The document discusses social media analytics and big data. It covers topics like social media data, big data characteristics, steps in social media analytics including listening, analyzing, engaging and integrating data. It also discusses sentiment analysis of social data, tools for social media analytics and big data, and challenges around opinion spam and fake reviews.
Jaganadh G presented on social media analytics at the Politics and Social Media conference. He discussed how social media has evolved from a passive platform to an interactive space for sharing ideas and opinions. He outlined various social media tools and how sentiment analysis can be used to understand public opinion and predict events from social media data, including in political science and economics. He proposed two themes for studying political movements in India using social media data and discussed how parties use social media and its impact.
This document discusses how social media can be used to help businesses in several ways:
1. Social media can drive brand awareness, relevance, and value by amplifying messaging to more people.
2. It provides ways to increase sales through new customer acquisition, increased transactions, and product exposure.
3. It allows for improved customer support, public relations, loyalty, and intelligence gathering.
4. User-generated content like reviews provide a global platform for sharing opinions that can influence decisions. Hashtags help group posts by topic to understand sentiment.
1) Data analytics is the process of examining large data sets to uncover patterns and insights. It involves descriptive, predictive, and prescriptive analysis.
2) Descriptive analysis summarizes past events, predictive analysis forecasts future events, and prescriptive analysis recommends actions.
3) Major companies like Facebook, Amazon, Uber, banks and Spotify extensively use big data and data analytics to improve customer experience, detect fraud, personalize recommendations and gain business insights.
This document discusses various aspects of consumer psychology and factors that influence consumer buying decisions. It provides definitions of consumer psychology and consumers. It then outlines several key factors that influence how consumers choose businesses and products, including culture, social classes, family, personality, motivation, and cultural trends. It also provides examples of how McDonald's tailors its menu to different cultures. Finally, it briefly discusses the breakthroughs of advertising pioneer David Ogilvy and some common advertising styles such as slice-of-life, lifestyle, testimonial, fantasy, demonstration, and musical.
Traditional marketing focuses on print, television, radio and other offline advertising methods while digital marketing utilizes online channels like websites, social media, mobile and other digital technologies. Some key differences are that traditional marketing has higher costs but allows for more personal interactions while digital marketing has lower costs, global reach, and allows for easier tracking of results and improved targeting of audiences. Both have benefits for raising brand awareness but digital provides more measurable outcomes.
1. Sentiment analysis involves using natural language processing, statistics, or machine learning to identify and extract subjective information like opinions, attitudes, and emotions from text.
2. It can analyze sentiment at different levels of granularity, such as document, sentence, or entity level.
3. Sentiment analysis has many applications including understanding customer opinions, predicting election results, and improving marketing strategies.
4. Performing accurate sentiment analysis requires understanding the concept of an opinion as a quintuple that identifies the target, aspect, sentiment polarity, opinion holder, and time.
Jaganadh G presented on social media analytics at the Politics and Social Media conference. He discussed how social media has evolved from a passive platform to an interactive space for sharing ideas and opinions. He outlined various social media tools and how sentiment analysis can be used to understand public opinion and predict events from social media data, including in political science and economics. He proposed two themes for studying political movements in India using social media data and discussed how parties use social media and its impact.
This document discusses how social media can be used to help businesses in several ways:
1. Social media can drive brand awareness, relevance, and value by amplifying messaging to more people.
2. It provides ways to increase sales through new customer acquisition, increased transactions, and product exposure.
3. It allows for improved customer support, public relations, loyalty, and intelligence gathering.
4. User-generated content like reviews provide a global platform for sharing opinions that can influence decisions. Hashtags help group posts by topic to understand sentiment.
1) Data analytics is the process of examining large data sets to uncover patterns and insights. It involves descriptive, predictive, and prescriptive analysis.
2) Descriptive analysis summarizes past events, predictive analysis forecasts future events, and prescriptive analysis recommends actions.
3) Major companies like Facebook, Amazon, Uber, banks and Spotify extensively use big data and data analytics to improve customer experience, detect fraud, personalize recommendations and gain business insights.
This document discusses various aspects of consumer psychology and factors that influence consumer buying decisions. It provides definitions of consumer psychology and consumers. It then outlines several key factors that influence how consumers choose businesses and products, including culture, social classes, family, personality, motivation, and cultural trends. It also provides examples of how McDonald's tailors its menu to different cultures. Finally, it briefly discusses the breakthroughs of advertising pioneer David Ogilvy and some common advertising styles such as slice-of-life, lifestyle, testimonial, fantasy, demonstration, and musical.
Traditional marketing focuses on print, television, radio and other offline advertising methods while digital marketing utilizes online channels like websites, social media, mobile and other digital technologies. Some key differences are that traditional marketing has higher costs but allows for more personal interactions while digital marketing has lower costs, global reach, and allows for easier tracking of results and improved targeting of audiences. Both have benefits for raising brand awareness but digital provides more measurable outcomes.
1. Sentiment analysis involves using natural language processing, statistics, or machine learning to identify and extract subjective information like opinions, attitudes, and emotions from text.
2. It can analyze sentiment at different levels of granularity, such as document, sentence, or entity level.
3. Sentiment analysis has many applications including understanding customer opinions, predicting election results, and improving marketing strategies.
4. Performing accurate sentiment analysis requires understanding the concept of an opinion as a quintuple that identifies the target, aspect, sentiment polarity, opinion holder, and time.
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
This document discusses opinion leadership and the role of opinion leaders in influencing the adoption of innovations. It covers several key points:
1) Opinion leaders are individuals who informally influence others' attitudes and behaviors regarding innovations. Their adoption behaviors help determine the rate of adoption in a social system.
2) Information flows from mass media to opinion leaders and then from opinion leaders to their followers, in a two-step process. Opinion leaders have greater exposure to new ideas than their followers.
