DT in India, China and Japan

Facts and Figures

  • 50% of Indian companies think they’ll disrupt (being first to launch a new service or product):
    • In doing so 63% will use digital technologies to develop the new products and services.
  • 93% of Indian companies have trouble with digital transformation.
  • 74%, 67% and 61% of Indian companies will invest in cybersecurity, IoT technologies and AI, respectively…
    • In Europe and USA 100% do, but the figures were the same 3-4 years ago).
  • The world’s population is getting older, but India’s workforce average age is 29…
    • In Austin (Texas, USA) average age is 32.
    • Austin is “the new Silicon-Valley” where dozens of IT firms are moving their headquarters and R&D centers.
  • In 2019, the Asian giant will overtake the US in most-cited 50% research papers.
  • More than half of online entrepreneurs are women.
  • 160.000 individuals with disabilities have Internet businesses.
  • China’s mobile Internet users made 100 times more mobile payments than in the U.S.
    • By 2023, Asia expects a 17% growth in digital revenue.
  • The Innovation Network Corp. of Japan gets 44 bill $ in funding from the Government:
    • Which will grow to 4 bill $ to keep the firm functioning, at least, till 2034.
  • Comparing with other countries:
    • The US will invest into AI programs 2 bill $ over the next 5 years, according to the US Department of Defense.
    • In 2018, British AI funding reached over a 1,1 bill $ in public and private money.
    • The French AI investment plan amounts to 1.6 bill $.
  • Demographics in Japan may determine the scope and impact of digital transformation:
    • In 2018, 6 mill Japanese (over 28%) were over 65:
      • And every fifth person (20% of the total) was over 70, making the country the world’s most aged.
    • Professor Ryuichi Kaneko (Meiji University) says Japan’s population will decrease:
      • From 127 mill in 2015 to 88 mill in 2065 and7 mill in 2100.
  • Digital Transformation in India lags behind that of Europe, China and the USA…
    • But it has the highest potential growth in the medium term.
  • The current world leader in Digital Transformation is China:
    • Not only regarding investment levels or highly qualified professionals
    • But also in the broader use of digital technology by the population, with the biggest internal demand in the world.
  • Behind China, Japan, the US and Europe are making similar efforts in investing and researching in AI and innovation.

Sources: The above text is a creative synthesis elaborated from the following sources: Mark Minevich (techcrunch.com); Jenny W. Hsu (Alizila); Rajesh Janey (Business Today, India’s leading business magazine).

These sources have been selected from a total of 15 articles on the subject matter. Which in turn are the result of sifting through 99 articles.

THESES / MAIN IDEAS

  • Is overtaking the US faster than expected in AI research papers.
  • China is the leader in e-commerce and mobile payments.
  • It is becoming a model for other countries in its inclusive growth impulse by adopting digital technology.
  • Is the leader in global fintech payments and a role model for the rest of Asia:
    • Chinese companies are financing many fintechs across Asia.
  • When US executives consider low import prices from China and, at the same time, regard their own digital transformation as expensive and rigid, this will result in a slow digitalization of US manufacturing:
    • It is cheaper to go on importing from China, than making more efficient their own manufacturing processes.
  • Where Chinese companies are best

The Chinese companies most impacted by Digital Transformation compete in the following sectors –and specifically in the two top layers in each of them–:

  • Consumer electronics
    • Connected devices
    • Digital media content
  • Automotive
    • Supply-chain logistics
    • Connectivity-enabled services
  • Chemicals
    • Demand forecasting, production planning
    • Customized systems based on Internet of Everything (IoE)
  • Financial services
    • Reduction of nonperforming loans
    • More efficient banking operations
  • Real state
    • Online sourcing
    • Online marketing
  • Healthcare
    • Remote monitoring of patients with chronic diseases
    • Ecommerce for over-the-counter treatments
  • Accenture surveyed 2,000 respondents, and learnt that:
  • Over 60% Indian consumers would share their sensitive data for benefits such as:
    • Rapid loan approvals, discounts on the gym and personalized offers.
  • 81% of consumers admit being very cautious” about personal data privacy.
  • Indian consumers are ready to share their personal data with insurers and banks:
    • 76% for car insurance premiums based on safe driving.
    • 69% for life insurance premiums related to a healthy lifestyle.
    • 92% for the feeling of security.
    • 93% for rapid loan approval.
    • 91% for personalized offers based on their location and for retailer’s discounts.
    • 76% for updates on their bank accounts.
    • 76% for personalized savings recommendations.
  • Comparing consumers’ willingness to share significant personal data with financial firms in exchange for personalized services globally:
    • In Asia: 67% / In China: 81% / In Indonesia: 74%.
    • In the US: 50%.
    • In Australia: 42%.
    • In Europe only 40% (in both the UK and Germany).
  •  

    • Digital Transformation in India means a less-cash economy:
      • The Indian economy is operating with an estimated $33 billion less cash for the las 2 years, being replaced by digital transactions.
      • Today, 99% of Indian adults have enrolled in Aadhaar (the card that contains their digital identity, allowing them to make digital transactions).
      • At this pace, in less than 20 years, India will not use cash any longer.
      • As a consequence, every company (starting from retail business) needs to adapt to security rules and incorporate technological solutions for this cash-free context.
  • The world’s largest aging population means that a workforce newcomer has to maintain a whole group of retirees.
  • Japan is looking for the Super Smart Society 5.0”, meaning digitalization of society itself:
    • Societal consensus on ethical issues
    • Open-mindedness regarding interactions with machines.
    • Radical change of the entire healthcare industry.
    • And even pursuit of happiness.
  • Digital Transformation in Japan leads to excellence in manufacturing ormonozukuri”: the idea of maximum optimization of the production value chain:
  • The same idea inspiring industry leaders like NEC, Nissan, Toyota, Renault, Toshiba, Mazda or Sharp…
    • But now taking advantage of Industry 4.0 technology: predictive maintenance, real-time monitoring, cobots, etc.
  • Now companies are producing data in each department
    • Data that can be shared at the corporate level to build an intelligent company, not only intelligent elements inside the company.

