Public and private organizations in all sectors are using their data to give them insight about their companies, as well as a competitive advantage. This session explores some of the key areas that organizations need to be considering in developing a Big Data management strategy: 1) Why are we collecting Big Data? 2) How can we mine our Big Data; 3) What measures are needed to govern Big Data? ; 4) How do we manage sensitive information and ensure compliance with relevant legislation?; and 5) How do we manage the balance the value, risks, and costs of Big Data?
The REAL Impact of Big Data on PrivacyClaudiu Popa
The awesome promise of Big Data is tempered by the need to protect personal information. Data scientists must expertly navigate the legislative waters and acquire the skills to protect privacy and security. This talk provides enterprise leaders with answers and suggests questions to ask when the time comes to consider the vast opportunities offered by big data.
Big data security challenges and recommendations!cisoplatform
What will you learn:
- Key Insights on Existing Big Data Architecture
- Unique Security Risks and Vulnerabilities of Big Data Technologies
- Top 5 Solutions to mitigate these security challenges
The REAL Impact of Big Data on PrivacyClaudiu Popa
The awesome promise of Big Data is tempered by the need to protect personal information. Data scientists must expertly navigate the legislative waters and acquire the skills to protect privacy and security. This talk provides enterprise leaders with answers and suggests questions to ask when the time comes to consider the vast opportunities offered by big data.
Big data security challenges and recommendations!cisoplatform
What will you learn:
- Key Insights on Existing Big Data Architecture
- Unique Security Risks and Vulnerabilities of Big Data Technologies
- Top 5 Solutions to mitigate these security challenges
Due to the evolution of personalized, data-driven digital marketing, companies now have infinite amounts of personally identifiable information (PII) about their customers; and this stockpile of information continues to grow—at an exponential rate. In fact, according to the Pew Research Center, the volume of business data worldwide—across all industries—doubles every 1.2 years.
But how should you use this treasure trove of data? And at what point does the information known about your consumers—and the ways you use this information—risk consumer privacy? Is there such thing as too much data?
Attend this webinar to learn:
• What your responsibilities are in today’s ‘big data universe’
• How to use your data and meet compliance laws
• Tips for integrating data across channels and platforms
• How to implement the principles of ‘Privacy by Design’
Big Data is the "next" Bg Technology and Business and Hadoop is one of the important framework of Big Data. Hadoop is currently used by Yahoo, EBay and 100s of organisations.
As the Big Data use cases will grow, security of Big Data technologies, solutions and applications will become extremely important. In this presentation, I have described top 5 key security challenges related to developing Big Data solutions and applications.
data mining privacy concerns ppt presentationiWriteEssays
Data Mining and privacy Presentation
This is a sample presentation on data mining. The presetation looks at the critical Issues In Data Mining: Privacy, National Security And Personal Liberty Implications Of Data Mining
Threat Ready Data: Protect Data from the Inside and the OutsideDLT Solutions
Is your current state really threat ready?
Amit Walia, Senior Vice President, General Manager of Data Integration and Security at Informatica, shares how to protect data from the inside and the outside from the 2015 Informatica Government Summit.
How to Organize Patient Information to Protect Patients' DataHellmuth Broda
This presentation describes what organizational steps can be taken to separate personally identifiable information from the necessary administrative information. When such procedures are applied patient data can be secured and privacy rules followed.
What is Big Data?
Big Data Laws
Why Big Data?
Industries using Big Data
Current process/SW in SCM
Challenges in SCM industry
How Big data can solve the problems?
Migration to Big data for an SCM industry
Big Data Applications | Big Data Analytics Use-Cases | Big Data Tutorial for ...Edureka!
( ** Hadoop Training: https://www.edureka.co/hadoop ** )
This Edureka tutorial on "Big Data Applications" will explain various how Big Data analytics can be used in various domains. Following are the topics included in this tutorial:
1. Why do we need Big Data Analytics?
2. Big Data Applications in Health Care.
3. Big Data in Real World Clinical Analytics.
4. Big Data Analytics in Education Sector.
5. IBM Case Study in Education Section.
6. Big data applications and use cases in E-Commerce.
7. How Government uses Big Data analytics?
8. How Big data is helpful in E-Government Portal?
9. Big Data in IOT.
10. Smart city concept.
11. Big Data analytics in Media and Entertainment
12. Netflix example in Big data
13. Future Scope of Big data.
Check our complete Hadoop playlist here: https://goo.gl/hzUO0m
Big Data & Analytics for Government - Case StudiesJohn Palfreyman
This presentation explains the future challenges that Governments face, and illustrates how Big Data & Analytics technologies can help address these challenges. Four case studies - based on recent customer projects - are used to show the value that the innovative application of these technologies can bring.