3) Opinion leaders tend to be more innovative, cosmopolitan, and socially active than their followers. However, their level of innovativeness depends on the norms of their social system - in traditional systems, neither leaders nor followers
Sentiment analysis techniques are used to analyze customer reviews and understand sentiment. Lexical analysis uses dictionaries to analyze sentiment while machine learning uses labeled training data. The document describes using these techniques to analyze hotel reviews from Booking.com. Word clouds and scatter plots of reviews are generated, showing mostly negative sentiment around breakfast, staff, rooms and facilities. Topic modeling reveals specific issues to address like soundproofing, air conditioning and parking. The analysis helps the hotel manager understand customer sentiment and priorities for improvement.
This document discusses consumer self-concept and its relationship to product image and choice. It defines key terms like self-image, ideal self-image, social self-image, and ideal social self-image. Products are seen as having images determined by attributes like packaging and advertising that can be congruent or incongruent with consumers' self-concepts. Research has found relationships between self-image/product image congruity and consumer choice, though findings are debated for social and ideal social self-images. Product conspicuousness and personalization may also influence these relationships. Personality and striving for an ideal self can impact how self-concept relates to preferences and purchase intentions. A product's image can develop from how consumers
Sentiment Prediction and Analysis of Major Airline Tweets using Rule-Based and Machine Learning Classifications. General Assembly Data Science Capstone Project.
This document discusses sentiment analysis techniques for understanding customer opinions expressed in text. It describes how sentiment analysis uses natural language processing and machine learning algorithms to classify text sentiments as positive, negative, or neutral. Conducting sentiment analysis can provide businesses with valuable customer insight to improve products, services, and marketing strategies.
This document provides an introduction to consumer behavior. It defines consumer behavior as how individuals search for, purchase, use, and dispose of products and services. Understanding consumer behavior is important for developing effective marketing strategies that satisfy consumer needs. Key applications of consumer behavior include informing marketing mix decisions, target market selection, and public policy efforts. The document also outlines some advantages and disadvantages of applying a consumer behavior perspective.
This document outlines topics related to data analytics including the definition of data analytics, the data analytics process, types of data analytics, steps of data analytics, tools used, trends in the field, techniques and methods, the importance of data analytics, skills required, and benefits. It defines data analytics as the science of analyzing raw data to make conclusions and explains that many analytics techniques and processes have been automated into algorithms. The importance of data analytics includes predicting customer trends, analyzing and interpreting data, increasing business productivity, and driving effective decision-making.
Operational CRM generally refers to services that allow an organization to take care of their customers. It provides support for various business processes, which can include sales, marketing and service. Contact and call centers, data aggregation systems and web sites are a few examples of operational CRM. If your company has a high customer turnover, or perhaps high service costs, Operational CRM Solutions is a tool that can help you solve your problems. The high tech expertise of CRM gives you access to information about your customer as well as giving you a clear view of your customers needs.
t is a systematic approach to analyze customer data and interactions to improve various business processes in Sales, Marketing and Service. The main purpose of Analytical CRM is to gather customer information from various channels and gain knowledge about customers’ behaviors and buying pattern as much as possible. It helps an organization to develop new marketing strategy, campaign management, customer acquisition and retention.
Data analytics involves analyzing data to extract useful information. It is used to identify risks, improve business processes, verify effectiveness, and influence decisions. There are five categories: data analytics of transactions and operations; web analytics of website traffic; social analytics of social media; mobile analytics of device data; and big data analytics. Companies obtain user data from GPS, sensors, and social media to perform analyses that benefit organizations.
This document discusses a presentation given at the Sentiment Analysis Symposium in San Francisco in October 2012. The presentation introduces opinion mining and sentiment analysis, covering key concepts, applications, challenges, and techniques. Some of the main topics discussed include defining opinion mining and sentiment analysis, analyzing public mood and opinions on social media, predicting future trends from social data, and addressing challenges like determining the credibility and trustworthiness of opinions.
Social CRM - Concept, Benefits and Approach to adoptFabio Cipriani
A call for reviewing current CRM Strategy, Processes and Mindset throughout companies
- Concept
- Comparison with traditional CRM
- Benefits
- Approach for adoption
- How to put it to work
The various factors which influences the retail consumers are discussed in the presentation. various factors like culture, personal, psychological factors etc., are explained in this ppt.
The document discusses consumer attitudes and how they are formed from various sources of information and experiences. It also discusses several models of attitude formation, including the tricomponent model involving cognition, affect, and conation. Marketers can influence attitude change through advertising by appealing to personality traits or resolving conflicting attitudes. Personality is shaped by both innate and learned factors and influences consumer behavior. Marketers segment consumers based on personality traits like openness to experiences and self-monitoring behavior.
This document discusses the scope, growth, and career opportunities in analytics. It defines analytics as the process of analyzing large datasets to discover useful patterns and insights. Analytics helps organizations make better, faster decisions by identifying opportunities for improvement. The analytics market in India is worth $375 million currently and is expected to increase to $1.15 billion. Analytics jobs in India range from 500 to 800 analysts out of every 10,000 employees at a company. Salaries for analytics professionals increase with more years of experience, ranging from 3.2 lakhs for 0-2 years of experience to over 27 lakhs for more than 12 years of experience. The field of analytics is highly in demand and is considered the sexiest job of
The document discusses post-purchase consumer behavior, including the stages of post-purchase satisfaction and dissatisfaction. It describes how after making a purchase, consumers will evaluate whether they are satisfied or dissatisfied based on if the product meets their expectations. If satisfied, they may become repeat customers, but if dissatisfied they may switch brands, spread negative word-of-mouth, or take actions like complaining or legal action. The document includes a flow chart outlining potential post-purchase behaviors and responses to satisfaction and dissatisfaction.
Motivation is an inner psychological force that activates and directs behavior toward goals. It stems from needs and wants and is influenced by individual traits and learning. Highly motivated employees tend to work more efficiently and be more productive.
