Forecast to contribute £216 billion to the UK economy via business creation, efficiency and innovation, and generate 360,000 new jobs by 2020, big data is a key area for recruiters.
In this QuickView:
- Big data in numbers
- Top 10 industries hiring big data professionals
- Top 10 qualifications sought by hirers
- Top 10 database and BI skills sought by hirers
- Getting started in big data: popular big data techniques and vendors
The document discusses big data, analytics, and their applications. It defines big data as large, complex datasets that are difficult to manage with traditional databases. Big data is characterized by its volume, velocity, and variety. Examples are given of how retailers, telecom companies, and e-retailers use big data analytics to gain insights. The document also outlines approaches to analytic development and discusses how various organizations use big data analytics in practice.
This document discusses big data, providing definitions and outlining its key characteristics of volume, velocity, and variety. It describes processes involved like integrating disparate data stores and employing Hadoop MapReduce. Sources of big data are identified as mobile devices, sensors, social media, etc. Tools used include distributed servers, storage, and databases. Statistics on data generated by companies like Facebook and Twitter are provided. Applications of big data include improving science, healthcare, finance, and security. Advantages include access to vast information, while disadvantages include costs and privacy issues.
This document contains confidential information about Target Soft Systems and should not be shared outside of proposal evaluators. It discusses big data, which refers to extremely large data sets that are difficult to analyze using traditional tools. Big data is defined by its volume, velocity, and variety. The document lists some applications of big data analytics in fields like healthcare, finance, and security. It also discusses technologies commonly used for big data analytics, including NoSQL databases and Hadoop.
The document discusses how big data benefits consumers in 5 key ways: 1) It allows companies to improve customer service based on feedback collected from reviews and social media. 2) Product improvements are made based on customer feedback collected online. 3) Big data helps connect consumers with relevant deals and advertisements. 4) Security measures are constantly improving to prevent hacking based on data collected. 5) Big data helps prevent and solve crimes when used by government and law enforcement.
Big data refers to the massive amounts of digital data being created every day from various sources such as social media, sensors, photos, videos, and online activities. This data is characterized by its volume, velocity, variety, and veracity. New technologies allow businesses and organizations to analyze these large, diverse, and complex data sets to gain insights and add value in many ways such as improving customer targeting, optimizing processes, enhancing health research, bolstering security efforts, and upgrading city infrastructure. While big data is transforming many industries, its full potential is just beginning to be realized.
Tools and techniques adopted for big data analyticsJOSEPH FRANCIS
This document discusses tools and techniques for big data analytics. It begins by defining big data and explaining why big data analysis is important for businesses. It then outlines the characteristics and history of big data, as well as the challenges and phases of big data analysis. The document proceeds to describe several tools and techniques used for big data analytics, including machine learning, natural language processing, and visualization. It provides examples of how these tools and techniques have been applied through case studies of Indian elections, AirBnB, and Shoppers Stop.
Vikas Samant is a big data and data science engineer who works with Entrench Electronics and Pentaho. He provides an overview of big data, defining it as large volumes of structured, semi-structured, and unstructured data that businesses must process daily. He describes the key characteristics of big data using the 3Vs - volume, variety, and velocity, and sometimes a fourth V of veracity. The document then discusses data structures, data science, the data science process, and provides examples of big data use cases like optimizing funnel conversion, behavioral analytics, customer segmentation, and fraud detection. It concludes with an overview of big data technologies, vendors, what Hadoop is, and why Hadoop is widely adopted.
This presentation, by big data guru Bernard Marr, outlines in simple terms what Big Data is and how it is used today. It covers the 5 V's of Big Data as well as a number of high value use cases.
The document discusses big data, analytics, and their applications. It defines big data as large, complex datasets that are difficult to manage with traditional databases. Big data is characterized by its volume, velocity, and variety. Examples are given of how retailers, telecom companies, and e-retailers use big data analytics to gain insights. The document also outlines approaches to analytic development and discusses how various organizations use big data analytics in practice.
This document discusses big data, providing definitions and outlining its key characteristics of volume, velocity, and variety. It describes processes involved like integrating disparate data stores and employing Hadoop MapReduce. Sources of big data are identified as mobile devices, sensors, social media, etc. Tools used include distributed servers, storage, and databases. Statistics on data generated by companies like Facebook and Twitter are provided. Applications of big data include improving science, healthcare, finance, and security. Advantages include access to vast information, while disadvantages include costs and privacy issues.
This document contains confidential information about Target Soft Systems and should not be shared outside of proposal evaluators. It discusses big data, which refers to extremely large data sets that are difficult to analyze using traditional tools. Big data is defined by its volume, velocity, and variety. The document lists some applications of big data analytics in fields like healthcare, finance, and security. It also discusses technologies commonly used for big data analytics, including NoSQL databases and Hadoop.
The document discusses how big data benefits consumers in 5 key ways: 1) It allows companies to improve customer service based on feedback collected from reviews and social media. 2) Product improvements are made based on customer feedback collected online. 3) Big data helps connect consumers with relevant deals and advertisements. 4) Security measures are constantly improving to prevent hacking based on data collected. 5) Big data helps prevent and solve crimes when used by government and law enforcement.
Big data refers to the massive amounts of digital data being created every day from various sources such as social media, sensors, photos, videos, and online activities. This data is characterized by its volume, velocity, variety, and veracity. New technologies allow businesses and organizations to analyze these large, diverse, and complex data sets to gain insights and add value in many ways such as improving customer targeting, optimizing processes, enhancing health research, bolstering security efforts, and upgrading city infrastructure. While big data is transforming many industries, its full potential is just beginning to be realized.
Tools and techniques adopted for big data analyticsJOSEPH FRANCIS
This document discusses tools and techniques for big data analytics. It begins by defining big data and explaining why big data analysis is important for businesses. It then outlines the characteristics and history of big data, as well as the challenges and phases of big data analysis. The document proceeds to describe several tools and techniques used for big data analytics, including machine learning, natural language processing, and visualization. It provides examples of how these tools and techniques have been applied through case studies of Indian elections, AirBnB, and Shoppers Stop.
Vikas Samant is a big data and data science engineer who works with Entrench Electronics and Pentaho. He provides an overview of big data, defining it as large volumes of structured, semi-structured, and unstructured data that businesses must process daily. He describes the key characteristics of big data using the 3Vs - volume, variety, and velocity, and sometimes a fourth V of veracity. The document then discusses data structures, data science, the data science process, and provides examples of big data use cases like optimizing funnel conversion, behavioral analytics, customer segmentation, and fraud detection. It concludes with an overview of big data technologies, vendors, what Hadoop is, and why Hadoop is widely adopted.
