The Center of Applied Data Science (CADS) was established in 2014 in Malaysia and expanded to Singapore in 2018. CADS aims to establish a global standard in data science and analytics education. It has produced over 1,000 data professionals and advised both government and corporate clients through its BOLT methodology of building capabilities, operating solutions, learning skills, and transferring knowledge. Data science and analytics roles such as data scientists, data analysts, and data engineers are in high demand with increasing salaries and opportunities for career advancement.
The document discusses how analytics and data can help organizations improve performance and address common reasons why analytics projects fail. It provides examples of how a Swedish bank called Handelsbanken successfully uses an empowering culture and personalized service to remain highly profitable. The document advocates that organizations build cultures allowing sustainable performance, empower people, and communicate strategies clearly. It also discusses how tools like data visualization and storytelling can help internal auditors gain insights from big data and improve auditing.
Jumpstart a Lucrative Career in Data ScienceSharala Axryd
The Center of Applied Data Science (CADS) aims to make the world more sustainable through technology, data insights, and intelligence. CADS educates clients on data management, integration, and analysis to empower them and promote independence. As the first comprehensive data science training institution in ASEAN, CADS integrates learning, networking, and professional growth to train effective data scientists and cultivate the next generation of data professionals. There is high demand for data scientists as 90% of data in the last 2 years remains unused, with the potential value from data exceeding $300 billion annually. However, many countries in ASEAN still face gender gaps that CADS hopes to help address through education and training.
Big data refers to large and complex data sets that are difficult to process using traditional methods. The Center of Applied Data Science (CADS) was founded to address the need for data science talent in Malaysia by training the next generation of data professionals. CADS partners with leading organizations like the Data Incubator, Harvard Business School, and Coursera to provide rigorous data science education programs. The goal is to cultivate data talent and empower individuals and organizations to leverage big data for competitive advantage.
Digital Business Today: Where is it heading?Sharala Axryd
This document discusses trends in digital business and data science, including the future of technologies like edge computing, artificial intelligence, and the Internet of Things. It outlines eight categories of data scientists and notes that data visualization and chief data officer will be important roles. The new chief data officer will need skills in vision, managing multidisciplinary teams, multiple communication forms, computational thinking, and innovation. The Center of Applied Data Science provides certification in these areas.
1) The document discusses how 5 out of 10 future jobs have yet to be created due to technological innovation and the need for professionals to be agile and adopt new solutions.
2) By 2050, industries like banking and manufacturing will integrate automation and robotics according to economists, showing how digital disruption has already occurred.
3) Jobs that did not exist 10 years ago like data scientists, online community managers, and drone operators are highlighted to demonstrate how new roles are emerging while traditional jobs in areas like healthcare, education and law are threatened by advances in big data and machine learning.
The Future of Work is Here: Are You Prepared?Sharala Axryd
The document discusses how technology is changing the nature of work and the future workforce. Automation and AI will significantly impact jobs over the coming decades, with some jobs being replaced while new jobs are created. To stay relevant, professionals need to continually learn new skills. The future workforce will require skills in problem solving, critical thinking, and emotional intelligence rather than just technical skills. While AI will replace some jobs, it will also create new types of jobs. Malaysia needs to take advantage of new technologies like AI, IoT, and big data to increase productivity and improve livelihoods. However, it has not fully reached Industry Revolution 3.0 yet. Women and underrepresented groups also remain an untapped resource, and empowering them
When Big Data Meets Recruiting - HRM Asia March 2015 PresentationDheeraj Shastri
Big data refers to the massive volume of both structured and unstructured data from various sources that is difficult to process using traditional methods. Predictive analytics can help human resources derive insights from both internal data like past hires and attrition, and external data like economic trends. Applying big data and predictive analytics to recruiting can help recruiters identify pivotal roles, know their typical hiring funnel metrics, strategically plan recruitment activities based on data insights, determine the best sourcing methods based on a role's importance and scarcity of talent, and ultimately drive finding candidates and filling jobs faster while spending resources smarter.
This document provides an overview of the services offered by ADP TotalSource, including talent acquisition, payroll and HR administration, talent management, health and welfare benefits, business insights, risk mitigation and compliance, employee communication, and retirement savings plans. It highlights statistics on the benefits of outsourcing HR administration and adopting data-driven decision making. The document promotes ADP TotalSource as a provider that can help power and protect businesses with HR expertise and solutions across the entire employee lifecycle and as companies grow.
The document discusses how analytics and data can help organizations improve performance and address common reasons why analytics projects fail. It provides examples of how a Swedish bank called Handelsbanken successfully uses an empowering culture and personalized service to remain highly profitable. The document advocates that organizations build cultures allowing sustainable performance, empower people, and communicate strategies clearly. It also discusses how tools like data visualization and storytelling can help internal auditors gain insights from big data and improve auditing.
Jumpstart a Lucrative Career in Data ScienceSharala Axryd
The Center of Applied Data Science (CADS) aims to make the world more sustainable through technology, data insights, and intelligence. CADS educates clients on data management, integration, and analysis to empower them and promote independence. As the first comprehensive data science training institution in ASEAN, CADS integrates learning, networking, and professional growth to train effective data scientists and cultivate the next generation of data professionals. There is high demand for data scientists as 90% of data in the last 2 years remains unused, with the potential value from data exceeding $300 billion annually. However, many countries in ASEAN still face gender gaps that CADS hopes to help address through education and training.
Big data refers to large and complex data sets that are difficult to process using traditional methods. The Center of Applied Data Science (CADS) was founded to address the need for data science talent in Malaysia by training the next generation of data professionals. CADS partners with leading organizations like the Data Incubator, Harvard Business School, and Coursera to provide rigorous data science education programs. The goal is to cultivate data talent and empower individuals and organizations to leverage big data for competitive advantage.
Digital Business Today: Where is it heading?Sharala Axryd
This document discusses trends in digital business and data science, including the future of technologies like edge computing, artificial intelligence, and the Internet of Things. It outlines eight categories of data scientists and notes that data visualization and chief data officer will be important roles. The new chief data officer will need skills in vision, managing multidisciplinary teams, multiple communication forms, computational thinking, and innovation. The Center of Applied Data Science provides certification in these areas.
