Big data
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Big data refers to extremely large data sets that cannot be processed by traditional databases due to their volume, variety, and velocity. It has been defined by the 3 V's - volume of data, variety of data types, and velocity of data processing. While the term big data is new, the concept of collecting and storing large amounts of information for analysis has existed for decades. Organizations can use big data to gain insights and make more informed decisions, but it also presents challenges related to data types, processing, storage, security, and separating meaningful from meaningless information. When applied effectively, big data can provide benefits like cost savings, time reductions, and competitive advantages, but its limitations must be considered.
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Big data.
In this presentation, you can learn about big data in general, the three Vs of big data, how to store big data, and how to choose a big data store.
Big data
what is big data?,uses of big data,3V's,processing of big data,tools of big data,benefits of big data
Big data
Big data refers to large, complex datasets that traditional data processing applications are inadequate to handle. It is characterized by high volume, velocity, variety, and veracity. Big data comes from both structured and unstructured sources and requires new techniques and tools to capture, manage, and analyze it. Analyzing big data can provide insights, competitive advantages, and better decision making across many industries such as healthcare, finance, manufacturing, and retail. The market for big data and analytics is growing rapidly and is projected to be over $50 billion by 2017.
Overview of Big data(ppt)
It is a brief overview of Big Data. It contains History, Applications and Characteristics on BIg Data.
It also includes some concepts on Hadoop.
It also gives the statistics of big data and impact of it all over the world.
Big data
Big data refers to large datasets that cannot be processed using traditional computing techniques due to their size and complexity. It involves data from various sources like social media, online transactions, and sensors. Big data has three characteristics - volume, velocity, and variety. There are various technologies like Hadoop that can handle big data by distributing processing across clusters of computers. Hadoop provides a reliable and cost-effective way to process large datasets for both operational and analytical uses.
Big data
Big data refers to extremely large data sets that are too large to be processed with traditional data processing applications. It is characterized by 3 V's - velocity, or the fast rate at which data is generated; variety, as data comes in both structured and unstructured formats; and volume, with the amount of data reaching the petabyte scale. Examples of big data sources include social media sites, e-commerce sites, weather stations, telecom companies, and stock markets. Apache Hadoop is an open source software framework commonly used to develop applications that can process big data across distributed systems.
Business intelligence architectures.pdf
A business intelligence architecture has three main components: data sources, data warehouses/marts, and business intelligence methodologies. Data is gathered from various sources, stored, and then extracted to feed analytical models and support decision making. These include multidimensional analysis, data mining, time series analysis, and optimization models. The pyramid shows passive exploration and active data mining tools to generate and confirm hypotheses without prior assumptions. Optimization models determine the best solution from alternatives to support decisions at the top.
Big Data, Big Deal: For Future Big Data Scientists
Big Data, Big Deal is a document that discusses big data. It begins by defining big data as high-volume, high-velocity, and high-variety information that requires new processing methods. It then discusses the key drivers for big data, including technical drivers like increased data storage and social media, as well as business drivers like customer analytics and public opinion analysis. The document concludes by discussing challenges for big data like data quality, privacy, and the need for skilled data scientists with technical expertise, curiosity, storytelling abilities, and cleverness.
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Big data.
In this presentation, you can learn about big data in general, the three Vs of big data, how to store big data, and how to choose a big data store.
Big data
what is big data?,uses of big data,3V's,processing of big data,tools of big data,benefits of big data
Big data
Big data refers to large, complex datasets that traditional data processing applications are inadequate to handle. It is characterized by high volume, velocity, variety, and veracity. Big data comes from both structured and unstructured sources and requires new techniques and tools to capture, manage, and analyze it. Analyzing big data can provide insights, competitive advantages, and better decision making across many industries such as healthcare, finance, manufacturing, and retail. The market for big data and analytics is growing rapidly and is projected to be over $50 billion by 2017.
Overview of Big data(ppt)
It is a brief overview of Big Data. It contains History, Applications and Characteristics on BIg Data.