This presentation, by big data guru Bernard Marr, outlines in simple terms what Big Data is and how it is used today. It covers the 5 V's of Big Data as well as a number of high value use cases.
Fundamentals of Big Data in 2 minutes!!Simplify360
In today’s world where information is increasing every second, BIG DATA takes up a major role in transforming any business.
Learn the fundamentals of big data in just 2 minutes!
This document discusses various applications of big data across different domains. It begins by defining big data and its key characteristics of volume, variety and velocity. It then discusses how big data is being used in social media for recommendation systems, marketing, electioneering and influence analysis. Applications in healthcare discussed include personalized medicine, clinical trials, electronic health records, and genomics. Uses of big data in smart cities are also summarized, such as for smart transport, traffic management, smart energy, and smart governance. Specific examples and case studies are provided to illustrate the benefits and savings achieved from leveraging big data across these various sectors.
The Pros and Cons of Big Data in an ePatient WorldPYA, P.C.
PYA Principal Dr. Kent Bottles, who is also PYA Analytics’ Chief Medical Officer, presented “The Pros and Cons of Big Data in an ePatient World” at the ePatient Connections 2013 conference.
Big Data & Analytics (Conceptual and Practical Introduction)Yaman Hajja, Ph.D.
A 3-day interactive workshop for startups involve in Big Data & Analytics in Asia. Introduction to Big Data & Analytics concepts, and case studies in R Programming, Excel, Web APIs, and many more.
DOI: 10.13140/RG.2.2.10638.36162
People are sometimes intimidated by big data because it seems overwhelming and they’re much more familiar with using statistics on survey data or analyzing opinions from focus group data. But here are nine examples from companies like Netflix, Ceasars Entertainment, Walmart, eBay, and UPS, that could have conducted survey or focus group research have instead used big data to accomplish big things.
Big data refers to massive amounts of structured and unstructured data that is difficult to process using traditional databases. It is characterized by volume, variety, velocity, and veracity. Major sources of big data include social media posts, videos uploaded, app downloads, searches, and tweets. Trends in big data include increased use of sensors, tools for non-data scientists, in-memory databases, NoSQL databases, Hadoop, cloud storage, machine learning, and self-service analytics. Big data has applications in banking, media, healthcare, energy, manufacturing, education, and transportation for tasks like fraud detection, personalized experiences, reducing costs, predictive maintenance, measuring teacher effectiveness, and traffic control.
Big data analytics involves analyzing large volumes of data from multiple sources that are dynamically linked. It provides opportunities for better business and healthcare intelligence through targeted efforts. However, it also poses risks such as potential data breaches and loss. Controls like access logging and monitoring, encryption, and automated scanning are important to manage these risks. Analytics approaches include descriptive, diagnostic, predictive, and prescriptive methods. Police departments are starting to use predictive analytics software to generate individual and area threat scores based on various data sources, which raises privacy concerns. Staffing specialist skills and ensuring data quality are important for organizations using big data analytics.
Big data is a term for datasets that are so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying, updating and information privacy.
Lets ideate and discuss more:
www.extentia.com/contact-us
A top-down look at current industry and technology trends for Big Data, Data Analytics and Machine Learning (cognitive technologies, AI etc.). New slides added for Ark Group presentation on 1st December 2016.
The document discusses the promise and challenges of big data for businesses. It provides examples of how two companies successfully used big data to improve performance. An airline used big data to radically improve the accuracy of flight arrival time predictions, saving millions per year. Sears used big data to decrease the time needed to generate personalized promotions from 8 weeks to 1 week, creating higher quality promotions. While big data holds great potential, challenges remain around developing data science skills, overcoming cultural barriers, and addressing privacy concerns. Overall, the document argues that data-driven decision making will allow companies that embrace big data to outperform their competitors.
This document provides an overview of big data in various industries. It begins by defining big data and explaining the three V's of big data - volume, variety, and velocity. It then discusses examples of big data in digital marketing, financial services, and healthcare. For digital marketing, it discusses database marketers as pioneers of big data and how big data is transforming digital marketing. For financial services, it discusses how big data is used for fraud detection and credit risk management. It also provides details on algorithmic trading and how it crunches complex interrelated big data. Overall, the document outlines how big data is being leveraged across industries to improve operations, increase revenues, and achieve competitive advantages.
Big data refers to large datasets that cannot be processed using traditional computing techniques due to their size and complexity. It comes from a variety of sources like social media, online transactions, digital images, videos, sensors, and more. The volume of data is doubling every two years. Big data has three key aspects: volume, referring to the large amount of data; variety, as data comes in many formats; and velocity, as data streams in at high speed. Technologies like Hadoop and MapReduce can capture, store, search, share, and analyze big data across distributed systems in a cost-effective way to provide insights.
This document discusses big data, defining it as large volumes of diverse data that are growing rapidly and requiring new techniques to capture, curate, manage, and analyze. It covers the key characteristics of big data including volume, velocity, and variety. The document also outlines common sources of big data, tools used to manage and analyze it, applications of big data analytics, risks and benefits, and the future growth of big data.
This document provides an overview of big data in a seminar presentation. It defines big data, discusses its key characteristics of volume, velocity and variety. It describes how big data is stored, selected and processed. Examples of big data sources and tools used are provided. The applications and risks of big data are summarized. Benefits to organizations from big data analytics are outlined, as well as its impact on IT and future growth prospects.
Big data refers to very large data sets that are analyzed computationally to reveal patterns, trends, and relationships. It is characterized by 3Vs - volume, velocity, and variety. Big data has many applications in recent scenarios including politics, weather, medicine, media, and manufacturing. It is used in politics to analyze voter data beyond basic demographics. In weather, sensor data from devices is used to create more detailed weather maps and forecasts. Medicine uses big data to identify patterns in symptoms that can help predict and prevent diseases like heart failure. Media analyzes data on user behaviors to tailor content instead of relying on traditional formats. Manufacturing leverages big data for increased transparency and insights into performance issues.
The document discusses different types of data. It defines data as information that has been converted into a format suitable for processing by computers, usually binary digital form. Data types represent the kind of data that can be processed in a computer program, such as numeric, alphanumeric, or decimal. The main types of data discussed are strings, characters, integers, and floating point numbers.
