This document summarizes a presentation on creating value from big data and business analytics. It discusses research conducted with three large UK organizations: a mobile telecom operator, television company, and integrated transport authority. The research aimed to understand how these organizations leverage their big data and analytics capabilities to create business value. Key findings included using mobile network data for location-based services and public alerts, predictive modeling of viewers for targeted advertising, and travel data analysis to improve transport operations and customer experiences.
The document discusses big data, providing definitions and facts about the volume of data being created. It describes the characteristics of big data using the 5 V's model (volume, velocity, variety, veracity, value). Different types of data are mentioned, from unstructured to structured. Hadoop is introduced as an open source software framework for distributed processing and analyzing large datasets using MapReduce and HDFS. Hardware and software requirements for working with big data and Hadoop are listed.
Big data is large amounts of unstructured data that require new techniques and tools to analyze. Key drivers of big data growth are increased storage capacity, processing power, and data availability. Big data analytics can uncover hidden patterns to provide competitive advantages and better business decisions. Applications include healthcare, homeland security, finance, manufacturing, and retail. The global big data market is expected to grow significantly, with India's market projected to reach $1 billion by 2015. This growth will increase demand for data scientists and analysts to support big data solutions and technologies like Hadoop and NoSQL databases.
This document provides an overview of blockchain technology, how it works, and its applications. It defines blockchain as a decentralized digital ledger consisting of blocks that record transactions across networks so past transactions cannot be altered. The document outlines the history of blockchain, how it provides security through hashing and proof-of-work algorithms, and how cryptocurrencies use blockchain to be immune from counterfeiting without central authorities. It then provides an example of how a basic bitcoin transaction occurs between parties on the blockchain network.
- Big data refers to large volumes of data from various sources that is analyzed to reveal patterns, trends, and associations.
- The evolution of big data has seen it grow from just volume, velocity, and variety to also include veracity, variability, visualization, and value.
- Analyzing big data can provide hidden insights and competitive advantages for businesses by finding trends and patterns in large amounts of structured and unstructured data from multiple sources.
Fog computing refers to performing computing tasks closer to the source of data generation rather than solely relying on centralized cloud computing. It helps address issues like high bandwidth needs and latency by processing some data locally and only sending valuable aggregated data to the cloud. Fog computing is driven by the rise of IoT and is useful for applications requiring low latency like connected cars, smart grids, and healthcare. It aims to make decisions and processing occur as close to data generation as possible using localized computing resources and devices.
Edge Computing: Bringing the Internet Closer to YouMegan O'Keefe
The document discusses edge computing, which involves offloading compute and storage tasks from centralized cloud infrastructure to network edges in order to enable lower latency applications. It provides examples of edge computing use cases in various industries and discusses challenges and opportunities in building edge computing systems using technologies like Kubernetes. The global edge computing market is expected to reach $6.72 billion by 2022.
One of the most hyped IT buzzwords to have emerged in the last couple of years. Blockchain has found its way into major media headlines on a near-daily basis, but a year and a half ago, it was a word used by a relatively small number of people to describe the peer-to-peer distributed ledger technology.
The document discusses big data, providing definitions and facts about the volume of data being created. It describes the characteristics of big data using the 5 V's model (volume, velocity, variety, veracity, value). Different types of data are mentioned, from unstructured to structured. Hadoop is introduced as an open source software framework for distributed processing and analyzing large datasets using MapReduce and HDFS. Hardware and software requirements for working with big data and Hadoop are listed.
Big data is large amounts of unstructured data that require new techniques and tools to analyze. Key drivers of big data growth are increased storage capacity, processing power, and data availability. Big data analytics can uncover hidden patterns to provide competitive advantages and better business decisions. Applications include healthcare, homeland security, finance, manufacturing, and retail. The global big data market is expected to grow significantly, with India's market projected to reach $1 billion by 2015. This growth will increase demand for data scientists and analysts to support big data solutions and technologies like Hadoop and NoSQL databases.
This document provides an overview of blockchain technology, how it works, and its applications. It defines blockchain as a decentralized digital ledger consisting of blocks that record transactions across networks so past transactions cannot be altered. The document outlines the history of blockchain, how it provides security through hashing and proof-of-work algorithms, and how cryptocurrencies use blockchain to be immune from counterfeiting without central authorities. It then provides an example of how a basic bitcoin transaction occurs between parties on the blockchain network.
- Big data refers to large volumes of data from various sources that is analyzed to reveal patterns, trends, and associations.
- The evolution of big data has seen it grow from just volume, velocity, and variety to also include veracity, variability, visualization, and value.
- Analyzing big data can provide hidden insights and competitive advantages for businesses by finding trends and patterns in large amounts of structured and unstructured data from multiple sources.
Fog computing refers to performing computing tasks closer to the source of data generation rather than solely relying on centralized cloud computing. It helps address issues like high bandwidth needs and latency by processing some data locally and only sending valuable aggregated data to the cloud. Fog computing is driven by the rise of IoT and is useful for applications requiring low latency like connected cars, smart grids, and healthcare. It aims to make decisions and processing occur as close to data generation as possible using localized computing resources and devices.
Edge Computing: Bringing the Internet Closer to YouMegan O'Keefe
The document discusses edge computing, which involves offloading compute and storage tasks from centralized cloud infrastructure to network edges in order to enable lower latency applications. It provides examples of edge computing use cases in various industries and discusses challenges and opportunities in building edge computing systems using technologies like Kubernetes. The global edge computing market is expected to reach $6.72 billion by 2022.
One of the most hyped IT buzzwords to have emerged in the last couple of years. Blockchain has found its way into major media headlines on a near-daily basis, but a year and a half ago, it was a word used by a relatively small number of people to describe the peer-to-peer distributed ledger technology.
This document provides an overview of the Blue Prism robotic process automation (RPA) software. It describes how Blue Prism uses artificial intelligence and machine learning to automate repetitive tasks. The document outlines key features of Blue Prism including its ability to integrate with various backend systems, provide full visibility and control over automated processes, and use intelligent robots. It also summarizes the main components of Blue Prism including the Studio for building processes, Control for monitoring automation, and System for user management. Examples of industries using Blue Prism are listed as well as the typical return on investment from RPA implementations.
