The objective of this module is to provide an overview of the basic information on big data.
Upon completion of this module you will:
-Comprehend the emerging role of big data
-Understand the key terms regarding big and smart data
-Know how big data can be turned into smart data
-Be able to apply the key terms regarding big data
Becoming an analytics-driven organization helps companies reduce costs, increase
revenues and improve competitiveness, and this is why business intelligence and
analytics continue to be a top priority for CIOs. Many business decisions, however,
are still not based on analytics, and CIOs are looking for ways to reduce time to value
for deploying business intelligence solutions so that they can expand the use of
analytics to a larger audience of users.
Companies are also interested in leveraging the value of information in so-called big
data systems that handle data ranging from high-volume event data to social media
textual data. This information is largely untapped by existing business intelligence
systems, but organizations are beginning to recognize the value of extending the
business intelligence and data warehousing environment to integrate, manage, govern
and analyze this information.
Understanding big data and data analytics big dataSeta Wicaksana
Big Data helps companies to generate valuable insights. Companies use Big Data to refine their marketing campaigns and techniques. Companies use it in machine learning projects to train machines, predictive modeling, and other advanced analytics applications.
Gayatri Patel, eBay, presents at the Big Analytics 2012 Roadshow
The wonders of what data can do for an organization is measured in the productivity and competitiveness of their team's decisions. Some believe more data is the key. Agreed...but good decisions require more than just deriving intelligence from big data. In this dynamic market, the need to socialize and evolve ideas with other teams, quickly correlate information across sources, and test ideas to fail fast early are strong enablers to gain competitive footing. eBay¹s analytic and technology advancements garners insights and approaches that continue to help our employees tell their "data stories" and make better decisions.
Understanding big data and data analytics-Business IntelligenceSeta Wicaksana
Faster and more accurate reporting, analysis or planning; better business decisions; improved employee satisfaction and improved data quality top the list. Benefits achieved least frequently include reducing costs, and increasing revenues.
Becoming an analytics-driven organization helps companies reduce costs, increase
revenues and improve competitiveness, and this is why business intelligence and
analytics continue to be a top priority for CIOs. Many business decisions, however,
are still not based on analytics, and CIOs are looking for ways to reduce time to value
for deploying business intelligence solutions so that they can expand the use of
analytics to a larger audience of users.
Companies are also interested in leveraging the value of information in so-called big
data systems that handle data ranging from high-volume event data to social media
textual data. This information is largely untapped by existing business intelligence
systems, but organizations are beginning to recognize the value of extending the
business intelligence and data warehousing environment to integrate, manage, govern
and analyze this information.
Understanding big data and data analytics big dataSeta Wicaksana
Big Data helps companies to generate valuable insights. Companies use Big Data to refine their marketing campaigns and techniques. Companies use it in machine learning projects to train machines, predictive modeling, and other advanced analytics applications.
Gayatri Patel, eBay, presents at the Big Analytics 2012 Roadshow
The wonders of what data can do for an organization is measured in the productivity and competitiveness of their team's decisions. Some believe more data is the key. Agreed...but good decisions require more than just deriving intelligence from big data. In this dynamic market, the need to socialize and evolve ideas with other teams, quickly correlate information across sources, and test ideas to fail fast early are strong enablers to gain competitive footing. eBay¹s analytic and technology advancements garners insights and approaches that continue to help our employees tell their "data stories" and make better decisions.
Understanding big data and data analytics-Business IntelligenceSeta Wicaksana
Faster and more accurate reporting, analysis or planning; better business decisions; improved employee satisfaction and improved data quality top the list. Benefits achieved least frequently include reducing costs, and increasing revenues.
Big Data Analytics Architecture PowerPoint Presentation SlidesSlideTeam
Presenting this set of slides with name - Big Data Analytics Architecture Powerpoint Presentation Slides. This PPT deck displays twenty six slides with in depth research. Our topic oriented Big Data Analytics Architecture Powerpoint Presentation Slides presentation deck is a helpful tool to plan, prepare, document and analyse the topic with a clear approach. We provide a ready to use deck with all sorts of relevant topics subtopics templates, charts and graphs, overviews, analysis templates. Outline all the important aspects without any hassle. It showcases of all kind of editable templates infographs for an inclusive and comprehensive Big Data Analytics Architecture Powerpoint Presentation Slides presentation. Professionals, managers, individual and team involved in any company organization from any field can use them as per requirement.
This project is about "Big Data Analytics," and it provides a comprehensive overview of topics related to Data and Analytics and a short note on Cognitive Analytics, Sentiment Analytics, Data Visualization, Artificial intelligence & Data-Driven Decision Making along with examples and diagrams.
Data Profiling: The First Step to Big Data QualityPrecisely
Big data offers the promise of a data-driven business model generating new revenue and competitive advantage fueled by new business insights, AI, and machine learning. Yet without high quality data that provides trust, confidence, and understanding, business leaders continue to rely on gut instinct to drive business decisions.