This document discusses the rise of big data and how the volume of data being created is growing exponentially, with 2.5 quintillion bytes created daily from various sources like sensors, social media, images, videos and purchases. It outlines how traditional databases and data analytics are struggling to handle this unstructured data, leading to the emergence of new solutions like Hadoop. It also explores how new roles like data scientists are emerging to help organizations extract value from all this big data through advanced analytics.
Big data refers to the massive amounts of digital data being created every day from various sources such as social media, sensors, digital images, online transactions, and more. This data grows exponentially in volume, velocity, and variety. New technologies allow organizations to analyze diverse unstructured data to gain valuable insights about customers, optimize processes, improve health outcomes, enhance security, and more. While big data opens many opportunities, businesses must consider its implications and leverage associated technologies and analytical techniques to extract value from big data.
Big Data and Transport Understanding and assessing optionsLudovic Privat
Massive amounts of digital data are being generated from a variety of sources including sensors, devices and online activities, with estimates that the total "digital universe" will grow to 44 zettabytes by 2020. This data holds potential value when combined from different sources to provide new insights but also risks around privacy if used without appropriate protections. Transport authorities will need to evaluate how new and existing data sources can help improve operations, planning and safety while ensuring regulations keep pace with changing data practices.
The new Data Economy & The results of shifting social, cultural, and personal...Intel IT Center
The document discusses the emerging data economy and its social impacts. It notes that personal data is becoming a new type of asset and currency. Cultural frameworks and norms are in flux as data and digital technologies transform industries like music, movies, banking, politics and more. New experiences, ecosystems and relationships are being created through data. The data economy is growing rapidly and will continue to change as more data is acquired across devices and analyzed on new platforms.
Fundamentals of Big Data in 2 minutes!!Simplify360
In today’s world where information is increasing every second, BIG DATA takes up a major role in transforming any business.
Learn the fundamentals of big data in just 2 minutes!
This document discusses various applications of big data across different domains. It begins by defining big data and its key characteristics of volume, variety and velocity. It then discusses how big data is being used in social media for recommendation systems, marketing, electioneering and influence analysis. Applications in healthcare discussed include personalized medicine, clinical trials, electronic health records, and genomics. Uses of big data in smart cities are also summarized, such as for smart transport, traffic management, smart energy, and smart governance. Specific examples and case studies are provided to illustrate the benefits and savings achieved from leveraging big data across these various sectors.
The Pros and Cons of Big Data in an ePatient WorldPYA, P.C.
PYA Principal Dr. Kent Bottles, who is also PYA Analytics’ Chief Medical Officer, presented “The Pros and Cons of Big Data in an ePatient World” at the ePatient Connections 2013 conference.
Big Data & Analytics (Conceptual and Practical Introduction)Yaman Hajja, Ph.D.
A 3-day interactive workshop for startups involve in Big Data & Analytics in Asia. Introduction to Big Data & Analytics concepts, and case studies in R Programming, Excel, Web APIs, and many more.
DOI: 10.13140/RG.2.2.10638.36162
People are sometimes intimidated by big data because it seems overwhelming and they’re much more familiar with using statistics on survey data or analyzing opinions from focus group data. But here are nine examples from companies like Netflix, Ceasars Entertainment, Walmart, eBay, and UPS, that could have conducted survey or focus group research have instead used big data to accomplish big things.
Big data refers to massive amounts of structured and unstructured data that is difficult to process using traditional databases. It is characterized by volume, variety, velocity, and veracity. Major sources of big data include social media posts, videos uploaded, app downloads, searches, and tweets. Trends in big data include increased use of sensors, tools for non-data scientists, in-memory databases, NoSQL databases, Hadoop, cloud storage, machine learning, and self-service analytics. Big data has applications in banking, media, healthcare, energy, manufacturing, education, and transportation for tasks like fraud detection, personalized experiences, reducing costs, predictive maintenance, measuring teacher effectiveness, and traffic control.
Big data analytics involves analyzing large volumes of data from multiple sources that are dynamically linked. It provides opportunities for better business and healthcare intelligence through targeted efforts. However, it also poses risks such as potential data breaches and loss. Controls like access logging and monitoring, encryption, and automated scanning are important to manage these risks. Analytics approaches include descriptive, diagnostic, predictive, and prescriptive methods. Police departments are starting to use predictive analytics software to generate individual and area threat scores based on various data sources, which raises privacy concerns. Staffing specialist skills and ensuring data quality are important for organizations using big data analytics.
Big data is a term for datasets that are so large or complex that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying, updating and information privacy.
Lets ideate and discuss more:
www.extentia.com/contact-us
A top-down look at current industry and technology trends for Big Data, Data Analytics and Machine Learning (cognitive technologies, AI etc.). New slides added for Ark Group presentation on 1st December 2016.
The document discusses the promise and challenges of big data for businesses. It provides examples of how two companies successfully used big data to improve performance. An airline used big data to radically improve the accuracy of flight arrival time predictions, saving millions per year. Sears used big data to decrease the time needed to generate personalized promotions from 8 weeks to 1 week, creating higher quality promotions. While big data holds great potential, challenges remain around developing data science skills, overcoming cultural barriers, and addressing privacy concerns. Overall, the document argues that data-driven decision making will allow companies that embrace big data to outperform their competitors.
This document provides an overview of big data in various industries. It begins by defining big data and explaining the three V's of big data - volume, variety, and velocity. It then discusses examples of big data in digital marketing, financial services, and healthcare. For digital marketing, it discusses database marketers as pioneers of big data and how big data is transforming digital marketing. For financial services, it discusses how big data is used for fraud detection and credit risk management. It also provides details on algorithmic trading and how it crunches complex interrelated big data. Overall, the document outlines how big data is being leveraged across industries to improve operations, increase revenues, and achieve competitive advantages.
Big data refers to large datasets that cannot be processed using traditional computing techniques due to their size and complexity. It comes from a variety of sources like social media, online transactions, digital images, videos, sensors, and more. The volume of data is doubling every two years. Big data has three key aspects: volume, referring to the large amount of data; variety, as data comes in many formats; and velocity, as data streams in at high speed. Technologies like Hadoop and MapReduce can capture, store, search, share, and analyze big data across distributed systems in a cost-effective way to provide insights.
This document discusses big data, defining it as large volumes of diverse data that are growing rapidly and requiring new techniques to capture, curate, manage, and analyze. It covers the key characteristics of big data including volume, velocity, and variety. The document also outlines common sources of big data, tools used to manage and analyze it, applications of big data analytics, risks and benefits, and the future growth of big data.
