Data analytics

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Data Analytics

Data
Analytics

Data analytics involves
the processes and
activities designed to
obtain and evaluate
data to extract useful
information
What is data
analytics?

What is the data analytics for?
Results of the data analysis can be used to:
 Identify key areas of risk, frauds or misuse
 Improve business processes
 Verify the effectiveness of processes
 Influence on business decisions

Currently, there are five major categories in data analysis

Data analytics: in organizations and companies that
analyze traditional data: transactional and operational
Web analytics: analytics of data traffic on a website
Social analytics: social media data analysis
Mobile analytics: in mobile devices with the aim of analyzing
the data sent, received or transited by these devices
Big data analytics: analytics of big data volumes

Where data comes from?
•Companies get users data from…
GPS
SENSORS SOCIAL MEDIA

An example of data analytics at
American Institutes Research

The above information was taken from Joyanes
Aguilar (2016) “Sistemas de información en la
empresa”. For academic purposes only. The
images were also taken from different sources,
in the same way, for academic purposes only.
Universidad Colegio Mayor de Cundinamarca
Sistemas de información
Jeison Javier García Espinosa
David Fernando García Algarra

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Data analytics is the process of examining raw data to discover patterns, correlations, trends, and other valuable information. Its significance lies in its ability to transform data into actionable insights, ultimately leading to informed decision-making and improved business outcomes. From optimizing operational processes to enhancing customer experiences, data analytics offers a plethora of benefits across various sectors.

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Running title: TRENDS IN COMPUTER INFORMATION SYSTEMS 1 TRENDS IN COMPUTER INFORMATION SYSTEMS 4 Trends in Computer Information Systems, and the Rise to Business Intelligence Shad Martin School for Professional Studies St. Louis University ENG 2005 Dr. Rebecca Wood November 23, 2016 Introduction Our quest to increase our knowledge of Computer Information Systems has produced a number of benefits to humanity. The innovation humans have discovered in Computer Information Systems has led to new sub-areas of study for students and professionals to continue their progression to master all that Computer Information Systems has to offer. Amy Web of the Harvard Business Review reported 8 Tech Trends to Watch in 2016, She noted, “In order to chart the best way forward, you must understand emerging trends: what they are, what they aren’t, and how they operate. Such trends are more than shiny objects; they’re manifestations of sustained changes within an industry sector, society, or human behavior. Trends are a way of seeing and interpreting our current reality, providing a useful framework to organize our thinking, especially when we’re hunting for the unknown. Fads pass. Trends help us forecast the future” (Harvard Business Review, 2015). In short, Amy’s reference to understanding the emerging trends in Computer Information can provide a framework from which, students, professionals, and scientists to conscientiously create a path towards optimizing their efforts. Ensuring we have a fundamental approach to analyze data will enhance our understanding of this subject further. In this paper I will expound on three of the top trends used to provide insight into the data produced from the advancements in Computer Information Systems. These trends or methods are taking place in my workplace within a financial institution, and in many other industries. It is important to note this paper does not provide an inclusive list of all methodologies that exist. Individuals can now leverage analytics to synthesize insights from data to identify emerging risk, manage operational risks, identify trends, improve compliance, and customer satisfaction. Data in and by itself is not always useful. Regardless of the data source, trained professional must understand the best approach to structure the data to make it more useful. In this paper, I will touch on three popular methodology trends occurring in Computer Information Systems. Students and professionals who work with large data would benefit from having a solid understanding of the fundamental principles of Business Intelligence as data scientific approach and when to use these methodologies. The rise of Business Intelligence Computer Information Systems allow many companies to gather and generate large amounts of data on their customers, business activities, potential merger targets, and risks found in their organization. These large sets of data have given rise to vari.

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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