10 Event Technology Trends to Watch in 2016Eventbrite UK
We’ve picked 10 exciting, innovative technologies that are gathering pace and adoption, and are likely to start appearing on your radar in 2016. Get ahead of the curve by learning more about them.
Ross Chayka. Gartner Hype Cycle
Ross Chayka's personal websites:
UA - https://rchayka.one
EN - https://rosschayka.com
LIn - https://www.linkedin.com/in/rchayka
This document provides an introduction to fog computing. Fog computing is a model where data processing and applications occur at the edge of networks rather than solely in the cloud. This helps address limitations of cloud computing like high latency and bandwidth usage. Key characteristics of fog computing include low latency, geographical distribution, mobility support, and real-time interactions. Potential applications discussed are connected cars, smart grids, and smart traffic lights, which can benefit from fog computing's low latency and location awareness.
Data Science Tutorial | Introduction To Data Science | Data Science Training ...Edureka!
This Edureka Data Science tutorial will help you understand in and out of Data Science with examples. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:
1. Why Data Science?
2. What is Data Science?
3. Who is a Data Scientist?
4. How a Problem is Solved in Data Science?
5. Data Science Components
This document provides an introduction and overview of cloud computing. It defines cloud computing as a model that enables network access to configurable computing resources that can be rapidly provisioned and released with minimal management effort. The document discusses how cloud computing allows users and companies to avoid upfront infrastructure costs and adjust resources to meet fluctuating demand. It also examines different perspectives on cloud computing and provides definitions from industry leaders to clarify what cloud computing is and how it relates to concepts like utility computing.
This document provides an overview of big data. It defines big data as large volumes of diverse data that are growing rapidly and require new techniques to capture, store, distribute, manage, and analyze. The key characteristics of big data are volume, velocity, and variety. Common sources of big data include sensors, mobile devices, social media, and business transactions. Tools like Hadoop and MapReduce are used to store and process big data across distributed systems. Applications of big data include smarter healthcare, traffic control, and personalized marketing. The future of big data is promising with the market expected to grow substantially in the coming years.
Big Data may well be the Next Big Thing in the IT world. The first organizations to embrace it were online and startup firms. Firms like Google, eBay, LinkedIn, and Facebook were built around big data from the beginning.
This document summarizes a seminar on distributed computing. It discusses how distributed computing works using lightweight software agents on client systems and dedicated servers to divide large processing tasks. It covers distributed computing management servers, application characteristics that are suitable like long-running tasks, types of distributed applications, and security and standardization challenges. Advantages include improved price/performance and reliability, while disadvantages include complexity, network problems, and security issues.
Data science is a field that focuses on analyzing large amounts of data using tools and techniques to discover patterns and make business decisions. Data scientists utilize machine learning algorithms to develop predictive models from multiple sources of data in different formats. Data has become a valuable asset like oil in the 21st century that can help organizations improve decision making. The career is expected to grow exponentially and data scientists can earn more than average IT workers.
Grid computing allows for the sharing of computer resources across a network. It utilizes both reliable tightly-coupled cluster resources as well as loosely-coupled unreliable machines. The grid system balances resource usage to provide quality of service to participants. Grid computing works by having at least one administrative computer and middleware that allows computers on the network to share processing power and data storage. It has advantages like improved efficiency, resilience, and ability to handle large-scale applications, but also challenges around resource sharing and licensing across multiple servers.
This document provides an overview of big data. It begins by defining big data and noting that it first emerged in the early 2000s among online companies like Google and Facebook. It then discusses the three key characteristics of big data: volume, velocity, and variety. The document outlines the large quantities of data generated daily by companies and sensors. It also discusses how big data is stored and processed using tools like Hadoop and MapReduce. Examples are given of how big data analytics can be applied across different industries. Finally, the document briefly discusses some risks and benefits of big data, as well as its impact on IT jobs.
This document provides an overview of big data in various industries. It begins by defining big data and explaining the three V's of big data - volume, variety, and velocity. It then discusses examples of big data in digital marketing, financial services, and healthcare. For digital marketing, it discusses database marketers as pioneers of big data and how big data is transforming digital marketing. For financial services, it discusses how big data is used for fraud detection and credit risk management. It also provides details on algorithmic trading and how it crunches complex interrelated big data. Overall, the document outlines how big data is being leveraged across industries to improve operations, increase revenues, and achieve competitive advantages.
This document provides an overview of big data, including its definition, characteristics, sources, tools used, applications, benefits, and impact on IT. Big data is a term used to describe the large volumes of data, both structured and unstructured, that are so large they are difficult to process using traditional database and software techniques. It is characterized by high volume, velocity, variety, and veracity. Common sources of big data include mobile devices, sensors, social media, and software/application logs. Tools like Hadoop, MongoDB, and MapReduce are used to store, process, and analyze big data. Key applications areas include homeland security, healthcare, manufacturing, and financial trading. Benefits include better decision making, cost reductions
Green computing refers to using computing resources efficiently and minimizing environmental impact. It involves implementing energy-efficient policies and practices when setting up and operating IT systems. The goals of green computing include minimizing energy consumption, purchasing green energy, and reducing employee/customer travel requirements. Green cloud computing aims to achieve efficient infrastructure utilization and processing while minimizing energy usage. It uses techniques like dynamic resource allocation and powering down underutilized servers.
This document discusses edge computing and how it relates to IoT and AI. It defines key concepts like IoT, AI, machine learning, and cloud computing. It then explains that edge computing allows data from IoT devices to be processed locally instead of sending it to data centers, improving latency, security, costs and business uptime. Some applications of edge computing include autonomous vehicles, augmented reality, retail, and connected homes/offices.
Presentation by DHS S&T at the NY Blockchain 360 Conference regarding Blockchain's relevance to the Homeland Security Enterprise. Results of security and privacy research and development over the last 2+ years and next steps.
Real estate plays a significant role in the Indian Government’s stated goal of transforming India into a $5 trillion economy and blockchain can play a major role in realising this goal.
Ethereum is an open software platform based on blockchain technology that enables developers to
build and deploy decentralized applications.
Ethereum is a distributed public blockchain network.