This document provides an overview of big data in a seminar presentation. It defines big data, discusses its key characteristics of volume, velocity and variety. It describes how big data is stored, selected and processed. Examples of big data sources and tools used are provided. The applications and risks of big data are summarized. Benefits to organizations from big data analytics are outlined, as well as its impact on IT and future growth prospects.
Big data refers to very large data sets that are analyzed computationally to reveal patterns, trends, and relationships. It is characterized by 3Vs - volume, velocity, and variety. Big data has many applications in recent scenarios including politics, weather, medicine, media, and manufacturing. It is used in politics to analyze voter data beyond basic demographics. In weather, sensor data from devices is used to create more detailed weather maps and forecasts. Medicine uses big data to identify patterns in symptoms that can help predict and prevent diseases like heart failure. Media analyzes data on user behaviors to tailor content instead of relying on traditional formats. Manufacturing leverages big data for increased transparency and insights into performance issues.
The document discusses different types of data. It defines data as information that has been converted into a format suitable for processing by computers, usually binary digital form. Data types represent the kind of data that can be processed in a computer program, such as numeric, alphanumeric, or decimal. The main types of data discussed are strings, characters, integers, and floating point numbers.
This document discusses the rise of big data and how the volume of data being created is growing exponentially, with 2.5 quintillion bytes created daily from various sources like sensors, social media, images, videos and purchases. It outlines how traditional databases and data analytics are struggling to handle this unstructured data, leading to the emergence of new solutions like Hadoop. It also explores how new roles like data scientists are emerging to help organizations extract value from all this big data through advanced analytics.
Big data refers to the massive amounts of digital data being created every day from various sources such as social media, sensors, digital images, online transactions, and more. This data grows exponentially in volume, velocity, and variety. New technologies allow organizations to analyze diverse unstructured data to gain valuable insights about customers, optimize processes, improve health outcomes, enhance security, and more. While big data opens many opportunities, businesses must consider its implications and leverage associated technologies and analytical techniques to extract value from big data.
Big Data and Transport Understanding and assessing optionsLudovic Privat
Massive amounts of digital data are being generated from a variety of sources including sensors, devices and online activities, with estimates that the total "digital universe" will grow to 44 zettabytes by 2020. This data holds potential value when combined from different sources to provide new insights but also risks around privacy if used without appropriate protections. Transport authorities will need to evaluate how new and existing data sources can help improve operations, planning and safety while ensuring regulations keep pace with changing data practices.
The new Data Economy & The results of shifting social, cultural, and personal...Intel IT Center
The document discusses the emerging data economy and its social impacts. It notes that personal data is becoming a new type of asset and currency. Cultural frameworks and norms are in flux as data and digital technologies transform industries like music, movies, banking, politics and more. New experiences, ecosystems and relationships are being created through data. The data economy is growing rapidly and will continue to change as more data is acquired across devices and analyzed on new platforms.
The document summarizes the key findings of a study conducted by the Federal Trade Commission (FTC) on the practices of nine major data brokers. The FTC found that data brokers collect vast amounts of personal information about consumers from various sources without consumers' knowledge. Data brokers then use this data to create consumer profiles and inferences, and develop products for marketing, risk mitigation, and people search that are sold to various clients. While these products provide some benefits, the FTC raised concerns about consumers' lack of awareness and control over their personal data held by brokers. The report makes legislative and best practice recommendations to improve transparency and consumer choice.
This document discusses data mining with big data. It defines big data and data mining. Big data is characterized by its volume, variety, and velocity. The amount of data in the world is growing exponentially with 2.5 quintillion bytes created daily. The proposed system would use distributed parallel computing with Hadoop to handle large volumes of varied data types. It would provide a platform to process data across dimensions and summarize results while addressing challenges such as data location, privacy, and hardware resources.
This document discusses big data mining. It defines big data as large volumes of structured and unstructured data that are difficult to process using traditional methods due to their size. It describes the characteristics of big data including volume, variety, velocity, variability, and complexity. It also discusses challenges of big data such as data location, volume, hardware resources, and privacy. Popular tools for big data mining include Hadoop, Apache S4, Storm, Apache Mahout, and MOA. Hadoop is an open source software framework that allows distributed processing of large datasets across clusters of computers. Common algorithms for big data mining operate at the model and knowledge levels to discover patterns and correlations across distributed data sources.
This document discusses the rise of big data and the data economy. It begins by comparing the growth of transportation infrastructure like highways and the internet to the growth of digital data. It then discusses the various types of data being created, including website data, social media data, mobile data, and machine data. It explains that while the scale of data seems vast, most individual data points are worthless alone. The value comes from combining different types of data to generate new insights. It concludes by looking briefly at the history of data analysis and hypothesis testing, and how our approaches may need to evolve to analyze the vast amounts of data now available.
The New SAP Simon Dale, Mastering SAP: Enabling digital transformationSAP Asia Pacific
Making Sense of the New SAP: Simon Dale, General Manager, Innovation Sales, SAP Asia Pacific & Japan, keynotes at the 2015 Mastering SAP Technologies event in Melbourne. Simon describes the roadmap for digital transformation in your business.
The Data Economy: 2016 Horizonwatch Trend BriefBill Chamberlin
The slides provide a quick overview of the Data Economy trend. The slides provide summary information, a list of trends to watch and links to additional resources
Big data refers to the massive amounts of unstructured data that are growing exponentially. Hadoop is an open-source framework that allows processing and storing large data sets across clusters of commodity hardware. It provides reliability and scalability through its distributed file system HDFS and MapReduce programming model. The Hadoop ecosystem includes components like Hive, Pig, HBase, Flume, Oozie, and Mahout that provide SQL-like queries, data flows, NoSQL capabilities, data ingestion, workflows, and machine learning. Microsoft integrates Hadoop with its BI and analytics tools to enable insights from diverse data sources.
There are as many views and definitions of Data Mining as there are people working in and on the topic. Confusion reigns and people ask; what is it; why do we need it; and isn’t it just Data Mining rebranded? In this slide deck and presentation we set the scene an highlight the differences and need for Data Mining in order to give a framework for case studies and future projects.
So - why do we need it?
The economic, industrial, commercial, social, political and sustainability problems we face cannot be successfully addressed using the management techniques and models largely inherited from the Industrial Revolution. The world no longer appears infinite in resources, slow paced, linear and stable. We now see the limitations; feel the impact of rapid change; and we can conceptualize the non-linear and unstable nature of it all! We are also starting to comprehend the scale and the need for machine assistance.