1) The document discusses how 5 out of 10 future jobs have yet to be created due to technological innovation and the need for professionals to be agile and adopt new solutions.
2) By 2050, industries like banking and manufacturing will integrate automation and robotics according to economists, showing how digital disruption has already occurred.
3) Jobs that did not exist 10 years ago like data scientists, online community managers, and drone operators are highlighted to demonstrate how new roles are emerging while traditional jobs in areas like healthcare, education and law are threatened by advances in big data and machine learning.
The Future of Work is Here: Are You Prepared?Sharala Axryd
The document discusses how technology is changing the nature of work and the future workforce. Automation and AI will significantly impact jobs over the coming decades, with some jobs being replaced while new jobs are created. To stay relevant, professionals need to continually learn new skills. The future workforce will require skills in problem solving, critical thinking, and emotional intelligence rather than just technical skills. While AI will replace some jobs, it will also create new types of jobs. Malaysia needs to take advantage of new technologies like AI, IoT, and big data to increase productivity and improve livelihoods. However, it has not fully reached Industry Revolution 3.0 yet. Women and underrepresented groups also remain an untapped resource, and empowering them
When Big Data Meets Recruiting - HRM Asia March 2015 PresentationDheeraj Shastri
Big data refers to the massive volume of both structured and unstructured data from various sources that is difficult to process using traditional methods. Predictive analytics can help human resources derive insights from both internal data like past hires and attrition, and external data like economic trends. Applying big data and predictive analytics to recruiting can help recruiters identify pivotal roles, know their typical hiring funnel metrics, strategically plan recruitment activities based on data insights, determine the best sourcing methods based on a role's importance and scarcity of talent, and ultimately drive finding candidates and filling jobs faster while spending resources smarter.
This document provides an overview of the services offered by ADP TotalSource, including talent acquisition, payroll and HR administration, talent management, health and welfare benefits, business insights, risk mitigation and compliance, employee communication, and retirement savings plans. It highlights statistics on the benefits of outsourcing HR administration and adopting data-driven decision making. The document promotes ADP TotalSource as a provider that can help power and protect businesses with HR expertise and solutions across the entire employee lifecycle and as companies grow.
How to sustain analytics capabilities in an organizationSAS Canada
This presentation is part of Analytics Management Series that is designed to suggest paths towards effective decision-making in order to help sustain and grow analytical capabilities. It features thought leaders who actively manage complex analytical environments who share their best practices. How to sustain analytics capabilities in an organization features Daymond Ling, Senior Director, Modelling & Analytics (CIBC) on how organizations who want better performance and less problems can use data to their advantage.
Are you your company’s chief data officer? Given the scarcity of the official role, it’s likely that you’re not — at least in title. But that doesn't mean that you shouldn't operate like one. Do you approach data leadership as a C-level executive or a senior data head? Is your team’s output strategic or just operational? In this interactive keynote, one of the Windy City’s foremost data leaders will lead an interactive discussion on what it takes to lead like a chief, what it looks like, and how to get there and get it done.
The document discusses business data and the importance of aligning business data with business strategy. It defines business data as data collected and stored by businesses to support operations and decision making. It also discusses common types of business data like customer data, transactions, and social media data. The document emphasizes that a data strategy should be driven by business goals and outlines key elements of an effective data strategy like defining goals, governance, and aligning data initiatives with business objectives.
How CIOs are thinking about big data and the major opportunities, challenges and threats they face in managing the analytics unstructured information. Based on a survey of Canadian IT leaders
The document discusses how organizations can increase the value of their data through establishing an information architecture, empowering employees to make smarter use of data, and investing in data-driven decision making. It recommends focusing on teamwork between business and IT, breaking down data silos, choosing suitable analytical software, and improving processes. Related studies highlight the importance of data accessibility and quality for digital transformation and decreasing operating costs. The document invites connecting to discuss practical implementation.
Chief Data & Analytics Officer Fall Boston - PresentationSrinivasan Sankar
Data Asset Catalog & Metadata Management - Is It a Fad or Is It the Future?
Many have dubbed metadata as “the new black,” but is this accurate?
How to leverage metadata management to streamline data governance and ensure transparency
Improving data quality and ensuring consistency and accuracy of data across various reporting systems
Looking at the flip side: what are the additional training requirements and value-added for the business?
This document introduces ExcKlam, a consulting firm focused on sustainability, CSR, and risk management. It provides information on ExcKlam's management team and services. ExcKlam offers knowledge and intelligence, CSR and risk management, organization and business analysis, and sustainable project solutions. It works with partners and uses various frameworks and technologies to provide customized solutions and help clients with business growth and resilience. ExcKlam aims to create value and social impact through relationships and innovative projects.
Role of Analytics in Delivering Health Information to help fight Cancer in Au...Deanna Kosaraju
Voices 2014
Role of Analytics in Delivering Health Information to help fight Cancer in Australia
Katerina Andronis,
Deloitte Consulting, Australia and Chandana Unnithan,
Deakin University, Australia
DataOps: Nine steps to transform your data science impact Strata London May 18Harvinder Atwal
According to Forrester Research, only 22% of companies are currently seeing a significant return from data science expenditures. Most data science implementations are high-cost IT projects, local applications that are not built to scale for production workflows, or laptop decision support projects that never impact customers. Despite this high failure rate, we keep hearing the same mantra and solutions over and over again. Everybody talks about how to create models, but not many people talk about getting them into production where they can impact customers.
Harvinder Atwal offers an entertaining and practical introduction to DataOps, a new and independent approach to delivering data science value at scale, used at companies like Facebook, Uber, LinkedIn, Twitter, and eBay. The key to adding value through DataOps is to adapt and borrow principles from Agile, Lean, and DevOps. However, DataOps is not just about shipping working machine learning models; it starts with better alignment of data science with the rest of the organization and its goals. Harvinder shares experience-based solutions for increasing your velocity of value creation, including Agile prioritization and collaboration, new operational processes for an end-to-end data lifecycle, developer principles for data scientists, cloud solution architectures to reduce data friction, self-service tools giving data scientists freedom from bottlenecks, and more. The DataOps methodology will enable you to eliminate daily barriers, putting your data scientists in control of delivering ever-faster cutting-edge innovation for your organization and customers.