Enabling Big Data with Data-Level Security:The Cloud Analytics Reference Arch...Booz Allen Hamilton
; Booz Allen’s data lake approach enables agencies to embed security controls within each individual piece of data to reinforce existing layers of security and dramatically reduce risk. Government agencies – including military and intelligence agencies – are using this proven security approach to secure data and fully capitalize on the promise of big data and the cloud.
IQPC Enterprise IT Security Exchange, March 10, 2013
This presentation looks at the risks and rewards and security and privacy implications of Big Data Analytics.
Big data contains valuable information— some of it sensitive customer data—that can be a honeypot for internal and external attackers. Given the risk involved, organizations must proactively enhance defenses and prevent data breaches. The four steps outlined in this deck, help organizations to develop a holistic approach to data security and privacy.
Big Data and Security - Where are we now? (2015)Peter Wood
Peter Wood started looking at Big Data as a solution for Advanced Threat Protection in 2013. This presentation examines how Big Data is being used for security in 2015, how this market is developing and how realistic vendor offerings are.
A discussion of the role of taxonomies and other controlled vocabularies in the managing of large amounts of data for researchers, focusing in particular on searchability and data visualization. Presented by Marjorie M.K. Hlava, president of Access Innovations, Inc., for the SLA Military Libraries Division 2013 Workshop, December 12, 2013.
Currently, the Yahoo EC Taiwan team provides business performance matrix to users by acquiring data from the Web production and Back office ERP systems. The reporting system is built using traditional BI technologies such as RDBMS, ETL tools, OLAP tools, home-made reporting tools, store procedures, web pages,?. With increasing usage growth of the user browsing data in the business decision on daily basis, The ability to provided data analytics on these Big Data is getting more and more important and needed. The traditional RDBMs have reaching its limit in process big data while connecting to OLAP tool. We started with the feasibility of connecting MicroStrategy with Hive 0.9 and created a prototype system to test in two scenarios – ad-hoc query to Hive and performance test of the predefined MicroStrategy Intelligent Cube for ad-hoc analytics. We did the performance test on Ad-hoc query via HiveQL and query from MicroStrategy cube, and will share the result in the session. Based on our test results, we will be able to provide the following applications to different types of users. A) Ad-hoc query running against Hadoop can allow well trained data analyst or power users to have deeper analysis on data within Hadoop. B) OLAP reports running against MicroStrategy Intelligent Cube can provide quicker response time on ad-hoc analytics with predefined data in Cube.
Due to the evolution of personalized, data-driven digital marketing, companies now have infinite amounts of personally identifiable information (PII) about their customers; and this stockpile of information continues to grow—at an exponential rate. In fact, according to the Pew Research Center, the volume of business data worldwide—across all industries—doubles every 1.2 years.
But how should you use this treasure trove of data? And at what point does the information known about your consumers—and the ways you use this information—risk consumer privacy? Is there such thing as too much data?
Attend this webinar to learn:
• What your responsibilities are in today’s ‘big data universe’
• How to use your data and meet compliance laws
• Tips for integrating data across channels and platforms
• How to implement the principles of ‘Privacy by Design’
Big Data is the "next" Bg Technology and Business and Hadoop is one of the important framework of Big Data. Hadoop is currently used by Yahoo, EBay and 100s of organisations.
As the Big Data use cases will grow, security of Big Data technologies, solutions and applications will become extremely important. In this presentation, I have described top 5 key security challenges related to developing Big Data solutions and applications.
data mining privacy concerns ppt presentationiWriteEssays
Data Mining and privacy Presentation
This is a sample presentation on data mining. The presetation looks at the critical Issues In Data Mining: Privacy, National Security And Personal Liberty Implications Of Data Mining
Threat Ready Data: Protect Data from the Inside and the OutsideDLT Solutions
Is your current state really threat ready?
Amit Walia, Senior Vice President, General Manager of Data Integration and Security at Informatica, shares how to protect data from the inside and the outside from the 2015 Informatica Government Summit.
How to Organize Patient Information to Protect Patients' DataHellmuth Broda
This presentation describes what organizational steps can be taken to separate personally identifiable information from the necessary administrative information. When such procedures are applied patient data can be secured and privacy rules followed.
What is Big Data?
Big Data Laws
Why Big Data?
Industries using Big Data
Current process/SW in SCM
Challenges in SCM industry
How Big data can solve the problems?
Migration to Big data for an SCM industry
Big Data Applications | Big Data Analytics Use-Cases | Big Data Tutorial for ...Edureka!