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A report from the Economist Intelligence Unit The evolving role of data in decision- making Sponsored by and © The Economist Intelligence Unit Limited 20131 The evolving role of data in decision-making About the report 2 Introduction 3 Big data: a misunderstood and misused term 4 The use of big data is becoming entrenched 6 The Alice in Wonderland problem 7 It’s all about the culture 9 The price of neglect and the need for resources 11 Conclusion 12 Appendix: survey results 13 Contents 1 2 3 4 5 © The Economist Intelligence Unit Limited 20132 The evolving role of data in decision-making About the report In March and April 2013 the Economist Intelligence Unit surveyed 212 global executives on behalf of CSC and EMC to investigate their views on the increasing integration of data, especially “big data”, into organisations’ decision-making processes. Those polled are based in North America (36%), Asia-Pacifi c (26%), Western Europe (37%), and Eastern Europe (1%). Executives hail from 19 sectors and represent 16 functional roles, and all work for companies with revenues exceeding US$1bn. In addition, in-depth interviews were conducted with experts from a variety of industries. Our thanks are due to the following for their time and insight (in alphabetical order): l Christopher Frank, Vice President, fi nancial services fi rm l Yuri Levin, Distinguished Professor of Operations Management and Director of Master of Management Analytics program, Queen’s University l Al Raymond, Head of U.S. Privacy & Social Media Compliance ,TD Bank l William Ruh, Vice President and Global Technology Director, General Electric The report was written by Lynn Grenier and edited by Christine Emba. © The Economist Intelligence Unit Limited 20133 The evolving role of data in decision-making As the volume of data has grown and the methods of analysing them have improved, organisations have been integrating data more fi rmly into the decision-making process. However, increasing numbers of traditional and non-traditional data sources are inundating companies with data in volumes and types they may not have seen before. Companies are fi nding an increasing gap between the acquisition of data and their meaningful use. Companies have focused extensively on the opportunities and challenges presented by “big data”, recognising that leveraging it to gain competitive advantage can yield signifi cant pay- offs. Research from MIT’s Sloan School of Business indicates that companies that engage in “data-driven decision-making” enjoy a 5 to 6% increase in output and productivity over fi rms that do not. These results are replicated in other metrics, such as asset utilisation, return on equity and market value. The evolving role of data in decision-making, sponsored by CSC and EMC, examines how big data is used in organisations and what impact it has on decision-making. It looks at organisati.

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The purpose of diagnostic analytics is to give a business more actionable information than descriptive analytics alone. It enables you to find out why something is working (or not working), allowing you to correct any wrong assumptions you may have had. The benefits of diagnostic analytics include: Greater insights: It allows for deeper insights into your data when used in conjunction with other types of data analytics. Forming and testing hypotheses: Having evidence of what has previously happened helps businesses to form and test new hypotheses more easily. Identifying anomalies: Diagnostic analytics helps you determine whether data outliers were one-off anomalies or useful, significant findings. Avoiding future mistakes: It helps you identify when and where something didn’t perform well, enabling you to improve efficiency, reduce waste, and avoid costly mistakes. Ease of understanding: Diagnostic findings are generally simple to understand, and once they have been turned into data visualizations, they can be easily shared with stakeholders. Limitations of diagnostic analytics One limitation of diagnostic analytics is that it is easy to mistake correlation for causation. For example, there is a correlation between ice cream sales and bee stings, but that doesn’t mean that one caused the other. They are in fact both dependent on a third factor (warm temperatures). So any correlations in your data must always be fully investigated before assuming a causal link. Diagnostic analytics can’t predict the future, or make suggestions about what should be done — it can only explain why something happened, and any further information can only be gained either from a knowledgeable person making educated guesses or from predictive or prescriptive analytics. Nor does it answer the question “What should we do?” — this is answered by the field of prescriptive analytics. Diagnostic analytics doesn’t give definitive answers. It can’t tell you that A definitely caused B, only that a certain percentage of people who encountered event A did (or did not) encounter event B. The accuracy of outcomes can be improved, however, with better-quality data, larger data sets, and the involvement of domain experts in interpreting the data. How to use diagnostic analytics in your business The first step in diagnostic analytics is deciding on the questions you want answers to. These may include questions like: "What causes customers to cancel their subscriptions to our online product?" "Why has web traffic decreased by so much this month?" "Why are so many of our employees quitting their jobs this year?" "Why do sales always increase in November?" You should ensure that you have access to a reasonably large data set containing good-quality data that’s relevant to your question. This will help you to draw useful inferences and avoid making decisions based on outliers or the opinions of a vocal minority. Some examples of the kinds of data sets that are large enough to be set.

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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

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All types of machine automated systems are generating large amount of data in different forms likestatistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper weare discussing issues, challenges, and application of these types of Big Data with the consideration of bigdata dimensions. Here we are discussing social media data analytics, content based analytics, text dataanalytics, audio, and video data analytics their issues and expected application areas. It will motivateresearchers to address these issues of storage, management, and retrieval of data known as Big Data. Aswell as the usages of Big Data analytics in India is also highlighted.

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All types of machine automated systems are generating large amount of data in different forms like statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we are discussing issues, challenges, and application of these types of Big Data with the consideration of big data dimensions. Here we are discussing social media data analytics, content based analytics, text data analytics, audio, and video data analytics their issues and expected application areas. It will motivate researchers to address these issues of storage, management, and retrieval of data known as Big Data. As well as the usages of Big Data analytics in India is also highlighted.

BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...

BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...

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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.

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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.

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All types of machine automated systems are generating large amount of data in different forms likestatistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper weare discussing issues, challenges, and application of these types of Big Data with the consideration of bigdata dimensions. Here we are discussing social media data analytics, content based analytics, text dataanalytics, audio, and video data analytics their issues and expected application areas. It will motivateresearchers to address these issues of storage, management, and retrieval of data known as Big Data. Aswell as the usages of Big Data analytics in India is also highlighted.

BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...

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All types of machine automated systems are generating large amount of data in different forms like statistical, text, audio, video, sensor, and bio-metric data that emerges the term Big Data. In this paper we are discussing issues, challenges, and application of these types of Big Data with the consideration of big data dimensions. Here we are discussing social media data analytics, content based analytics, text data analytics, audio, and video data analytics their issues and expected application areas. It will motivate researchers to address these issues of storage, management, and retrieval of data known as Big Data. As well as the usages of Big Data analytics in India is also highlighted.

BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...

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BIG DATA ANALYTICS: CHALLENGES AND APPLICATIONS FOR TEXT, AUDIO, VIDEO, AND S...

According to Altimeter Group research, the average enterprise-class company owns 178 social media accounts, while 13 departments—from marketing to customer support to legal-- actively engage in social media. Yet social media— and as a result, social data— are still largely isolated from business-critical enterprise data sourced from platforms such as Customer Relationship Management, Business Intelligence and market research. This lack of a holistic view of social signals in the context of other enterprise and external data can lead to partially-informed decisions, missed opportunity, and increased risk and cost, as the organization makes decisions without the benefit of critical input from external constituencies. In this Altimeter Group research report reflecting input from 35 enterprise-class organizations and technology ecosystem contributors, industry analyst Susan Etlinger lays out an imperative for Social Data Intelligence, identifying key dimensions that organizations must understand, pragmatic steps they can take toward mature integration, and how successful businesses are already using social data in the context of other critical enterprise data to drive measurable value throughout the organization.

[Report] Social Data Intelligence, by Susan Etlinger

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Social Data Intelligence: Integrating Social and Enterprise Data for Competit...

Social Data Intelligence: Integrating Social and Enterprise Data for Competit...

Social Data Intelligence: Integrating Social and Enterprise Data for Competit...

With increased utilization of data within their operational and strategic processes, enterprises need to ensure data quality and accuracy. Data curation is a process that can ensure the quality of data and its fitness for use. Traditional approaches to curation are struggling with increased data volumes, and near real-time demands for curated data. In response, curation teams have turned to community crowd-sourcing and semi-automatedmetadata tools for assistance. This chapter provides an overview of data curation, discusses the business motivations for curating data and investigates the role of community-based data curation, focusing on internal communities and pre-competitive data collaborations. The chapter is supported by case studies from Wikipedia, The New York Times, Thomson Reuters, Protein Data Bank and ChemSpider upon which best practices for both social and technical aspects of community-driven data curation are described. E. Curry, A. Freitas, and S. O’Riáin, “The Role of Community-Driven Data Curation for Enterprises,” in Linking Enterprise Data, D. Wood, Ed. Boston, MA: Springer US, 2010, pp. 25-47.

The Role of Community-Driven Data Curation for Enterprises

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The Role of Community-Driven Data Curation for Enterprises

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20240508 QFM014 Elixir Reading List April 2024.pdf

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Data analytics

1. Data Analytics

2. Data analytics involves the processes and activities designed to obtain and evaluate data to extract useful information What is data analytics?

3. What is the data analytics for? Results of the data analysis can be used to:  Identify key areas of risk, frauds or misuse  Improve business processes  Verify the effectiveness of processes  Influence on business decisions

4. Currently, there are five major categories in data analysis

5. Data analytics: in organizations and companies that analyze traditional data: transactional and operational Web analytics: analytics of data traffic on a website Social analytics: social media data analysis Mobile analytics: in mobile devices with the aim of analyzing the data sent, received or transited by these devices Big data analytics: analytics of big data volumes

6. Where data comes from? •Companies get users data from… GPS SENSORS SOCIAL MEDIA

7. An example of data analytics at American Institutes Research

8. The above information was taken from Joyanes Aguilar (2016) “Sistemas de información en la empresa”. For academic purposes only. The images were also taken from different sources, in the same way, for academic purposes only. Universidad Colegio Mayor de Cundinamarca Sistemas de información Jeison Javier García Espinosa David Fernando García Algarra