The Chief Data Officer: Tomorrow's Corporate RockstarKatrina Read
The transformative power of data and analytics is being harnessed by organisations around the world to make smarter, quicker and more analytical-driven decisions. At the helm of this transformation is the Chief Data Officer – a strategic leader who employs data and analytics to create tangible business value, and who is rapidly attracting rock star status.
A SMART Seminar conducted on 3 May 2013 by Ian Bertram.
Leveraging information for decision making, assessing its value and ensuring frictionless sharing of information within the enterprise and beyond is what will fuel success in the current and future economy. New use cases with insatiable demand for real-time access to socially mediated and context-aware insights make information management in the 21st century dramatically different.
For more information, see http://goo.gl/a6F2c
The document discusses big data and its importance for businesses. It provides several definitions of big data from different sources that commonly refer to large and complex datasets that are difficult to process using traditional methods due to their size and speed. Big data represents an opportunity for businesses to gain valuable insights and optimize their operations, customer service, and decision making. However, it also poses challenges for storage, analysis, and privacy. The document advocates the need for businesses to make full use of all their enterprise data and leverage in-memory and streaming analytics to extract value from big data.
Real-World Data Governance: How to Write a Data Steward Job DescriptionDATAVERSITY
A Data Steward Job Description is a list of job responsibilities that a Data Steward uses for tasks, or functions, and responsibilities of them in their everyday role. It includes to whom they report, the qualifications or skills needed by the person, and sometimes even includes a salary range. The job description of a Data Steward is not a new job description or different from their other job description. Is this confusing? We thought so.
This Real-World Data Governance webinar with Bob Seiner will focus on defining the typical responsibilities for every data steward all at once, no matter the industry, their role in the organization, or their role in the Data Governance program. Bob will focus on a list of competencies required for people to become great Data Stewards.
The session will include:
Components of a Data Steward Job Description
Seiner’s Rules for Becoming a Data Steward and How They Apply
Getting the Data Steward Involved in the Writing
Evaluating a Data Steward Based on the Job Description
Is a Job Description Even Necessary
Data Catalog as the Platform for Data IntelligenceAlation
Data catalogs are in wide use today across hundreds of enterprises as a means to help data scientists and business analysts find and collaboratively analyze data. Over the past several years, customers have increasingly used data catalogs in applications beyond their search & discovery roots, addressing new use cases such as data governance, cloud data migration, and digital transformation. In this session, the founder and CEO of Alation will discuss the evolution of the data catalog, the many ways in which data catalogs are being used today, the importance of machine learning in data catalogs, and discuss the future of the data catalog as a platform for a broad range of data intelligence solutions.
The document discusses a big data webinar series hosted by Cronos Big Data Services that focuses on using big data in various industries. It provides an overview of key big data concepts and Cronos' offerings, which include workshops, proofs of concept, and implementations related to big data analytics. The document also outlines how Cronos helps organizations leverage big data to gain insights and make better decisions.
Data Governance Best Practices and Lessons LearnedDATAVERSITY
Best practices and lessons learned are powerful tools used to assess an organization’s readiness and initial activities associated with delivering a Data Governance program. There are two criteria to determine if something is best practice for your organization. And the definition of data governance best practice is best way to learn from others and begin with the end in mind.
Bob Seiner will share industry data governance best practices in this month’s installment of the RWDG webinar series. Learn how to use the best practices defined in this webinar to address opportunities to improve your organization’s data governance implementation. Attend this webinar and learn that assessing your organization may not be as difficult as you think.
During this webinar Bob will discuss:
How to define data governance best practices for your organization
Criteria used to determine if a practice is best practice
How to assess your organization against industry best practice
Assessing risks associated with best practice gaps
Addressing opportunities to improve gaps uncovered in the assessment
Machine learning - What they don't teach you on Coursera ODSC London 2016Harvinder Atwal
I’ll show some example of live models at MoneySuperMarket. However, the main theme will be that there is far more to successful implementation of Machine Learning than just creating good algorithms. There needs to be just as much effort, if not more, put into selling the benefits to the business, working with developers and engineers to put the model into production, building testing into the process and ongoing maintenance of the solution.
Data Done Right: Ensuring Information IntegritySharala Axryd
It’s the ultimate “garbage in, garbage out” quandary. Data can be an organization’s most valuable asset — but only to the degree its quality can be validated and trusted. Without the right guidelines, processes, and solutions in place to control the way applications, systems, databases, messages, and documents are managed, "dirty" data can permeate systems across the enterprise, negatively impacting everything from strategic planning to day-to-day decision making. High-quality data will ensure more efficiency in driving a company’s success because of the dependence on fact-based decisions, instead of habitual or human intuition.
To gain a better understanding of this topic, this speaking session will examine:
- what data quality and master data management is
- why they are so crucial for successful business operations and strategies
- how to improve data quality by organizational, procedural and technological means
The document discusses key aspects of transforming a learning institution into a data-driven university (DDU). It outlines that a DDU aims to utilize data analytics to make higher education smarter and optimize management processes. Some key success factors for a DDU include developing industry-ready talent with skills in analytics, digital, and business and achieving operational excellence through analytics to build competitive resilience. The document also provides parameters that define a digital ecosystem and discusses barriers to digital transformation in education.
How to sustain analytics capabilities in an organizationSAS Canada
This presentation is part of Analytics Management Series that is designed to suggest paths towards effective decision-making in order to help sustain and grow analytical capabilities. It features thought leaders who actively manage complex analytical environments who share their best practices. How to sustain analytics capabilities in an organization features Daymond Ling, Senior Director, Modelling & Analytics (CIBC) on how organizations who want better performance and less problems can use data to their advantage.
Are you your company’s chief data officer? Given the scarcity of the official role, it’s likely that you’re not — at least in title. But that doesn't mean that you shouldn't operate like one. Do you approach data leadership as a C-level executive or a senior data head? Is your team’s output strategic or just operational? In this interactive keynote, one of the Windy City’s foremost data leaders will lead an interactive discussion on what it takes to lead like a chief, what it looks like, and how to get there and get it done.
The document discusses business data and the importance of aligning business data with business strategy. It defines business data as data collected and stored by businesses to support operations and decision making. It also discusses common types of business data like customer data, transactions, and social media data. The document emphasizes that a data strategy should be driven by business goals and outlines key elements of an effective data strategy like defining goals, governance, and aligning data initiatives with business objectives.
