFORMATION SYSTEMS 4
Trends in Computer Information Systems, and the Rise to Business Intelligence
Shad Martin
School for Professional Studies
St. Louis University
ENG 2005 Dr. Rebecca Wood
November 23, 2016
Introduction
Our quest to increase our knowledge of Computer Information Systems has produced a number of benefits to humanity. The innovation humans have discovered in Computer Information Systems has led to new sub-areas of study for students and professionals to continue their progression to master all that Computer Information Systems has to offer. Amy Web of the Harvard Business Review reported 8 Tech Trends to Watch in 2016, She noted, “In order to chart the best way forward, you must understand emerging trends: what they are, what they aren’t, and how they operate. Such trends are more than shiny objects; they’re manifestations of sustained changes within an industry sector, society, or human behavior. Trends are a way of seeing and interpreting our current reality, providing a useful framework to organize our thinking, especially when we’re hunting for the unknown. Fads pass. Trends help us forecast the future” (Harvard Business Review, 2015). In short, Amy’s reference to understanding the emerging trends in Computer Information can provide a framework from which, students, professionals, and scientists to conscientiously create a path towards optimizing their efforts. Ensuring we have a fundamental approach to analyze data will enhance our understanding of this subject further.
In this paper I will expound on three of the top trends used to provide insight into the data produced from the advancements in Computer Information Systems. These trends or methods are taking place in my workplace within a financial institution, and in many other industries. It is important to note this paper does not provide an inclusive list of all methodologies that exist. Individuals can now leverage analytics to synthesize insights from data to identify emerging risk, manage operational risks, identify trends, improve compliance, and customer satisfaction. Data in and by itself is not always useful. Regardless of the data source, trained professional must understand the best approach to structure the data to make it more useful. In this paper, I will touch on three popular methodology trends occurring in Computer Information Systems. Students and professionals who work with large data would benefit from having a solid understanding of the fundamental principles of Business Intelligence as data scientific approach and when to use these methodologies.
The rise of Business Intelligence
Computer Information Systems allow many companies to gather and generate large amounts of data on their customers, business activities, potential merger targets, and risks found in their organization. These large sets of data have given rise to vari.
Difference B/w Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data
The most popular and rapidly evolving technologies in the world are Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data. All firms, large and small, are increasingly looking for IT experts who can filter through the data and help with the efficient implementation of sound business decisions. In light of the current competitive environment, Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data are essential technologies that drive company growth and development. In this topic, “Difference Between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, And Big Data,” we will examine the key definitions and skills needed to obtain them. We will also examine the main differences between Data Analytics, Data Analysis, Data Mining, Data Science, Machine Learning, and Big Data. So let’s start by briefly introducing each concept.
Data Analysis vs Data Analytics
Data Analysis is the process of analyzing, organizing, and manipulating a collection of data to extract relevant information. An “Analytics platform” is a piece of software that enables data and statistics to be generated and examined systematically, whereas a “business analyst” is a person who applies an analytical method to a collection of information for a specific goal. As this is becoming increasingly popular the corporate sector has started to broadly accept it. Data Analysis makes it easy to understand the data. It provides an important historical context for understanding what has occurred recent past. To master Power BI check out Power BI Online Course
Data Analytics includes both decision-making processes and performance enhancement through relevant forecasts. Businesses may utilize data analytics to enhance business decisions, evaluate market trends, and analyze customer satisfaction, all of which can lead to the creation of new, enhanced products and services. Using Data Analytics, it is possible to make more accurate forecasts for the future by examining previous data. To master Data Analytics Skills visit Data Analytics Course in Pune
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Data Analytics
Data Analysis
Data Analytics is analytics that is used to make conclusions based on data.
Data Analysis is a subset of data analytics that is used to analyze data and derive specific insights from it.
Using historical data and customer expectations, businesses may develop a solid business strategy.
Making the most of historical data helps organizations identify new possibilities promote business growth and make more effective decisions.
The term “data analytics” refers to the collecting and assessment of data that involves one or more users.
Who needs Big Data? What benefits can organisations realistically achieve with Big Data? What else required for success? What are the opportunities for players in this space? In this paper, Cartesian explores these questions surrounding Big Data.
www.cartesian.com
Big data refers to the vast amount of structured and unstructured data that inundates organizations on a daily basis. This data comes from various sources such as social media, sensors, digital transactions, mobile devices, and more.