Modeling our situation !
Sophisticated computer models for weather systems are now complemented by ecological, economic, conflict and resource modeling of varying depth and accuracy. However, the key is always the accuracy and coverage of the primary data. We started with modest databases and data mining, but they mostly proved inadequate, and we are now amassing vast databases on every aspect of life - people, planet and machines. This ‘BIG DATA’ explosion demands a rethink of how, what, and where we gather data; the way we analyze and model; and the way we make decisions.
So - what is the big difference?
Data Mining was limited, planer, simple, linear and constrained to a few relationships amongst people: what they did, where they went, who they knew and so on. In contrast; Big Data is unbounded, spans all peoples and machines in all domains and activities with application to every aspect of life, business, industry, government and sustainability etc. It also takes into account the non-linear nature of relationships and events.
“Big Data is an almost unconscious outcome of the desire and need to sustain all peoples on a rapidly smaller looking planet”
Data mining is an important part of business intelligence and refers to discovering interesting patterns from large amounts of data. It involves applying techniques from multiple disciplines like statistics, machine learning, and information science to large datasets. While organizations collect vast amounts of data, data mining is needed to extract useful knowledge and insights from it. Some common techniques of data mining include classification, clustering, association analysis, and outlier detection. Data mining tools can help organizations apply these techniques to gain intelligence from their data warehouses.
The document provides an overview of data mining concepts including association rules, classification, and clustering algorithms. It introduces data mining and knowledge discovery processes. Association rule mining aims to find relationships between variables in large datasets using the Apriori and FP-growth algorithms. Classification algorithms build a model to predict class membership for new records based on a decision tree. Clustering algorithms group similar records together without predefined classes.
The document is a chapter from a textbook on data mining written by Akannsha A. Totewar, a professor at YCCE in Nagpur, India. It provides an introduction to data mining, including definitions of data mining, the motivation and evolution of the field, common data mining tasks, and major issues in data mining such as methodology, performance, and privacy.
We Are Social's comprehensive new Digital in 2016 report presents internet, social media, and mobile usage statistics and trends from all over the world. It contains more than 500 infographics, including global data snapshots, regional overviews, and in-depth profiles of the digital landscapes in 30 of the world's key economies. For a more insightful analysis of the numbers contained in this report, please visit http://bit.ly/DSM2016ES.
Introduction to big data – convergences.saranya270513
Big data is high-volume, high-velocity, and high-variety data that is too large for traditional databases to handle. The volume of data is growing exponentially due to more data sources like social media, sensors, and customer transactions. Data now streams in continuously in real-time rather than in batches. Data also comes in more varieties of structured and unstructured formats. Companies use big data to gain deeper insights into customers and optimize business processes like supply chains through predictive analytics.
This document discusses big data and why organizations should care about it. It defines big data as large volumes of diverse data that present challenges to analyze and extract value from. The world is generating much more data from sources like sensors, devices and digital content. Organizations that can analyze big data in real-time will have competitive advantages over those that cannot. The document provides examples of big data sources and opportunities it provides for different industries. Early adopters of big data technologies will be organizations already dealing with large data or those in industries experiencing rapid changes.
Big data document (basic concepts,3vs,Bigdata vs Smalldata,importance,storage...Taniya Fansupkar
This document provides an overview of big data, including its definition, origins, characteristics, importance, and opportunities and challenges. It describes big data as large volumes of diverse data that require new technologies and techniques to capture, curate, manage and process within a tolerable time. Big data is characterized by its volume, velocity and variety. Analyzing big data can provide benefits such as cost reductions, time reductions, new product development and smart decision making. It also discusses storing, processing and analyzing data at the edge of networks.
This document provides an overview of big data presented by five individuals. It defines big data, discusses its three key characteristics of volume, velocity and variety. It explains how big data is stored, selected and processed using techniques like Hadoop and MapReduce. Examples of big data sources and tools are provided. Applications of big data across various industries are highlighted. Both the risks and benefits of big data are summarized. The future growth of big data and its impact on IT is also outlined.
Big data refers to extremely large data sets that are too large to be processed using traditional data processing applications. It is characterized by high volume, variety, and velocity. Examples of big data sources include social media, jet engines, stock exchanges, and more. Big data can be structured, unstructured, or semi-structured. Key characteristics include volume, variety, velocity, and variability. Analyzing big data can provide benefits like improved customer service, better operational efficiency, and more informed decision making for organizations in various industries.
The document discusses big data, which refers to massive amounts of data from various sources that are difficult to analyze using traditional database tools. It defines big data and provides examples of types of data. It also discusses characteristics of big data like volume, variety, velocity, variability, and value. The document then discusses Apache Hadoop, a popular framework for storing and processing large datasets, and how big data is used in marketing for recommendation engines. Finally, it discusses how big data can provide value by making information more transparent and accessible.
1.Introduction
2.Overview
3.Why Big Data
4.Application of Big Data
5.Risks of Big Data
6.Benefits & Impact of Big Data
7.Conclusion
‘Big Data’ is similar to ‘small data’, but bigger in size
But having data bigger it requires different approaches:
Techniques, tools and architecture
An aim to solve new problems or old problems in a better
way
Big Data generates value from the storage and processing
of very large quantities of digital information that cannot be
analyzed with traditional computing techniques.
This document provides an analysis of big data, including its characteristics, applications, and analytics techniques used by businesses. It discusses that big data is data that is too large to be processed by traditional databases and software. It has characteristics of volume, velocity, variety, and veracity. The document outlines tools for big data like Hadoop, MongoDB, Apache Spark, and Apache Cassandra. It explains that big data analytics helps businesses gain insights from vast amounts of structured and unstructured data to improve decision making.
This Presentation is completely on Big Data Analytics and Explaining in detail with its 3 Key Characteristics including Why and Where this can be used and how it's evaluated and what kind of tools that we use to store data and how it's impacted on IT Industry with some Applications and Risk Factors
Big data is very large data that is difficult to process using traditional methods. It is characterized by high volume, velocity, and variety. Examples of real-life big data implementations include using social media to understand customer behavior, tracking social media for marketing campaigns, and analyzing medical data to predict readmissions. Challenges include integrating diverse data sources and ensuring ethical access. Common techniques for processing big data are parallel database management systems and MapReduce frameworks like Hadoop.