How CIOs are thinking about big data and the major opportunities, challenges and threats they face in managing the analytics unstructured information. Based on a survey of Canadian IT leaders
The document discusses how organizations can increase the value of their data through establishing an information architecture, empowering employees to make smarter use of data, and investing in data-driven decision making. It recommends focusing on teamwork between business and IT, breaking down data silos, choosing suitable analytical software, and improving processes. Related studies highlight the importance of data accessibility and quality for digital transformation and decreasing operating costs. The document invites connecting to discuss practical implementation.
Chief Data & Analytics Officer Fall Boston - PresentationSrinivasan Sankar
Data Asset Catalog & Metadata Management - Is It a Fad or Is It the Future?
Many have dubbed metadata as “the new black,” but is this accurate?
How to leverage metadata management to streamline data governance and ensure transparency
Improving data quality and ensuring consistency and accuracy of data across various reporting systems
Looking at the flip side: what are the additional training requirements and value-added for the business?
This document introduces ExcKlam, a consulting firm focused on sustainability, CSR, and risk management. It provides information on ExcKlam's management team and services. ExcKlam offers knowledge and intelligence, CSR and risk management, organization and business analysis, and sustainable project solutions. It works with partners and uses various frameworks and technologies to provide customized solutions and help clients with business growth and resilience. ExcKlam aims to create value and social impact through relationships and innovative projects.
Role of Analytics in Delivering Health Information to help fight Cancer in Au...Deanna Kosaraju
Voices 2014
Role of Analytics in Delivering Health Information to help fight Cancer in Australia
Katerina Andronis,
Deloitte Consulting, Australia and Chandana Unnithan,
Deakin University, Australia
DataOps: Nine steps to transform your data science impact Strata London May 18Harvinder Atwal
According to Forrester Research, only 22% of companies are currently seeing a significant return from data science expenditures. Most data science implementations are high-cost IT projects, local applications that are not built to scale for production workflows, or laptop decision support projects that never impact customers. Despite this high failure rate, we keep hearing the same mantra and solutions over and over again. Everybody talks about how to create models, but not many people talk about getting them into production where they can impact customers.
Harvinder Atwal offers an entertaining and practical introduction to DataOps, a new and independent approach to delivering data science value at scale, used at companies like Facebook, Uber, LinkedIn, Twitter, and eBay. The key to adding value through DataOps is to adapt and borrow principles from Agile, Lean, and DevOps. However, DataOps is not just about shipping working machine learning models; it starts with better alignment of data science with the rest of the organization and its goals. Harvinder shares experience-based solutions for increasing your velocity of value creation, including Agile prioritization and collaboration, new operational processes for an end-to-end data lifecycle, developer principles for data scientists, cloud solution architectures to reduce data friction, self-service tools giving data scientists freedom from bottlenecks, and more. The DataOps methodology will enable you to eliminate daily barriers, putting your data scientists in control of delivering ever-faster cutting-edge innovation for your organization and customers.
The Chief Data Officer: Tomorrow's Corporate RockstarKatrina Read
The transformative power of data and analytics is being harnessed by organisations around the world to make smarter, quicker and more analytical-driven decisions. At the helm of this transformation is the Chief Data Officer – a strategic leader who employs data and analytics to create tangible business value, and who is rapidly attracting rock star status.
A SMART Seminar conducted on 3 May 2013 by Ian Bertram.
Leveraging information for decision making, assessing its value and ensuring frictionless sharing of information within the enterprise and beyond is what will fuel success in the current and future economy. New use cases with insatiable demand for real-time access to socially mediated and context-aware insights make information management in the 21st century dramatically different.
For more information, see http://goo.gl/a6F2c
The document discusses big data and its importance for businesses. It provides several definitions of big data from different sources that commonly refer to large and complex datasets that are difficult to process using traditional methods due to their size and speed. Big data represents an opportunity for businesses to gain valuable insights and optimize their operations, customer service, and decision making. However, it also poses challenges for storage, analysis, and privacy. The document advocates the need for businesses to make full use of all their enterprise data and leverage in-memory and streaming analytics to extract value from big data.
Real-World Data Governance: How to Write a Data Steward Job DescriptionDATAVERSITY
A Data Steward Job Description is a list of job responsibilities that a Data Steward uses for tasks, or functions, and responsibilities of them in their everyday role. It includes to whom they report, the qualifications or skills needed by the person, and sometimes even includes a salary range. The job description of a Data Steward is not a new job description or different from their other job description. Is this confusing? We thought so.
This Real-World Data Governance webinar with Bob Seiner will focus on defining the typical responsibilities for every data steward all at once, no matter the industry, their role in the organization, or their role in the Data Governance program. Bob will focus on a list of competencies required for people to become great Data Stewards.
The session will include:
Components of a Data Steward Job Description
Seiner’s Rules for Becoming a Data Steward and How They Apply
Getting the Data Steward Involved in the Writing
Evaluating a Data Steward Based on the Job Description
Is a Job Description Even Necessary
Data Catalog as the Platform for Data IntelligenceAlation
Data catalogs are in wide use today across hundreds of enterprises as a means to help data scientists and business analysts find and collaboratively analyze data. Over the past several years, customers have increasingly used data catalogs in applications beyond their search & discovery roots, addressing new use cases such as data governance, cloud data migration, and digital transformation. In this session, the founder and CEO of Alation will discuss the evolution of the data catalog, the many ways in which data catalogs are being used today, the importance of machine learning in data catalogs, and discuss the future of the data catalog as a platform for a broad range of data intelligence solutions.
The document discusses a big data webinar series hosted by Cronos Big Data Services that focuses on using big data in various industries. It provides an overview of key big data concepts and Cronos' offerings, which include workshops, proofs of concept, and implementations related to big data analytics. The document also outlines how Cronos helps organizations leverage big data to gain insights and make better decisions.
Data Governance Best Practices and Lessons LearnedDATAVERSITY
Best practices and lessons learned are powerful tools used to assess an organization’s readiness and initial activities associated with delivering a Data Governance program. There are two criteria to determine if something is best practice for your organization. And the definition of data governance best practice is best way to learn from others and begin with the end in mind.