( ** Hadoop Training: https://www.edureka.co/hadoop ** )
This Edureka tutorial on "Big Data Applications" will explain various how Big Data analytics can be used in various domains. Following are the topics included in this tutorial:
1. Why do we need Big Data Analytics?
2. Big Data Applications in Health Care.
3. Big Data in Real World Clinical Analytics.
4. Big Data Analytics in Education Sector.
5. IBM Case Study in Education Section.
6. Big data applications and use cases in E-Commerce.
7. How Government uses Big Data analytics?
8. How Big data is helpful in E-Government Portal?
9. Big Data in IOT.
10. Smart city concept.
11. Big Data analytics in Media and Entertainment
12. Netflix example in Big data
13. Future Scope of Big data.
Check our complete Hadoop playlist here: https://goo.gl/hzUO0m
Big Data & Analytics for Government - Case StudiesJohn Palfreyman
This presentation explains the future challenges that Governments face, and illustrates how Big Data & Analytics technologies can help address these challenges. Four case studies - based on recent customer projects - are used to show the value that the innovative application of these technologies can bring.
Enabling Big Data with Data-Level Security:The Cloud Analytics Reference Arch...Booz Allen Hamilton
; Booz Allen’s data lake approach enables agencies to embed security controls within each individual piece of data to reinforce existing layers of security and dramatically reduce risk. Government agencies – including military and intelligence agencies – are using this proven security approach to secure data and fully capitalize on the promise of big data and the cloud.
IQPC Enterprise IT Security Exchange, March 10, 2013
This presentation looks at the risks and rewards and security and privacy implications of Big Data Analytics.
Big data contains valuable information— some of it sensitive customer data—that can be a honeypot for internal and external attackers. Given the risk involved, organizations must proactively enhance defenses and prevent data breaches. The four steps outlined in this deck, help organizations to develop a holistic approach to data security and privacy.
Big Data and Security - Where are we now? (2015)Peter Wood
Peter Wood started looking at Big Data as a solution for Advanced Threat Protection in 2013. This presentation examines how Big Data is being used for security in 2015, how this market is developing and how realistic vendor offerings are.
A discussion of the role of taxonomies and other controlled vocabularies in the managing of large amounts of data for researchers, focusing in particular on searchability and data visualization. Presented by Marjorie M.K. Hlava, president of Access Innovations, Inc., for the SLA Military Libraries Division 2013 Workshop, December 12, 2013.
Currently, the Yahoo EC Taiwan team provides business performance matrix to users by acquiring data from the Web production and Back office ERP systems. The reporting system is built using traditional BI technologies such as RDBMS, ETL tools, OLAP tools, home-made reporting tools, store procedures, web pages,?. With increasing usage growth of the user browsing data in the business decision on daily basis, The ability to provided data analytics on these Big Data is getting more and more important and needed. The traditional RDBMs have reaching its limit in process big data while connecting to OLAP tool. We started with the feasibility of connecting MicroStrategy with Hive 0.9 and created a prototype system to test in two scenarios – ad-hoc query to Hive and performance test of the predefined MicroStrategy Intelligent Cube for ad-hoc analytics. We did the performance test on Ad-hoc query via HiveQL and query from MicroStrategy cube, and will share the result in the session. Based on our test results, we will be able to provide the following applications to different types of users. A) Ad-hoc query running against Hadoop can allow well trained data analyst or power users to have deeper analysis on data within Hadoop. B) OLAP reports running against MicroStrategy Intelligent Cube can provide quicker response time on ad-hoc analytics with predefined data in Cube.
Bridging the gap between privacy and big data Ulf Mattsson - Protegrity Sep 10Ulf Mattsson
Big Data systems like Hadoop provide analysis of massive amounts of data to open up “Big Answers”, identifying trends and new business opportunities. The massive scalability and economical storage also provides the opportunity to monetize collected data by selling it to a third party.
However, the biggest issue with Big Data remains security. Like any other system, the data must be protected according to regulatory mandates, such as PCI, HIPAA and Privacy laws; from both external and internal threats – including privileged users.
So how can we bridge the gap between access to vast amounts of data, and security of more and more types of data, in this rapidly evolving new environment?
In this webinar, Ulf Mattsson explores the issues and provide solutions to bring together data insight and security in Big Data. With deep knowledge in advanced data security technologies, Ulf explains the best practices in order to safely unlock the power of Big Data.
Privacy, Permissions and the Evolution of Big DataVision Critical
In the a world with stronger privacy regulations, how can you get customer consent to access the data that drives your marketing and your business? Big Data has allowed business to tap the power of customer data, but increased public attention to privacy—and pressure for government regulation—means that organizations can't assume they’ll have unfettered access to consumer data.