IRJET- A Scrutiny on Research Analysis of Big Data Analytical Method and Clou...IRJET Journal
This document discusses big data analytical methods, cloud computing, and how they can be combined. It explains that big data involves large amounts of structured, semi-structured, and unstructured data from various sources that requires significant computing resources to analyze. Cloud computing provides a way for big data analytics to be offered as a service and processed efficiently using cloud resources. The integration of big data and cloud computing allows organizations to gain business intelligence from large datasets in a flexible, scalable and cost-effective manner.
Bigdata.
Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Challenges include capture, storage, analysis, data curation, search, sharing, transfer, visualization, querying, updating and information privacy. The term "big data" often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. "There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem."[2] Analysis of data sets can find new correlations to "spot business trends, prevent diseases, combat crime and so on."[3] Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet search, fintech, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics,[4] connectomics, complex physics simulations, biology and environmental research.[5]
Data sets grow rapidly - in part because they are increasingly gathered by cheap and numerous information-sensing Internet of things devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks.[6][7] The world's technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s;[8] as of 2012, every day 2.5 exabytes (2.5×1018) of data are generated.[9] One question for large enterprises is determining who should own big-data initiatives that affect the entire organization.[10]
Relational database management systems and desktop statistics- and visualization-packages often have difficulty handling big data. The work may require "massively parallel software running on tens, hundreds, or even thousands of servers".[11] What counts as "big data" varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target. "For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration."
Big data is still relatively new and it is very exciting. The opportunities, if not necessarily endless, are are at least incredibly rich and varied. Aiming to bridge the link between Big Data as a Technology and Big Data as Business Value, we hope our presentation will help frame some of your thinking on how to use and benefit from this topical development.
Enterprises are facing exponentially increasing amounts of data that is breaking down traditional storage architectures. NetApp addresses this "big data challenge" through their "Big Data ABCs" approach - focusing on analytics, bandwidth, and content. This enables customers to gain insights from massive datasets, move data quickly for high-speed applications, and securely store unlimited amounts of content for long periods without increasing complexity. NetApp's solutions provide a foundation for enterprises to innovate with data and drive business value.
Big Data Analytics: Recent Achievements and New ChallengesEditor IJCATR
The era of Big data is being generated by everything around us at all times. Every digital process and social media
exchange produces it. Systems, sensors and mobile devices transmit it. Big data is arriving from multiple sources at an alarming
velocity, volume and variety. To extract meaningful value from big data, you need optimal processing power, analytics
capabilities and skills. Big data has become an important issue for a large number of research areas such as data mining,
machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The combination of
big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas
as social media and social networks. These new challenges are focused mainly on problems such as data processing, data
storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and
tracking data, among others. In this paper, discussion about the new concept big data and data analytic their concept, tools
and methodologies that is designed to allow for efficient data mining and information sharing fusion from social media and of
the new applications and frameworks that are currently appearing under the “umbrella” of the social networks, social media
and big data paradigms.
This document provides an overview of big data, including its definition, characteristics, storage and processing. It discusses big data in terms of volume, variety, velocity and variability. Examples of big data sources like the New York Stock Exchange and social media are provided. Popular tools for working with big data like Hadoop, Spark, Storm and MongoDB are listed. The applications of big data analytics in various industries are outlined. Finally, the future growth of the big data industry and market size are projected to continue rising significantly in the coming years.
Big data presents opportunities for communications service providers (CSPs) to capture new revenue streams by optimizing large amounts of structured and unstructured customer data. To take advantage, CSPs must develop a strategic plan and roadmap to transform how they use customer data, identifying specific business values. Success stories show how CSPs have improved operational efficiency, provided targeted marketing offers, and created new business models through partnerships. The document recommends CSPs formulate a big data strategy and business case with measurable outcomes to guide strategic transformation and monetization of big data opportunities.
Big data offers opportunities for companies to gain competitive advantages through improved customer intimacy, product innovation, and operations. The document discusses how various companies are leveraging big data across industries. It notes that 45% of companies have implemented big data initiatives in the past two years and over 90% of Fortune 500 companies will have initiatives underway soon. Harnessing big data's potential requires understanding where it can create value within a company and having the right organizational structure, technology investments, and plan to capture those benefits.
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BHOLENDRA SINGH RESUME - Sr. Software Engineer at India Today GroupBholendra Singh
I am an Android and Flutter mobile application developer with over 6.5+ years of experience. I am skilled in various programming languages and tools, including Android, Flutter (Hybrid), Java, Kotlin, Dart, Firebase, and Google Cloud. I am always ready to take on new challenges, learn new technologies, and solve real-time problems using my expertise.
Webinar - Compensation Data Demystified: Unveiling Expert InsightsPayScale, Inc.
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Big data is changing
the world dramatically
right before our eyes –
from the amount of
data being produced to
the way in which it’s
structured and used.
This QuickView provides an overview to
what big data is, how big it is, how it’s
mined, refined and used, what the key
roles are and the popular big data
techniques used by suppliers.
Contents
Explosive Growth
What is Big Data?
Management of Big Data
How Big is Big Data?
Big Data in Numbers
5 Predictions For The $125 Billion Big Data
Analytics Market in 2015
5 Key Big Data Positions Used in a Big Data Flow
Top 10 Related IT Skills
Top 10 Industries Hiring Big Data Professionals
Top 10 Qualifications Sought by Hirers
Top 10 Database and BI skills Sought by Hirers
Key Big Data Terms Demystified
Popular Big Data Techniques and Vendors
“The most valuable commodity I know of is information”.
- Gordon Gekko
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Explosive Growth
The explosion of the data industry,
which has been likened to oil in the 18th
Century: an immensely, untapped
valuable asset, is fueling extraordinary
demand for “big data” skilled
professionals. Estimates suggest that
between 2012-17, use of big data could
contribute £216 billion to the UK
economy via business creation,
efficiency and innovation, and generate
58,000 new jobs. Big data is big business.
According to a report conducted by
leading business analytics software and
services company SAS, over the past five
years big data job growth has risen at an
annual rate of 212%.This presents both
challenges and opportunities for
businesses. A study by the Royal
Academy of Engineering shows that
British industry will need 1.25 million
new graduates in science, technology,
engineering and maths subjects
between now and 2020 to maintain
current employment numbers in an
ever-evolving market.
With no guarantee that universities will
produce the number of graduates
needed to meet the demand, or that
companies will invest in training and
development for existing staff,
companies who are seeking to
implement a big data strategy will need
to pursue a defined hiring strategy.