MTBiz is for you if you are looking for contemporary information on business, economy and especially on banking industry of Bangladesh. You would also find periodical information on Global Economy and Commodity Markets.
Magenta advisory: Data Driven Decision Making –Is Your Organization Ready Fo...BearingPoint Finland
It’s nice to have loads of data. Nevertheless, many managers start to sweat when it comes to genuinely fact-based decision making. This study reveals the keys to leveraging big data successfully.
Similar to Odgers Berndtson and Unico Big Data White Paper (20)
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
【社内勉強会資料_Octo: An Open-Source Generalist Robot Policy】
Odgers Berndtson and Unico Big Data White Paper
1. WHITE PAPER:
Big Data &
Predictive Analytics;
THE CHALLENGES AND OPPORTUNITIES FOR ALL.
2. INTRODUCTION
ABOUT THIS DOCUMENT
UNLOCKING THE POWER
OF PREDICTIVE ANALYTICS
DEFINING PREDICTIVE ANALYTICS
THE FIVE KEY UNDERSTANDINGS
WITH BIG DATA AND PREDICTIVE ANALYTICS
WHAT IS POSSIBLE TODAY?
THREE PREDICTIONS
FOR THE FUTURE
QA
APPENDIX; INDUSTRY ATTENDEES
OF THE SYDNEY AND MELBOURNE EXECUTIVE BREAKFASTS
02
03
04
05
06
09
09
11
14
Contents
3. 02
Big Data remains one of the most significant
opportunities for business, but also one of the
most poorly leveraged. In numbers, only 0.5 per
cent of data is ever actually analysed, meaning
that organisations are not only wasting precious
resources collecting and storing data, they are
missing out on the significant insights that data
can provide back into the business.
What businesses are good at is the capture of
raw data. Studies have found that 90 per cent
of the world’s data has been created in just the
past two years, and the rate of data collection
is not slowing down – we are expecting an
increase of 4,300 per cent of yearly data
production by the year 2020.
This rate of data creation – and capture – has
the potential to exasperate the overwhelming
challenges that businesses are facing in data
analysis. Businesses are aware of the need to
scale their investments into Big Data analysis.
A Wikibon forecast expects the global Big Data
market to grow from $18.3 billion in 2014 to
$92.2 billion in 2026 – a compound annual
growth rate of 14.4 per cent. IDC, meanwhile,
predicts that through 2020, spending on cloud-
based Big Data and Analytics technology will
grow 4.5 times faster than spending on on-
premises solutions.
Perhaps the most clear indicator of the raw
demand for data solutions can be found in the
venture capital space, where Big Data startups
picked up 11 per cent of all tech venture capital
in 2015. Big Data is not just a trend – it’s
equal parts pain point and opportunity, and
organisations are keen to resolve the former
and capitalise on the latter.
Introduction
“Artificial Intelligence, or more correctly Machine
Learning, is a powerful new technology that
must be understood by every company. The
potential impact that AI/ML will have on
businesses is significant and will enable
companies to provide new services and drive
greater insights more quickly. Every executive
must understand this opportunity and what
impact it could have on their operations.”
–David Thodey, Chair CSIRO
4. 03
This white paper looks at where
the biggest data opportunities are,
and how businesses can capitalise
on these by better understanding
what solutions will be of benefit for
their business.
Unico in collaboration with Odgers Berndtson engaged senior enterprise
executives from a wide cross-section of industries during two business
roundtables in October 2016. Please see the attendance list in appendix one.
The insights from these discussions are captured throughout the document.
From page 14 you will find a Q A section, in which the industry attendees
highlighted their key questions around the implementation of and use of
predictive analytics and data analysis.
About this document
This white paper was generated to
capture real world experience and
thinking in data use and analysis.
5. 04
A KPMG study found that for CEOs, Big Data
and analytics was a top-3 investment priority.
Two thirds of CEOs, according to the report,
were concerned that their organisation wasn’t
being disruptive enough, and this concern was
in significant part driving interest in data; CEOs
saw it as a path in developing new products
and services, while realising greater savings
and efficiencies.
