Merely one mention of artificial intelligence invokes the pictures of robots, chatbots, or self-driving cars. Artificial intelligence, a branch of computer science deals with devising technology and machines that are capable of performing tasks that require human intelligence. AI is everywhere and it has not stepped down since its invasion. The new decade will witness massive investments by global technology giants into AI technologies.

In the race to acquire and develop the most efficient and ingenious technologies, countries worldwide have been setting guidelines and investing immensely in the field of AI. Following are the statistics of the global AI readiness Index conducted by Oxford Insights and the International Development Research Centre (IDRC) in 2019. The index covers 194 countries and territories and ranks them according to their preparedness to implement AI in the delivery of public services and internal operations. Each country is allocated a score that considers 11 input metrics under 4 clusters: governance, infrastructure and data, skills and education, and government and public services. The report suggests that the rankings are dominated by countries that have robust economies, an abundance of data and information, and a government with a strong vision.

• Singapore comes first in the list of artificial intelligence readiness with a score of 9.186.
• The top 20 positions are acquired by Canada, New Zealand, Australia, and some European countries, and Asian countries. China, currently 21^st in the global rankings will improve in the upcoming years because of the government’s inclination to optimize data and artificial intelligence.
• Most of the countries in North America were awarded higher ranks in the list while the African and Asia-Pacific regions were the worst performers.

Country	Rank	Score
Singapore	1	9.186
United Kingdom	2	9.069
Germany	3	8.810
United States of America	4	8.804
Finland	5	8.772
Sweden	6	8.674
Canada	6	8.674
France	8	8.608
Denmark	9	8.601
Japan	10	8.582

 

• India stands third in the Asia Pacific region after Singapore and Hong Kong and 19^th in the global index overall. According to surveys, it is one of the most prepared economies with regard to government readiness to artificial intelligence. But despite this availability, it lacks creativity, innovation, and awareness which are the reasons behind Indian start-ups lagging behind those of the American and other developed countries. 76% of start-ups in India believe that there is a dearth of skilled professionals in the field of AI and hence hesitate from using these technologies in their business. But the established companies like Amazon, Google, and Apple are employing AI technologies like deep learning, language processing, and machine learning and are acquiring start-ups to provide new and improved experiences across their services.

Present and expected Scenario

China, USA, Japan, United Kingdom, and Germany are the leading countries in AI research. China recently announced its intention to become ‘a principal world center of artificial intelligence innovation’ by 2030. And America’s distinct pool of research technology knowledge and business market power will contribute to helping them achieve their AI development objectives.

Although the United States remains highly invested in AI and the latest technologies, it seems likely that China might win over America in the race. Despite having the 5 tech-giants – Amazon, Facebook, Apple, Google, and Microsoft, it looks as if the US will be lagging behind developing countries like China. In the US, the resources are available in ample amounts but are fragmented and the potential isn’t utilized efficiently. Also, the national leadership is quite weak and there is a lack of systematic vision.

On the other hand, China apart from having the latest computers, smartphones, gadgets and other electronic devices is now focusing on system-wide AI. This includes robotics, advanced medical equipment, and autonomous vehicles (AVs). China’s growing internet economy and shift of industries towards data analytics and AI trends will be a major contributing factor in its AI boom. Some examples of Chinese AI companies that are developing are UBTECH Robotics which is developing humanoid robots and SenseTime which is building facial recognition technology.

Thus, although the United States still remains ahead of others in the AI industry, if China implements the policies and strategies effectively, it will soon manage to replace the USA. Therefore, from the increasing demand and usage of AI-driven technologies, it can be concluded that AI, machine learning, and data science will thrive as the most in-demand skills in the near future.

Singapore’s artificial intelligence industry will be booming in the upcoming decade. Singapore is one of the first countries to announce a national AI strategy in 2017. One of the most effective ideas that were put into action by the government of Singapore to achieve this was the establishment of the AI Ethics Advisory Council. This council has been established to assist the government to develop standards and guidelines and issue practical guidance for the voluntary adoption of AI in businesses and organizations. (Everything you need to know about Singapore’s AI strategy)

Singapore is a trailblazer in the Asia-Pacific region when it comes to accepting new technologies. Singapore has chosen five big AI-driven development projects that address national challenges keeping into consideration the impact on society and economy with an aim to become the global hub of AI technologies. Also, educational institutions there have increased emphasis on fields such as mathematics, statistics, computer science, and information technology to allow students to understand the significance and trend of data science, machine learning, and artificial intelligence. Business and management organizations too are focusing on data trends as well apart from their regular curriculum (Analyze data and build stunning reports using our services). UAE and Singapore were trendsetters in bringing AI decision-making at the cabinet-level. They have justified the strengths and strategies that have the potential to make these countries the leading producers of AI technologies.

Present and expected Scenario

China, USA, Japan, United Kingdom, and Germany are the leading countries in AI research. China recently announced its intention to become ‘a principal world center of artificial intelligence innovation’ by 2030. And America’s distinct pool of research technology knowledge and business market power will contribute to helping them achieve their AI development objectives.

Although the United States remains highly invested in AI and the latest technologies, it seems likely that China might win over America in the race. Despite having the 5 tech-giants – Amazon, Facebook, Apple, Google, and Microsoft, it looks as if the US will be lagging behind developing countries like China. In the US, the resources are available in ample amounts but are fragmented and the potential isn’t utilized efficiently. Also, the national leadership is quite weak and there is a lack of systematic vision.