Sources: The above text is a creative synthesis elaborated from the following sources: Elizabeth Masamune (bluenotes.anz.com); Diya Koshy George (yourstory.com); Will Knight (Technology Review).

These sources have been selected from a total of 16 articles on the subject matter. Which in turn are the result of sifting through 105 articles.

SCENARIOS

  • We will continue to see important investment movements from Alibaba, Tencent, JD.com, Xiaomi and Didi Chuxing…
  • Other countries in Asia should look at China as role model, as it moves to a cashless society, building more financial inclusivity for the underprivileged.
  • Communications between users and companies in China are widely carried out using digital communications:
    • Social media
    • Apps
    • Automated chatbots,
    • Bots writing emails
    • Websites…
  • The country experiments the most advanced multichannel and omnichannel implementations:
    • Multichannel: the user can communicate using every available channel… even in person.
    • Omnichannel: it’s a step further than multichannel: different channels are traced all together:
      • Not only the user has multiple channels, but he or she will find a similar response from every of them (with the same up-to-date data, speech or writing style).
  • Will have the youngest workforce by 2022 and the largest by 2027.
  • If Indian feminine workforce reaches the world’s average 48%, India could increase its Gross Domestic Product by 700 bill $.
  • The Government publishes data that people can use to make up irrigation policies, allocating the budget and reducing corruption.
  • Companies are considering the barriers that hamper their transformation –such as cybersecurity, regulations or weak digital governance— as American and European companies started doing since 2012 (with the birth and later expansion of Big Data).

Sources: The above text is a creative synthesis elaborated from the following sources:  Diya Koshy George (yourstory.com); Fintech News Hong Kong.

These sources have been selected from a total of 9 articles on the subject matter. Which in turn are the result of sifting through 71 articles.

RECOMMENDATIONS

  • In a context where every company is living this same change process, the advantage will come from boosting the process, or taking the maximum value of it.
  • In order to achieve this, firms should eliminate waste and apply improvement efforts, looking for the following management symptoms in order to relate them to data initiatives:
  • Overproduction: production of larger quantities, or sooner than necessary, or additional nonessential plans or the use of higher quality equipment than necessary…
    • Data democratization doesn’t mean “more data than needed”…
    • But “only the needed data to the right person at the right moment.
  • That is Smart Data.
  • We should not give more information than necessary: extra copies, reports that nobody will read…
  • Only the key data to make the decision.
  • That is Smart Visual Data.
  • Waits or idle time: waiting times, interruptions or inactive time due to lack of data, information or orders, plans or material…
    • Employees waiting for the previous activity, laboratory results, shortage of equipment and other reasons that contribute to stoppages, waiting for emails, faxes, materials, the work of a partner…
    • Establish metrics (high quality metrics are data… really valuable data) about the processes involved in the value chain.
  • Unnecessary transport: related to the maldistribution and deficient planning of the internal movement of resources, transport documents, materials, equipment…
    • If the data we need is already available in some Open Data project, or in the Data Pool of some other company or partner, the best option is sharing the data, not implementing the whole data lifecycle from scratch (locating, loading, cleaning and processing).
    • Open Data allows companies to:
      • Understand potential
      • Construct, launch and suggest new products and services and reduce acquisition costs and redundancy.
  • Over-processing: processes that cause excess use of…
    • Raw material, equipment, energy.
    • Paperwork, outdated forms, inadequate software.
  • As soon as data is processed enough to be applied for the necessary decision, use it:
    • Example: if you need to make a decision about a whole country, you don’t need to obtain and show the data for every neighborhood or even cities.
  • Excess of stock: excessive, unnecessary or premature inventories that can lead to:
    • Material losses.
    • Additional personnel to control that excess.
    • Costs for early purchase.
    • Stacked materials, open files, waiting supplies, unread emails…
  • Big Data implies the possibility of having “terabytes” of data…
  • But how much of this information has been actually applied to make decisions?
  • The more data, the more chances to make mistakes and the more expensive the whole process.
  • Excess of movement: search files, search information, look at manuals, look at catalogs..
    • If we can look at the data in only one spot, we’ll interpret them more easily and quickly.
    • That’s why Data Warehouses (central repositories of structured and processed data) are not going to be replaced by Data Lakes…
    • But Data Lakes are reinforcing Data Warehouses with more information when it’s necessary.
  • Quality defects: errors in…
    • Design
    • Measurements and plans
    • Use of incorrect work methods
    • Low skilled labor.
  • The result is a product of poor quality and customer dissatisfaction. In short, defects…
  • The more iterative and agile the development and delivery of products, the more feedback and better fit-for-purpose and fit-for-use we’ll build our products and services.
  • Bad talent management: ideas, skills, improvements are lost due to…
    • Having a low-skilled workforce:
      • Poorly trained
      • Poorly informed
      • And with a lack of motivation.
    • The solution is to:
      • Empower employees to discover their hidden creativity.
      • Let people talk constructively and make mistakes under control.
      • We have to experiment in order to learn.

Sources: The above text is a creative synthesis elaborated from the following sources:  Jenis Sheth (Supply Chain Game Changer).

These sources have been selected from a total of 6 articles on the subject matter. Which in turn are the result of sifting through 56 articles.