Maslow's hierarchy of needs proposes that people are motivated to fulfill physiological, safety, social, esteem and self-actualization needs in that order. ERG theory is an alternative that groups these needs into existence, relatedness and growth. McClelland's needs theory identifies the needs for power, affiliation and achievement as key motivators. Expectancy theory and equity theory are process-based theories that explain how motivation occurs based on desired outcomes and perceptions of fair treatment.
The document contains a sample question paper for a digital marketing exam. It includes 3 sections - the first with 5 short questions, the second with 3 long-form questions, and the third being a case study on the company Swiggy. The case study details Swiggy's origins, business model, use of digital marketing including social media, and competition in the Indian food delivery market. It concludes with 3 discussion questions about Swiggy's digital marketing strategy and how it can create a successful future business model.
This document provides an overview of key concepts related to big data and data analytics. It discusses what big data is in terms of volume, velocity, and variety. Examples of big data sources and amounts of data from companies like Google, Facebook, and Twitter are provided. Architectures for processing big data using Hadoop and MapReduce are described. Different types of business analytics including descriptive, predictive, and prescriptive analytics are summarized. Research areas involving big data analytics are outlined. Finally, references on big data and analytics are listed.
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
This document discusses opinion leadership and the role of opinion leaders in influencing the adoption of innovations. It covers several key points:
1) Opinion leaders are individuals who informally influence others' attitudes and behaviors regarding innovations. Their adoption behaviors help determine the rate of adoption in a social system.
2) Information flows from mass media to opinion leaders and then from opinion leaders to their followers, in a two-step process. Opinion leaders have greater exposure to new ideas than their followers.
3) Opinion leaders tend to be more innovative, cosmopolitan, and socially active than their followers. However, their level of innovativeness depends on the norms of their social system - in traditional systems, neither leaders nor followers
Sentiment analysis techniques are used to analyze customer reviews and understand sentiment. Lexical analysis uses dictionaries to analyze sentiment while machine learning uses labeled training data. The document describes using these techniques to analyze hotel reviews from Booking.com. Word clouds and scatter plots of reviews are generated, showing mostly negative sentiment around breakfast, staff, rooms and facilities. Topic modeling reveals specific issues to address like soundproofing, air conditioning and parking. The analysis helps the hotel manager understand customer sentiment and priorities for improvement.
This document discusses consumer self-concept and its relationship to product image and choice. It defines key terms like self-image, ideal self-image, social self-image, and ideal social self-image. Products are seen as having images determined by attributes like packaging and advertising that can be congruent or incongruent with consumers' self-concepts. Research has found relationships between self-image/product image congruity and consumer choice, though findings are debated for social and ideal social self-images. Product conspicuousness and personalization may also influence these relationships. Personality and striving for an ideal self can impact how self-concept relates to preferences and purchase intentions. A product's image can develop from how consumers
Sentiment Prediction and Analysis of Major Airline Tweets using Rule-Based and Machine Learning Classifications. General Assembly Data Science Capstone Project.
This document discusses sentiment analysis techniques for understanding customer opinions expressed in text. It describes how sentiment analysis uses natural language processing and machine learning algorithms to classify text sentiments as positive, negative, or neutral. Conducting sentiment analysis can provide businesses with valuable customer insight to improve products, services, and marketing strategies.
This document provides an introduction to consumer behavior. It defines consumer behavior as how individuals search for, purchase, use, and dispose of products and services. Understanding consumer behavior is important for developing effective marketing strategies that satisfy consumer needs. Key applications of consumer behavior include informing marketing mix decisions, target market selection, and public policy efforts. The document also outlines some advantages and disadvantages of applying a consumer behavior perspective.
This document outlines topics related to data analytics including the definition of data analytics, the data analytics process, types of data analytics, steps of data analytics, tools used, trends in the field, techniques and methods, the importance of data analytics, skills required, and benefits. It defines data analytics as the science of analyzing raw data to make conclusions and explains that many analytics techniques and processes have been automated into algorithms. The importance of data analytics includes predicting customer trends, analyzing and interpreting data, increasing business productivity, and driving effective decision-making.
Operational CRM generally refers to services that allow an organization to take care of their customers. It provides support for various business processes, which can include sales, marketing and service. Contact and call centers, data aggregation systems and web sites are a few examples of operational CRM. If your company has a high customer turnover, or perhaps high service costs, Operational CRM Solutions is a tool that can help you solve your problems. The high tech expertise of CRM gives you access to information about your customer as well as giving you a clear view of your customers needs.
t is a systematic approach to analyze customer data and interactions to improve various business processes in Sales, Marketing and Service. The main purpose of Analytical CRM is to gather customer information from various channels and gain knowledge about customers’ behaviors and buying pattern as much as possible. It helps an organization to develop new marketing strategy, campaign management, customer acquisition and retention.
Data analytics involves analyzing data to extract useful information. It is used to identify risks, improve business processes, verify effectiveness, and influence decisions. There are five categories: data analytics of transactions and operations; web analytics of website traffic; social analytics of social media; mobile analytics of device data; and big data analytics. Companies obtain user data from GPS, sensors, and social media to perform analyses that benefit organizations.
This document discusses a presentation given at the Sentiment Analysis Symposium in San Francisco in October 2012. The presentation introduces opinion mining and sentiment analysis, covering key concepts, applications, challenges, and techniques. Some of the main topics discussed include defining opinion mining and sentiment analysis, analyzing public mood and opinions on social media, predicting future trends from social data, and addressing challenges like determining the credibility and trustworthiness of opinions.
Social CRM - Concept, Benefits and Approach to adoptFabio Cipriani
A call for reviewing current CRM Strategy, Processes and Mindset throughout companies
- Concept
- Comparison with traditional CRM
- Benefits
- Approach for adoption
- How to put it to work
The various factors which influences the retail consumers are discussed in the presentation. various factors like culture, personal, psychological factors etc., are explained in this ppt.