It also includes some concepts on Hadoop.
It also gives the statistics of big data and impact of it all over the world.
Big data
Big data refers to large datasets that cannot be processed using traditional computing techniques due to their size and complexity. It involves data from various sources like social media, online transactions, and sensors. Big data has three characteristics - volume, velocity, and variety. There are various technologies like Hadoop that can handle big data by distributing processing across clusters of computers. Hadoop provides a reliable and cost-effective way to process large datasets for both operational and analytical uses.
Big data
Big data refers to extremely large data sets that are too large to be processed with traditional data processing applications. It is characterized by 3 V's - velocity, or the fast rate at which data is generated; variety, as data comes in both structured and unstructured formats; and volume, with the amount of data reaching the petabyte scale. Examples of big data sources include social media sites, e-commerce sites, weather stations, telecom companies, and stock markets. Apache Hadoop is an open source software framework commonly used to develop applications that can process big data across distributed systems.
Business intelligence architectures.pdf
A business intelligence architecture has three main components: data sources, data warehouses/marts, and business intelligence methodologies. Data is gathered from various sources, stored, and then extracted to feed analytical models and support decision making. These include multidimensional analysis, data mining, time series analysis, and optimization models. The pyramid shows passive exploration and active data mining tools to generate and confirm hypotheses without prior assumptions. Optimization models determine the best solution from alternatives to support decisions at the top.
Big Data, Big Deal: For Future Big Data Scientists
Big Data, Big Deal is a document that discusses big data. It begins by defining big data as high-volume, high-velocity, and high-variety information that requires new processing methods. It then discusses the key drivers for big data, including technical drivers like increased data storage and social media, as well as business drivers like customer analytics and public opinion analysis. The document concludes by discussing challenges for big data like data quality, privacy, and the need for skilled data scientists with technical expertise, curiosity, storytelling abilities, and cleverness.
Big data Presentation
Content:
Introduction
What is Big Data?
Big Data facts
Three Characteristics of Big Data
Storing Big Data
THE STRUCTURE OF BIG DATA
WHY BIG DATA
HOW IS BIG DATA DIFFERENT?
BIG DATA SOURCES
BIG DATA ANALYTICS
TYPES OF TOOLS USED IN BIG-DATA
Application Of Big Data analytics
HOW BIG DATA IMPACTS ON IT
RISKS OF BIG DATA
BENEFITS OF BIG DATA
Future of big data
Big data-ppt-
The document discusses big data, defining it as large volumes of data from various sources that cannot be analyzed using traditional methods. It outlines three key characteristics of big data - volume, velocity and variety. Volume refers to the huge amount of data, velocity to the speed at which data is generated and processed, and variety to the different data types. The document also discusses how big data is stored, processed using tools like Hadoop, and analyzed to provide insights. It highlights some applications and risks of big data as well as its impact on IT.
Big data
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Data Mining With Big Data
Abstract:
Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.
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Big Data
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The Growing Importance of Data Cleaning
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Read More: https://expressanalytics.com/blog/growing-importance-of-data-cleaning/
Bigdata Analytics using Hadoop
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Big data
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Thilga
This document provides an introduction to big data including:
- An overview of what big data is and the challenges it presents in terms of capture, curation, storage, search, sharing, transfer, analysis and visualization of large, complex datasets.
- The 3Vs of big data - volume, velocity and variety - and examples of the scale of data being generated every day from sources like social media, sensors and scientific instruments.
- The technologies and architectural approaches needed to harness big data including Hadoop, Spark, data warehouses, graph databases, and cloud computing platforms.
5 v of big data
The document discusses the 5 V's of big data: Volume, Velocity, Variety, Veracity, and Value. Volume refers to the vast amounts of data generated every second from sources like social media and sensors. Velocity is the speed at which new data is created, such as credit card transactions. Variety means the different types of data including structured, unstructured, and semi-structured. Veracity addresses the uncertainty in data quality. Value ensures the large amounts of data can be analyzed and applied to business cases.