This SXSW Interactive workshop, presented by Andrew Grenville, chief research officer at Vision Critical, and Tyler Douglas, chief marketing officer at Vision Critical, helped participants prepare to balance privacy concerns with the need for data.
This presentation:
- Shares fresh data on consumer attitudes towards privacy
- Explores successful models for obtaining consumer consent for data access
- Identifies ways to provide value to customers who share data
For more information about the session: http://bit.ly/sxswconsent
To learn more about the study behind this presentation, please see: http://www.visioncritical.com/communities-consent-white-paper
Building Confidence in Big Data - IBM Smarter Business 2013 IBM Sverige
Success with big data comes down to confidence. Without confidence in the underlying data, decision makers may not trust and act on analytic insight. You need confidence in your data – that it’s correct, trusted, and protected through automated integration, visual context, and agile governance. You need confidence in your ability to accelerate time to value, with fast deployments of big data appliances. Learn how clients have succeeded with big data by building confidence in their data, ability to deploy, and skills. Presenter: David Corrigan, Big Data specialist, IBM. Mer från dagen på http://bit.ly/sb13se
Trivadis TechEvent 2016 Big Data Privacy and Security Fundamentals by Florian...Trivadis
In Big Data we focus on the 4 V's: Volume, Velocity, Varity and Veracity. But another important topic is often not in the focus: Privacy and Security. Yet as important and if not considered from the beginning it might put your Big Data project at risk. Learn about most important Privacy and Security fundamentals in Big Data, you should take into account in your next Big Data project.
Panel SXSW : The Internet of Me : Impact of biometrics and machine learning o...Pierre-Majorique Léger
Abstract : The explosion of consumer based sensors and machine learning is at the core of advancements in biometrics and neurophysiology, and never has so much data been available. Dedicated technologies and applications have started to disrupt the way we understand ourselves, our health and the way we interact with each other and Things. The Canadian Space Agency, and CAE Inc. will be discussing how these developments are impacting their technology roadmaps and vision, and how tech centers like the Tech3Lab qualify emotion and cognitive responses to allow for a better understanding of users health and performance. Mar 13, 2017 | 10:00am – 10:45am
Here are some tips on hiring and retaining top Big Data talent. Features : how to source candidates, how to interview them, interview techniques and mistakes.
Listen to video of presentation and download slides here : http://elephantscale.com/2017/03/building-successful-big-data-team-demand-webinar/
What exactly is big data? What exactly is big data? .pptxTusharSengar6
big data is data that contains greater variety, arriving in increasing volumes and with more velocity. This is also known as the three “Vs.” Put simply, big data is larger, more complex data sets, especially from new data sources.
Big Data Analytics: Challenges And Applications For Text, Audio, Video, And S...IJSCAI Journal
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...gerogepatton
All types of machine automated systems are generating large amount of data in different forms likestatistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper weare discussing issues, challenges, and application of these types of Big Data with the consideration of bigdata dimensions. Here we are discussing social media data analytics, content based analytics, text dataanalytics, audio, and video data analytics their issues and expected application areas. It will motivateresearchers to address these issues of storage, management, and retrieval of data known as Big Data. Aswell as the usages of Big Data analytics in India is also highlighted.
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...ijscai
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted.
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...gerogepatton
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted.
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...gerogepatton
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted.
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...gerogepatton
All types of machine automated systems are generating large amount of data in different forms likestatistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper weare discussing issues, challenges, and application of these types of Big Data with the consideration of bigdata dimensions. Here we are discussing social media data analytics, content based analytics, text dataanalytics, audio, and video data analytics their issues and expected application areas. It will motivateresearchers to address these issues of storage, management, and retrieval of data known as Big Data. Aswell as the usages of Big Data analytics in India is also highlighted.
Similar to Your organization and Big Data: Managing access, privacy, and security (20)
the innovation process is becoming more complex as it responds to transformative changes in the way research is done. However, we have few empirical research tools that help us understand the magnitude of these changes and how they have altered the innovation system. In response to this, we develop citation measures that weight the relationships between patented inventions based on their semantic similarity. Measuring the semantic similarity between over 5 million U.S. patents and over 52 million citations, we define and demonstrate four distinct metrics that measure: knowledge translation, knowledge integration, knowledge diffusion, and knowledge scope. Applying these measures provides novel empirical demonstrations of how the research environment has changed in recent decades. We show that researchers have drawn from increasingly distant knowledge sources, and that knowledge diffusion has occurred at an ever-accelerating pace. These citation distance measures show substantial promise in furthering our understanding of the research process and improving our assessment of scientific impact
Senior Project and Engineering Leader Jim Smith.pdfJim Smith
I am a Project and Engineering Leader with extensive experience as a Business Operations Leader, Technical Project Manager, Engineering Manager and Operations Experience for Domestic and International companies such as Electrolux, Carrier, and Deutz. I have developed new products using Stage Gate development/MS Project/JIRA, for the pro-duction of Medical Equipment, Large Commercial Refrigeration Systems, Appliances, HVAC, and Diesel engines.