While the Bitcoin blockchain is used to track ownership of digital currency (bitcoins), the Ethereum
blockchain focuses on running the programming code of any decentralized application.
Ether is a cryptocurrency whose blockchain is generated by the Ethereum platform. Ether can be
transferred between accounts and used to compensate participant mining nodes for computations
performed.
Presentation given by Douglas Smith of HolyTornado! at the Marketing Innovations Summit in Genk, Belgium on the need for innovation in retail. http://www.marketinginnovation.eu/
This document summarizes a presentation about the relationships between operations research (OR), data science, and business analytics. It begins by defining OR as applying analytical methods to help make better decisions, noting its broad scope. OR traditionally uses techniques like optimization, simulation, and forecasting. Data science also uses these techniques and focuses on descriptive, predictive, and prescriptive models. While OR and data science practitioners use similar methods, data scientists tend to have stronger software skills. The presentation argues that to be effective, OR practitioners need to expand their skills to work with new data types and technologies, and ensure their work is embedded within organizations to drive prescriptive analytics and cultural change. Bringing together soft OR methods, hard analytics techniques,
This document provides an overview of the Blue Prism robotic process automation (RPA) software. It describes how Blue Prism uses artificial intelligence and machine learning to automate repetitive tasks. The document outlines key features of Blue Prism including its ability to integrate with various backend systems, provide full visibility and control over automated processes, and use intelligent robots. It also summarizes the main components of Blue Prism including the Studio for building processes, Control for monitoring automation, and System for user management. Examples of industries using Blue Prism are listed as well as the typical return on investment from RPA implementations.
10 Event Technology Trends to Watch in 2016Eventbrite UK
We’ve picked 10 exciting, innovative technologies that are gathering pace and adoption, and are likely to start appearing on your radar in 2016. Get ahead of the curve by learning more about them.
Ross Chayka. Gartner Hype Cycle
Ross Chayka's personal websites:
UA - https://rchayka.one
EN - https://rosschayka.com
LIn - https://www.linkedin.com/in/rchayka
This document provides an introduction to fog computing. Fog computing is a model where data processing and applications occur at the edge of networks rather than solely in the cloud. This helps address limitations of cloud computing like high latency and bandwidth usage. Key characteristics of fog computing include low latency, geographical distribution, mobility support, and real-time interactions. Potential applications discussed are connected cars, smart grids, and smart traffic lights, which can benefit from fog computing's low latency and location awareness.
Data Science Tutorial | Introduction To Data Science | Data Science Training ...Edureka!
This Edureka Data Science tutorial will help you understand in and out of Data Science with examples. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial:
1. Why Data Science?
2. What is Data Science?
3. Who is a Data Scientist?
4. How a Problem is Solved in Data Science?
5. Data Science Components
This document provides an introduction and overview of cloud computing. It defines cloud computing as a model that enables network access to configurable computing resources that can be rapidly provisioned and released with minimal management effort. The document discusses how cloud computing allows users and companies to avoid upfront infrastructure costs and adjust resources to meet fluctuating demand. It also examines different perspectives on cloud computing and provides definitions from industry leaders to clarify what cloud computing is and how it relates to concepts like utility computing.
This document provides an overview of big data. It defines big data as large volumes of diverse data that are growing rapidly and require new techniques to capture, store, distribute, manage, and analyze. The key characteristics of big data are volume, velocity, and variety. Common sources of big data include sensors, mobile devices, social media, and business transactions. Tools like Hadoop and MapReduce are used to store and process big data across distributed systems. Applications of big data include smarter healthcare, traffic control, and personalized marketing. The future of big data is promising with the market expected to grow substantially in the coming years.
Big Data may well be the Next Big Thing in the IT world. The first organizations to embrace it were online and startup firms. Firms like Google, eBay, LinkedIn, and Facebook were built around big data from the beginning.
This document summarizes a seminar on distributed computing. It discusses how distributed computing works using lightweight software agents on client systems and dedicated servers to divide large processing tasks. It covers distributed computing management servers, application characteristics that are suitable like long-running tasks, types of distributed applications, and security and standardization challenges. Advantages include improved price/performance and reliability, while disadvantages include complexity, network problems, and security issues.
Data science is a field that focuses on analyzing large amounts of data using tools and techniques to discover patterns and make business decisions. Data scientists utilize machine learning algorithms to develop predictive models from multiple sources of data in different formats. Data has become a valuable asset like oil in the 21st century that can help organizations improve decision making. The career is expected to grow exponentially and data scientists can earn more than average IT workers.
Grid computing allows for the sharing of computer resources across a network. It utilizes both reliable tightly-coupled cluster resources as well as loosely-coupled unreliable machines. The grid system balances resource usage to provide quality of service to participants. Grid computing works by having at least one administrative computer and middleware that allows computers on the network to share processing power and data storage. It has advantages like improved efficiency, resilience, and ability to handle large-scale applications, but also challenges around resource sharing and licensing across multiple servers.
This document provides an overview of big data. It begins by defining big data and noting that it first emerged in the early 2000s among online companies like Google and Facebook. It then discusses the three key characteristics of big data: volume, velocity, and variety. The document outlines the large quantities of data generated daily by companies and sensors. It also discusses how big data is stored and processed using tools like Hadoop and MapReduce. Examples are given of how big data analytics can be applied across different industries. Finally, the document briefly discusses some risks and benefits of big data, as well as its impact on IT jobs.
This document provides an overview of big data in various industries. It begins by defining big data and explaining the three V's of big data - volume, variety, and velocity. It then discusses examples of big data in digital marketing, financial services, and healthcare. For digital marketing, it discusses database marketers as pioneers of big data and how big data is transforming digital marketing. For financial services, it discusses how big data is used for fraud detection and credit risk management. It also provides details on algorithmic trading and how it crunches complex interrelated big data. Overall, the document outlines how big data is being leveraged across industries to improve operations, increase revenues, and achieve competitive advantages.
