Data Science, Analytics and AI: Gamechangers for the Future of Work
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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.
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Data Science, Analytics and AI: Gamechangers for the Future of Work
- 1. 11 Copyright © 2019 CADS and/or its affiliates. All rights reserved. | CADS Confidential – Internal/Restricted/Highly Restricted Sharala Axryd Founder and CEO of The Center of Applied Data Science Data Science, Analytics & AI: Gamechangers for the Future of Work
- 2. 22 Copyright © 2019 CADS and/or its affiliates. All rights reserved. | CADS Confidential – Internal/Restricted/Highly Restricted The Center of Applied Data Science Establish a global standard in Data Science and Analytics • Produced more than 1000 data professionals • Advised government bodies & corporate entities from various verticals 2017 2018 • Implemented BOLT approach to deliver organization wide transformation projects • Produced 200 + Fresh Graduate Data Professionals for the Industry via Data Star Program CADS aims to be the voice of Data Science & Analytics in ASEAN 2014 2015 & 2016 Strategic Partnerships with world leading institutions Malaysia Since 2014 Singapore Since 2018
- 3. 33 Copyright © 2019 CADS and/or its affiliates. All rights reserved. | CADS Confidential – Internal/Restricted/Highly Restricted BOLT = Build.Operate.Learn.Transfer Build Build capabilities to address business challenges. Capabilities include data-driven strategy, analytics solutions, talent and skills Operate Operate and sustain the capability in a Managed Services environment Transfer Transfer knowledge and skills to create a self-sustainable analytics eco-system Learn Learn by upskilling talent with the necessary analytics skills Our Capabilities Our Offerings
- 4. 44 Copyright © 2019 CADS and/or its affiliates. All rights reserved. | CADS Confidential – Internal/Restricted/Highly Restricted
- 5. 55 Copyright © 2019 CADS and/or its affiliates. All rights reserved. | CADS Confidential – Internal/Restricted/Highly Restricted The Role of Data Professionals
- 6. 66 Copyright © 2019 CADS and/or its affiliates. All rights reserved. | CADS Confidential – Internal/Restricted/Highly Restricted The Career of the Future NOW Data Science,Analytics andAI is all about using the right piece of information to solve the puzzle/challenge the company has been facing Companies Struggle to ManageTheir Data New Data Privacy Regulations Increase the Need for Data Professionals Data Science, Analytics and AI are Still Evolving Data Professionals Have In-Demand Skills A Staggering Amount of Data Growth High Likelihood of Career Advancement Opportunities
- 7. 77 Copyright © 2019 CADS and/or its affiliates. All rights reserved. | CADS Confidential – Internal/Restricted/Highly Restricted Job title Average Salaries for APAC Data Scientists roles Chief Data Scientist RM460,500 (62% increment) Senior Data Scientist RM284,344 (50% increment) Data Scientist RM188,652 (15% increment) Junior Data Scientist RM164,080 Data Analysts roles Data Analyst (Experience) RM253,750 (37% increment) Data Analyst (Entry) RM185,600 Data Engineers roles Senior Data Engineer RM161,100 (42% increment) Data Engineer RM113,000 In a new field like big data analytics, promotions are based more on qualifications and proven problem-solving rather than seniority. The more you learn, the more qualifications & accreditations you have, the quicker you will move up the ladder.
- 8. 88 Copyright © 2019 CADS and/or its affiliates. All rights reserved. | CADS Confidential – Internal/Restricted/Highly Restricted Job title Salaries in Malaysia per Annum (2018) Data Science Management roles Chief Technology Officer RM384,000 – RM636,000 IT Director RM288,000 – RM444,000 Project Manager RM216,000 – RM360,000 Data Science Professional roles Senior Financial/Business Analyst RM96,000 - RM132,000 Financial/Business Analyst RM66,000 - RM108,000 Software Development roles Chief Technology Officer RM384,000 - RM636,000 IT Director RM288,000 - RM444,000 Project Manager RM216,000 - RM360,000 In a new field like big data analytics, promotions are based more on qualifications and proven problem-solving rather than seniority. The more you learn, the more qualifications & accreditations you have, the quicker you will move up the ladder.
- 9. 99 Copyright © 2019 CADS and/or its affiliates. All rights reserved. | CADS Confidential – Internal/Restricted/Highly Restricted 10 essential traits of a Data Scientist 1. Business Expertise –They need to understand the business demands and be aware of the constraints. 2. Data Maneuverability – Experience using BI tools.The knowledge and understanding of when to use various BI tools vs. Reporting tools, based on what information is needed. 3. Knowledge of SQL – Experience creating queries, including complex queries. 4. Curiosity – Recognize that they don’t know everything and have the ability to figure out the answers and learn more. 5. IT Infrastructure Knowledge – Degree in computer science, MIS or similar education that would ensure they understand data architectures and data integration issues. 6. Statistical Expertise – Solid education and experience working with statistics and statistical models. 7. Understanding of How to Access Data 8. Personality – Ability to express new ideas and findings to management. 9. Insight – Knowledge of business impact associated with their findings. 10. Teamwork – Work together with other data scientists as well as business users to understand what information would be helpful.The ability to think outside the realm of “regular reports”.
- 10. 1010 Copyright © 2019 CADS and/or its affiliates. All rights reserved. | CADS Confidential – Internal/Restricted/Highly Restricted
- 11. 1111 Copyright © 2019 CADS and/or its affiliates. All rights reserved. | CADS Confidential – Internal/Restricted/Highly Restricted
- 12. 1212 Copyright © 2019 CADS and/or its affiliates. All rights reserved. | CADS Confidential – Internal/Restricted/Highly Restricted “21st century kids are being taught by 20th century adults using 19th century curriculum techniques on an 18th century calendar!”
- 13. 1313 Copyright © 2019 CADS and/or its affiliates. All rights reserved. | CADS Confidential – Internal/Restricted/Highly Restricted “The illiterate of the 21st century will not be those who cannot read and write, but those who cannot learn, unlearn, and relearn.” — AlvinToffler, American Writer, Futurist, Businessman
- 14. 1414 Copyright © 2019 CADS and/or its affiliates. All rights reserved. | CADS Confidential – Internal/Restricted/Highly Restricted ThankYou
Notas do Editor
- To be used as 1st and Last slide
- 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.
- Source: https://www.robertwalters.com/content/dam/robert-walters/global/files/salary-survey/salary-survey-2018-southeast-asia-greater-china.pdf
- Agenda slide
- 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.