The document discusses consumer attitudes and how they are formed from various sources of information and experiences. It also discusses several models of attitude formation, including the tricomponent model involving cognition, affect, and conation. Marketers can influence attitude change through advertising by appealing to personality traits or resolving conflicting attitudes. Personality is shaped by both innate and learned factors and influences consumer behavior. Marketers segment consumers based on personality traits like openness to experiences and self-monitoring behavior.
This document discusses the scope, growth, and career opportunities in analytics. It defines analytics as the process of analyzing large datasets to discover useful patterns and insights. Analytics helps organizations make better, faster decisions by identifying opportunities for improvement. The analytics market in India is worth $375 million currently and is expected to increase to $1.15 billion. Analytics jobs in India range from 500 to 800 analysts out of every 10,000 employees at a company. Salaries for analytics professionals increase with more years of experience, ranging from 3.2 lakhs for 0-2 years of experience to over 27 lakhs for more than 12 years of experience. The field of analytics is highly in demand and is considered the sexiest job of
The document discusses post-purchase consumer behavior, including the stages of post-purchase satisfaction and dissatisfaction. It describes how after making a purchase, consumers will evaluate whether they are satisfied or dissatisfied based on if the product meets their expectations. If satisfied, they may become repeat customers, but if dissatisfied they may switch brands, spread negative word-of-mouth, or take actions like complaining or legal action. The document includes a flow chart outlining potential post-purchase behaviors and responses to satisfaction and dissatisfaction.
Motivation is an inner psychological force that activates and directs behavior toward goals. It stems from needs and wants and is influenced by individual traits and learning. Highly motivated employees tend to work more efficiently and be more productive.
Maslow's hierarchy of needs proposes that people are motivated to fulfill physiological, safety, social, esteem and self-actualization needs in that order. ERG theory is an alternative that groups these needs into existence, relatedness and growth. McClelland's needs theory identifies the needs for power, affiliation and achievement as key motivators. Expectancy theory and equity theory are process-based theories that explain how motivation occurs based on desired outcomes and perceptions of fair treatment.
The document contains a sample question paper for a digital marketing exam. It includes 3 sections - the first with 5 short questions, the second with 3 long-form questions, and the third being a case study on the company Swiggy. The case study details Swiggy's origins, business model, use of digital marketing including social media, and competition in the Indian food delivery market. It concludes with 3 discussion questions about Swiggy's digital marketing strategy and how it can create a successful future business model.
This document provides an overview of key concepts related to big data and data analytics. It discusses what big data is in terms of volume, velocity, and variety. Examples of big data sources and amounts of data from companies like Google, Facebook, and Twitter are provided. Architectures for processing big data using Hadoop and MapReduce are described. Different types of business analytics including descriptive, predictive, and prescriptive analytics are summarized. Research areas involving big data analytics are outlined. Finally, references on big data and analytics are listed.
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.
Big Data & Business Analytics: Understanding the MarketspaceBala Iyer
This document provides an overview of big data and business analytics. It discusses the growth of data and importance of analytics to businesses. The key topics covered include defining big data and data science, analyzing the analytics ecosystem and key players, examining use cases of analytics at companies like Target and Whirlpool, and providing recommendations for building an analytics capability and working with analytics vendors. The presentation emphasizes how data-driven decisions can improve business performance but also notes challenges to overcome like skills shortages and changing organizational culture.
Opportunities and methodological challenges of Big Data for official statist...Piet J.H. Daas
1) The document discusses opportunities and challenges of using Big Data for official statistics. It describes Big Data as data that is difficult to collect, store, or process using conventional statistical systems due to issues of volume, velocity, structure, or variety.
2) The author outlines their experiences at Statistics Netherlands using various Big Data sources like traffic sensor data, mobile phone data, and social media data. They discuss methodological challenges in accessing and analyzing large volumes of data, dealing with noisy and unstructured data, and addressing issues of selectivity.
3) The document emphasizes the need for new skills like data science, high performance computing, and people with open and pragmatic mindsets to work with Big Data. It also addresses privacy
Victoria L. Lemieux presented on using mixed-initiative social media analytics and visualization to innovate regulatory practices. The methodology uses sentiment analysis and visual analytics tools to explore historical Twitter data and measure public trust in regulatory processes. It aims to provide regulatory agencies support in notice-and-comment rulemaking by analyzing social media sentiment and discussion. The presentation highlighted the mixed-initiative social media analytics methodology and visualization tool developed, potential applications in regulatory impact assessment and rulemaking, and lessons learned from the project.
The document discusses how Twitter data analytics can provide valuable insights into user behavior, trends, and sentiment by analyzing the massive amounts of data generated every second on Twitter, including the volume of tweets, semantics, and metadata. Popular tools for Twitter data analytics include Twitter's built-in platform as well as third party tools like Sprout Social and integrating Twitter with Google Analytics. The benefits and challenges of Twitter data analytics for business marketing, public sentiment analysis, and competitive intelligence are also examined.
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.
Applications of Big Data Analytics in BusinessesT.S. Lim
The document discusses big data and big data analytics. It begins with definitions of big data from various sources that emphasize the large volumes of structured and unstructured data. It then discusses key aspects of big data including the three Vs of volume, variety, and velocity. The document also provides examples of big data applications in various industries. It explains common analytical methods used in big data including linear regression, decision trees, and neural networks. Finally, it discusses popular tools and frameworks for big data analytics.
This document provides an overview and agenda for building an analytics capability. It discusses key topics such as:
- The importance of big data and analytics for business decisions
- Building an analytics capability requires the right people, processes, and technology
- Companies can build capabilities internally, outsource work, or use a hybrid approach
- When outsourcing analytics work, firms need to consider issues like vendor skills, data protection, and intellectual property ownership
The document discusses the paradigm shift in social science research enabled by big data. Key points:
- Advances in data collection technologies and analytics tools now allow researchers to study social phenomena at an unprecedented scale, depth, and scope. This represents a potential scientific paradigm shift toward computational social science.
- Factors driving this shift include the massive growth of digital data from various sources, reduced costs of data collection, and new capabilities for large-scale empirical research.