By working with hiring specialists to tap
into the existing talent pool and extract
the hard to find candidates to meet their
objectives, businesses will benefit
significantly.
According to Accenture, one of the world’s biggest parcel
companies and also among the world’s largest big data
users, spending $1bn annually to store and study 16
petabytes of data from every conceivable point of its
business. The enormity of this statistic underlines how
valuable big data is (in the right hands), how important it
is for businesses to acquire, interpret and use the right
data, and just how exciting the market potential is.
Source: SAS
1.2New Grads Needed
Million
212Job Growth
%
58,000
New Jobs
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What is Big Data?
Buzzword? catchphrase?
technology? In the last decade
there has been a lot said about big
data with hundreds of definitions,
such as:
“Big data is the derivation of value from
traditional relational database-driven business
decision making, augmented with new sources
of unstructured data” - Oracle
“Datasets whose size is beyond the ability of
typical database software tools to capture,
store, manage, and analyze” - McKinsey
“Big data is the data characterized by 3
attributes: volume, variety and velocity” - IBM
David Wellman’s succinct offering captures
the essence of what big data is really about -
“Big Data is not about the size of the data, it’s
about the value within the data.” Taking this
point back to Accenture’s research on the
$1bn packaging company, the value of big
data is about the specific purpose and intent
it’s used for and ultimately it’s impact on the
bottom line. To paraphrase William Bruce
Cameron “not everything that can be counted
counts”.
Technological Factors
Driving the Growth of Big
Data
New sources of data are being created
through:
• Digitisation of existing processes and
services, for example online banking, email
and medical records
• Automatic generation of data, such as web
server logs that record web page requests
• Reduction in the cost and size of sensors
found in aeroplanes, buildings and the
environment
• Production of new gadgets that collect and
transmit data, for example GPS location
information from mobile phones and
capacity updates from ‘smart’ waste bins
Enhanced Computing
Capabilities Driving Big
Data Include:
• Improved data storage at higher densities,
for lower cost
• Greater computing power for faster and
more complex calculations
• Cloud computing (remote access to shared
computing resources via a device connected
to a network), facilitating cheaper access to
data storage, computation, software and
other services
• Recent advances in statistical and
computational techniques, which can be
used to analyse and extract meaning from
big data
• Development of new tools such as Apache
Hadoop (which enables large data sets to be
processed across clusters of computers) and
extension of existing software, such as
Microsoft Excel.
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Mining
Big data can be acquired from a vast,
and increasing, number of sources.
These include images, sound recordings,
user click streams that measure internet
activity, and data generated by computer
simulations (such as those used in
weather forecasting). Key to managing
data collection are metadata, which is
data about data. An email, for example,
automatically generates metadata
containing the addresses of the sender
and recipient, and the date and time it
was sent, to aid the manipulation and
storage of email archives. Producing
metadata for big data sets can be
challenging, and may not capture all the
nuances of the data.
Refining
Data may undergo numerous processes
to improve quality and usability before
analysis, including:
Extraction – pulling out required
information from the initial data and
expressing it in a structured form
Cleansing – detecting and then
correcting or removing corrupt or
inaccurate records standardization –
formatting data to aid interoperability
Linkage – connecting records from
different sources.
.
Management of Big Data
Use
Analytics are used to gain insight from
data. They typically involve applying an
algorithm (a sequence of calculations) to
data to find patterns, which can then be
used to make predictions or forecasts. Big
data analytics encompass various
inter-related techniques, including the
following examples.
Data mining - identifies patterns by sifting
through data. It can be applied to user click
streams to understand how customers use
web pages to inform web page design.
Machine learning - describes systems that
learn from data. For example, a system
that compares documents in two different
languages can infer translation rules;
human correction of any errors in the rules
can result in the system learning how to
improve the software.
Simulation - can be used to model the
behaviour of complex systems. For
example, building a trading simulation can
help to assess the effectiveness of
measures to reduce insider trading.
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How Big is Big Data?
Research group IDC predicts the digital universe
will reach 40 zettabytes in size – that’s 45 trillion
gigabytes – by 2020. That’s a 50-fold growth in just
one decade. There is now almost as many bits of
data as there are known stars in the universe.
2013: 4.4 zettabytes, 2020: 44 zettabytes.
Source: Oracle 2012
What is a Zettabyte?
1,000,000,000,000 Gigabytes
1,000,000,000,000 Terabytes
1,000,000,000,000 Petabytes
1,000,000,000,000 Exabytes
1,000,000,000,000 Zettabytes
0
5
10
15
20
25
30
35
40
45
50
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
1 terabyte
holds the
equivalent
of roughly
210 single-
sided DVDs In 20007,
the estimated infomation
content of all human knowledge
was 295 exabytes
Data is growing at a 40 % compound annual rate, reaching nearly 45 ZB by 2020
Data in zettabytes ( ZB )
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64,000UKorganisationswith100ormore
staffwillhaveimplemented
bigdataanalyticsby2020
2009 2020
By 2020 over 1/3 of all
data will live in or pass
through the cloud
346,000
Big data job opportunities
created in the economy
in the UK by 2020
The digital universe will grow from 3.2 zettabytes today
to 40 zettabytes in only 6 years
0 6
Big Data in Numbers
Individuals create 70 % of all dataEnterprises store 80 % of all data
Data production will be 44 times greater in 2020 than it was in 2009
222%
2017
The UK is forecasting a 222%
increase in big data jobs by 2017
x+100
41%2011 2013
Rise in “big data” jobs
throughout the UK
Source: IDC/SAS
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Predictions For The
$125 Billion Big Data
Analytics Market in
2015
The big data and analytics market
will reach $125 billion worldwide in
2015, according to IDC and The
International Institute of Analytics
(IIA).
Here are their top five predictions
for 2015:
1. Over the next five years spending on
cloud-based big data and analytics (BDA)
solutions will grow three times faster than
spending for on-premise solutions.
2. Shortag of skilled staff will persist. In the
US alone there will be 181,000 deep
analytics roles in 2018 and five times that
many positions requiring related skills in
data management and interpretation. In the
UK, there will be 47,000 big data roles by
2017, a 222% increase on 2013.
3. Growth in applications incorporating
advanced and predictive analytics, including
machine learning, will accelerate in 2015.
These apps will grow 65% faster than apps
without predictive functionality.
4. 70% of large organisations already
purchase external data and 100% will do so
by 2019. In parallel more organisations will
begin to monetise their data by selling them
or providing value-added content.