Understanding big data
This document discusses big data, defining it as the exponential growth and availability of both structured and unstructured data. It describes big data using the three V's: volume, velocity, and variety. It also discusses two additional dimensions of big data: variability and complexity. The document explains that analyzing big data can lead to cost reductions, time reductions, new product development, and better business decisions. It provides examples of how companies like eBay, Amazon, Walmart, and Facebook handle and analyze large amounts of data.
What is big data ? | Big Data Applications
Big data is similar to ‘small data’ but bigger in size. It is a term that describes the large volume of data both structured and unstructured. Big data generates value from the storage and processing of very large quantities of digital information that cannot be analyzed with traditional computing techniques
Big data
This document proposes a theme on big data analytics research. It notes that the world's data storage capacity doubles every 40 months and discusses how big data can provide value across many areas like health, policymaking, education and more. The proposal recommends that Hong Kong develop a state-of-the-art big data platform to make a difference in areas like smart cities and support aging populations. It outlines objectives like large-scale machine learning from big data and discusses how Hong Kong is well-positioned for this research with experts across universities and potential collaborators in industry. The expected outcomes include new methodologies, applications impacting society and industry, and educational programs to cultivate big data leaders.
Big data characteristics, value chain and challenges
Abstract—Recently the world is experiencing an deluge of
data from different domains such as telecom, healthcare
and supply chain systems. This growth of data has led to
an explosion, coining the term Big Data. In addition to the
growth in volume, Big Data also exhibits other unique
characteristics, such as velocity and variety. This large
volume, rapidly increasing and verities of data is becoming
the key basis of completion, underpinning new waves of
productivity growth, innovation and customer surplus. Big
Data is about to offer tremendous insight to the
organizations, but the traditional data analysis
architecture is not capable to handle Big Data. Therefore,
it calls for a sophisticated value chain and proper analytics
to unearth the opportunity it holds. This research
identifies the characteristics of Big Data and presents a
sophisticated Big Data value chain as finding of this
research. It also describes the typical challenges of Big
Data, which are required to be solved. As a part of this
research twenty experts from different industries and
academies of Finland were interviewed.
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Identify and analyze the greatest insights from big data
Identify and analyze the greatest insights from big data by leveraging technologies that can effectively handle the growing volume, velocity, and variety of data.
Bigdata
This document discusses big data analytics and how it is transforming business intelligence. It defines big data analytics as combining large datasets ("big data") with advanced analytic techniques. It describes big data using the three V's: volume, variety, and velocity. Volume refers to the large size of datasets. Variety means data comes from many different sources and formats. Velocity means data streams in continuously and in real-time. The document provides examples of how companies are using big data analytics to discover new insights and track changing customer behavior.
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Big data Presentation
Content:
Introduction
What is Big Data?
Big Data facts
Three Characteristics of Big Data
Storing Big Data
THE STRUCTURE OF BIG DATA
WHY BIG DATA
HOW IS BIG DATA DIFFERENT?
BIG DATA SOURCES
BIG DATA ANALYTICS
TYPES OF TOOLS USED IN BIG-DATA
Application Of Big Data analytics
HOW BIG DATA IMPACTS ON IT
RISKS OF BIG DATA
BENEFITS OF BIG DATA
Future of big data
Big data-ppt-
The document discusses big data, defining it as large volumes of data from various sources that cannot be analyzed using traditional methods. It outlines three key characteristics of big data - volume, velocity and variety. Volume refers to the huge amount of data, velocity to the speed at which data is generated and processed, and variety to the different data types. The document also discusses how big data is stored, processed using tools like Hadoop, and analyzed to provide insights. It highlights some applications and risks of big data as well as its impact on IT.
Big data
The document discusses big data issues and challenges. It defines big data as large volumes of structured and unstructured data that is growing exponentially due to increased data generation. Some key challenges discussed include storage and processing limitations of exabytes of data, privacy and security risks, and the need for new skills and training to manage and analyze big data. Examples are given of large data projects in various domains like science, healthcare, and commerce that are driving big data growth.