The critical foundation and first step to deliver high quality data in support of a data-driven view that truly leverages the value of big data is data profiling - a proven capability to analyze the actual data content and help you understand what's really there.
View this webinar on-demand to learn five core concepts to effectively apply data profiling to your big data, assess and communicate the quality issues, and take the first step to big data quality and a data-driven business.
Applying Data Quality Best Practices at Big Data ScalePrecisely
Global organizations are investing aggressively in data lake infrastructures in the pursuit of new, breakthrough business insights. At the same time, however, 2 out of 3 business executives are not highly confident in the accuracy and reliability of their own Big Data. Regaining that confidence requires utilizing proven data quality tools at Big Data scale.
In this on-demand webinar, discover how to ensure your data lake is a trusted source for advanced business insights that lead to new revenue, cost savings and competitiveness. You will have the opportunity to:
• Compare your organization’s data lake “readiness” against initial findings from our upcoming annual Big Data Trends survey
• Gain insight into where and how to leverage data quality best practices for Big Data use cases
• Explore how a ‘Develop Once, Deploy Anywhere’ approach, including to native Big Data infrastructures such as Hadoop and Spark, facilitates consistent data quality patterns
Slides from May 2018 St. Louis Big Data Innovations, Data Engineering, and Analytics User Group meeting. The presentation focused on Data Modeling in Hive.
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...ijscai
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted.
Big Data Analytics | What Is Big Data Analytics? | Big Data Analytics For Beg...Simplilearn
The presentation about Big Data Analytics will help you know why Big Data analytics is required, what is Big Data analytics, the lifecycle of Big Data analytics, types of Big Data analytics, tools used in Big Data analytics and few Big Data application domains. Also, we'll see a use case on how Spotify uses Big Data analytics. Big Data analytics is a process to extract meaningful insights from Big Data such as hidden patterns, unknown correlations, market trends, and customer preferences. One of the essential benefits of Big Data analytics is used for product development and innovations. Now, let us get started and understand Big Data Analytics in detail.
Below are explained in this Big Data analytics tutorial:
1. Why Big Data analytics?
2. What is Big Data analytics?
3. Lifecycle of Big Data analytics
4. Types of Big Data analytics
5. Tools used in Big Data analytics
6. Big Data application domains
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Big Data Applications | Big Data Application Examples | Big Data Use Cases | ...Simplilearn
In this Big Data presentation, we will be discussing the Big data growth over the last few years followed by the various big data applications. We will look into the various sectors where big data is used such as weather forecast, healthcare, media and entertainment, logistics, travel & tourism and finally in the government & law enforcement sector.
We will be discussing how below industries are using Big Data presentation:
1. Weather forecast
2. Media and entertainment
3. Healthcare
4. Logistics
5. Travel n tourism
6. Government and law enforcement
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
The objective of this module is to provide an overview of the basic information on big data.
Upon completion of this module you will:
-Comprehend the emerging role of big data
-Understand the key terms regarding big and smart data
- Know how big data can be turned into smart data
- Be able to apply the key terms regarding big data
Duration of the module: approximately 1 – 2 hours
Big Data Analytics Architecture PowerPoint Presentation SlidesSlideTeam
Presenting this set of slides with name - Big Data Analytics Architecture Powerpoint Presentation Slides. This PPT deck displays twenty six slides with in depth research. Our topic oriented Big Data Analytics Architecture Powerpoint Presentation Slides presentation deck is a helpful tool to plan, prepare, document and analyse the topic with a clear approach. We provide a ready to use deck with all sorts of relevant topics subtopics templates, charts and graphs, overviews, analysis templates. Outline all the important aspects without any hassle. It showcases of all kind of editable templates infographs for an inclusive and comprehensive Big Data Analytics Architecture Powerpoint Presentation Slides presentation. Professionals, managers, individual and team involved in any company organization from any field can use them as per requirement.
This project is about "Big Data Analytics," and it provides a comprehensive overview of topics related to Data and Analytics and a short note on Cognitive Analytics, Sentiment Analytics, Data Visualization, Artificial intelligence & Data-Driven Decision Making along with examples and diagrams.
Data Profiling: The First Step to Big Data QualityPrecisely
Big data offers the promise of a data-driven business model generating new revenue and competitive advantage fueled by new business insights, AI, and machine learning. Yet without high quality data that provides trust, confidence, and understanding, business leaders continue to rely on gut instinct to drive business decisions.
The critical foundation and first step to deliver high quality data in support of a data-driven view that truly leverages the value of big data is data profiling - a proven capability to analyze the actual data content and help you understand what's really there.
View this webinar on-demand to learn five core concepts to effectively apply data profiling to your big data, assess and communicate the quality issues, and take the first step to big data quality and a data-driven business.