For the CFO, Big Data offers the opportunity
to develop a broader understanding of a
businesses’ ever more complex corporate
governance responsibilities. For these
executives, moving from a retrospective an
intuition-driven decision making process, to
one based on data, will help the business
be more proactive and holistic in the way it
handles its economics.
The CMO benefits too. Currently, marketers see
predictive analytics as a holy grail to their work.
A Forrester Consulting study found that 89 per
cent of B2B marketers had identified predictive
analytics as being critical to their roadmaps
in 2016, and that 78 per cent of them see B2B
marketing as expanding to deal acceleration
from demand generation. From that statistic it’s
easy to deduce that predictive analytics will play
a key role in a critical redefinition of marketing
in many businesses.
Unlocking The Power
OF PREDICTIVE ANALYTICS
Most members of the C-suite community now
regard Big Data and analytics to be a critical part
of their role into the future.
“It is apparent that boards and executive teams are under enormous
pressure to land on a ‘future’ operating model for their businesses.
A robust and well tested strategic plan that is dynamic and data
driven is essential for the new digital era and therefore the demand
for executives that understand this data driven world is significant
and will only continue to grow especially when we consider how
wrong we can be if we don’t use all the tools at our disposal.”
–Paul Rush Partner, Odgers Berndtson
6. 05
Once an organisation has collected and
centralised its data, it is then ready to
have predictive analytics applied to it, in
order to derive key pieces of information.
It is important that organisations do
this step; without the analysis applied
to the data, the ROI in collecting and
storing the data is non-existent and all
the data will be good for is searching.
It’s in the analysis that the value of big
data is unlocked.
Predictive analytics specialises in
making use of unstructured data, or
what we generally refer to as ‘Big Data.’
Structured data makes use of fixed fields
– spreadsheets or relational databases,
for example. Unstructured data includes
photos, webpages and digital documents;
it’s the data that isn’t meaningful when
placed into neat boxes and categories.
The concept of Big Data can also be
understood in two ways. It could be
related to the number of samples
or observations that exceed certain
threshold; or it could be the number of
dimensions exceed certain number, even
Because predictive analytics includes automated processes, applications of the
technology often have strong Machine Learning capabilities built in, in order to
automatically “learn,” adapt, and update as new data is collected.
But Machine Learning is not always going to generate the best results from the data,
either. If the data is highly linear, then an investment in Machine Learning can be
wasteful and inefficient. For this reason it’s important to fully understand the nature of
the data before developing the algorithms with which to analyse it.
if the number of samples is relatively
small. For an easy example, researchers
might have DNA genes data consisting of
more than 10,000 genes per sample, but
have less than 100 samples. This is still
Big Data. In general, we classify small
data as having equal to or less than 15
attributes, medium data of between 15 –
25 attributes, and Big Data as more than
25 attributes.
There are two primary challenges in
leveraging Big Data; the first is that
unstructured data is proving very difficult
to leverage by organisations, , and with
around 80 per cent of all data being
unstructured, tools and services that help
organisations make sense of it all, such
as predictive analytics, are immeasurably
valuable. The other challenge, however, is
that for machine learning or data mining
algorithms, the high dimensionality
of many examples of big data is a
significant technical challenge that needs
to be overcome - often through custom
algorithms - before an organisation will
be able to derive meaningful and accurate
insights from the data.
Predictive analytics can be
used to:
• Predict future trends/events
• Identify patterns
• Identify casual relationships
between things
• Image recognition
• Text mining and processing
Defining
PREDICTIVE ANALYTICS
7. 06
UNDERSTAND THAT NOT
ALL DATA IS EQUAL
The sheer weight of data being created means
that it’s simply impossible to give every byte
of it equal weighting. To make effective use of
data, and the solutions that are implemented
to leverage it, enterprise needs to focus on the
nature of the business and the kind of data it
will derive best value from, and then identify
how it captures and applies analytics to the
datasets. In effect, we need to get back to
basics and understand and redefine Big Data
before we can start to work with it.
Data can be split into three distinct categories:
structured, unstructured and semi-structured.
Structured data is what people typically
visualise when they think of data; it looks like
numbers in a spreadsheet. This is relatively
simple to analyse.
Unstructured and semi-structured data is
more difficult to analyse, and might take more
unusual forms such as:
• Any digital image has data behind it which
can be analysed.
• Word documents full of words that can be
analysed with text processing algorithms.