5. Rich media (video, audio, image) analytics
will at least triple in 2015 and emerge as the
key driver for BDA technology investment.
Hiring Big Data Specialists: The Key
Roles
Data Analyst
Big Data Developer
Data Modeler
Big Data Architect
Business Data Analyst
Data Scientist
SAS Data Analyst
SAP Data Analyst
SQL Data Analyst
Data Warehousing (DWH) Developer
Data Centre Architect
Master Data Analyst
Data Governance Manager
Big Data Consultant
Data Warehousing (DWH) Analyst
Data Integration Developer
Data Migration Analyst
Master Data Consultant
Data Migration Manager
Data Business Analyst
Oracle Data Warehousing (DWH) Developer
Market Data Engineer
Data Migration Project Manager
Data Centre Project Manager
Data Centre Consultant
Data Protection Manager
SAS Data Integration (DI) Studio Developer
125$BillionIDC and (IIA)
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Big Data top 10s
For the six months to 3
March 2015, IT jobs
within the UK citing big
data also mentioned the
following IT skills in
order of popularity. The
figures indicate the
number of jobs and
their proportion against
the total number of IT
job ads sampled that
cited big data.
Top 10 Industries
1 Finance
2 Marketing
3 Banking
4 Retail
5 Telecoms
6 Advertising
7 Games
8 Pharmaceutical
9 Investment Banking
10 Legal
Top 10 Qualifications
1 Degree
2 phD
3 Security Cleared
4 VCP4
5 SQL
6 MBA
7 Microsoft Certification
8 DV Cleared
9 ISEB
10 PMI Cirtification
Database & Business
Intelligence
1 Hadoop
2 noSQL
3 SQL Server
4 Data Warehouse
5 MongoDB
6 Apache Hive
7 mySQL
8 Data Mining
9 Apache Cassandra
10 SQL Server Integration Serv.
Top 10 Related IT Skills
1 Java
2 Hadoop
3 Agile Software Dev.
4 Analytics
5 SQL
6 Business Inteligence
7 Finance
8 NoSQL
9 Python
10 SQL Server
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Key Big Data Terms
Demystified
Hadoop
Hadoop is a complex software ecosystem central
to a broad range of state-of-art big data
technologies (learn more about what is Hadoop).
Companies that work with data at super-massive
scale inevitably need expert engineers who can
work nimbly within the Hadoop framework.
NoSQL
NoSQL (commonly referred to as "Not Only SQL")
represents a completely different framework of
databases that allows for high-performance,
agile processing of information at massive scale.
In other words, it is a database infrastructure
that as been very well-adapted to the heavy
demands of big data.
MongoDB
MongoDB is a leading NoSQL database that is
very popular among companies with big data
initiatives. Demand for talented engineers with
MongoDB familiarity is very high.
Cassandra
Cassandra is a popular NoSQL technology stack
that was originally developed at Facebook, and is
now deployed at large number of companies
with big data initiatives.
Business Intelligence (BI)
BI is a critical capability in any data-driven
organisation, responsible for making data visible
and actionable for smarter decision-making. BI
teams accomplish this by developing tools that
make data easy to digest – i.e. data reporting,
visualisation, and query platforms such as
dashboards and OLAP tools. BI
developers/analysts require sharp technical skills
and comfort working with large database
systems
Database Administrators (DBA)
DBAs are vital engineers at any company with
data infrastructure. The role of DBA has actually
become more complex over the years. In the
past, data may have been adequately managed
on a single server. But the big data infrastructure
of today is often comprised of a medley of
intricate, interconnected data platforms,
potentially involving large clusters of massively
parallel processing servers. Demand for this type
of DBA talent is very high.
Key Big Data Positions Used
in a Big Data Flow
Data Hygienists make sure that data coming
into the system is clean and accurate, and stays
that way over the entire data lifecycle.
Data Explorers sift through mountains of data
to discover the data you actually need.
Business Solution Architects put the
discovered data together and organise it so that
it's ready to analyse.
Data Scientists take this organised data and
create sophisticated analytics models that, for
example, help predict customer behavior and
allow advanced customer segmentation and
pricing optimization.
Campaign Experts turn the models into results.
They have a thorough knowledge of the technical
systems that deliver specific marketing
campaigns, such as which customer should get
what message when.
11. Popular Big Data Techniques and Vendors
Business Intelligence (BI)/Online Analytical Processing
(OLAP):
Users interactively analyse multidimensional data
users can roll-up, drill-down, and slice data
BI tools provide dashboard and report capabilities
Cluster Analysis:
Segment objects (e.g., users) into groups based on similar
properties or attributes
Data Mining:
Process to discover and extract new patterns in large data sets
Predictive Modeling:
A model is created to best predict the probability of an outcome
SQL:
A computer language that manages (e.g., query, insert, delete,
extract) data from a relational database
Crowdsourcing:
A process for collecting data from a large community or
distributed group of people
Idea submission is a common crowdsourcing activity
Textual Analysis:
Computer algorithms that analyse natural language
Topics can be extracted from text along with their linkages
Sentiment Analysis:
A form of textual analysis that determines a positive, negative, or
neutral reaction
Often used in marketing brand campaigns
Network analysis:
A methodology to analyse the relationship among nodes (e.g.,
people)
On social media platforms, it can be used to create the social
graph of follower and friends’ connections among users
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Technique Vendor
TransactionalDataNon-transactionalSocialData
12. Reference Shelf:
Cebr "Data Equity: unlocking the value of big data, 2012"
Hadoop Summit 2014
IDC "Big Data and Analytics and Enterprise Applications Will Continue to Drive
Software Market Growth Until 2018"
Computer Weekly: "IT Department for Big Data Projects"
McKinsey "Big Data is the data the next frontier for innovation"
McKinsey "Big data: The next frontier for innovation, competition, and
productivity"
David Wellman "What is Big Data"
IDC: "Worldwide Big Data and Analytics Predictions for 2015"
Accenture Big Success with Big Data Survey
Onrec: "How is the online recruitment IT sector faring"
ITJobs Watch "Big Data skills in IT jobs"
POSTnote "Big Data: An Overview"
The International Institute of Analytics "Analytics predictions for 2015"
Data Jobs "Key Big Data Terms Demystified"
HBR "Five Roles You Need on Your Big Data Team"
CION Insight "Digital Universe Expands at an Alarming Rate"
The Telegraph "Big data skills will lead to big IT jobs"
MIT: "The Big Data Conundrum: How to Define It?"
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