My experience includes:
Managed customized engineered refrigeration system projects with high voltage power panels from quote to ship, coordinating actions between electrical engineering, mechanical design and application engineering, purchasing, production, test, quality assurance and field installation. Managed projects $25k to $1M per project; 4-8 per month. (Hussmann refrigeration)
Successfully developed the $15-20M yearly corporate capital strategy for manufacturing, with the Executive Team and key stakeholders. Created project scope and specifications, business case, ROI, managed project plans with key personnel for nine consumer product manufacturing and distribution sites; to support the company’s strategic sales plan.
Over 15 years of experience managing and developing cost improvement projects with key Stakeholders, site Manufacturing Engineers, Mechanical Engineers, Maintenance, and facility support personnel to optimize pro-duction operations, safety, EHS, and new product development. (BioLab, Deutz, Caire)
Experience working as a Technical Manager developing new products with chemical engineers and packaging engineers to enhance and reduce the cost of retail products. I have led the activities of multiple engineering groups with diverse backgrounds.
Great experience managing the product development of products which utilize complex electrical controls, high voltage power panels, product testing, and commissioning.
Created project scope, business case, ROI for multiple capital projects to support electrotechnical assembly and CPG goods. Identified project cost, risk, success criteria, and performed equipment qualifications. (Carrier, Electrolux, Biolab, Price, Hussmann)
Created detailed projects plans using MS Project, Gant charts in excel, and updated new product development in Jira for stakeholders and project team members including critical path.
Great knowledge of ISO9001, NFPA, OSHA regulations.
User level knowledge of MRP/SAP, MS Project, Powerpoint, Visio, Mastercontrol, JIRA, Power BI and Tableau.
I appreciate your consideration, and look forward to discussing this role with you, and how I can lead your company’s growth and profitability. I can be contacted via LinkedIn via phone or E Mail.
Jim Smith
678-993-7195
jimsmith30024@gmail.com
The Team Member and Guest Experience - Lead and Take Care of your restaurant team. They are the people closest to and delivering Hospitality to your paying Guests!
Make the call, and we can assist you.
408-784-7371
Foodservice Consulting + Design
Artificial intelligence (AI) offers new opportunities to radically reinvent the way we do business. This study explores how CEOs and top decision makers around the world are responding to the transformative potential of AI.
The case study discusses the potential of drone delivery and the challenges that need to be addressed before it becomes widespread.
Key takeaways:
Drone delivery is in its early stages: Amazon's trial in the UK demonstrates the potential for faster deliveries, but it's still limited by regulations and technology.
Regulations are a major hurdle: Safety concerns around drone collisions with airplanes and people have led to restrictions on flight height and location.
Other challenges exist: Who will use drone delivery the most? Is it cost-effective compared to traditional delivery trucks?
Discussion questions:
Managerial challenges: Integrating drones requires planning for new infrastructure, training staff, and navigating regulations. There are also marketing and recruitment considerations specific to this technology.
External forces vary by country: Regulations, consumer acceptance, and infrastructure all differ between countries.
Demographics matter: Younger generations might be more receptive to drone delivery, while older populations might have concerns.
Stakeholders for Amazon: Customers, regulators, aviation authorities, and competitors are all stakeholders. Regulators likely hold the greatest influence as they determine the feasibility of drone delivery.
Oprah Winfrey: A Leader in Media, Philanthropy, and Empowerment | CIO Women M...CIOWomenMagazine
This person is none other than Oprah Winfrey, a highly influential figure whose impact extends beyond television. This article will delve into the remarkable life and lasting legacy of Oprah. Her story serves as a reminder of the importance of perseverance, compassion, and firm determination.
3. 2016 Ontario Connections.
Big data is high-volume, high-velocity and
high-variety information assets that demand
cost-effective, innovative forms of information
processing for enhanced insight and decision
making. http://www.gartner.com/it-glossary/big-data/
Big data is a term that describes large volumes
of high velocity, complex, and variable data
that require advanced techniques and
technologies to enable the capture, storage,
distribution, management, and analysis of the
information.
http://www.techamerica.org/Docs/fileManager.cfm?f=techamerica-
bigdatareport-final.pdf
Defining
Big Data
4. 2016 Ontario Connections.