This document provides an overview of big data, including its definition, characteristics, sources, tools used, applications, benefits, and impact on IT. Big data is a term used to describe the large volumes of data, both structured and unstructured, that are so large they are difficult to process using traditional database and software techniques. It is characterized by high volume, velocity, variety, and veracity. Common sources of big data include mobile devices, sensors, social media, and software/application logs. Tools like Hadoop, MongoDB, and MapReduce are used to store, process, and analyze big data. Key applications areas include homeland security, healthcare, manufacturing, and financial trading. Benefits include better decision making, cost reductions
Green computing refers to using computing resources efficiently and minimizing environmental impact. It involves implementing energy-efficient policies and practices when setting up and operating IT systems. The goals of green computing include minimizing energy consumption, purchasing green energy, and reducing employee/customer travel requirements. Green cloud computing aims to achieve efficient infrastructure utilization and processing while minimizing energy usage. It uses techniques like dynamic resource allocation and powering down underutilized servers.
This document discusses edge computing and how it relates to IoT and AI. It defines key concepts like IoT, AI, machine learning, and cloud computing. It then explains that edge computing allows data from IoT devices to be processed locally instead of sending it to data centers, improving latency, security, costs and business uptime. Some applications of edge computing include autonomous vehicles, augmented reality, retail, and connected homes/offices.
Presentation by DHS S&T at the NY Blockchain 360 Conference regarding Blockchain's relevance to the Homeland Security Enterprise. Results of security and privacy research and development over the last 2+ years and next steps.
Real estate plays a significant role in the Indian Government’s stated goal of transforming India into a $5 trillion economy and blockchain can play a major role in realising this goal.
Ethereum is an open software platform based on blockchain technology that enables developers to
build and deploy decentralized applications.
Ethereum is a distributed public blockchain network.
While the Bitcoin blockchain is used to track ownership of digital currency (bitcoins), the Ethereum
blockchain focuses on running the programming code of any decentralized application.
Ether is a cryptocurrency whose blockchain is generated by the Ethereum platform. Ether can be
transferred between accounts and used to compensate participant mining nodes for computations
performed.
Presentation given by Douglas Smith of HolyTornado! at the Marketing Innovations Summit in Genk, Belgium on the need for innovation in retail. http://www.marketinginnovation.eu/
This document summarizes a presentation about the relationships between operations research (OR), data science, and business analytics. It begins by defining OR as applying analytical methods to help make better decisions, noting its broad scope. OR traditionally uses techniques like optimization, simulation, and forecasting. Data science also uses these techniques and focuses on descriptive, predictive, and prescriptive models. While OR and data science practitioners use similar methods, data scientists tend to have stronger software skills. The presentation argues that to be effective, OR practitioners need to expand their skills to work with new data types and technologies, and ensure their work is embedded within organizations to drive prescriptive analytics and cultural change. Bringing together soft OR methods, hard analytics techniques,
Role of Data in Digital TransformationVMware Tanzu
Data plays a big role in building the kinds of experiences demanded by the market today. In this session, we’ll unpack what goes into building a data-driven app, case studies of how organizations have successfully overcome siloed data and analytics to bring new predictive features into their applications, and what your next steps for data should be on your digital transformation journey.
Speaker: Les Klein, EMEA CTO Data, Pivotal
Accenture Technology Vision for Retail 2016accenture
The document summarizes key points from Accenture's 2016 Technology Vision for Retail report. It discusses how retailers must focus on putting people (customers, employees, business partners) first in the digital age. It also covers how retailers can leverage intelligent automation, build a liquid workforce, engage in the platform economy, manage predictable disruption, and ensure digital trust. The document provides examples and recommendations for retailers on each of these technology trends.
Using data effectively worskhop presentationcommunitylincs
This document discusses the value of data for non-profit organizations. It explains that data can help organizations better target services, improve advocacy and fundraising, and demonstrate impact. The document provides examples of open government data sources and case studies of organizations using data effectively. It also discusses potential barriers to using data and where organizations can find help and support.
In the second article of the series, sponsored by Avanade, we look at how many business leaders don't view digital as central to their organisations and are avoiding partaking in a business makeover that would empower employees to utilise data analysis. What does it take to compete with those who have made the organisational change?
Overview of major factors in big data, analytics and data science. Illustrates the growing changes from data capture and the way it is changing business beyond technology industries.
The document discusses a pilot project to create a virtual presence for the Ontario Public Service (OPS) on Second Life, a virtual world platform. The goal is to attract potential job applicants, especially youth, by creating interactive career experience areas and measuring the effectiveness of online outreach. Key lessons from the pilot include the need for proper marketing, accessibility, and defining success metrics. Moving forward, expanding the presence on Second Life and using other social media for OPS careers and digital strategy initiatives are recommended.
This document summarizes a presentation by PwC on data and analytics in the digital age. PwC consists of data professionals who help clients leverage their data and manage risks. Recent projects include analyzing payroll, designing websites, and building systems to visualize customer orders. The presentation covers how digital transformation allows companies to use analytics to stay competitive. It also demonstrates a data visualization tool to support digital transformations.
Presentation is about online macro environment and digital marketing environment. Further, market place analysis, SWOT analysis, online market place map, PESTLE analysis, digital economy defined, digital immigrants vs digital natives, innovation vs disruptive innovation, non existing businesses, etc.
Using the Cloud to Attract, Engage & Retain Your CustomersWainhouse Research
The document discusses how companies can use digital engagement and cloud communications to attract, engage, and retain customers. It provides examples of how government, higher education, transportation, and healthcare organizations are using digital tools like messaging, video, location services, and analytics via the cloud to improve customer experiences. Overall the document promotes the idea that the evolving needs of today's digital customers require companies to adopt new cloud-based communications technologies and services to effectively engage with their diverse customer bases.
UNLOCK YOUR DIGITAL VALUE POTENTIAL - BOOZ DIGITAL AMSTERDAM 2013Femke-Anna van Zanten
Most players see digital as incremental instead of transformative. Digital is not just an add-on, and as such, incremental steps will not be enough. Re-imagining in a broader context is key. Learn here how to Re-imagine your business, and create Digital Value: new insights, frameworks and case examples.
The document discusses creating value from data and overcoming hype around data science. It summarizes that data science has the potential to create value through customer insights, improved processes, and new products, but realizing this value is challenging. Three key challenges are 1) extracting meaningful information from data, 2) bringing business and IT together in joint data science programs and organizations, and 3) developing data skills and an organizational culture that supports data-driven decision making. Overcoming these challenges is necessary to build mature data science capabilities and unlock the full value of data.