Applying Data Quality Best Practices at Big Data ScalePrecisely
Global organizations are investing aggressively in data lake infrastructures in the pursuit of new, breakthrough business insights. At the same time, however, 2 out of 3 business executives are not highly confident in the accuracy and reliability of their own Big Data. Regaining that confidence requires utilizing proven data quality tools at Big Data scale.
In this on-demand webinar, discover how to ensure your data lake is a trusted source for advanced business insights that lead to new revenue, cost savings and competitiveness. You will have the opportunity to:
• Compare your organization’s data lake “readiness” against initial findings from our upcoming annual Big Data Trends survey
• Gain insight into where and how to leverage data quality best practices for Big Data use cases
• Explore how a ‘Develop Once, Deploy Anywhere’ approach, including to native Big Data infrastructures such as Hadoop and Spark, facilitates consistent data quality patterns
Slides from May 2018 St. Louis Big Data Innovations, Data Engineering, and Analytics User Group meeting. The presentation focused on Data Modeling in Hive.
BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...ijscai
All types of machine automated systems are generating large amount of data in different forms like
statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we
are discussing issues, challenges, and application of these types of Big Data with the consideration of big
data dimensions. Here we are discussing social media data analytics, content based analytics, text data
analytics, audio, and video data analytics their issues and expected application areas. It will motivate
researchers to address these issues of storage, management, and retrieval of data known as Big Data. As
well as the usages of Big Data analytics in India is also highlighted.
Big Data Analytics | What Is Big Data Analytics? | Big Data Analytics For Beg...Simplilearn
The presentation about Big Data Analytics will help you know why Big Data analytics is required, what is Big Data analytics, the lifecycle of Big Data analytics, types of Big Data analytics, tools used in Big Data analytics and few Big Data application domains. Also, we'll see a use case on how Spotify uses Big Data analytics. Big Data analytics is a process to extract meaningful insights from Big Data such as hidden patterns, unknown correlations, market trends, and customer preferences. One of the essential benefits of Big Data analytics is used for product development and innovations. Now, let us get started and understand Big Data Analytics in detail.
Below are explained in this Big Data analytics tutorial:
1. Why Big Data analytics?
2. What is Big Data analytics?
3. Lifecycle of Big Data analytics
4. Types of Big Data analytics
5. Tools used in Big Data analytics
6. Big Data application domains
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of the Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
Big Data Applications | Big Data Application Examples | Big Data Use Cases | ...Simplilearn
In this Big Data presentation, we will be discussing the Big data growth over the last few years followed by the various big data applications. We will look into the various sectors where big data is used such as weather forecast, healthcare, media and entertainment, logistics, travel & tourism and finally in the government & law enforcement sector.
We will be discussing how below industries are using Big Data presentation:
1. Weather forecast
2. Media and entertainment
3. Healthcare
4. Logistics
5. Travel n tourism
6. Government and law enforcement
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
This course will enable you to:
1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
4. Get an overview of Sqoop and Flume and describe how to ingest data using them
5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
9. Gain a working knowledge of Pig and its components
10. Do functional programming in Spark
11. Understand resilient distribution datasets (RDD) in detail
12. Implement and build Spark applications
13. Gain an in-depth understanding of parallel processing in Spark and Spark RDD optimization techniques
14. Understand the common use-cases of Spark and the various interactive algorithms
15. Learn Spark SQL, creating, transforming, and querying Data frames
Learn more at https://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
The objective of this module is to provide an overview of the basic information on big data.
Upon completion of this module you will:
-Comprehend the emerging role of big data
-Understand the key terms regarding big and smart data
- Know how big data can be turned into smart data
- Be able to apply the key terms regarding big data
Duration of the module: approximately 1 – 2 hours
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
El objetivo de este modulo es ofrecer una vision general sobre el impacto futuro del Big Data.
Una vez completado este modulo, podrá:
Obtener una valiosa información de las predicciones para el futuro del Big Data
Conseguir un mayor conocimiento que permita reconocer algunas de las tendencias que están surgiendo
Adquirir una vision general de las oportunidades que pueden beneficiar a su negocio con el Big Data
Comprender algunos de los desafíos iniciales que podrías tener con el Big Data
El objetivo de este módulo es proporcionar una visión general de la ética que rodea al Big Data y la legislación que rige.
Una vez completado este módulo, podrá:
- Adquirir conocimientos sobre cómo reconocer la necesidad de regular el Big Data
- Identificar la diferencia entre privacidad y protección de datos
- Comprender la necesidad de implementar acciones de protección de datos en su propio negocio
El objetivo de este módulo es obtener una visión general sobre cómo utilizar los datos externos para mejorar el negocio.
Una vez completado este módulo, podrá:
Comprender los fundamentos de los datos externos y dónde encontrarlos
Comprender que ya existe una gran cantidad de datos abiertos que pueden ser utilizados
Reconocer los beneficios de utilizar datos externos para mejorar el negocio
El objetivo de este módulo es obtener una visión general sobre cómo utilizar los datos de los que ya se dispone para mejorar el negocio.