“Insights from Big Data can enable you to
make better decisions. They can help you
facilitate growth and organizational
transformation, reduce costs and manage
volatility and risk. This enables you to
capitalize on new sources of revenue and
generate more value for your organization.”
Financial Accounting Advisory Services (n.d.). Big data strategy to support the CFO and
governance agenda
The
value of
Big Data
7. 2016 Ontario Connections.
Big Data tends to be measured in terms of
terabytes and petabytes (1024 terabytes).
Definitions of “big” are relative, and fluctuate,
especially as storage capacities increase over
time.
Data is generated by every computerized
system in the organization, including human
resources solutions, supply-chain management
software, and social media tools for marketing.
Volume
8. 2016 Ontario Connections.
Google indexes 20 billion pages per day.
Twitter has more than 500 million users and 400
million tweets per day.
Facebook generates 2.7 million
‘Likes’, 500 TB processed, and 300 million photos
that are uploaded per day.
http://bit.ly/1SVxPwp; http://bit.ly/1SVy76j; http://bloom.bg/1SVyldK
Examples
of volume
10. 2016 Ontario Connections.
Organizations generate various types of structured,
semi-structured, and unstructured data.
Structured data is the tabular type found in
spreadsheets or relational databases (about 10% of
most data).
Text, images, audio, and video are examples of
unstructured data, which sometimes lacks the
structural organization required by machines for
analysis
Variety
12. 2016 Ontario Connections.
Velocity refers to the rate at which data is
generated and the speed at which it should be
analyzed and acted upon.
The proliferation of digital devices such as
smartphones has led to an unprecedented rate
of data creation and is driving a growing need
for real-time analytics and evidence-based
planning
Velocity
14. 2016 Ontario Connections.
Some data is inherently unreliable; for
example, customer comments in social media,
as they entail judgment.
We need to deal with imprecise and uncertain
data. Is the data that is being stored, and
mined meaningful to the problem being
analyzed?
Veracity
15. 2016 Ontario Connections.
Big Data is often characterized by relatively
“low value density”. That is, the data received
in the original form usually has a low value
relative to its volume. However, a high value
can be obtained by analyzing large volumes of
such data.
Value
16. 2016 Ontario Connections.
Value is any application of big data
that:
• Drives revenue increases (e.g. customer
loyalty analytics)
• Identifies new revenue opportunities,
improves quality and customer satisfaction
(e.g., Predictive Maintenance),
• Saves costs (e.g., fraud analytics)
• Drives better outcomes (e.g., patient care).
Value
19. 2016 Ontario Connections.
Blogs, tweets, social networking sites (such as
LinkedIn and Facebook), blogs, news feeds,
discussion boards, and video sites all fall under
Big Data.
Social
media
20. 2016 Ontario Connections.
Machine-generated data constitutes a wide variety
of devices, from RFIDs to sensors, such as optical,
acoustic, seismic, thermal, chemical, scientific, and
medical devices, and even the weather.
Machine-
generated
data
21. 2016 Ontario Connections.
From the GPS systems in our cars, in planes, and ships, to
GPS apps on smartphones, we use GPS to guide our
movements.
GPS is used to track our movements, such as emergency
beacons, and retailers who use in-store WiFi networks to
access shoppers’ smartphones and track their shopping
habits.
Location Based Services (LBS) allow us to deliver services
based on the location of moving objects such as cars or
people with mobile phones.
GPS
and
spatial
data
23. 2016 Ontario Connections.
It is generally thought that the true value of Big Data is
seen only when it is used to drive decision making.
You need efficient processes to turn high volumes of
fast-moving and varied data into meaningful insights.
As information managers, you might not be doing the
analysis, but you have a crucial role to play in
managing this data to enable this analysis.
Big Data
analytics:
How do
we mine
our data?
24. 2016 Ontario Connections.
Text analytics extract information from textual
data.
• Social network feeds, emails, blogs, online forums, survey
responses, corporate documents, news, and call centre
logs are examples of textual data held by organizations.
Text analytics enable organizations to convert
large volumes of human generated text into
meaningful summaries, which support
evidence-based decision-making.
Text
analytics
25. 2016 Ontario Connections.
Audio analytics analyze and extract information
from unstructured audio data. Customer call
centres and healthcare are the primary
application areas of audio analytics.
• Call centres use audio analytics for efficient analysis
of recorded calls to improve customer experience,
evaluate agent performance, and so forth.
• In healthcare, audio analytics support diagnosis and
treatment of certain medical conditions that affect the
patient’s communication patterns
(e.g.,schizophrenia), or analyze an infant’s cries to
learn about the infant’s health and emotional status.