Data Mining With Big Data
Abstract:
Big Data concern large-volume, complex, growing data sets with multiple, autonomous sources. With the fast development of networking, data storage, and the data collection capacity, Big Data are now rapidly expanding in all science and engineering domains, including physical, biological and biomedical sciences. This paper presents a HACE theorem that characterizes the features of the Big Data revolution, and proposes a Big Data processing model, from the data mining perspective. This data-driven model involves demand-driven aggregation of information sources, mining and analysis, user interest modeling, and security and privacy considerations. We analyze the challenging issues in the data-driven model and also in the Big Data revolution.
Moneytree - Data Aggregation with SWF
An outline of how Moneytree uses Amazon SWF to coordinate our backend aggregation workflow. Focuses on how to run a large scale distributed system with a few developers while still sleeping at night.
Introduction to BigData
Hadoop is a Java framework for managing large datasets distributed across clusters of commodity hardware. It allows for the distributed processing of large datasets across clusters of computers using simple programming models. Hadoop features distributed storage and processing of data and is designed to scale up from single servers to thousands of machines, each offering local computation and storage. It provides reliable, scalable, and distributed computing and storage for big data applications.
Big Data
This document discusses big data and its characteristics. It notes that big data emerged in the early 2000s and was initially adopted by online companies like Google and Facebook. It then defines big data as large datasets that cannot be managed by traditional databases due to their huge size. Examples are provided of the immense amount of data generated every minute on social media platforms and in other industries. The document concludes by explaining that big data is classified according to volume, velocity and variety, and that it requires new storage and processing solutions due to its large, unstructured and qualitative nature.
The Growing Importance of Data Cleaning
The process of data cleaning involves the process of transformation of data from a raw format to a format that is compatible with your and use case.
Read More: https://expressanalytics.com/blog/growing-importance-of-data-cleaning/
Bigdata Analytics using Hadoop
Big data refers to large amounts of data that are beyond the processing capabilities of typical database software. It is characterized by its volume, velocity, and variety. Hadoop is an open-source software framework that can distribute data and processing across clusters of computers to solve big data problems. Hadoop uses HDFS for storage and MapReduce as a programming model to process large datasets in parallel across clusters.
Introduction to Big Data & Big Data 1.0 System
This document provides an introduction to big data and big data 1.0 systems. It discusses how the volume of data being created is growing exponentially and outlines the 5 V's of big data: volume, velocity, variety, veracity, and value. It describes how Hadoop is an open-source software framework that was inspired by Google's MapReduce and is designed to process large amounts of unstructured data across clusters of computers. Hadoop became very popular for organizations working with big data during the early 2010s, representing the first generation or "1.0" of big data systems.
Big data peresintaion
The document provides an outline on big data and Hadoop. It discusses what big data is, how it is generated from various sources, its key characteristics of volume, velocity and variety. It describes the benefits of big data including cost reduction, time reduction, and supporting business decisions. It then explains what Hadoop is and its main components including HDFS, MapReduce, NameNode and DataNode. Hadoop allows distributed processing of large data sets across commodity servers to store and process large amounts of data.
Big data
This document defines big data and discusses its key characteristics and applications. It begins by defining big data as large volumes of structured, semi-structured, and unstructured data that is difficult to process using traditional methods. It then outlines the 5 Vs of big data: volume, velocity, variety, veracity, and variability. The document also discusses Hadoop as an open-source framework for distributed storage and processing of big data, and lists several applications of big data across various industries. Finally, it discusses both the risks and benefits of working with big data.
Thilga
This document provides an introduction to big data including:
- An overview of what big data is and the challenges it presents in terms of capture, curation, storage, search, sharing, transfer, analysis and visualization of large, complex datasets.
- The 3Vs of big data - volume, velocity and variety - and examples of the scale of data being generated every day from sources like social media, sensors and scientific instruments.
- The technologies and architectural approaches needed to harness big data including Hadoop, Spark, data warehouses, graph databases, and cloud computing platforms.