- The new approaches enabled by big data help address traditional tradeoffs in research between generalizability, control, and realism. Large, unobtrusively collected data sets allow for more realistic and controlled studies of real-world phenomena.
IRJET - Election Result Prediction using Sentiment AnalysisIRJET Journal
This document proposes a method to predict election results using sentiment analysis of social media data. It involves collecting data from Twitter, Facebook, and Instagram using their APIs. The data will then be preprocessed by removing special characters and URLs. Popular machine learning algorithms like Naive Bayes and SVM will be trained on the preprocessed data to classify tweets as positive, negative, or neutral sentiment toward political parties. The classified tweets will then be analyzed to predict the outcome of elections.
This document provides a review of techniques, tools, and platforms for analyzing social media data. It discusses the types of social media data and formats available, as well as tools for accessing, cleaning, analyzing, and visualizing social media data. Some key challenges of social media research are the restricted access to comprehensive data sources, lack of tools for in-depth analysis without programming, and need for large data storage and computing facilities to support research at scale. The document provides a methodology and critique of current approaches and outlines requirements to better support social media research.
Minne analytics presentation 2018 12 03 final compressedBonnie Holub
A large transportation company needed help optimizing their transportation model to reduce costs. Teradata developed a transportation optimization model and user interface tool that takes forecasted volumes and determines the most optimal transportation modes and routes to deliver products to customers while considering capacities, constraints, and business rules. The tool selects the lowest cost solutions for each material/customer pair and allows users to conduct "what-if" analysis of scenarios to further reduce total costs.
Minne analytics presentation 2018 12 03 final compressedBonnie Holub
Monday was another great conference by MinneAnalytics! #MinneFRAMA was a great success with over 1,100 attendees at Science Museum of Minnesota. Alison Rempel Brown is a great host! A Teradata colleague told me that her post about my presentation "blew up" with hits and she got over 2K views, and 60+ likes. I'm proud to be a part of this great #datascience organization brining #machinelearning and #artificialintelligence #analytics to our #bigdata clients. If you want my slides, here they are.
McKinsey Global Institute Big data The next frontier for innova.docxandreecapon
McKinsey Global Institute
Big data: The next frontier for innovation, competition, and productivity 27
2. Bigdatatechniquesand technologies
A wide variety of techniques and technologies has been developed and adapted to aggregate, manipulate, analyze, and visualize big data. These techniques and technologies draw from several fields including statistics, computer science, applied mathematics, and economics. This means that an organization that intends to derive value from big data has to adopt a flexible, multidisciplinary approach. Some techniques and technologies were developed in a world with access to far smaller volumes and variety in data, but have been successfully adapted so that they are applicable to very large sets of more diverse data. Others have been developed more recently, specifically to capture value from big data. Some were developed by academics and others by companies, especially those with online business models predicated on analyzing big data.
This report concentrates on documenting the potential value that leveraging big data can create. It is not a detailed instruction manual on how to capture value, a task that requires highly specific customization to an organization’s context, strategy, and capabilities. However, we wanted to note some of the main techniques and technologies that can be applied to harness big data to clarify the way some
of the levers for the use of big data that we describe might work. These are not comprehensive lists—the story of big data is still being written; new methods and tools continue to be developed to solve new problems. To help interested readers find a particular technique or technology easily, we have arranged these lists alphabetically. Where we have used bold typefaces, we are illustrating the multiple interconnections between techniques and technologies. We also provide a brief selection of illustrative examples of visualization, a key tool for understanding very large-scale data and complex analyses in order to make better decisions.
TECHNIQUES FOR ANALYZING BIG DATA
There are many techniques that draw on disciplines such as statistics and computer science (particularly machine learning) that can be used to analyze datasets. In this section, we provide a list of some categories of techniques applicable across a range of industries. This list is by no means exhaustive. Indeed, researchers continue to develop new techniques and improve on existing ones, particularly in response to the need
to analyze new combinations of data. We note that not all of these techniques strictly require the use of big data—some of them can be applied effectively to smaller datasets (e.g., A/B testing, regression analysis). However, all of the techniques we list here can be applied to big data and, in general, larger and more diverse datasets can be used to generate more numerous and insightful results than smaller, less diverse ones.
A/B testing. A technique in which a control group is compa ...
A short Introduction to the Influence of Big Data in today's world and how it's helping the organization and industry to be familiar with their clients and partners.
This document discusses how big data and analytics will help the world of charities. It argues that the financial flows to charities, their operations, and government policy will all need to shift as data technology rapidly grows. Charities will need to address issues around who owns and manages the increasing data being collected. This data, from sources like charities, donors, news, and third parties, is currently being used by various stakeholders like funders, charities, and social enterprises to make funding decisions, identify opportunities, and drive impact. A case study is presented on a hackathon that used data and one on how data is influencing estate planning and charitable giving conversations.
This document discusses how big data and analytics will help the world of charities. It argues that the financial flows to charities, their operations, and government policy will all need to shift as data technology rapidly grows. Charities will need to address issues around who owns and manages the increasing data being collected. This data, from sources like charities, donors, news, and third parties, is currently being used by various stakeholders like funders, charities, and social enterprises to make funding decisions, identify opportunities, and drive impact. A case study is presented on how data was used in a hackathon event. The role of data in influencing estate planning and planned giving conversations is also discussed.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
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(𝐓𝐋𝐄 𝟏𝟎𝟎) (𝐋𝐞𝐬𝐬𝐨𝐧 𝟏)-𝐏𝐫𝐞𝐥𝐢𝐦𝐬
𝐃𝐢𝐬𝐜𝐮𝐬𝐬 𝐭𝐡𝐞 𝐄𝐏𝐏 𝐂𝐮𝐫𝐫𝐢𝐜𝐮𝐥𝐮𝐦 𝐢𝐧 𝐭𝐡𝐞 𝐏𝐡𝐢𝐥𝐢𝐩𝐩𝐢𝐧𝐞𝐬:
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𝐄𝐱𝐩𝐥𝐚𝐢𝐧 𝐭𝐡𝐞 𝐍𝐚𝐭𝐮𝐫𝐞 𝐚𝐧𝐝 𝐒𝐜𝐨𝐩𝐞 𝐨𝐟 𝐚𝐧 𝐄𝐧𝐭𝐫𝐞𝐩𝐫𝐞𝐧𝐞𝐮𝐫:
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2. Contents
• Social Media Data
• BigData
• Steps of social media analytics
• Sensitive Analysis
• Social Media Analytics tools
• Big data Analytics Software
• Applications of Social Media Analytics
• Opportunities & Challenges
Aug 28, 2018 2
7. Social Media Data
•The amount of data we produce every day is truly mind-
boggling. There are2.5 quintillion bytes of data (1000 EB)
created each day
•Over the last two years alone 90 percent of the data in
the world was generated.