Marketing ecosystem: 7 challenges facing marketers todayTony Davis
The marketing technology landscape has exploded. Marketers are spoiled for choice with an array of platforms, products, integrations and connections. In this presentation, we take a step back from the technology to look at 7 major challenges faced by marketers wanting to navigate the marketing ecosystem.
89% of consumers switch to a competitor after a poor CX Abhishek Sood
89% of consumers switch to a competitor following a poor customer experience, according to an Oracle study. But how can you use digital technology to improve your customers' experience?
Uncover how several prominent businesses embraced digital technologies to retain customers and increase profits. For example, Domino's Pizza had a 23% growth in profit after it allowed customers to track their deliveries online.
Discover the 4 factors that can make a digital transformation project profitable and worthwhile.
Research Presentation: How Numbers are Powering the Next Era of MarketingMediaPost
This document discusses how numbers and data are powering the next era of marketing. It notes that nearly 4 in 10 CMOs say they do not have the right tools and resources to meet their marketing objectives. Companies are turning to providers like Accenture Interactive to bring together full marketing solutions using consulting, technology, and analytics. The CMO agenda is being disrupted by the explosion of data from sources like mobile devices, social media, and the internet of things. This data needs to be captured, analyzed, and used to optimize marketing interactions and drive continuous discovery. Strong collaboration between marketing and IT is needed to take advantage of big data technologies and manage various data environments.
This document discusses key technology trends impacting the retail industry in 2016, as identified by IBM. It covers four main dynamics of transformation: analytics, cloud computing, mobile and social engagement, and security. Analytics and cognitive computing allow retailers to gain insights from big data to personalize customer experiences. Cloud computing enables speed, agility and flexible infrastructure upgrades. Mobile and social technologies connect retailers with customers in real-time and on-the-go. Security is a growing concern as data volumes increase and attack sophistication rises. The document provides an overview of IBM solutions that address these trends, such as analytics platforms, cloud services, and security offerings to help retailers adapt to ongoing disruption and digital transformation in retail.
This document discusses how a big box retailer utilized big data to improve its business. It outlines the steps the retailer took:
1) It identified where big data could create advantages, such as predictive analytics to forecast sales declines. This would allow the retailer to be more proactive.
2) It built future capability scenarios to determine how to leverage big data, such as using social media data to predict problems.
3) It defined the benefits and roadmap for implementing big data, including investing millions over 5 years for a positive return. Benefits would include more consistent, faster information and insights.
The document provides details on how the retailer methodically planned and aligned its big data strategy to its business needs
1) The document discusses how marketing has shifted from an "art" to a "science" due to changes in technology and customer expectations. Advances like social media, mobile devices, and cloud computing have given customers more control over brand conversations and empowered them to expect personalized, seamless experiences across channels.
2) It proposes a "Customer Value model" with four interconnecting layers: customer value at the core, surrounded by customer journey, customer value analytics, and finally company value. This model aims to continuously link customer insights and data to business decisions in real-time in order to maximize value for both customers and the company.
3) Achieving customer value now requires understanding customer needs, behaviors, and motiv
WeSpline is a community-built database and social network that connects companies, individuals, startups, enterprises, investors and other organizations. It uses artificial intelligence like text mining and machine learning to power intelligent search and recommendation functions. The goal is to foster innovation by making it easier for users to find potential partners, opportunities, and information around the world.
4th Modern Marketing Reckoner by MMA Global India & Group M: 60+ experts on W...Social Samosa
The Modern Marketing Reckoner (MMR) is a comprehensive resource packed with POVs from 60+ industry leaders on how AI is transforming the 4 key pillars of marketing – product, place, price and promotions.
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Discussion on Vector Databases, Unstructured Data and AI
https://www.meetup.com/unstructured-data-meetup-new-york/
This meetup is for people working in unstructured data. Speakers will come present about related topics such as vector databases, LLMs, and managing data at scale. The intended audience of this group includes roles like machine learning engineers, data scientists, data engineers, software engineers, and PMs.This meetup was formerly Milvus Meetup, and is sponsored by Zilliz maintainers of Milvus.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data LakeWalaa Eldin Moustafa
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
STATATHON: Unleashing the Power of Statistics in a 48-Hour Knowledge Extravag...sameer shah
"Join us for STATATHON, a dynamic 2-day event dedicated to exploring statistical knowledge and its real-world applications. From theory to practice, participants engage in intensive learning sessions, workshops, and challenges, fostering a deeper understanding of statistical methodologies and their significance in various fields."
1. Hull University Business School
Creating value from Big Data and Business
Analytics
Version 1.0
July 2014
Prof.
Richard
Vidgen
Management
Systems,
HUBS
E:
r.vidgen@hull.ac.uk
2. Hull University Business School
Introduc?on
• This
presenta?on
is
a
summary
of
the
report
prepared
for
the
EPSRC’s
Nemode
programme:
– Vidgen,
R.,
(2014).
Crea?ng
business
value
from
Big
Data
and
business
analy?cs:
organisa?onal,
managerial
and
human
resource
implica?ons.
University
of
Hull
Research
Memorandum,
no.
94,
ISBN
978-‐1-‐906422-‐31-‐8.
• The
full
report
can
be
accessed
at
– hWp://www2.hull.ac.uk/hubs/research/research-‐
memoranda.aspx
4. Hull University Business School
Value
Crea4on
[Big]
Data
The
[Big]
ques?on:
how
do
we
get
from
data
to
value?