Una vez completado este módulo, podrá:
Comprender cómo aprovechar los datos existentes que ya tiene
Conocer la ubicación de los datos internos que ya se encuentran en su empresa
Mejorar su conocimiento sobre cómo los datos pueden ayudar a desarrollar su marca
El objetivo de este módulo es proporcionar una visión general sobre lo que entendemos por Big Data.
Una vez completado este módulo, podrá:
- Comprender el papel emergente del Big Data
- Entender los términos clave del Big Data y Smart Data
- Saber cómo Big Data puede convertirse en Smart Data
- Ser capaz de aplicar los términos clave en relación con el Big Data
Dwe m4 cyber bullying and conflict resolutionData-Set
You will understand the difference between online and offline cyberbullying and digital drama
You will learn how cyberbullying can occur in education and what a cyberbullying educational policy should include
You will learn the different types of cyberbullying and how to react to cyberbullying and negativity online
You will be able to address countering hate speech online
Dwe m3 digital footprint netiquette and reputation Data-Set
You will learn what your digital footprint is and how you can leave traces that can never be erased
You will be able to find out the size of your digital footprint by using different tools such as the Personal Digital Footprint Calculator
You will also become aware of your digital shadow and what it comprises of
You will check out your netiquette and see if it is correct or acceptable when using the internet
You will understand your online reputation and, by incorporating the 4 tips, be able to protect it
Learning Objective
You will learn the difference between online an offline identities. You will learn whether authenticity or anonymity is more important
You will understand different personas and how it can be presented or perceived by others
You will learn about the difference between real self vs online self
You will know why it is important and how to be your true self online
Learning objectives
You will learn how to look after your personal health, safety, relationships and work-life balance in a digital setting
You will know how to be digitally responsible in a way that doesn’t harm others
You will understand the impacts of social media on your psychological wellbeing and what you can do
You will learn how digital technology impacts your physical health
You will become aware of how digital knowledge is a poor substitute for learning in the real world
The objective of this module is to provide an overview of the basic information on big data.
Upon completion of this module you will:
-Comprehend the emerging role of big data
-Understand the key terms regarding big and smart data
-Know how big data can be turned into smart data
-Be able to apply the key terms regarding big data
The objective of this module is to gain an overview of how to use the data you already have available in order to improve your business.
Upon completion of this module you will:
Gain an understanding of how to take advantage of the existing data you already have
Comprehend the location of where internal data already lies within your company
Improve your knowledge on how data can help build your brand
The objective of this module is to gain an overview of the ethics surrounding big data and the legislation that governs it.
Upon completion of this module you will:
- Gain knowledge on how to recognize the necessity of regulating big data
- Obtain an understanding of the difference between privacy and data protection
- Understand the need to implement data protection actions into your own business
The objective of this module is to gain an overview of the ethics surrounding big data and the legislation that governs it.
Upon completion of this module you will:
- Gain knowledge on how to recognize the necessity of regulating big data
- Obtain an understanding of the difference between privacy and data protection
- Understand the need to implement data protection actions into your own business
The objective of this module is to provide an overview of what the future impacts of big data are likely to be.
Upon completion of this module you will:
Gain valuable insight into the predictions for the future of Big Data
Be better placed to recognise some of the trends that are emerging
Acquire an overview of the possible opportunities your business can have with Big Data
Understand some of the start up challenges you might have with Big Data
The objective of this module is to gain an overview of the ethics surrounding big data and the legislation that governs it.
Upon completion of this module you will:
- Gain knowledge on how to recognize the necessity of regulating big data
- Obtain an understanding of the difference between privacy and data protection
- Understand the need to implement data protection actions into your own business
The objective of this module is to gain an overview of the ethics surrounding big data and the legislation that governs it.
Upon completion of this module you will:
- Gain knowledge on how to recognize the necessity of regulating big data
- Obtain an understanding of the difference between privacy and data protection
- Understand the need to implement data protection actions into your own business
Data set Improve your business with your own business dataData-Set
The objective of this module is to gain an overview of how to use the data you already have available in order to improve your business.
Upon completion of this module you will:
-Gain an understanding of how to take advantage of the existing data you already have
-Comprehend the location of where internal data already lies within your company
-Improve your knowledge on how data can help build your brand
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Show drafts
volume_up
Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
1. This programme has been funded with
support from the European Commission
Module 1:
Introduction
to Big Data
2. DATA SET SKILLS FOR BUSINESS
Module 1:
Introduction
to
Big Data
The objective of this module is to provide an overview of the
basic information on big data.
Upon completion of this module you will:
-Comprehend the emerging role of big data
-Understand the key terms regarding big and smart data
-Know how big data can be turned into smart data
-Be able to apply the key terms regarding big data
Duration of the module: approximately 1 – 2 hours
3. DATA SET SKILLS FOR BUSINESS
2) The V‘s of data
4) Case study3) How does big
data become
smart data?
• A Brief History of
Data
• What is Big Data?