Aug 28, 2018 7
8. Big Data
• Big data is the term for a
collection of data sets so large
and complex that it becomes
difficult to process using on-
hand database management
tools or traditional data
processing applications
• Systems / Enterprises
generate huge amount of data
from Terabytes to and even
Petabytes of information
• It’s very difficult to manage
such huge data……
Aug 28, 2018 8
9. 2009
800,000 petabytes
2020
35 zettabytes
as much Data and Content
Over Coming Decade
Business leaders frequently make
decisions based on information they don’t
trust, or don’t have1in3
83%
of CIOs cited “Business intelligence
and analytics” as part of their visionary
plans
to enhance competitiveness
Business leaders say they don’t have
access to the information they need to do
their jobs
1in2
of CEOs need to do a better job
capturing and understanding
information rapidly in order to make
swift business decisions
60%
… And Organizations Need Deeper
Insights
Of world’s data
is unstructured
90%
BIG DATA
9
Aug 28, 2018 9
10. Extracting insight from an immense volume, variety and velocity of data, in
context, beyond what was previously possible.
Big Data
Aug 28, 2018 10
12. The Challenge: Bring Together a Large Volume and Variety of Data
to Find New Insights
Identify criminals and threats from
disparate video, audio, and data
feeds
Make risk decisions based on real-time
transactional data
Predict weather patterns to plan
optimal wind turbine usage, and
optimize capital expenditure on asset
placement
Detect life-threatening
conditions at hospitals in time to
intervene
Multi-channel customer sentiment
and experience a analysis
12
Aug 28, 2018 12
14. The New Customer Influence Path
14
Awareness Consideration Purchase
Source: Evans et al. (2010), Social Media Marketing: The Next Generation of Business Engagement
Aug 28, 2018
15. Steps of social media analytics
• social media analytics framework around four
critical steps – listen, analyze, engage and
integrate – to effectively use social media for
intelligent decision making
• Listen - identifying and collecting relevant
social media data. Data-gathering tools (free
or subscription-based) can help organizations
collect customers’ tweets, blog posts, status
updates, etc.,
Aug 28, 2018 15
16. Steps of social media analytics-Analyze
• analyzing the collected data to understand
customer sentiment.
• Removing the “noise” around the data will help
improve the accuracy of the analysis.
• Semantic analysis is an advanced data-cleansing
method that groups large amounts of data based
on the relationship between words and/or
phrases.
• Semantic analysis goes beyond classifying
customer comments into positive, negative and
neutral, and provides insights into what
customers think about products, including what
they like and what improvements they would like
to see.
Aug 28, 2018 16
17. Steps of social media analytics -
Engage
• Engage - Customers who are engaged with
companies through social media spend 20% to
40% more than other customers, reveals a Bain &
Co. study of more than 3,000 customers.
• Analyzing social media posts provides a deeper
perspective on trending topics, hot brands and
the type of content that is being shared.
• Predictive analytics can also be used to
understand what would interest customers, and
the ideal time to publish content.
Aug 28, 2018 17
18. Steps of social media analytics -
Integrate
• Integrate - this stage involves integrating unstructured
data across the organization with enterprise structured
data to obtain a 360-degree view of customers. To
achieve this, organizations must integrate their social
media platforms with their existing master data
management (MDM) systems.
• it can automatically add relevant social media data to
the master customer file. It can also update customer
profiles whenever changes are made in source systems
to reflect the latest customer information.
Aug 28, 2018 18
19. Sentiment Analysis of Social Media Data
• Sentiment
– A thought, view, or attitude, especially one based
mainly on emotion instead of reason
• Sentiment Analysis
– opinion mining
– use of natural language processing (NLP) and
computational techniques to automate the
extraction or classification of sentiment from
typically unstructured text
19Aug 28, 2018
20. Emotions
20
Source: Bing Liu (2011) , “Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data,” Springer, 2nd Edition,
Love
Joy
Surprise
Anger
Sadness
Fear
Aug 28, 2018
21. Sentiment Analysis and
Opinion Mining
• Computational study of
opinions,
sentiments,
subjectivity,
evaluations,
attitudes,
appraisal,
affects,
views,
emotions,
ets., expressed in text.