Bob Seiner will share industry data governance best practices in this month’s installment of the RWDG webinar series. Learn how to use the best practices defined in this webinar to address opportunities to improve your organization’s data governance implementation. Attend this webinar and learn that assessing your organization may not be as difficult as you think.
During this webinar Bob will discuss:
How to define data governance best practices for your organization
Criteria used to determine if a practice is best practice
How to assess your organization against industry best practice
Assessing risks associated with best practice gaps
Addressing opportunities to improve gaps uncovered in the assessment
Machine learning - What they don't teach you on Coursera ODSC London 2016Harvinder Atwal
I’ll show some example of live models at MoneySuperMarket. However, the main theme will be that there is far more to successful implementation of Machine Learning than just creating good algorithms. There needs to be just as much effort, if not more, put into selling the benefits to the business, working with developers and engineers to put the model into production, building testing into the process and ongoing maintenance of the solution.
Data Done Right: Ensuring Information IntegritySharala Axryd
It’s the ultimate “garbage in, garbage out” quandary. Data can be an organization’s most valuable asset — but only to the degree its quality can be validated and trusted. Without the right guidelines, processes, and solutions in place to control the way applications, systems, databases, messages, and documents are managed, "dirty" data can permeate systems across the enterprise, negatively impacting everything from strategic planning to day-to-day decision making. High-quality data will ensure more efficiency in driving a company’s success because of the dependence on fact-based decisions, instead of habitual or human intuition.
To gain a better understanding of this topic, this speaking session will examine:
- what data quality and master data management is
- why they are so crucial for successful business operations and strategies
- how to improve data quality by organizational, procedural and technological means
The document discusses key aspects of transforming a learning institution into a data-driven university (DDU). It outlines that a DDU aims to utilize data analytics to make higher education smarter and optimize management processes. Some key success factors for a DDU include developing industry-ready talent with skills in analytics, digital, and business and achieving operational excellence through analytics to build competitive resilience. The document also provides parameters that define a digital ecosystem and discusses barriers to digital transformation in education.
Technology has transformed the way people work. Leaders can resolidify their teams by developing a robust Workforce Augmented Strategy to adjust their leadership behaviour, embrace digital workforce platforms and deepen their engagement with digitally enabled workers.
Malaysian Insurance Institute (MII) together with The Center of Applied Data Science (CADS) Founder and CEO Sharala Axryd will run a webinar for leaders to create a center of excellence for data literacy that addresses business needs and talent potential identification.
In doing so, leaders will be able to:
- improve employee engagement and talent retention
- improve data literacy and close competency gap
- digitize operations and automate process
Data Scientist Job, Career & Salary | Data Scientist Salary | Data Science Ma...Edureka!
The document discusses data science as a career field. It covers the following key points:
1. The demand for data scientists is growing rapidly due to the increasing amounts of data being generated.
2. Projections indicate the number of data science jobs will grow by 364,000 by 2020, and businesses analyzing data could earn $430 billion more than those not analyzing data.
3. Data scientists can earn average salaries of $111,000 per year, while data engineers and developers earn over $90,000. Salaries increase significantly with experience.
Learn All about Data Science from the Best Private University in KarnatakaREVA University
Completing Masters in Data Science degree can reshape your career path, though it demands dedication and time to gain the necessary skills and land the right job. To assist you, we've crafted a detailed plan for building a career in Data Science.
The document summarizes the Certified Data Science Professional (CDSPTM) certification program. It discusses the growing data science market size and job demand. The certification provides foundational training in topics like mathematics, programming, machine learning, and data visualization. It is an affordable and self-paced online program that includes learning resources and practice exams. Obtaining the CDSPTM validation increases one's chances of employment and salary in the data science field.
ydney's Data Analytics Advantage Launching Your Data-Driven Careerajisha1710
Australia has reached the peak of establishing itself as a worldwide data analytics hub. If you appreciate data management and statistics, being a data analyst may be the ideal option. The data analytics training program in Sydney provides the ideal learning environment due to its extensive acquisition of cutting-edge technology.
The document discusses how Cloudera helps customers with their data and analytics journeys. It recommends that customers (1) build a data-driven culture, (2) assemble the right cross-functional team, and (3) adopt an agile approach to data projects by starting small and iterating often. Successful customers operationalize insights efficiently and implement data governance appropriately for their needs and maturity.
Advances in technology for capturing information have led to the promise of “Big Data” to dramatically alter the business environment. However, technology is only an enabler of aggregation and analysis. Many firms struggle to convert information to business knowledge and insights. Learn how organizations are using data to improve skill development at all levels and developing models for organizational structures to link these skills to executive decision-making.
Speakers: Dan McGurrin, Ph.D., NC State and Pamela Webber, Cisco
Information Rich, Knowledge Poor: Overcoming Insurers’ Data ConundrumDeloitte United States
The ability to effectively harvest and harness data across the enterprise is quickly emerging as a competitive differentiator in the financial services industry. In the insurance sector specifically, a number of pioneers are already making healthy strides toward mastering information management, but for most companies that have not yet fully invested in this transformation, growing market mania around "Big Data" and looming regulatory changes that demand increased data transparency continue to generate considerable anxiety.
While many insurers have already spent and continue to spend heavily on core-system and technology modernization, most still find their efforts have fallen short of expectations and needs when it comes to information management. If data is expected to be realized as a strategic asset, insurers can no longer continue to merely tweak existing systems and business models to clear this data management hurdle.
However, operationalizing information management enterprise-wide is neither an easy nor short-term exercise, as demonstrated by programs already under way at companies that have pioneered the effort. But for many, the potential benefits to be derived from successfully organizing, governing, consuming and analyzing available data assets — both internal and external — are likely well worth the investment.
Still, to achieve holistic data fluency, optimize data exploitation and realize a positive ROI, insurers will need to dismantle numerous roadblocks embedded in their current infrastructure, hardware and software, corporate culture, and business models.
Information rich, knowledge poor explores challenges and potential solutions to mastering information management and realizing data as a strategic asset.
Data Governance Best Practices, Assessments, and RoadmapsDATAVERSITY
When starting or evaluating the present state of your Data Governance program, it is important to focus on best practices such that you don’t take a ready, fire, aim approach. Best practices need to be practical and doable to be selected for your organization, and the program must be at risk if the best practice is not achieved.