• Sources of Data
• The Importance of
Big Data
1) The emerging role of
data in VET and
enterprise
• Turning Big Data into Value
• Smart Data Applications
• How to Start Smart?
• Big Data Challenges
• How Target
used the Power
of Big Data
• Volume
• Velocity
• Variety
• Veracity
• Value
4. DATA SET SKILLS FOR BUSINESS
THE EMERGING ROLE OF DATA IN
VET AND ENTERPRISE
1. What is Big Data?
2. Classification of Data
3. Sources of Data
4. The Importance of Big Data
5. “Big Data is the foundation of all of
the megatrends that are happening
today, from social to mobile to the
cloud to gaming.”
Chris Lynch
6. DATA SET SKILLS FOR BUSINESS
WHAT IS BIG DATA?
There are some things that are so big, that they have implications for
everyone, whether we want it or not.
Big data is one of those things, and it is completely transforming the way
we do business and is impacting most other parts of our lives.
The basic idea behind the phrase “Big Data” is that everything we do is
increasingly leaving a digital trace, which we can use and analyse.
Big Data therefore refers to our ability to make use of the everincreasing
volumes of data.
“Data of a very large
size, typically to the
extent that its
manipulation and
management
present significant
logistical
challenges.“
Oxford English Dictionary,
2013
7. DATA SET SKILLS FOR BUSINESS
CLASSIFICATION OF DATA
“Data” is defined as ‘the quantities, characters, or symbols on which operations are
performed by a computer, which may be stored and transmitted in the form of
electrical signals and recorded on magnetic, optical, or mechanical recording media’,
as a quick google search would show.
“Big Data” refers to copious amounts of data which are:
-too large to be processed
-too copious to be analyzed by traditional tools
-not stored or managed efficiently.
However, there is also huge potential in the analysis of Big Data.
Proper management and study of data can help companies make better decisions
based on usage statistics and user interests, thereby helping their growth. Some
companies have even come up with new products and services, based on feedback
received from Big Data analysis opportunities.
8. DATA SET SKILLS FOR BUSINESS
STRUCTURED
DATA
UNSTRUCTURED
DATA
SEMI-
STRUCTURED
DATA
1 2 3
CLASSIFICATION OF DATA
Classification is essential for the study of any subject. So Big
Data is widely classified into three main types, which are:
9. DATA SET SKILLS FOR BUSINESS
STRUCTURED
DATA
1
Structured Data is used to refer to the data which is
already stored in databases, in an ordered manner. It
accounts for about 20% of the total existing data.
There are two sources of structured data- machines and
humans.
All the data received from sensors, web logs and financial
systems are classified under machine-generated data.
These include medical devices, GPS data, data of usage
statistics captured by servers and applications and the
huge amount of data that usually move through trading
platforms, to name a few.
Human-generated structured data mainly includes all the
data a human input into a computer, such as his name
and other personal details. When a person clicks a link on
the internet, or even makes a move in a game, data is
created.
10. DATA SET SKILLS FOR BUSINESS
STRUCTURED
DATA
Employee_ID Employee_Name Gender Department Salary_In_Euros
2365 Rajesh Kulkarni Male Finance 65000
3398 Pratibha Joshi Female Admin 65000
7465 Shushil Roy Male Admin 50000
7500 Shubhojit Das Male Finance 50000
7699 Priya Sane Female Finance 55000
An 'Employee' table in a database is
an example of Structured Data.
Example of Structured
Data
1
11. DATA SET SKILLS FOR BUSINESS
UNSTRUCTURED
DATA
2
Unstructured data is the opposite of
structured data- they have no clear format in
storage.
About 80% of the total data accounted for is
unstructured big data. Most of the data a person
encounters belongs to this category- and until
recently, there was not much to do to it except
storing it or analyzing it manually.
Unstructured data is also classified based on its
source, into machine-generated or human-
generated. Machine-generated data accounts
for all the satellite images, the scientific data
from various experiments and radar data
captured by various facets of technology.
12. DATA SET SKILLS FOR BUSINESS
UNSTRUCTURED
DATA
2 Human-generated unstructured data is found in abundance
across the internet, since it includes social media data, mobile
data and website content. This means that the pictures we upload
to out Facebook or Instagram handles, the videos we watch on
YouTube and even the text messages we send all contribute to the
gigantic heap that is unstructured data.
Example of Unstructured
Data
Output returned by 'Google
Search.’
13. DATA SET SKILLS FOR BUSINESS
SEMI-
STRUCTURED
DATA
3
Semi-structured data appears to be
unstructured at a glance so can be
difficult to analyze.
Information that is not in the traditional
database format as structured data, but contain
some organizational properties which make it
easier to process, are included in semi-structured
data.
For example, NoSQL documents are
considered to be semi-structured,
since they contain keywords that can
be used to process the document
easily
14. DATA SET SKILLS FOR BUSINESS
SEMI-
STRUCTURED
DATA
An email message is one example of semi-structured data. It
includes well-defined data fields in the header such as sender
etc., while the actual body of the message is unstructured.