– Reviews, blogs, discussions, news, comments, feedback, or any other
documents
21
Source: Bing Liu (2011) , “Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data,” Springer, 2nd Edition,
Aug 28, 2018
22. Applications of Sentiment Analysis
• Consumer information
– Product reviews
• Marketing
– Consumer attitudes
– Trends
• Politics
– Politicians want to know voters’ views
– Voters want to know policitians’ stances and who
else supports them
• Social
– Find like-minded individuals or communities
22Aug 28, 2018
23. Classification Based on
Supervised Learning
• Sentiment classification
– Supervised learning Problem
– Three classes
• Positive
• Negative
• Neutral
23
Source: Bing Liu (2011) , “Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data,” Springer, 2nd Edition,
Aug 28, 2018
24. Opinion words in
Sentiment classification
• topic-based classification
– topic-related words are important
• e.g., politics, sciences, sports
• Sentiment classification
– topic-related words are unimportant
– opinion words (also called sentiment words)
• that indicate positive or negative opinions are
important,
e.g., great, excellent, amazing, horrible, bad, worst
24
Source: Bing Liu (2011) , “Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data,” Springer, 2nd Edition,
Aug 28, 2018
25. Sentiment Analysis Architecture
25Vishal Kharde and Sheetal Sonawane (2016), "Sentiment Analysis of Twitter Data: A Survey of Techniques,"
International Journal of Computer Applications, Vol 139, No. 11, 2016. pp.5-15
Positive
tweets
Negative
tweets
Word
features
Features
extractor
Features
extractor
Positive Negative
TweetClassifier
Training
set
Aug 28, 2018
26. Sentiment Classification Based on Emotions
26Vishal Kharde and Sheetal Sonawane (2016), "Sentiment Analysis of Twitter Data: A Survey of Techniques,"
International Journal of Computer Applications, Vol 139, No. 11, 2016. pp.5-15
Based on Positive Emotions
Feature Extraction
Positive Negative
Tweeter
Classifier
Training Dataset
Tweeter Streaming API 1.1
Positive tweets Negative tweets
Tweet preprocessing
Based on Negative Emotions
Generate Training Dataset for Tweet
Test Dataset
Aug 28, 2018
27. Sentiment Classification Techniques
27Source: Jesus Serrano-Guerrero, Jose A. Olivas, Francisco P. Romero, and Enrique Herrera-Viedma (2015),
"Sentiment analysis: A review and comparative analysis of web services," Information Sciences, 311, pp. 18-38.
Sentiment
Analysis
Machine
Learning
Approach
Lexicon-
based
Approach
Corpus-based
Approach
Supervised
Learning
Unsupervised
Learning
Dictionary-
based
Approach
Statistical
Semantic
Decision Tree
Classifiers
Linear
Classifiers
Rule-based
Classifiers
Probabilistic
Classifiers
Support Vector
Machine (SVM)
Deep Learning
(DL)
Neural Network
(NN)
Bayesian
Network (BN)
Maximum
Entropy (ME)
Naïve Bayes
(NB)
Aug 28, 2018
28. SJSU Washington Square
Research Project
Twitter Sentiment Analysis for Understanding
Citizens’ Trust in Government
• Collected over 1m tweets from January 2013 from
60 accounts
• 20 cities, 20 mayors, 20 police departments
• Analysis was done using R (for data retrieval,
preparation, and computation) and Excel (for
plotting)
• Use topsy.com as an alternative: lists top 1000
tweets from historical data
Aug 28, 2018 28
30. SJSU Washington Square
Methodology: Data Collection
• Topsy API was used to retrieve the tweets
• An API URL example:
http://otter.topsy.com/search.js?q=@hfxgov&offset=0&mintime=1356978601&maxtime=140890
5001&type=tweet&nohidden=0&perpage=100&page=1&apikey=09C43A9B270A470B8EB8F2946
A9369F3
• A batch script in R was executed to retrieve these tweets
• The API response: a JSON data file (a tree/XML like format)
Aug 28, 2018 30
31. SJSU Washington Square
Methodology: Data Preparation
• The retrieved data was cleansed by removing:
• symbols
• punctuations
• special characters
• URLs
• numbers
Aug 28, 2018 31
32. SJSU Washington Square
• Bag of Words approach was used for sentiment analysis.
• stemming: Each tweet was stemmed into the group of English words
• Matching: A match of each word was searched in the lexicon database
(total 6135 words in the lexicon; 2230 positive and 3905 negative)
• Scoring: Positive and negative matches were summed to define a score
of each tweet
• Polarity: (P-N)/(P+N), where P=total sum of positive sentiment words;
N=total sum of negative sentiment words
• Results were grouped and combined.
Aug 28, 2018 32
Methodology: Sentiment Analysis
35. Word-of-mouth
Voice of the Customer
• 1. Attensity
– Track social sentiment across brands and
competitors
– http://www.attensity.com/home/
• 2. Clarabridge
– Sentiment and Text Analytics Software
– http://www.clarabridge.com/
35Aug 28, 2018
36. 36
Attensity: Track social sentiment across brands and competitors
http://www.attensity.com/
http://www.youtube.com/watch?v=4goxmBEg2Iw#!Aug 28, 2018
37. 37
Clarabridge: Sentiment and Text Analytics Software
http://www.clarabridge.com/
http://www.youtube.com/watch?v=IDHudt8M9P0
Aug 28, 2018
38. Purpose of Social Media analytics tools
• With analytics tools for social media you are able
to quickly and easily see the most important
metrics of your brand performance.
• audience growth graph - number of new
likes/follows on a social media profile on a day-
to-day basis
• total engagement chart - information about how
your audience interacts with your content.
• Demographics - paint a better picture of what
your current audience
Aug 28, 2018 38
40. E-Popular Tools
(“Social Media Monitoring/Analysis")
• Radian 6
• Social Mention
• Overtone OpenMic
• Microsoft Dynamics Social Networking
Accelerator
• SAS Social Media Analytics
• Lithium Social Media Monitoring
• RightNow Cloud Monitor
40
Source: Wiltrud Kessler (2012), Introduction to Sentiment Analysis
Aug 28, 2018
47. Opinion Spamming
• Opinion Spamming
– "illegal" activities
• e.g., writing fake reviews, also called shilling
– try to mislead readers or automated opinion mining
and sentiment analysis systems by giving
undeserving positive opinions to some target entities
in order to promote the entities and/or by giving
false negative opinions to some other entities in
order to damage their reputations.