On the other hand, China apart from having the latest computers, smartphones, gadgets and other electronic devices is now focusing on system-wide AI. This includes robotics, advanced medical equipment, and autonomous vehicles (AVs). China’s growing internet economy and shift of industries towards data analytics and AI trends will be a major contributing factor in its AI boom. Some examples of Chinese AI companies that are developing are UBTECH Robotics which is developing humanoid robots and SenseTime which is building facial recognition technology.

Thus, although the United States still remains ahead of others in the AI industry, if China implements the policies and strategies effectively, it will soon manage to replace the USA. Therefore, from the increasing demand and usage of AI-driven technologies, it can be concluded that AI, machine learning, and data science will thrive as the most in-demand skills in the near future.

Analytics trends in the business world

Data is power for small-scale businesses.  Ensuring the compatibility of adopted strategies with the business goals and objectives is supremely important for any business to grow. With ample insights into the usage of their products and services, smaller businesses can learn about customer interests, predict trends and align their marketing and product management approach. And this is when analytics comes into the picture.

What is analytics?

Analytics is the systematic analysis of data using statistical, computer programming and operations research techniques to derive patterns and meaningful insights from it and utilise them to make decisions effectively. It helps obtain a comprehensive analysis to understand what is working and what needs to be improved. It is time that small & medium businesses start owning data analytics and use it to their advantage.

For small scale businesses with limited resources and assets, social media pages and company websites can be tracked efficiently to gather customer data (Read: Image Analytics and AI in social media marketing). But merely gathering this data doesn’t complete the job. The data obtained requires sorting and visualisation. Tools like Google Analytics and Tableau provide analytics reports free of cost, hence are considered as the most feasible options for data analysis for smaller companies. R and Apache Spark are a few of the other popular business analytics tools.

In 2019, the following data analytics trends were prominent in the business world-

1. Deep learning- Deep learning, a part of machine learning has advanced techniques that are helping companies and firms to improve their decision-making abilities and facilitate operational work. It is based on artificial neural networks with representation learning. These are capable of sorting and structuring a large amount of data. An example of application of deep learning for many businesses is content recommendation. Businesses should start using deep learning to analyse the user’s taste and recommend content accordingly thus improving customer experience and easing marketing efforts.

2. Machine learning- The adoption of machine learning is increasing by leaps and bounds, and that’s not surprising given its benefits, from eliminating manual tasks to deducing useful insights from data. A subset of artificial intelligence, machine learning is the study of algorithms that improve on their own. Customer Segmentation & Lifetime Value Prediction are some of the most popular applications of machine learning in business.

3. Dark data- Dark data is the data that is acquired through computational operations but is not used to predict trends and draw conclusions from. Unused email correspondences and raw survey data are examples of dark data. This data obtained is analysed to obtain information like the customer’s preferred mode of communication. The traditional unstructured data that inundates the data centres in firms can be interpreted easily using dark data analytics and can be used to promote the growth of the company.

Small-scale businesses can use data analytics and machine learning algorithms to track customers throughout the sales and transaction cycle and can use analytics reports to find answers to questions like at what stage products and services are brought most frequently, thus improving customer service.

Analytics techniques provide detailed insights into customer behaviour and this knowledge is remarkably valuable to developing businesses. Analysing customer reviews, frequently asked questions and conversations between sales assistants and customers can contribute in setting standards to improve customer service hence boost growth. Analytics can also be used to manage budget and expenditure efficiently. Businesses with abundant resources can invest in multiple marketing strategies and opt for business intelligence solutions but smaller businesses can neither afford nor apply these on them. In such cases, analytics plays a crucial role. By focusing on a limited number of established and tested sales strategies the smaller companies can manage to increase the RoI.

Data Analytics and Business during the pandemic

While the global health professionals and scientists are joining hands to accelerate the research and development process and devise standards to curb the spread of the coronavirus, a major section of the business world comprising of the small-scale businesses is struggling to survive the economic and global supply chain disruptions. But on one hand, as this crisis complicated the lives of businessmen, on the other it opened newer dimensions and perspectives for them specifically in merging with the field of data science and data analytics.  A recent survey conducted by Sisence, a Business Intelligence software company, suggests that businesses are shifting massively to data analytics to help them sustain the pandemic and expand their business.

Analytics is booming since and before the rise of the Covid-19 pandemic. Encountering the sudden downfall in revenues, businesses are using analytics reports to cater to the changing demands and needs of their customers and search for ways for better performance in their respective ventures (Read: Stories of small business: resilience amid COVID-19)

Some insights into the survey:

• The survey demonstrates that 55% businesses, during the pandemic are using analytics techniques to improve efficiency and 47% and 45% of the total that were surveyed are using the same to support customers and predict future outcomes respectively.

• The usage of analytics has catapulted significantly in the smaller firms and companies. 68% small businesses are using analytics for operational purposes, 56% in finance, 50% in sales and 45% in products.

Most importantly, this survey reveals that the data professionals remain optimistic about the business opportunities and the importance of analytics in businesses despite the growing economic challenges. 79% of those surveyed believe we’ll survive this global crisis and there are no chances of data analytics trends getting diminished in the near future.

Why did the small-scale businesses suddenly observe an ardent spike in the usage of data analytics during Covid-19 pandemic?

Small businesses are focusing on improving their efficiency and building a strong customer base. They are certainly not established enough to opt for a single streamlined business intelligence solution, like other larger companies. Therefore, to draw insights and convert them into actions, data analytics is the most suitable tool for them. And this is why the businesses that have employed less than 200-300 people have shown the maximum increase in the usage of data analytics during pandemic.

By 2021, 265% growth is expected in e-commerce sales which implies huge amount of production of data. And with two out of every three companies opting for data analytics in the present to manage their data, the increasing demand for analytics skills seems justified.