This is harder to analyse, but forms most of
what we term ‘Big Data’, and is where most
information is actually contained.
Five Key Understandings
WITH BIG DATA AND PREDICTIVE ANALYTICS
Capitalising on the opportunities that predictive
analytics enables requires business to take
a measured, five-prong approach to the
planning, rollout and subsequent management
of technology solutions:
01
8. 07
NOT ALL ‘ANALYSIS’ IS EQUAL
(AND PEOPLE ARE A CRITICAL
PART OF THE PROCESS)
There is a difference between analysis and
analytics. Analysis unlocks the true value of
data by extracting meaningful insights from
what has been collected through the analytics
strategy. To put it simply: Data analytics without
analysis is just data.
It is important to have a human element
involved in all analytics strategies to properly
extract the insights behind what the data
is telling us, and to hold the data analytics
ROBUST ALGORITHMS
Evolving technologies such as we’ve seen
in Hadoop, parallel processing, and cheaper
storage techniques, are combining to make
the collection and storage of data easy for
businesses of all sizes. However, data of that
size and scale also requires advanced and
robust algorithms to be able to leverage the
insights in a productive and efficient manner.
algorithms to account. Machine Learning and
robust algorithms are certainly able to take the
busywork out of a data scientist’s role, however,
it is important that the role not be made
redundant; instead the role of a data scientist
should transition to something more strategic to
the business, and data scientists in the future
will need to have a better understanding of the
value and purpose of data at all other branches
of the business’ operations.
Most critically, organisations need to consider
developing bespoke algorithms that speak to
the data that is important to their business.
Algorithms and their formulas need to be able
to cut through the “noise” of the sheer mass
of data out there, and collect (and analyse)
only the relevant economic, locational and
behavioural data sets.
Five Key Understandings
WITH BIG DATA AND PREDICTIVE ANALYTICS (CONT.)
02
03
9. 08
Five Key Understandings
WITH BIG DATA AND PREDICTIVE ANALYTICS (CONT.)
WE NEED TO UNLOCK THE
VALUE OF MACHINE LEARNING
If the data is non-linear, it needs to have
Machine Learning applications applied to be
able to generate meaningful insights over
the short, medium, and long term. Machine
Learning is, basically, the function that helps
technology get better with time.
Machine Learning is important to the analysis
of large quantities of unstructured data, as we
find in Big Data. Solutions that have robust
USING DATA TO PREDICT OUTCOMES
The key purpose of the collection and analysis of Data is to identify trends and opportunities, and to
use that information to gain competitive advantage to better position the business.
It’s easy to fall into a trap whereby an organisation draws data into the business and then extracts
insights looking backwards; at what it already understands. Properly configuring the Big Data
insights strategy from the outset is essential to commercial success.
Machine Learning processes built into them
are able to extract value from the multitudes of
sources that input data into an organisation, and
do so without human supervision. Furthermore,
Machine Learning applications become better
the more data that is fed into them. With this
in mind, effective Data Analytics projects in
the future will implement Machine Learning
processes as standard.
04
05
10. 09
Three predictions
FOR THE FUTURE
What does the future hold for Big Data and
predictive analytics? Based on the predictions
and trends for the current state of the Big Data
industry, we can expect three key themes to
emerge over the coming years:
BIG DATA IS ACCELERATING
INTO EVEN BIGGER DATA
The first trend, as noted earlier in this paper,
is that Big Data is only going to become
more overwhelming in terms of how much
is captured and stored, both in terms of
structured and unstructured data.
This presents challenges. When businesses
as a whole are currently only analysing 0.5
per cent of the data coming into the business,
whatever challenges they are facing in
increasing that percentage will be exasperated
as the organisation brings in more and more
data. At the same time, a greater understanding
of what can be done with data, as well as
01 improved tools, Machine Learning, and analysis
strategies, there is also going to be much
greater datasets to play with, which provide in
turn much better quality data to draw insights
out of.
We can also expect to see an increase in
complex data analysis being done in real time,
assisted by automation and Machine Learning.
Previously this has been difficult to achieve, but
now it’s possible to generate actionable insights
out of your data as it comes in; for example,
model parameters can be updated in real time
as data becomes available.
11. 10
Three predictions
FOR THE FUTURE (CONT.)