5. Hull University Business School
Value
Crea4on
[Big]
Data
Business
analy4cs
capability
Research
approach
Proposi4on
1:
An
organiza?on's
business
analy?cs
capability
mediates
between
[Big]
data
and
the
crea?on
of
value
6. Hull University Business School
Process
Technology
Organiza?on/
management
People
Value
Crea4on
[Big]
Data
Business
analy4cs
capability
Research
approach
Proposi4on
2:
An
organiza?on's
business
analy?cs
capability
is
usefully
viewed
as
a
socio-‐technical
entanglement
7. Hull University Business School
Research
method
• Case
studies
of
three
large
UK
organiza?ons
with
Big
data
and
established
business
analy?cs
func?ons
• Data
collec?on
by
interview
of
heads
of
analy?cs
–
recorded
and
transcribed
8. Hull University Business School
Case
studies
MobCo*
–
a
leading
UK
mobile
telecoms
operator
MediaCo*
–
a
UK
television
company
CityTrans*
-‐
An
integrated
transport
authority
for
a
large
UK
city
*Organiza?on
names
are
pseudonymous
10. Hull University Business School
Data
and
value
crea?on
-‐
MobCo
VALUE
CREATION
• Crea?on
of
data
products
based
on
mobile
phone
usage
and
loca?on
awareness
(e.g.,
an?-‐credit
card
fraud,
loca?on-‐based
marke?ng)
• Poten?al
for
public
service
offerings
(e.g.,
flood
warning
by
text
message)
• Substan?al
revenue
opportuni?es
for
analy?cs
-‐
poten?al
to
uplih
TOTAL
revenues
by
20%
(35%
external,
65%
internal)
DATA
• Customer
profiles/
demographic
data
• Data
generated
by
mobile
phone
users
(customers)
interac?ng
with
the
mobile
phone
network
• Loca?on
of
user
can
be
tracked
11. Hull University Business School
MobCo:
opportuni?es
for
value
crea?on
• “You
can
send
them
a
text
message.
We
want
to
tell
everybody
that’s
within
an
area
that’s
at
risk
from
flood
damage
or
if
there's
been
a
chemical
spill,
or
a
terrorist
alert
…
Or,
even
doing
tsunami
warnings
with
everybody
that’s
within
a
mile
of
shore.”
(Head
of
Analy?cs,
MobCo)
12. Hull University Business School
Data
and
value
crea?on
-‐
MediaCo
VALUE
CREATION
• Adver?sing
revenues
• Marke?ng
and
promo?on
of
content
• Social
benefit
through
educa?on
re
promo?on
of
content
novel
to
viewer
DATA
• User
viewing
habits
tracked
online
with
detailed
Web
analy?cs
• Poten?al
for
links
to
external
data
(e.g.,
credit-‐ra?ngs,
social
media,
weather)
13. Hull University Business School
MediaCo:
opportuni?es
for
value
crea?on
• “So
we’ve
built
a
predicIve
model,
that
model
was
audited
by
PWC
as
being
80%
plus
accurate
in
terms
of
when
we
say
you’re
an
ABC1
1634
housewife
with
children,
how
many
Imes
are
we
geUng
that
right?
And
15%
of
all
of
our
video
on
demand
inventory
will
be
traded
on
that
product,
at
a
price
premium
of
about
20%
to
30%
above
our
exisIng
price
premium
for
the
product
that
you
currently
can
trade
on.”
(Head
of
Analy?cs,
MediaCo)
14. Hull University Business School
MediaCo:
opportuni?es
for
value
crea?on
• “it
isn’t
about
just
because
I
watch
comedy
you’re
not
introducing
me
to
more
and
more
and
more
comedy,
you’re
helping
me
to
discovery
something
in
factual
perhaps
that
I
would
never
even
consider.
And
I
may
not
enjoy
it
but
it
perhaps
will
evoke
some
kind
of
reacIon.”
(Head
of
Analy?cs,
MediaCo)
15. Hull University Business School
Data
and
value
crea?on
-‐
CityTrans
VALUE
CREATION
• Improvements
to
reliability
and
quality
of
service
• Insights
into
the
specific
customer
experience
(not
an
averaged
out
experience)
• Replacement
of
expensive
qualita?ve
surveys
by
automated
travel
analysis
• Poten?al
to
ini?ate
behavioural
change
in
passengers
to
spread
the
network
load
DATA
• Data
collected
by
?cke?ng
system
(smart
travel
cards
(STCs)
and
contactless
payment
cards
(CPCs)
• Data
is
stored
at
the
disaggregate
level,
with
some
(anonymised)
personal
informa?on
• CityTrans
has
access
to
customer
data
sets
outside
the
public
transport
realm
16. Hull University Business School
CityTrans:
opportuni?es
for
value
crea?on
• “We’ve
also
been
looking
whether
there
is
an
opportunity
to
provide
customers
with
be[er
informaIon
about
the
busiest
Imes
and
places
on
our
network,
in
order
to
encourage
some
customers
to
shi
their
travel
Imes
slightly.
If
we
can
shi
even
some
customers
from
the
most
crowded
Imes
and
places,
all
customers
could
have
a
be[er
journey
experience.”
(Head
of
Analy?cs,
CityTrans)
18. Hull University Business School
What
do
organiza?ons
need
to
think
about
when
embarking
on
the
analy?cs
journey?
19. Hull University Business School
!
Data and value
1. Ensure data
quality
Data must be ‘fit for purpose’, including legacy data
2. Build
permissions
platforms
Organizations will develop customer self-serve permissions portals.
Assurance of trust is paramount – organizations must be transparent
about how data is used and generate trust that it is secure
3. Apply
anonymization
Establish confidence in the data anonymization process before data is
shared
4. Share value Value created from data may need to be shared with the data originator
5. Build data
partnerships
Value is likely to arise from data partnerships rather than selling data as a
commodity to third parties
6. Create public
and private value
The data managed by the organizations can be used for public and
societal benefit as well as commercial benefit (e.g., flood warnings)
7. Legislation and
regulation
Changes in legislation may result in fundamental shifts in what can be
done with customer data (for example a “right to be forgotten”)
!
20. Hull University Business School
2.
Permissions
plaoorms
• “We’re
actually
going
a
step
further
and,
we
are
launching
a
new
permissions
pla]orm
and
if
you
are
a
MobCo
customer
you
will
see
your
personal
details
…
you
can
log
on,
enter
your
credenIals,
and
you
will
see
all
the
data
that
we
hold
about
you.
That
data
will
be
field-‐editable,
so
you
can
go
in
there
and
you
can
delete
it,
or
you
can
make
it
more
specific.
And
then,
depending
on
who
you
are,
let’s
say
that
you
have
a
resemblance
to
a
demographic
character,
it’s
just
who
you
are,
you
will
see
a
series
of
use
cases,
that
we
would
like
to
use
your
data
in.