5 v of big data
The document discusses the 5 V's of big data: Volume, Velocity, Variety, Veracity, and Value. Volume refers to the vast amounts of data generated every second from sources like social media and sensors. Velocity is the speed at which new data is created, such as credit card transactions. Variety means the different types of data including structured, unstructured, and semi-structured. Veracity addresses the uncertainty in data quality. Value ensures the large amounts of data can be analyzed and applied to business cases.
Understanding big data
This document discusses big data, defining it as the exponential growth and availability of both structured and unstructured data. It describes big data using the three V's: volume, velocity, and variety. It also discusses two additional dimensions of big data: variability and complexity. The document explains that analyzing big data can lead to cost reductions, time reductions, new product development, and better business decisions. It provides examples of how companies like eBay, Amazon, Walmart, and Facebook handle and analyze large amounts of data.
What is big data ? | Big Data Applications
Big data is similar to ‘small data’ but bigger in size. It is a term that describes the large volume of data both structured and unstructured. Big data generates value from the storage and processing of very large quantities of digital information that cannot be analyzed with traditional computing techniques
Big data
This document proposes a theme on big data analytics research. It notes that the world's data storage capacity doubles every 40 months and discusses how big data can provide value across many areas like health, policymaking, education and more. The proposal recommends that Hong Kong develop a state-of-the-art big data platform to make a difference in areas like smart cities and support aging populations. It outlines objectives like large-scale machine learning from big data and discusses how Hong Kong is well-positioned for this research with experts across universities and potential collaborators in industry. The expected outcomes include new methodologies, applications impacting society and industry, and educational programs to cultivate big data leaders.
Big data characteristics, value chain and challenges
Abstract—Recently the world is experiencing an deluge of
data from different domains such as telecom, healthcare
and supply chain systems. This growth of data has led to
an explosion, coining the term Big Data. In addition to the
growth in volume, Big Data also exhibits other unique
characteristics, such as velocity and variety. This large
volume, rapidly increasing and verities of data is becoming
the key basis of completion, underpinning new waves of
productivity growth, innovation and customer surplus. Big
Data is about to offer tremendous insight to the
organizations, but the traditional data analysis
architecture is not capable to handle Big Data. Therefore,
it calls for a sophisticated value chain and proper analytics
to unearth the opportunity it holds. This research
identifies the characteristics of Big Data and presents a
sophisticated Big Data value chain as finding of this
research. It also describes the typical challenges of Big
Data, which are required to be solved. As a part of this
research twenty experts from different industries and
academies of Finland were interviewed.
BigData and Beyond
John Avery discusses opportunities in big data and graph analytics for financial services. Big data involves storing, processing, and analyzing large amounts of structured and unstructured data across distributed systems. It can help financial firms reduce the effort to analyze more data and enable new types of analyses. Graph analytics uses relationships in data to power applications in areas like risk management, trading, and regulatory compliance. Firms should explore applying these technologies to problems like counterparty risk analysis, market surveillance, and systemic risk oversight.
Big data, Big decision
This document discusses how enterprises can leverage big data. It notes that no single solution will meet all needs and not all solutions will be a good fit. It recommends enterprises use big data if improvements and returns on investment are measurable, and outlines steps for getting started such as starting small and organically, reusing existing resources, and initially focusing on internal information. The overall message is that successfully using big data depends on enterprise goals and capabilities.
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Bda assignment can also be used for BDA notes and concept understanding.
Big data refers to large and complex datasets that are difficult to analyze using traditional methods. It is characterized by high volume, velocity, and variety of data from numerous sources. Big data analytics uses tools like Hadoop and Spark to extract meaningful insights from large, unstructured datasets in real-time. This allows companies to gain valuable business insights, reduce costs, enhance customer experience, innovate products, and make faster decisions.
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Whitepaper: Thriving in the Big Data era Manage Data before Data Manages you
Paper Overview -
Every day, we create 2.5 quintillion bytes of data — so much that 90% of the data in the world today has been created in the last two years alone.
