Digital transformation has aided businesses in embracing change and remaining competitive in an increasingly digital world.

Data is the sole driver of today’s world. It has become the most valuable asset, and without it, one cannot hope to prosper in the present congested market. Companies must efficiently use their data because it can be a differentiating factor for corporate growth. This necessitates the effective integration of data analytics and Artificial Intelligence (AI) into business processes.

Integrating big data analytics allows for faster, more informed decision-making and access to real-time data. It has the ability to be a game-changer in businesses’ efforts to transform digitally. Numerous data analytics services companies are extended their services and helping enterprises in creating new business models and achieving great heights. 

Implementing big data analytics and AI into existing business models can yield real results, allowing firms to undertake a successful digital transformation. The transformation process aims to use digital technology to build or modify consumer experiences, culture, and business processes, to suit changing customer wants and market conditions.

This is where AI comes into play. It has the potential to help businesses create a digital product development life cycle, become more imaginative, agile, and adaptable than ever before.

How Big Data Aids Digital Transformation?

Digital transformation has aided businesses in embracing change and remaining competitive in an increasingly digital world. The usefulness of big data in digital transformation is determined by an organization’s ability to combine the two to enable business process digitization and automation. This digitalization and automation leads to digital transformation in product development, more efficiency, new business models, and innovation.

Businesses can also use big data analytics to get precise information about an individual or various groups of clients. This can include information such as which websites do they spend time on, what they purchase, purchasing frequency, etc. Businesses can use this data to make improvements to meet their customers’ future wants while also creating goals on how to achieve those demands. As a result, to complete their digital transformation, organisations must use big data and data analytics.

Artificial Intelligence and Digital Transformation: What’s the Connection?

When Artificial Intelligence became a part of many organisations’ business goals, the digital transformation went another step forward with the ability to integrate different systems and automate various daily tasks. These technologies are crucial in the digital transformation because they allow your company to make better use of the data it collects in a variety of ways.

As well as resulting in faster and more efficient operations and, as a result, increased output. AI enables us to make use of all of this data to propel the organization forward, whether by improving current products and services or exploring new inventive initiatives.

How Big Data Analytics and AI can help you create value through digitization? 

Emerging technologies, notably AI, are slowly infiltrating the everyday lives of businesses all around the world, much like the internet revolutionised the business world in the past.

Data analytics services for small business and medium businesses are also offered by numerous companies. Having said this, the question is no more when will all small and medium-sized businesses be digitally transformed, but which technologies will be important and prioritised. AI and big data may be employed in a variety of industries by businesses.

Digital transformation must be used to add value to a product, with artificial intelligence detecting information to assist the experts involved in various stages of the digital product development process, such as design, execution, and delivery.

With everything digitalized, you may start looking at the phases of the project that need to be tweaked; for example, AI in the design phase increases research, development, and forecasts the future steps.

In the execution phase, continuous maintenance is an important stage, and this is where AI and Machine Learning can assist. These are real-time replies to the research. When it comes to delivering a product, it’s all about the customer experience. Businesses can leverage data analytics services and solutions to ensure they are on the right track.

Much has been stated about how AI and Big Data Analytics can enable businesses to stay ahead of the curve in their digital transformation, giving them a competitive advantage over their competitors. Let’s take a look at why organisations need to be digital in product developmentand why should they use big data analytics and artificial intelligence in their digital transformation efforts:

• Using Data Analytics Services, businesses can readily measure and model risks using big data tools.
• By automating and optimising common procedures and procedures, businesses can save time and money.
• Many big data analysis technologies enable businesses to have a deeper understanding of their customers and actively develop loyal customers rather than simply responding to their needs and inquiries.
• Create quicker business decisions based on cognitive technology outputs.
• Businesses can learn about customer-related trends and patterns and focus on client retention by utilising big data analytics.

In a nutshell, big data analytics and AI may be used in a variety of ways to assist businesses in achieving successful digital transformation. Companies must keep in mind, however, that digital business transformation is a continuous process, and they must continue to come up with new strategies and leverage big data analytics services for enhancing business intelligence and keep up with the changing environment even after they have achieved success.

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The Beijing International Big Data Exchange, which was set up in the capital city on Wednesday with a registered capital of 200 million yuan ($30.46 million), is expected to play a key role in China’s digital economy promotion efforts and facilitate more data-based transactions in the country.

Established by Beijing Financial Holdings Group and government agencies like the Beijing Municipal Bureau of Economy and Information Technology, the exchange will become critical infrastructure for data security, data operations and cross-border data transactions.

“It will play an important role in cultivating a sound data exchange market to unleash the value of data and drive digital economy. It will be first applied in Beijing, Tianjin and Hebei province and expanded nationwide and globally later,” said Yin Yong, vice-mayor of Beijing.