And
within
those
use
cases
there
will
be
a
series
of
incenIves
and
you
can
go
through
and
grant
and
revoke
a
percentage
of
these
use
cases,
according
to
your
comfort
level.”
(Head
of
Analy?cs,
MobCo)
21. Hull University Business School
3.
Anonymiza?on
• “I
think
we’ve
put
enough
checks
in
place
such
that
there’s
nothing
personally
idenIfiable
in
the
cloud.
What
happens
is
we,
once
you
register
with
us
we
create
a
globally
unique
idenIfier,
so
it’s
like
a
12
digit
number
or
something.
So
everything
gets
anonymized,
then
from
that
point
forward
that
idenIfier
and
all
of
your
behavioural
data
is
all
put
in
the
cloud
…
you
know,
there’s
nothing
that’s
idenIfiable
down
to
the
individual,
at
best
what
you’re
going
to
get
is
just
viewing
behaviour.”
(Head
of
Analy?cs,
MediaCo)
22. Hull University Business School
!
Organizational and management
8. Corporate
analytics strategy
An analytics strategy is needed with a clear articulation of how and where
value will be created
9. Organizational
change
Becoming a data-driven organization will involve organizational and
cultural change and innovation
10. Deep domain
knowledge
The business analytics function will need to build deep understanding of
the organization and its business domain if it is to create lasting value
11. Team
structure
The business analytics team requires a mix of data scientists, business
analysts, and IT specialists
12. Academic
partnering
Data science expertise and resource can be acquired through partnering
with Universities
13. Ethics
process
Ethics committees should be established to provide oversight of how data
is used and to protect the reputations and brands of organizations
14. Agility The agile practices of software development can be adopted and
modified to provide a process model for analytics projects
15. Explore and
exploit
Analytics teams should exploit in response to identified problems (80%)
and have slack resource to explore new opportunities (20%)
!
23. Hull University Business School
9.
Organiza?onal
change
• “but
it
is
a
cultural
recogni?on
that
being
data-‐centric
as
a
company
is
a
way
to
more
effec?vely
compete”
(Head
of
Analy?cs,
MobCo)
24. Hull University Business School
Business
analy?cs
group
Data
scien?sts
Business
analysts
IT
development
and
opera?ons
11.
Team
structure
25. Hull University Business School
13.
Ethics
processes
• “Yeah,
we
do
have
a
saying
internally
that
we
want
to
be
spooky
but
not
creepy..Now,
the
difference
between
spooky
and
creepy
is,
it’s
very,
very
thin.
And
spooky’s
sort
of,
you
know,
“Ooh,
how
do
they
do
that?”
and
creepy
is
sort
of,
“Ugh,
how
do
they
do
that?”
And
that’s
a
fine
line.”
(Head
of
Analy?cs,
MobCo)
26. Hull University Business School
!
Technology, people and tools
Technology
16. Visualization
as story-telling
Visualization of data is not simply a technical feature – it is part of the
story-telling
17. Technologies While technology is in a state of flux an agnostic approach is advisable
People and tools
18. Data scientist
personal
attributes
The data scientist must be curious, problem-focused, able to work
independently, and capable of co-creating and communicating stories to
the business that form the basis for actionable insight into data
19. Data scientist
as ‘bricoleur’
The tools and techniques don’t matter as much as the ability of the data
scientist to cobble together solutions using the tools at hand (‘bricolage’)
20. Acquisition
and retention
Data scientists are attracted by interesting data to work with and retained
if they are given interesting problems to work on and have career paths
!
27. Hull University Business School
16.
Story-‐telling
• “You
need
to
have
the
story
about
what
does
this
mean
for
your
organizaIon
and
what
acIon
decision-‐makers
should
take.
Your
data
scienIst
needs
to
take
the
complicated
maths
and
explain
the
conclusions
in
such
a
way
that
someone
who
is
not
a
data
analyst
can
understand
it.
In
some
ways,
that
may
be
the
hardest
skill
for
the
data
scienIst
”
(Head
of
Analy?cs,
CityTrans)
28. Hull University Business School
17.
Technologies
• “The
technology
has
never
been
anything
that’s
standing
in
our
way.
The
technology,
you
can
go
to
any
one
of
five
or
six
different
vendors
to
get
what
we
require.
That’s
not
the
hard
part.”
(Head
of
Analy?cs,
MobCo)
• “We
started
with
SPSS
iniIally,
but
we
found
it
wasn’t
flexible
enough
and
didn’t
enable
machine
learning
in
the
cloud,
and
that’s
what
we
needed.”
(Head
of
Analy?cs,
MediaCo)
29. Hull University Business School
18.
Data
scien?st
aWributes
• “…
a
lot
of
my
analysts
certainly
will
describe
how
they
were
just
fundamentally
curious
around
how
the
world
is
structured,
or
curious
as
to
why,
you
know,
pa[erns
emerge
the
way
they
emerge.
So
it
wasn’t
about
the
vocaIon
necessarily
itself,
but
it
was
an
element
of
curiosity.
And
that
curiosity
is
what
you
want
in
an
analyst.”
(Head
of
Analy?cs,
MediaCo)
31. Hull University Business School
Business
analy?cs
and
organiza?onal
change
• Pergrew’s
(1988)
model
of
organiza?onal
change
refers
to
the
context,
content,
and
process
of
change
• These
are
applied
to
analy?cs
as
– CONTEXT
-‐
analy?cs
eco-‐system
(why
change
is
needed)
– CONTENT
-‐
analy?cs
maturity
(what
change
is
needed)
– PROCESS
-‐
analy?cs
road-‐map
(how
change
will
be
enacted)
Pergrew,
A.
M.
(1988).
The
management
of
strategic
change.
B.
Blackwell.
32. Hull University Business School
Data
assets
Analy?cs
strategy
ICT
strategy
and
technologies
Data
CONTEXT
Business
analy?cs
eco-‐system
What
data
is
available
and
of
what
quality?
What
technologies
are
used
to
store
and
analyze
the
data?