Join Bob Seiner for an important webinar focused on industry best practice around standing up formal Data Governance. Learn how to assess your organization against the practices and deliver an effective roadmap based on the results of conducting the assessment.
In this webinar, Bob will focus on:
- Criteria to select the appropriate best practices for your organization
- How to define the best practices for ultimate impact
- Assessing against selected best practices
- Focusing the recommendations on program success
- Delivering a roadmap for your Data Governance program
Most companies recognize the importance of data and Data Governance. Yet, many companies are failing in their efforts to become data-driven. Increasing investment in technology has not addressed the problem. In fact, the increasing complexity has made matters worse. In order to succeed, organizations must address the most difficult issue that is holding them back: cultural change and the human side of Data Governance.
In this session, Ron Huizenga will discuss human factors as the major impediment to business adoption, as well as how to address them.
This document outlines the program learning outcomes for the B412 - Analytics for Business Decision Making program. The goal of the program is to prepare students to analyze various data to assist multi-faceted business decision making through developing skills in data analysis, programming, statistics, and databases. The vocational program learning outcomes include extracting, transforming, and loading data to support problem solving and decision making, developing predictive models using operational and marketing data, and effectively communicating analytics results to support business decision making.
Cisco established an Analytics Center of Excellence (CoE) to accelerate the company's competitive advantage through data-driven insights. The CoE aims to understand past performance, manage current operations, and influence future outcomes. It works with business functions and a governing body of senior leaders to prioritize initiatives, establish processes, and cultivate a culture where analytics drives decision-making. The long-term goal is to transform Cisco into a company where analytics provides a clear competitive differentiator.
Is your big data journey stalling? Take the Leap with Capgemini and ClouderaCloudera, Inc.
Transitioning to a Big Data architecture is a big step; and the complexity of moving existing analytical services onto modern platforms like Cloudera, can seem overwhelming.
The document summarizes the Master of Business Analytics degree offered by Deakin University. It is a 1.5 year full-time or 3 year part-time degree that builds knowledge in business analytics theories, concepts and practice. The degree consists of 8 core units and 4 elective units. It provides hands-on experience with business analytics tools and focuses on developing skills across enterprise information management, descriptive/predictive/prescriptive analytics, and the business value of analytics. The degree is designed to prepare students for careers in the growing field of business analytics.
This document provides an overview of the Statistical Analysis System (SAS) software. It discusses SAS's history beginning in 1966 as a project to analyze agricultural data. SAS now has over 7,000 employees and provides software to 96 of the Fortune 100 companies. The document also describes SAS Enterprise BI Server, including its key features for business intelligence and a case study of its successful implementation at the US Department of Housing and Urban Development.
Licensed to Analyze? Strata Data NY 2019 IADSS Session - Usama Fayyad, Hamit ...IADSS
The latest insight into IADSS Research was shared with analytics community at Strata Data NY 2019 by O'reilly. IADSS Co-founders Usama Fayyad and Hamit Hamutcu talked about the current status of data science job market, the wasted cost of data science recruitment and role definitions & required skill-sets for most common roles in data science.
Please check out IADSSglobal on Twitter and visit www.iadss.org for more information.
Leverage Data Strategy as a Catalyst for InnovationGlorium Tech
The document discusses leveraging data strategy as a catalyst for innovation. It provides an overview of how a data strategy can help organizations innovate with data. It outlines key components of developing a winning data strategy, including understanding the current state, developing the strategy and implementation plan, executing use cases iteratively, and establishing governance. The document also discusses common challenges to innovation with data and provides examples of innovation use cases across different industries.
Becoming Data-Driven Through Cultural ChangeCloudera, Inc.
We've arrived at a crossroads. Big data is an initiative every business knows they should take on in order to evolve their business, but no one knows how to tackle the project.
This is the first in a series of webinars that describe how to break down the challenge into three major pieces: People, Process, and Technology. We'll discuss the industry trends around big data projects, the pitfalls with adopting a modern data strategy, and how to avoid them by building a culture of data-driven teams.
Similar to Data Science, Analytics and AI: Gamechangers for the Future of Work (20)
The Future Agenda: Digitising Democracy and the Fake News SagaSharala Axryd
The document discusses digitizing democracy and fake news. It provides an overview of The Center of Applied Data Science (CADS), which aims to empower clients through data education. It then discusses definitions of fake news and Malaysia's Anti-Fake News Act of 2018. Several countries around the world have also implemented or discussed laws against fake news. To combat misinformation, the root causes of its proliferation must be addressed. Connecting with alternative sources of information online can spread misinformation, so mainstream media should not be suppressed.
This document discusses careers in data science and provides information about data science roles. It summarizes that data scientists apply expertise to make predictions and answer business questions, data engineers build and optimize systems for data analysis, and data analysts deliver value by analyzing data and communicating results. It also discusses how big data can be used to cure disease, prevent crime, and explore planets, and emphasizes that digital disruption has already occurred.
Those Who Rule The Data, Rule The WorldSharala Axryd
Though 85% of global companies are trying to be data-driven, only 37% of that number say they’ve been successful.
In this Information Generation, leaders are being pressed to rewrite the rules for how they organize, develop, manage, and engage their 21st-century businesses. More precious than oil or gold, data can prove to be the crucial x-factor between gaining a competitive edge and facing extinction.
Success at Work through the Power of Analytical ThinkingSharala Axryd
The document is a presentation about success at work through analytical thinking presented by Sharala Axryd of The Center of Applied Data Science (CADS). CADS aims to make the world more sustainable through technology, insights, and intelligence. They educate clients on data management, integration, and analysis to empower clients and enable their independence from CADS services. The presentation discusses crucial 21st century skills like analytical thinking, creativity, and communication skills. It also notes that training for soft skills is the top priority for talent development and discusses how digital technologies can transform industries like oil and gas.
Rethinking Employment in an Automated EconomySharala Axryd
The document discusses how technology is automating many jobs and changing the nature of work. It suggests that while automation may eliminate some occupations, it will change most jobs by automating certain tasks. Companies need to rethink which job roles and skills are best suited for humans versus machines. To prepare for these changes, workers will need to learn new skills through retraining. The role of HR is also evolving to help companies with digital transformation, talent acquisition, performance management, diversity and strategic planning. Mastering skills like adaptability, learning new technologies, and communicating value will help individuals succeed in the changing job market.