47
Source: http://www.cs.uic.edu/~liub/FBS/fake-reviews.html
Aug 28, 2018
48. Forms of Opinion spam
• fake reviews (also called bogus reviews)
• fake comments
• fake blogs
• fake social network postings
• deceptions
• deceptive messages
48
Source: http://www.cs.uic.edu/~liub/FBS/fake-reviews.html
Aug 28, 2018
50. Professional Fake Review Writing Services
(some Reputation Management companies)
• Post positive reviews
• Sponsored reviews
• Pay per post
• Need someone to write positive reviews about our
company (budget: $250-$750 USD)
• Fake review writer
• Product review writer for hire
• Hire a content writer
• Fake Amazon book reviews (hiring book reviewers)
• People are just having fun (not serious)
50
Source: http://www.cs.uic.edu/~liub/FBS/fake-reviews.html
Aug 28, 2018
54. Opinion Spamming – eg.
• Big data analytics can accumulate the wisdom of
crowds, reveal patterns, and yield best practices.
• For a real-world example, in events related to the
2013 Boston Marathon bombings, social
networks of marathon participants and general
high-performance computational techniques
were combined to cluster and analyze large sets
of candid photos and video shots — ultimately
leading to the discovery of the perpetrators.
Aug 28, 2018 54
55. Impact of Data and Analytics on
Social Media in 2018
Targeted Advertising
• According to Nielsen survey of 28,000 global
Internet users, 92% of consumers trust
recommendations from friends and family
more than any other form of advertising.
• Seventy percent of customers place their
trust in online consumer reviews – making this
medium the second most trusted form of
advertising.
Aug 28, 2018 55
56. Impact of Data and Analytics on
Social Media in 2018
Converting unstructured data into Knowledge
• According to Gartner, 80% of enterprise data –
documents, e-mails, call logs, corporate blogs and
the like – is unstructured (i.e., it does not fit into
any traditional database).
• Advanced social analytics can help organizations
analyze and quickly draw inferences from
burgeoning unstructured social media and
enterprise data, and convert it into actionable
insights.
Aug 28, 2018 56
57. Impact of Data and Analytics on
Social Media in 2018
• “Search Engine Optimized” marketing - is a
technique used to boost webpages to the top
search results returned whenever
• Predictive analytics
• Personalized marketing communication -
relevance of advertisements to you will be
determined by what you post online, what you
watch, what you share, etc,
• In 2018, many companies are going to invest in
hiring digital marketers and data analysts, so they
can take advantage of all that data lying out there
on the internet and create better and more
efficient marketing strategies.
Aug 28, 2018 57
59. Teradata Aster Analytics platform
• The Teradata Aster Analytics platform includes the Aster
Database, Aster SNAP Framework, Aster R, SQL-MapReduce
framework, SQL-GR and the Aster Analytics Portfolio.
• The suite provides business users with a set of tools and
modules that enable them to efficiently uncover data insights
for the entire data discovery lifecycle, using advanced data
analytic functions.
• The tools address a range of business analytics scenarios,
including customer churn, path to purchase, fraud analysis,
manufacturing optimization and product affinity.
• Aster SQL-GR is a Graph processing engine for performing
Graph analytics on big data sets in the Aster Database.
Aug 28, 2018 59
60. Apache Hadoop
• Apache Hadoop is a framework that allows the distributed
processing of large data sets across clusters of commodity
computers using a simple programming model
• Map Reduce -Scenario
Aug 28, 2018 60
62. Image Recognition Analytics
• Social networking sites such as Facebook, Pinterest, Instagram and Flickr
receive and host billions of photos, with thousands added every minute.
Some of the images can be of brands, company logos and products,
without any text to reference them.
• Since traditional social media monitoring tools can only track text (such as
user comments and posts mentioning a brand), marketers often do not
know what customers are referring to, who is using their company’s
products, or if counterfeit versions of those products exist.
• Analytics with image recognition capabilities can help companies
overcome this challenge and leverage images to enhance their market
knowledge and extend their reach. Advanced image analytics with pixel-
level analysis is gradually gaining acceptance among large retailers and
advertising agencies.
• Companies such as Piqora and Curalate have developed image recognition
technologies for social media sites such as Facebook, Pinterest and
Instagram – allowing them to identify the most popular shared images
from their Web sites, the most influential individual visitors, and the traffic
that an image diverts to a target Web site.
Aug 28, 2018 62
63. Is this ethical — what about data
protection?
• some social media platforms do have some
form of open access user data (for example,
Twitter and Facebook)
• some sell their data to companies (for
example, Instagram)
• some platforms keep their user data entirely
confidential (for example, Snapchat).
Aug 28, 2018 63
64. Effects of Social Media Analytics
• Analysis of social media data collected by a retailer could for
instance reveal that unmarried females between 25 and 35
are suitable candidates for a discount offer on gym
equipment.
• The Future: Big Data Will Continue to Accelerate the Intrusion
of Social Media Companies into People’s Privacy
• A study published by researchers from Cambridge and
Stanford Universities shows that Facebook can use its data to
predict people’s personality with more accuracy than close
friends and families.
• any action you take on browsers and search engines today will
most likely link back to your social media profile, leaving
behind a long trail of digital footprint that can be used for
detecting your next moves.
Aug 28, 2018 64
65. Effects of Social Media Analytics -
Contd
• data collection and analytics is probably going to be
around for as long as we users are still giving out our
data on the internet.
• regulations such as General Data Protection
Regulation (GDPR) provide hope for some semblance
of data protection and privacy. This doesn’t mean that
we should openly publish all our personal information
on our social media accounts however.
• It’s best to follow this rule: if it’s not something you’re
comfortable with the entire world knowing, don’t post
it on the internet.
Aug 28, 2018 65
67. SJSU Washington SquareOpportunities
• “…data is useless without the skill to analyze it.
• A McKinsey Global Institute study states that the US will
face a shortage of about 190,000 data scientists and 1.5
million managers and analysts who can understand and
make decisions using Big Data by 2018.
Aug 28, 2018 67
68. Issues & Challenges
Dozens of questions must be addressed still…
what is the best architecture for the physical
data storage infrastructure?
how should data workers be situated within a
managerial hierarchy?
what security protocols should be introduced
to protect the integrity of the data ?
what is the appropriate ethical stance on
handling personal data?
Aug 28, 2018 68