If you wanted to find out who is emailing whom and when
(information contained in the header), a relational database
might be a good choice. But if you’re more interested in the
message content, big data tools, such as natural language
processing, will be a better ft.
Example of Semi-
structured Data:
Personal data stored in a XML
file.
3
15. DATA SET SKILLS FOR BUSINESS
Social media data is providing remarkable insights to companies on
consumer behavior and sentiment that can be integrated with CRM
data for analysis, with 230 million tweets posted on Twitter per day, 2.7
billion Likes and comments added to Facebook every day, and 60 hours
of video uploaded to YouTube every minute (this is what we mean by
velocity of data).
Machine data consists of information generated from industrial
equipment, real-time data from sensors that track parts and monitor
machinery (often also called the Internet of Things), and even web logs
that track user behavior online. At arcplan client CERN, the largest
particle physics research center in the world, the Large Hadron Collider
(LHC) generates 40 terabytes of data every second during experiments.
Regarding Transactional data, large retailers and even B2B companies
can generate multitudes of data on a regular basis considering that their
transactions consist of one or many items, product IDs, prices, payment
information, manufacturer and distributor data, and much more.
Source
of
Data
16. WhatsApp users
share
347,222
photos.
EVERY
MINUTE OF
EVERY DAY
E-mail users send
204,000,000
messages
YouTube users
upload
4,320
minutes of new
videos.
Google recieves over
4,000,000
search queries.Facebook users
share
2,460,000
pieces of content.
Twitter users tweet
277,000
times.
Amazon makes
83,000$
in online sales.
Instagram users
post
216,000
new photos.
Social Media Data Examples
DATA SET SKILLS FOR BUSINESS
17. Machine Data
Machine data is everywhere. It is created by everything from planes and elevators to
traffic lights and fitness-monitoring devices.
More recently, machine data has gained further attention as use of the Internet of
Things, Hadoop and other big data management technologies has grown.
Application, server and business process logs, call detail records and sensor data are
prime examples of machine data. Internet clickstream data and website activity logs also
factor into discussions of machine data.
Combining machine data with other enterprise data types for analysis is expected to
provide new views and insight on business activities and operations. Machine-generated
data is the lifeblood of the Internet of Things (IoT).
DATA SET SKILLS FOR BUSINESS
18. DATA SET SKILLS FOR BUSINESS
Simply put, IoT is the concept of basically
connecting any device with an on and off
switch to the Internet (and/or to each
other). This includes everything from
cellphones, coffee makers, washing
machines, headphones, lamps, wearable
devices and almost anything else you can
think of. This also applies to components
of machines, for example a jet engine of
an airplane or the drill of an oil rig.
Machine Data
Internet of Things (IoT )
19. Transactional Data
Transactional data are information directly derived as a result of
transactions. Unlike other sorts of data, transactional data contains a
time dimension which means that there is timeliness to it and over time,
it becomes less relevant.
Rather than being the object of transactions like the product being
purchased or the identity of the customer, it is more of a reference data
describing the time, place, prices, payment methods, discount values,
and quantities related to that particular transaction, usually at the point
of sale.
DATA SET SKILLS FOR BUSINESS
20. Transactional Data
Purchases Returns Invoices Payments Credits
Donations Trades Dividends Contracts Interest
Payroll Lending Reservations Signups Subscriptions
Examples of transactional data:
DATA SET SKILLS FOR BUSINESS
21. THE IMPORTANCE OF BIG DATA
DATA SET SKILLS FOR BUSINESS
The importance of big data
does not revolve around how
much data a company has but
how a company utilizes the
collected data.
Every company uses data in its
own way; the more efficiently
a company uses its data, the
more potential it has to grow.
The company can take data
from any source and analyze it
to find answers which will
enable:
22. THE IMPORTANCE OF BIG DATA
• Some tools of Big Data can bring cost advantages to business when large
amounts of data are to be stored and these tools also help in identifying
more efficient ways of doing business.
Cost Savings
• The high speed of tools and in-memory analytics can easily identify new
sources of data which helps businesses analyzing data immediately and
make quick decisions based on the learnings.
Time Reductions
• By knowing the trends of customer needs and satisfaction through
analytics you can create products according to the wants of customers.
New Product Development
• By analyzing big data you can get a better understanding of current
market conditions. For example, by analyzing customers’ purchasing
behaviors, a company can find out the products that are sold the most
and produce products according to this trend.
Understanding the Market Conditions
• Big data tools can do sentiment analysis. Therefore, you can get
feedback about who is saying what about your company. If you want to
monitor and improve the online presence of your business, then, big
data tools can help in all this.
Control Online Reputation
DATA SET SKILLS FOR BUSINESS
23. DATA SET SKILLS FOR BUSINESS
Why not take a BREAK and
WATCH Video 1 in the Resource
section:
What is Big Data and why does it matter?