Empowerment of Women through STEM Education in MalaysiaSharala Axryd
This document discusses empowering women through STEM education in Malaysia. It notes that STEM achievement gaps emerge as early as kindergarten for girls due to lack of role models, peer influence, and gender stereotypes. Early introduction of STEM skills and a growth mindset are important to develop meaningful learning for both boys and girls. Promoting women in STEM fields can unlock significant economic potential for Malaysia by addressing the underrepresentation of women. Mentors and role models and challenging gender stereotypes are keys to engaging more girls and women in STEM careers.
This document discusses the importance of data-driven decision making. It contains quotes from experts emphasizing how data is a valuable asset and currency for companies. The document outlines the steps companies should take to become more data-driven, including understanding business goals, exploring available data and analytic capabilities, assessing skills, and selecting tools that align with goals and skills. It also provides an example of Handelsbanken, a Swedish bank that could benefit from these practices. The document discusses challenges like data silos and the need for communication and centralized strategies, and stresses the importance of learning from failures through a test-and-learn culture.
This document discusses the importance of using storytelling techniques when presenting data insights to others. It notes that people are more likely to remember stories than statistics, and stories are more persuasive than statistics alone. Effective data storytelling involves structuring the narrative through chronological or reverse-chronological ordering, depending on the audience. It is important to provide full context and avoid misleading visualizations when telling data stories. Data stories should question assumptions and avoid making claims not supported by the data.
The document discusses data and internet usage in Malaysia. It notes that 87.4% of Malaysians, or 28 million people, use the internet with smartphones being the main access point. Most Malaysians use internet for messaging apps and 31 million have Facebook accounts. The document also discusses Sarawak state government's digital transformation training program which aims to train 500 people in the first year. It explores how understanding business goals, data capabilities, skills, and tools are important for becoming a data-driven organization.
Achieving greater heights at work through the power of data and analytical th...Sharala Axryd
The document discusses the importance of women embracing data and digitalization. It notes that including women in technology conversations brings valuable perspectives to drive innovation. It also notes that many future jobs will require new skills, so retraining will be important. Women who perform technology-related tasks receive higher pay increases. However, there is a lack of female role models in technology fields. When women are left out of decision making processes, products can fail to consider women's needs. Embracing data and digital skills will help women adapt to changing skills needs and have more career opportunities.
Cracking the Code: Data Science Tackles Investment ManagementSharala Axryd
The document discusses how data science can be used to enhance investment management operations. It describes how machine learning algorithms can be used to power robo advisors that provide tailored investment recommendations to clients based on their risk tolerance, behavior, and preferences. Neural networks can also be used for fraud detection by analyzing customer behavior and transactions to identify suspicious activities. Predictive analytics uses historical data to build models to analyze current data, while scenario-based analytics considers alternative future outcomes. The document also discusses how data science can help reduce cognitive biases that investors tend to have.
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."
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.
Build applications with generative AI on Google CloudMárton Kodok
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...Kaxil Naik
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Aggregage
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
As just one example of an organization putting data to use in a significant way, industry expert Ronald Van Loon describes how Airbus is using Big Data to save millions of dollars per year. Airbus, a global leader in the aerospace industry, has tapped into data to be more efficient, productive and innovative. For example, the many terabytes of data generated by their airplanes are now used to inform predictive and timely maintenance programs that keep airplanes flying—and customers happy. In another use case, Walmart has learned to use Big Data to get extremely granular and targeted, and drive supermarket performance. Analysts have used data to learn that people in some areas buy more strawberry Pop Tarts when preparing for emergencies, to discover in real time that cookies weren’t selling on Halloween because merchandisers forgot to display them and to realize a drop in product sales was due to a pricing error. In the first case, extra Pop Tarts were stocked (and sold). In the case of the cookies, the problem was rectified within hours. And in the third, once the problem was spotted, it was fixed right away. It used to take two to three weeks to identify a problem. Now it takes 20 minutes, using data and analytics.
Here are six things that should make you realize data science is the career of the future.
1. Companies Struggle to Manage Their Data
Businesses have opportunities to collect data from customers regarding transactions, website interactions and more. But, according to the 2018 Data Security Confidence Index from Gemalto, 65 percent of the businesses polled said they couldn’t analyze or categorize all the data they had stored. Plus, 89 percent knew that if they could analyze information properly, they’d have a competitive edge.
As a data scientist, you can help companies make progress with the data they gather, making it pay off for them both quickly and over time.
2. New Data Privacy Regulations Increase the Need for Data Scientists
In May 2018, the General Data Protection Regulation (GDPR) took effect for countries in the European Union. In 2020, California will enact a similar regulation for data privacy. The GDPR increased the reliance companies have on data scientists due to the need for real-time analytics and storing data responsibly.
One aspect of the GDPR allows customers to request that companies delete some kinds of data, necessitating that companies understand where and how they store such information.
In today’s society, people are understandably more wary about giving up data to businesses than people from past generations. People know data breaches happen, and that they have severe consequences.
Companies can no longer afford to treat their data irresponsibly. And, the GDPR and California’s data privacy rules are likely only the beginning. Data scientists can help businesses use data in a beneficial way that aligns with those privacy stipulations.
3. Data Science Is Still Evolving
Careers without growth potential stay stagnant, usually indicating that jobs within those respective fields must drastically change to remain relevant. Data science appears to have abundant opportunities to evolve over the next decade or so. Since it shows no signs of slowing down, that’s good news for people wanting to enter the field.
One minor change likely to emerge soon is that data science job titles will get more specific. A person working as a data scientist at one company is not necessarily doing the same thing as an individual in that same role at another enterprise.
As job titles — and data science careers — get more specific, people studying for data science careers can start to specialize and do the work that’s most meaningful to them. A 2017 reader poll by KDnuggets found most respondents believed the demand for data science is several years away from reaching a peak, and the average timeframe for that event was eight to nine years.
4. Data Scientists Have In-Demand Skills
Research shows 94 percent of data science graduates have gotten jobs in the field since 2011. One of the indicators that data science careers are well-suited for the future is the dramatic increase in data science job posts. Statistics from Indeed.com show a steady increase in the number of data science jobs listed over the years.