Audio
analytics
26. 2016 Ontario Connections.
Video analytics involves a variety of techniques to
monitor, analyze, and extract meaningful information
from video streams.
The increasing prevalence of closed-circuit television
(CCTV) cameras and of video-sharing websites are
the two leading contributors to the growth of
computerized video analysis. A key challenge,
however, is the sheer size of video data.
Video
analytics
27. 2016 Ontario Connections.
Social media analytics refer to the analysis of
structured and unstructured data from social
media channels.
• Social networks (e.g., Facebookand LinkedIn)
• Blogs (e.g., Blogger and WordPress)
• Microblogs (e.g.,Twitter and Tumblr)
• Social news (e.g., Digg and Reddit)
• Socia bookmarking (e.g., Delicious and StumbleUpon)
• Media sharing (e.g., Instagram and YouTube)
• Wikis (e.g., Wikipedia and Wikihow)
• Question-and-answer sites (e.g., Yahoo! Answers and
Ask.com)
• Review sites (e.g., Yelp, TripAdvisor)
Social
media
analytics
28. 2016 Ontario Connections.
Predictive analytics comprise a variety of
techniques that predict future outcomes based
on historical and current data, e.g., predicting
customers’ travel plans based on what they
buy, when they buy, and even what they say on
social media.
Predictive
analytics
30. 2016 Ontario Connections.
Canadian federal institutions reported 256 data breaches
in 2014-2015, up from 228 the year before. The main
culprit was identified as the use portable storage
devices:
• More than two-thirds of the agencies had not formally
assessed the risks surrounding the use of all types of
portable storage devices;
• More than 90 per cent did not track all devices
throughout their life cycle;
• One-quarter did not enforce the use of encrypted
storage devices.
http://bit.ly/27Say7c
Security
concerns
31. 2016 Ontario Connections.
• More data translates = higher risk of exposure in the event of a
breach.
• More experimental usage = the organization's governance and
security protocol is less likely to be in place
• New types of data are uncovering new privacy implications, with
few privacy laws or guidelines to protect that information (e.g.,
cell phone beacons that broadcast physical location, & health
devices such as medical, fitness and lifestyle trackers).
• Data linkage and combined sensitive data. The act of combining
multiple data sources can create unanticipated sensitive data
exposure.
Considerations
for Big Data
32. 2016 Ontario Connections.
“The protection of information and
information systems from unauthorized
access, use, disclosure, disruption,
modification, or destruction in order to
provide confidentiality, integrity, and
availability.” National Institutes of Standards and Technology
Information
security:
Definition
33. 2016 Ontario Connections.
“The claim of individuals, groups
or institutions to determine for
themselves when, how and to
what extent, information about
them is communicated to others.”
International Association of Privacy Professionals
Data
privacy:
Definition
34. 2016 Ontario Connections.
Under the federal Personal Information Protection and
Electronic Documents Act (PIPEDA), “personal
information” is “information about an identifiable
individual, but does not include the name, title or
business address or telephone number of an
employee of an organization.”
Regulatory
framework
for big
data
35. 2016 Ontario Connections.
The protection of personal information in
Canada rests on three fundamental goals:.
• Transparency – providing people with a basic understanding of
how their personal information will be used in order to gain
informed consent
• Limiting use plus consent – the use of that information only for
the declared purpose for which it was initially collected, or
purposes consistent with that use; and,
• Minimization – limiting the personal information collected to what
is directly relevant and necessary to accomplish the declared
purpose and the discarding of the data once the original purpose
has been served.
PIPEDA
and Big
Data
36. 2016 Ontario Connections.
Organizations that attempt to implement Big Data
initiatives without a strong governance regime in place,
risk placing themselves in ethical dilemmas without set
processes or guidelines to follow.
A strong ethical code, along with process, training,
people, and metrics, is imperative to govern what
organizations can do within a Big Data program.
Big Data
governance
37. 2016 Ontario Connections.
Data used for Big Data analytics can be gathered
combined from different sources, and create new data
sets.
Organizations must make sure that all security and
privacy requirements that are applied to their original
data sets are tracked and maintained across Big Data
processes throughout the information life cycle, from
data collection to disclosure or retention/destruction.
Respecting
the original
intent of the
information
gathered
38. 2016 Ontario Connections.
Data that has been processed, enhanced, or changed
by Big Data should be anonymized to protect the
privacy of the original data source, such as customers
or vendors.
Data that is not properly anonymized prior to external
release (or in some cases, internal as well) may result
in the compromise of data privacy, as the data is
combined with previously collected, complex data
sets.