-Donna Green (Ted X Talk)
25. THE 5 V‘s OF DATA
Volume
Velocity
Variety
Veracity
Value
90% of the data in the
world today has been
created in the last 2 years
alone.
Literally the speed of light!
Data doubles every 40
months.
Structured, semi-
structured and
unstructured data.
Because of the anonimity of
the Internet or possibly false
identities, the reliability of data
is often in question.
Having access to big data
is no good unless we can
turn it into value.
The magnitude
of the data
being generated.
The speed at
which data is being
generated and
aggregated.
The different types
of data.
The trustworthiness
of the data in terms
of accuracy in quality.
The economic
value of the data.
Big Data does a pretty good job of telling us what happened, but not why it happened or
what to do about it. The 5 V‘s represent specific characteristics and properties that can
help us understand both the challenges and advantages of big data initiatives.
DATA SET SKILLS FOR BUSINESS
26. AGE FRIENDLY ECONOMY | FUTURE OPPORTUNITIES FOR SMES
VOLUME
Volume refers to the vast amounts of data generated every
second.
Just think of all the emails, twitter messages, photos, video
clips, sensor data etc. we produce and share every second.
On Facebook alone we send 10 billion messages per
day, click the "like' button 4.5 billion times and upload
350 million new pictures each and every day.
This increasingly makes data sets too large to store and
analyse using traditional database technology. With big
data technology we can now store and use these data sets
with the help of distributed systems, where parts of the
data is stored in different locations and brought together
by software.
DATA SET SKILLS FOR BUSINESS
27. AGE FRIENDLY ECONOMY | FUTURE OPPORTUNITIES FOR SMES
VELOCITY
Velocity refers to the speed at which new data is generated
and the speed at which data moves around.
Just think of social media messages going viral in
seconds, the speed at which credit card transactions
are checked for fraudulent activities, or the
milliseconds it takes trading systems to analyse social
media networks to pick up signals that trigger
decisions to buy or sell shares.
Big data technology allows us now to analyse the data
while it is being generated, without ever putting it into
databases.
DATA SET SKILLS FOR BUSINESS
28. AGE FRIENDLY ECONOMY | FUTURE OPPORTUNITIES FOR SMES
VARIETY
Variety refers to the different types of data we can now
use. In the past we focused on structured data that neatly
fits into tables or relational databases.
Think of photos, video sequences or social media
updates.
With big data technology we can now harness differed
types of data including messages, social media
conversations, photos, sensor data, video or voice
recordings and bring them together with more traditional,
structured data.
DATA SET SKILLS FOR BUSINESS
29. AGE FRIENDLY ECONOMY | FUTURE OPPORTUNITIES FOR SMES
VERACITY
Veracity refers to the trustworthiness of the data.
With many forms of big data, quality and accuracy are less
controllable.
Just think of Twitter posts with hash tags,
abbreviations, typos and colloquial speech as well as
the reliability and accuracy of content.
Big data and analytics technology now allows us to work
with these type of data. The volumes often make up for
the lack of quality or accuracy.
30. AGE FRIENDLY ECONOMY | FUTURE OPPORTUNITIES FOR SMES
VALUE
Value: It is all well and good having access to
big data but unless we can turn it into value it
is useless. So you can safely argue that 'value'
is the most important V of Big Data. It is
important that businesses make a business
case for any attempt to collect and leverage
big data. It is so easy to fall into the buzz trap
and embark on big data initiatives without a
clear understanding of costs and benefits.
Big data can deliver value in almost any area of business or
society:
It helps companies to better understand and serve customers:
Examples include the recommendations made by Amazon or
Netflix.
It allows companies to optimize their processes: Uber is able to
predict demand, dynamically price journeys and send the closest
driver to the customers.
It improves our health care: Government agencies can now predict
flu outbreaks and track them in real time and pharmaceutical
companies are able to use big data analytics to fast-track drug
development.
It helps us to improve security: Government and law enforcement
agencies use big data to foil terrorist attacks and detect cyber crime.
It allows sport stars to boost their performance: Sensors in balls,
cameras on the pitch and GPS trackers on their clothes allow
athletes to analyze and improve upon what they do.
DATA SET SKILLS FOR BUSINESS
31. HOW DOES BIG DATA
BECOME SMART DATA
1. Turning Big Data into Value
2. Smart Data Applications
3. How to Start Smart?
4. Big Data Challenges
DATA SET SKILLS FOR BUSINESS
32. SMART DATA APPLICATIONS
DATA SET SKILLS FOR BUSINESS
Every business in the world
needs data to thrive.
Data is what tells you who your
customers are and how they
operate, and it’s what can
guide you to new insights and
new innovations but first
it is neccessary to find the right
area of interest first.