More specifically, there has been a 256 percent increase in them since 2013, which suggests companies recognize the worth of data scientists and want to add them to their teams.
5. A Staggering Amount of Data Growth
People generate data daily, but most probably don’t even think about it. According to a study about current and future data growth, 5 billion consumers interact with data daily, and that number will increase to 6 billion by 2025, representing three-quarters of the world’s population.
Additionally, the amount of data in the world in 2018 totaled 33 zettabytes, but projections show a rise to 133 zettabytes by 2025. Data production is on the rise, and data scientists will be at the forefront of helping enterprises use it effectively.
6. High Likelihood of Career Advancement Opportunities
LinkedIn recently picked data scientist as its most promising career of 2019. One of the reasons it got the top spot was that the average salary for people in the role is $130,000. LinkedIn’s study also looked at the likelihood that people could get promotions as data scientists and gave a career advancement score of nine out of 10.
Employees must show initiative to seize the chances to excel in data science roles, of course, but LinkedIn’s conclusions suggest companies intend to keep data scientists on their teams for the long run. If businesses didn’t view data scientists as applicable to their future competitiveness and prosperity, they likely wouldn’t offer promotions.
According to Glassdoor, for three years in a row starting in 2016, data science is the highest paid field to get into.
Of course, this follows the basic laws of economics - supply and demand. The demand for data science is very high, while the supply is too low.
Think about computer science years ago. The internet was becoming a thing and people were making a lot of money on it. Everybody wanted to become a programmer, a web-designer or anything, just to be in the CS industry. Salaries were super high and it was exceptional to be there. As time passed by, the salaries got lower as the supply of CS guys (and girls) started to catch up with the demand. That said, the industry is still above average in terms of pay.
The same thing is happening to the data science industry right now. Demand is really high and supply is really low, so the salaries are still very high and people are very much willing to get into data science.
Demand:
What are some examples of data science?
Google. They are the definition of data science. Everything they do is data driven from their search engine (google.com), through their YouTube efforts, maximization of ad revenue, etc. Even their HR team is using the scientific method to evaluate strategies that make the employees feel better at work so they can be more productive. Google is not the best place to work just by chance.
Amazon. Each product recommendation that you get comes from Amazon’s sophisticated data science algorithms. Actually, Amazon has implemented an algorithm that can predict with some certainty if you are going to buy a certain product. If the probability is high enough, they move it to the storage unit closest to you so when you actually purchase it, it could be delivered the same day.
Facebook. Facebook is generating ad revenue like crazy since it has all that personal data for all its users. Since you interact with the platform, they know if you prefer cat videos or dog videos, so they know if you are a cat person or a dog person. They know what sports you are into, what food you prefer, the amount of money that you are willing to spend online. In this way, they can target their users in extraordinary ways, thus companies just love to use it as a medium.
That being said, not only huge companies have a data science division. Small businesses, blogs, local businesses,etc. use Google analytics for their needs and have seen huge gains from it. This is also a part of data science. You don’t need to be doing machine learning to monetize on data science.
Now, if your competitors are relying on data-driven decision making and you aren’t, they will surpass you and steal your market share. Therefore, you must either adapt and employ data science tools and techniques, or you will simply be forced out of business.
Supply:
Data science was driven by technology change, thus it was impossible to exist 20 years ago (slow computers, low computational power, primitive programming languages, etc.)
However, when it came about, traditional education was not ready, so there are still very, very few programs that educate aspiring data scientists. That said, there are still not enough people exploiting the opportunities in this industry. Having a low supply of labor, salaries will remain high. Thus, this is a good field to get into.
Conclusion:
Keeping in mind that the demand will continue to grow, I expect that the result would be something like the CS field - demand will grow faster than the supply for a long time.
So, yes, data science is on the rise, both from a company’s perspective and from an employee’s perspective. This makes data science a great field to get into at the moment.
Since 2000 digital disruption has demolished 52% of the Fortune 500, with tech disrupting many industries such as music, publishing and retail. There are many cases already of established players who failed to ignore customer demands and reacted too slowly. Remember Blockbuster? We now have Netflix. Other examples abound:
Companies like Amazon, Volkswagen and McDonalds are all at the top of their game through fostering and leveraging innovative, even disruptive, supply chains built around strategic relationships and mutual trust
In four years, Airbnb has completely disrupted the hotel industry and today has more than 100 million users
Robotic process automation helped an international insurer cut down reporting times from 90 to 12 minutes, with 100% accuracy
Electric carmaker Tesla, which produces a fraction of vehicles compared with major US automakers, has achieved a higher market capitalisation than any — based on its prospects, not profits. It uses personalized digital marketing, as opposed to a dealer network, to drive sales.
Since 2000 digital disruption has demolished 52% of the Fortune 500, with tech disrupting many industries such as music, publishing and retail. There are many cases already of established players who failed to ignore customer demands and reacted too slowly. Remember Blockbuster? We now have Netflix. Other examples abound:
Companies like Amazon, Volkswagen and McDonalds are all at the top of their game through fostering and leveraging innovative, even disruptive, supply chains built around strategic relationships and mutual trust
In four years, Airbnb has completely disrupted the hotel industry and today has more than 100 million users
Robotic process automation helped an international insurer cut down reporting times from 90 to 12 minutes, with 100% accuracy
Electric carmaker Tesla, which produces a fraction of vehicles compared with major US automakers, has achieved a higher market capitalisation than any — based on its prospects, not profits. It uses personalized digital marketing, as opposed to a dealer network, to drive sales.
A college degree at the start of a working career does not answer the need for the continuous acquisition of new skills, especially as career spans are lengthening. Vocational training is good at giving people job-specific skills, but those, too, will need to be updated over and over again during a career lasting decades. – The Economist
Fortunately it doesn’t take much time or money to boost your skills to make you more competitive. You just need to have a strategy for ensuring that your knowledge and skills are always up-to-date. Even if you aren’t in a technical job, technical skills like software and social media help everyone. Creative skills like graphic design and photography are also useful in a variety of jobs. Skills like project management, team leadership, and conflict resolution are critical to anyone’s success. In short, knowledge work is an area that will continue to grow; career options will become more varied and require ongoing education to remaining current.
2nd last slide. Final slide will be the same as the 1st slide.