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This white paper will explain what Big Data is and provide practical examples, concluding with a message how to put data your data to work.
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Oceans of big data: Take the plunge or wade in slowly?
In a recent study, Deloitte identified some of the hurdles that keep organizations from making greater use of business analytics. These include poor technology infrastructure, the quality and amount of data being collected and leadership that may not support or even understand the use of analytics.
This presentation defines big data, explains why you should care about big data, and suggests when big data should be used. The potential of big data is immense, but it can also become an expensive distraction. Once you remove constraints on the size, type, source and complexity of useful data, you can ask the ‘crunchy’ questions that are critical to the success of your business.
Analysis on big data concepts and applications
The term, Big Data ‘ h a s been referred as a large amount of data that cannot be handled by traditional database
systems. It consists of large volumes of data which is been generated at a very fast rate, these cannot be handled and processed by
traditional data management tools, so it requires a new set of tools or frameworks to handle these types of data. Big data
works under V’s namely Volume, Velocity, and Variety. Volume refers to the size of the data whereas Velocity refers to the
speed that the data is being generated. Variety refers to different formats of data that is generated. Mostly in today’s world
thee average volumes of unstructured data like audio, video, image, sensor data etc. One can get these types of data through
social media, enterprise data, and Transactional data. Through Big data analytics, one can able to examine large data sets
containing a variety of data types. Primary goals of big data analytics are to help the organizations to take important decisions
by appointing data scientists and other analytics professionals to analyses large volumes of data. Challenges one can face
during large volume of data, especially machine-generated data, is exploding, how fast that data is growing every year, with
new sources of data that are emerging. Through the article, the authors intend to decipher the notions in an intelligible
manner embodying in text several use-cases and illustrations
Big data lecture notes
Big data is high-volume, high-velocity, and high-variety data that is difficult to process using traditional data management tools. It is characterized by 3Vs: volume of data is growing exponentially, velocity as data streams in real-time, and variety as data comes from many different sources and formats. The document discusses big data analytics techniques to gain insights from large and complex datasets and provides examples of big data sources and applications.
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Big data
- 2. Big Data... Can be defined asextremely large data assetsthat traditional databasescannot deal with. Big Data can be described in terms of the “3 V’s”(Volume, Variety and Velocity) - Volume refers to the total amount of all types of data - Variety refers to the different types of data - Velocity refers to the speed of data processing
- 3. History 70 yearsago,peoplewere attemptingto increase the volume of data,in an event commonly known asthe “information explosion” While the term “big data “ isrelatively new,the act of gathering and storing large amount of information for eventual analysisisagesold.The concept gained momentum in the early 2000s when industry analyst Doug Laney articulated the now-mainstream definition of big data asthe three Vs:
- 4. Current Issues/Challenges By uncoveringhidden patterns,unknown correlations,market trends,consumer satisfaction and etc,a Businesscan use BigDatato make more informed marketing decisions. ● Challenges ○ Number of types of data ○ Data processing ● Issues ○ Storage ○ Security
- 5. Lim itationsBe n e fits ● Cost savings ● Time reduction ● New Product development ● Understanding market conditions ● Controllingonline reputation All leading to… COMPETITIVEADVANTAGE ● Discerning unrelated and/or meaningless information ● Unreliable security due to outsourcing ● Transferability ● Inconsistencies in Data Collection
- 7. Risk vs Rew ard Organizationsmust decide whether Big Dataisworth using
- 8. Applying Big Data to Healthcare BigData hascontributed alot towardsimprovingthe quality of education, by keepingdata on patients,doctors are able to perform life savingproceduresasquickly aspossible ● Improving healthcare quality and coordination, so that outcomes are consistent with professional knowledge ● Provide support for reformed payments ● Reducing healthcare cost However… ● Security of patients in healthcare is non negotiable ● Doctors can be overly reliant on Big Data
- 10. Questions? An d m a yb e a n sw e rs :)
- 11. Thank You!