AUTOMATION WILL DEMOCRATISE
DATA, AND MAKE IT VALUABLE
TO ALL WITHIN THE BUSINESS
It will become important that all people within
the organisation, from CEO to marketing and
on to HR, will be able to use data in their
role. Gartner’s now-infamous prediction that
an organisation’s marketing team will spend
more on technology than the IT team by 2017
stems in no small part from the expected
increase in spending on data.
DATA SCIENTISTS WILL BECOME
ONE OF THE MOST VALUABLE
RESOURCES IN COMPANIES
Australia is facing a skills shortage as
data scientists become more and more in
demand. This is going to push up wages and
movement between jobs, and given that data
scientists will be in demand across most
industries, businesses will need to develop a
strategy to obtain – and then retain – the data
scientist talent.
Third parties might be a solution, with
organisations gaining access to data scientist
skills by engaging with a trusted partner for
building the analytics strategy. For others, 457
visas or outsourcing overseas might be the way
to go.
02
03
Equally, SMEs can expect to have access
to increasingly sophisticated tools for data
analysis, as these technologies become more
established in the market. In order to assist
individuals and businesses make use of the data
across the organisation, and to compensate for
a lack of internal skills in SMEs, automation will
become a more prominent tool that businesses
invest in as past of their Big Data spend.
Data scientists will be in high demand because
they will provide businesses with competitive
advantage. There will be off-the-shelf analytics
solutions available, but businesses will know
that if their rival uses the same analytics that
they are, there will be no competitive advantage.
Instead, these businesses will turn to their data
scientists to develop bespoke applications that
will give them access to data insights that their
competition does not have.
12. 11
QA
How much time do you spend
setting up the framework - what
are the timelines?
Why bespoke and not
off-the-shelf?
Across two senior executive roundtable
events held in Melbourne and Sydney
in October 2016, business leaders were
engaged in a discussion on how Big
Data and predictive analytics can be
applied to their own businesses and
business practices. These questions
were a key focus for enterprise:
The time frames involved in setting up
and executing an algorithm can vary
substantially, and can be anything from
a couple of weeks to months in duration.
Factors that can affect the rollout time
include; the front end and the kind of
There are advantages to both bespoke
and off-the-shelf solutions. If your Big
Data goal is simply to be able to search
and extract data, then there are off-the-
shelf solutions that can comfortably meet
those needs, from a variety of vendors.
The added benefit of these solutions
is that they are significantly more cost
effective to get up and running than
bespoke solutions.
Bespoke solutions have the advantage of
being highly customisable; for example,
reports that are to be generated, the
amount of data that needs to be input,
and whether there needs to be a period
of collecting new data in order to test the
strength of the algorithm.
locational data is often important, and
often difficult to derive real understanding
from with an off-the-shelf solution.
Of even more interest to a lot of
organisations is that bespoke solutions
provide genuine competitive advantage.
You can be fairly certain that if you’re
using an off-the-shelf solution, then so
too is your competitors. But the insights
delivered through a bespoke solution
are likely to be to the benefit of your
organisation alone.
13. 12
QA
(CONT.)
How do you embark on a proof
of concept for one of these
things when it can be especially
difficult to convince large
organisations to make these kinds
of expenditures?
How should the data transfer be
interpreted? Is it something that
can be technology-driven, or is
human involvement important at
this stage of the analysis cycle?
How do I deal with concerns
within my organisation
that staff fear of their
jobs as a consequence of
Big Data analytics?
Businesses are vaguely aware of the
need to invest in ‘big data’ already, so for
data scientists or IT executives to sell up
into management or the board, there is
already a base awareness there.
The ability for predictive analytics to
allow an organisation to get on the front
foot with its competitive strategy is the
There is absolutely the need to have
human eyeballs looking at various points
in the analytics cycle. Automation and
machine learning is very effective in
collecting data and creating meaningful
insights out of it. However, applying
those insights to real-world scenarios
requires a human understanding of the
data as well.
It’s true that the insights generated by
Big Data algorithms might highlight the
redundancy or inefficiency within some
roles within the organisation, however,
we see Big Data as a net creator of
jobs, and those staff would have the
opportunity to take on new roles within
the organisation.