Re-
Identification
39. 2016 Ontario Connections.
Matching data sets from third parties may provide
valuable insights that could not be obtained with
your data alone.
You need to consider and evaluate the adequacy of
the security and privacy data protections in place at
the third-party organizations.
Third-
party
use
40. 2016 Ontario Connections.
Big data’s potential for predictive analysis raises
particular concerns for data security and privacy.
• Think of the famous case of Target, which sent
coupons to a teenage girl, based upon her
shopping preferences, which suggested she
was pregnant, as well as her due date (Target
was accurate). The girl’s family found out
about her pregnancy through these coupons.
• Did the girl know that her shopping information
would be used for this purpose?
• Was she informed of Target’s privacy policy?
The risks of
predictive
analytics
41. 2016 Ontario Connections.
There are growing concerns that Big Data is
straining the privacy principles of identifying
purposes and limited use.
Consumers are called upon to agree to privacy
policies and consent forms that no one has the
time to read. The burden is increasingly placed
on the consumers, as these policies take the
form of disclaimers for the orgnizations.
Increasing
burden on
the
consumer
42. 2016 Ontario Connections.
“Just because commercial
organizations can collect
personal information and run it
through the revealing algorithms
of predictive analytics, doesn’t
mean that they should.”
Jennifer Stoddard
Can we
vs.
should
we?
43. 2016 Ontario Connections.
A useful tool is the Privacy Maturity Model
designed by the American Institute of Certified
Public Accountants (AICPA) or the Canadian
Institute of Chartered Accountants (CICA).
These sections are particularly relevant:
• 1.2.3: Personal information identification and classification
• 1.2.4: Risk assessment
• 1.2.6: Infrastructure and systems management
• 3.2.2: Consent for new purposes and uses
• 4.2.4: Information developed about individuals
• 8.2.1: Information security program.
http://bit.ly/1R3VcQZ
Privacy
assessment
46. 2016 Ontario Connections.
Strong data governance policies
and procedures are important:
• Who owns the data?
• Who is responsible for protecting the
data?
• How is data collected?
• What data is collected?
• How is the data retained?
Handling
&
retaining
data
47. 2016 Ontario Connections.
What security & privacy regulations apply to your
data?
What are the compliance provisions of your
agreements with any third parties or service providers.
What are their privacy and security policies?
Developing a solid compliance framework with a risk-
based map for implementation and maintenance.
Compliance
48. 2016 Ontario Connections.
Develop case scenarios where you would use Big
Data.
Identify what data will be used and how.
Identify possible risks
In this way, you are prepared for when you actually
use the Big Data, rather than be in a position to react
if something goes wrong.
Data
use
cases
49. 2016 Ontario Connections.
Tell your customers what personal data you
collect and how you use it.
Provide consistent consent mechanisms
across all products
Ensure that customers have the means to
withdraw their consent at the individual device
level.
Manage
consent
50. 2016 Ontario Connections.
Have rigorous controls over who has access to
the data.
Have periodic review of who has access rights,
and ensure that rights are removed
immediately, as and when required.
Access
management
51. 2016 Ontario Connections.
Remove all Personally Identifiable
Information (PII) from a data set and turn it into non-
identifying data.
Monitor anonymization requirements and analyze
the risks of re-identification.
Anonymization
52. 2016 Ontario Connections.
Maintain your responsibility to your customers
when you share data with third parties.
Include specific Big Data provisions within
contractual agreements.
Monitor third parties for compliance with data-
sharing agreements.
Data
sharing
56. 2016 Ontario Connections.
Database of 191 million U.S. voters exposed
on Internet
• An independent computer security researcher uncovered a database of
information on 191 million voters that is exposed on the open Internet.
The database includes names, addresses, birth dates, party affiliations,
phone numbers and emails of voters in all 50 U.S. states.
• A representative with the U.S. Federal Elections Commission, which
regulates campaign financing, said the agency does not have
jurisdiction over protecting voter records.
• Regulations on protecting voter data vary from state to state, with many
states imposing no restrictions. California, for example, requires that
voter data be used for political purposes only and not be available to
persons outside of the United States.
Government
breach
57. 2016 Ontario Connections.
Anthem
• Health insurer Anthem’s database was hacked
into. The personal information of 78.8 million
people was potentially stolen.
• The data breach extended into multiple brands
Anthem, Inc. uses to market its healthcare plans,
including, Anthem Blue Cross, Anthem Blue Cross
and Blue Shield, Blue Cross and Blue Shield of
Georgia, Empire Blue Cross and Blue Shield,
Amerigroup, Caremore, and UniCare.
Corporate
breach