33. SMART DATA APPLICATIONS
• Fraud
detection/Preventi
on
• Brand sentiment
analysis
• Real time pricing
• Product
placement
• Micro-targeted
advertising
• Monitor patient
visits
• Patient care and
safety
• Reduce
readmittance rates
• Smart meter-stream
analysis
• Proactive equipment
repair
• Power and
consuption matching
• Cell tower
diagnostics
• Bandwidth
allocation
• Proactive
maintenance
• Decreasing time
to market
• Supply planning
• Increasing product
quality
• Network intrusion
detection and
prevention
• Disease outbreak
detection
• Unsafe driving
detection and
monitoring
• Route and time
planning for
public transport
FINANCIAL SERVICES RETAIL TELECOM MANUFACTURING
HEALTHCARE UTILITIES, OIL & GAS PUBLIC SECTOR TRANSPORTATION
DATA SET SKILLS FOR BUSINESS
34. AGE FRIENDLY ECONOMY | FUTURE OPPORTUNITIES FOR SMES
HOW TO START SMART?
Even though data analysis and visualization tools have come a long way in
the past decade, big data analysis still relies on human intervention and
coordination to be successful. You need to know how to ask the right
questions, how to eliminate your own bias, and how to form actionable
insights rather than basic conclusions.
1. Review your
data.
•What data do you
have?
•How is it used?
•Do you have the
expertise to
manage your data?
2. Ask the right
questions.
• What data do
you have and
how is it used?
• Are you being
specific
enough?
3. Draw the
conclusions.
•Could an expert
help to sense-
check your results?
•Can you validate
your hypotheses?
•What further data
do you need?
DATA SET SKILLS FOR BUSINESS
35. BIG DATA CHALLENGES
LACK OF TALENT
To successfully implement
a big data project requires
a sophisticated team of
developers, data scientists
and analysts who also have
a sufficient amount of
domain knowledge to
identify valuable insights.
It‘s easy to get caught up in the hype and opportunity of big data. However, one of
the reasons big data is so underutilized is because big data and big data
technologies also present many challenges.
One survey found that 55% of big data projects are never completed. So what‘s
the problem with big data?
SCALABILITY
Many organizations fail
to take into account
how quickly a big data
project can grow and
evolve. Big data
workloads also tend to
be bursty, making it
difficult to allocate
capacity for resources.
ACTIONABLE
INSIGHTS
A key challenge for
data science teams is
to identify a clear
business objective and
the appropriate data
sources to collect and
analyze to meet that
objective.
DATA
QUALITY
Common causes of
dirty data include:
user imput errors,
duplicate data and
incorrect data
linking.
SECURITY
Specific challenges include:
- User authentication for every
team and team member
accessing the data
- Restricting access based on a
user‘s need
- Recording data access
histories and meeting other
comliance regulations
- proper use of encryprion on
data in-transit and at rest
COST
MANAGEMENT
Businesses pursuing big
data projects must
remember the cost of
training, maintenance
and expansion
DATA SET SKILLS FOR BUSINESS
36. DATA SET SKILLS FOR BUSINESS
Why not take a BREAK and READ
Article 1 in the Resources section:
The emerging role of Big Data in key
development issues: Opportunities,
challenges, and concerns
-Nir Kshetr
37. AGE FRIENDLY ECONOMY | FUTURE OPPORTUNITIES FOR SMES
CASE STUDY:
How AG used the Power of Big Data
THE BACKGROUND
Every time you go shopping, you share intimate details
about your consumption patterns with retailers. And many
of those retailers are studying those details to figure out
what you like, what you need, and which coupons are
most likely to make you happy. AG – Europe’s largest golf
retailer, for example, has figured out how to data-mine its
way into imminent retirees wallets, before they actually
retire.
DATA SET SKILLS FOR BUSINESS
38. AGE FRIENDLY ECONOMY | FUTURE OPPORTUNITIES FOR SMES
CASE STUDY:
How AG used the Power of Big Data
THE SOURCE OF AG’s BIG DATA
AG assigns every customer a Guest ID number, tied to their
credit card, name, or email address that becomes a bucket
that stores a history of everything they've bought and any
demographic information AG has collected from them or
bought from other sources. Using that, their analyst looked
at historical buying data for all the men who had signed up
their registries in the past.
DATA SET SKILLS FOR BUSINESS
39. AGE FRIENDLY ECONOMY | FUTURE OPPORTUNITIES FOR SMES
CASE STUDY:
How AG used the Power of Big Data
THE BIG BIG DATA CONCLUSION
Analyst ran test after test, analyzing the data, and before
long some useful patterns emerged. Gloves, for example.
Lots of men buy golf gloves, but one of Pole’s colleagues
noticed that men on the golf registry were buying smaller
golf peripheral especially golf gloves in the six months
leading up to their retirement. Another analyst noted that
in this 6 month window the frequency of visits to stores
increased.
DATA SET SKILLS FOR BUSINESS
40. DATA SET SKILLS FOR BUSINESS
Finish by READING Article 2 in
the Resources section:
Big Data:
The Management Revolution
by Andrew McAfee and Erik Brynjolfsson