For example, we created an algorithm
for one organisation, and over a period
of time the team managing it grew to
include a head data scientist, and three
people working underneath him. Because
salient point that needs to be made. Being
able to refine margins, staffing numbers,
inventory held, and so on based on real-
time information is a compelling business
case as organisations look to clamp
down on unnecessary wastage in their
spending, and find new and effective
ways to reach customers.
It’s also important to have skilled data
scientists looking at the insights, in order
to understand whether abnormalities
are signs of new trends, or otherwise
whether the algorithm needs updating to
meet changing dynamics in the market.
the insights generated from big data
tend to be so valuable, organisations
generally like to properly resource
their analytics teams.
Getting the staff on board with a Big
Data analytics strategy does require a
change management strategy to come
from the executive team, in order to
get everyone on board, but over the
longer term the career opportunities that
these technology solutions enable will
be compelling for staff at all levels of
the organisation.
14. 13
QA
(CONT.)
Is it true that some machine
learning algorithms ignore
clusters of data that it doesn’t
understand or are new?
How do you determine
weights for inputs into the
analytics algorithms?
What is the difference between
Machine Learning and
Deep Learning?
This can happen, and this is why it is
important to have skilled data scientists
looking at abnormalities in clusters of
data to determine whether the algorithm
needs to be adjusted.
The short answer is that you shouldn’t
be manually assessing weightings. The
mathematical algorithm should be
robust enough within the parameters
of defining the relationship between the
thing that you want to forecast, and the
explanatory variables.
Deep Learning is “the analysis and
learning of massive amounts of
unsupervised data, making it a valuable
tool for Big Data analytics where raw
data is largely unlabelled and un-
categorized.” Effectively, it is much the
same thing as Machine Learning, with
some variations in how the algorithm
is applied.
A good algorithm will also throw up red
flags when the clusters of abnormal data
are significant enough.
With bespoke Big Data analytics projects,
the mathematics team will approach
the problem with the understanding of
the outcomes, or the narrative that the
analytics will create for the business. The
weightings will be built into the equation
with that goal in mind.
Businesses should not be relying on a
single form of algorithm for their insights.
With two or three different algorithms
working on a single big data problem, the
insights being derived from the analytics
team will be more rounded and, therefore,
beneficial. Organisations should therefore
be investing in both Machine Learning
and Deep Learning analytics solutions.
“The success of data analysis relies heavily on the relevance
and quality of the source data and inputs, domain expertise
is critical. Best practice analytics brings together business,
science and technology to create a powerful differentiator.”
–Michael McKeon, Business Development Director,
Unico Enterprise Services
15. 14
Appendix; Industry Attendees
OF THE SYDNEY EXECUTIVE BREAKFASTS
ANDY HEDGES
ANTHONY LAU
CAMERON GARRETT
CHRIS EARNSHAW-NEES
DAVID BIGHEL
JASON JUMA-ROSS
KAREN LAWSON
KYLE BUNTING
MICHAEL MCKEON
MICHELLE ZIVKOVIC
PAUL RUSH
PETER BINKS
RICHARD MCMANUS
TIM SCHNEIDEMAN
Leadership Innovation Global Executive
ALAUD
Macquarie Group
Artis Group
ASX
Facebook
Slingshot Accelerator
TPG Telecom
UNICO
Directioneering
Odgers Berndtson
Crystal Bay Capital
Richard McManus
Hewlett Packard Enterprise
16. 15
Appendix; Industry Attendees
OF THE MELBOURNE EXECUTIVE BREAKFASTS
ADAM KYRIACOU
ANTHONY DEEBLE
ANTHONY MAGUIRE
BEN CHESTERMAN
DAVID ROBINSON
GENEVIEVE ELLIOTT
HAMISH COLEMAN
LARRY HOWARD
LOUISE HIGGINS
MARK GILBERT
MATTHEW PERRY
MICHAEL ALF
MICHAEL MCKEON
MICHELLE FITZGERALD
PAUL RUSH
PETER GAIDZKAR
RAPHAEL OWEN
RICHARD NEESON
ROBERT TURNER
Odgers Interim
Val Morgan
Telstra
Telstra
Assetic
Vicinity Centres
Vicinity Centres
Bluescope Steel
NOVA Entertainment
Telstra
DuluxGroup
KPMG Enterprise
UNICO
City of Melbourne
Odgers Berndtson
Reapit
Val Morgan
Original IT
Assetic