Reciproca?ng
selec?on
pressure
–
e.g.,
data
shapes
and
enables
the
content
of
the
analy?cs
strategy
while
the
analy?cs
strategy
simultaneously
shapes
the
data
collected
33. Hull University Business School
Analy?cs
strategy
Value
proposi?ons
Business
strategy
Strategy
CONTEXT
Business
analy?cs
eco-‐system
How
will
value
be
created
from
the
data?
Does
the
analy?cs
strategy
align
with
the
business
strategy?
34. Hull University Business School
Analy?cs
strategy
HR
strategy
People
CONTEXT
Business
analy?cs
eco-‐system
Data
scien?sts
and
analysts
Can
we
find,
train,
develop,
and
retain
analy?cs
people
with
the
relevant
skills
and
aWributes?
Do
we
have
an
appropriate
HR
strategy
to
support
the
development
of
business
analy?cs
in
the
organiza?on?
35. Hull University Business School
Data
assets
Analy?cs
strategy
Value
proposi?ons
ICT
strategy
and
technologies
HR
strategy
Business
strategy
Strategy
People
Data
CONTEXT
Business
analy?cs
eco-‐system
Data
scien?sts
and
analysts
36. Hull University Business School
CONTENT
Business
analy?cs
maturity
DegreeofbusinesstransformationHigh
HighLow
Low
Range of potential benefits
One.
Fragmented
Cultural change
Two.
Localised
Three.
Func?onal
Four.
Data-‐driven
Five.
Evidence-‐based
Six.
Essen?al
Developed
with
John
Morton,
Big
Data
advisor:
www.consultcpm.com
37. Hull University Business School
DegreeofbusinesstransformationHigh
HighLow
Low
Range of potential benefits
One.
Fragmented
Cultural change
Two.
Localised
Three.
Func?onal
Four.
Data-‐driven
Five.
Evidence-‐based
Six.
Essen?al
the
organiza?on
makes
ad
hoc
use
of
analy?cs
within
individual
departments,
such
as
marke?ng
CONTENT
Business
analy?cs
maturity
38. Hull University Business School
CONTENT
Business
analy?cs
maturity
DegreeofbusinesstransformationHigh
HighLow
Low
Range of potential benefits
One.
Fragmented
Cultural change
Two.
Localised
Three.
Func?onal
Four.
Data-‐driven
Five.
Evidence-‐based
Six.
Essen?al
the
organiza?on
begins
to
exploit
medium
sized
data
sets
and
starts
to
integrate
data
from
mul?ple
func?ons,
e.g.,
supply
chain,
marke?ng,
and
sales
39. Hull University Business School
CONTENT
Business
analy?cs
maturity
DegreeofbusinesstransformationHigh
HighLow
Low
Range of potential benefits
One.
Fragmented
Cultural change
Two.
Localised
Three.
Func?onal
Four.
Data-‐driven
Five.
Evidence-‐based
Six.
Essen?al
establishment
of
a
central
analy?cs
service
as
a
part
of
the
formal
organiza?onal
structure;
an
organiza?on-‐wide
emphasis
on
improving
revenue
and
margins
and
on
improving
opera?ons
40. Hull University Business School
CONTENT
Business
analy?cs
maturity
DegreeofbusinesstransformationHigh
HighLow
Low
Range of potential benefits
One.
Fragmented
Cultural change
Two.
Localised
Three.
Func?onal
Four.
Data-‐driven
Five.
Evidence-‐based
Six.
Essen?al
decisions
throughout
the
organiza?on
are
based
on
data
and
evidence;
management
know
to
ask
about
the
provenance
of
data
and
its
quality
and
know
how
to
interpret
the
results
of
analy?cs
41. Hull University Business School
CONTENT
Business
analy?cs
maturity
DegreeofbusinesstransformationHigh
HighLow
Low
Range of potential benefits
One.
Fragmented
Cultural change
Two.
Localised
Three.
Func?onal
Four.
Data-‐driven
Five.
Evidence-‐based
Six.
Essen?al
data
and
decisions
are
aligned
with
corporate
policy
and
strategy,
fully
integrated
into
business
processes,
and
supported
by
methods
such
as
randomized
controlled
experiments
as
the
basis
for
informed
ac?on
42. Hull University Business School
CONTENT
Business
analy?cs
maturity
DegreeofbusinesstransformationHigh
HighLow
Low
Range of potential benefits
One.
Fragmented
Cultural change
Two.
Localised
Three.
Func?onal
Four.
Data-‐driven
Five.
Evidence-‐based
Six.
Essen?al
management
and
decision-‐makers
focus
on
excep?ons/dilemmas,
ethical
considera?ons;
the
analy?cs
strategy
plays
a
greater
role
in
shaping
the
business
strategy
and
becomes
the
essence
of
how
the
organiza?on
competes
and
survives
in
its
environment
43. Hull University Business School
1.
Acquire
data
and
assess
quality
2.
Explore
value
proposi?ons
3.
(Re)define
analy?cs
strategy
4.
Plan
analy?cs
strategy
implementa?on
5.
Implement
analy?cs
strategy
6.
Evaluate
analy?cs
performance
and
maturity
2a.
Pilot
analy?cs
projects
(agile)
Data
issues
Proof
of
concept
Buy-‐in
PROCESS
Business
analy?cs
roadmap
44. Hull University Business School
In
summary
…
• Build
an
analy?cs
strategy
that
clearly
ar?culates
data
sources
and
value
proposi?ons
• Data
quality,
data
quality,
data
quality
…
• Analy?cs
involves
organiza?onal
and
cultural
change
–
it
will
take
?me
and
leadership
• Analy?cs
projects
are
not
simply/just
IT
projects
• Fail
fast,
fail
cheap,
learn
quickly
45. Hull University Business School
Further
informa?on
• Full
report:
– Vidgen,
R.,
(2014).
Crea?ng
business
value
from
Big
Data
and
business
analy?cs:
organisa?onal,
managerial
and
human
resource
implica?ons.
Hull
University
Business
School
Research
Memorandum,
no.
94,
ISBN
978-‐1-‐906422-‐31-‐8.
– hWp://www2.hull.ac.uk/hubs/pdf/NEMODE%20big
%20data%20scien?st%20report%20final.pdf
• Richard
Vidgen’s
blog:
– hWp://datasciencebusiness.wordpress.com