A data transaction system using blockchain and security computing technology was also launched, which will offer a series of services including data cleaning and data evaluation.

Fan Wenzhong, Party secretary and chairman of Beijing Financial Holdings Group, said: “The new exchange will strive to make the purchase and use of data more standardized and safer.”

The digital economy is a key focus for China in the 14th Five-Year Plan period (2021-25). Core industrial output is expected to account for 10 percent of the country’s GDP by 2025.

China’s digital trade has grown rapidly in the last few years. According to the Ministry of Commerce, the country’s digital trade, including exports and imports, surged by 6.7 percent to $203.6 billion in 2019, accounting for 26 percent of the total service trade volume.

“In this increasingly digital era of globalization, countries led by the US are striving to seize the high ground of digital trade,” said Liu Yingkui, head of international trade at the Academy of China Council for the Promotion of International Trade.

According to a report published by global consultancy firm McKinsey&Co, large gaps exist between a handful of leading countries and the rest of the world. The report ranked 139 countries on the basis of inflows and outflows of goods, services, finance, people and data. Singapore led the rankings, followed by the Netherlands, the United States and Germany.

China has grown more connected, reaching seventh place globally, but advanced economies in general remain more connected than developing countries.

Liu said China has accumulated a huge amount of data and data streams in a group of areas, including trans-border e-commerce and superfast 5G development. These are valuable foundations for the development of the digital economy.

“Once more companies join, the platform can leverage data and artificial intelligence to optimize industrial software and fill the gaps in industrial development with developed countries,” he said.

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AI may determine whether a company makes it to a human evaluation at all, according to Gartner’s latest study

More than 75% of venture capital (VC) and early-stage investor executive reviews will be informed using artificial intelligence (AI) and data analytics by 2025, according a recent industry study.

According to Gartner, by 2025, the AI- and data-science-equipped VC or PE investor will become commonplace. In addition, increased advanced analytics capabilities are rapidly shifting the early-stage venture investing strategy away from gut feel and qualitative decision making to a more modern platform-based quantitative process.

“Successful investors are purported to have a good ‘gut feel’ — the ability to make sound financial decisions from mostly qualitative information alongside the quantitative data provided by the technology company,” said Patrick Stakenas, senior research director at Gartner.

“However, this ‘impossible to quantify inner voice’ grown from personal experience is decreasingly playing a role in investment decision making. The traditional pitch experience will significantly shift by 2025 as VC and private equity (PE) investors turn to leveraging AI and data science insights for due diligence.”

The Gartner study also noted that information gathered from sources such as LinkedIn, PitchBook, Crunchbase and Owler, along with third-party data marketplaces, can be leveraged alongside diverse past and current investments.

“This data is increasingly being used to build sophisticated models that can better determine the viability, strategy and potential outcome of an investment in a short amount of time. Questions such as when to invest, where to invest and how much to invest are becoming almost automated,” said Stakenas.

Current AI technology is already capable of providing insights into customer desires and predicting future behaviour. Unique profiles can be built with little to no human input, which can be further developed via natural language processing AI that can determine qualities about an individual from real-time or audio recordings. 

While this technology is currently used primarily for marketing and sales purposes, by 2025, investment organisations will be leveraging it to determine which leadership teams are most likely to succeed.

“The personality traits and work patterns required for success will be quantified in the same manner that the product and its use in the market, market size and financial details are currently measured,” said Stakenas. “AI tools will be used to determine how likely a leadership team is to succeed based on employment history, field expertise and previous business success.”

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CTO & MD at AX Semantics, the SaaS-based, Natural Language Generation Platform that creates any content, in any language, at any scale.

2020 was a year most of us want to forget. The pandemic brought on economic, logistical and technological challenges on a massive global scale, leaving businesses scrambling to adapt. Amidst the upheaval, businesses turned to video conferencing platforms like Zoom and Google Meet to stay connected. Technologies like artificial intelligence (AI) and machine learning (ML) helped augment human efforts to take on everything from health to cybersecurity. Equally, businesses looked toward strategic execution and technology to remain agile among industry shifts and provide a greater return on investments. 

Businesses are now focused on what’s next and preparing for an economic surge in the latter half of 2021 once more people globally are vaccinated and the world returns to a more “normal” way of life. Here are four ways AI and ML will continue to shape multiple industries and integrate with other technologies to drive further innovation and change in the year ahead:

1. Increased Commercial Applications For “Federated ML”

“Federated ML” or a “cloud-in-a-pocket” approach will play a more prominent role. The applied principles and techniques employed with federated ML means data doesn’t need to be in the cloud anymore. Today’s devices can store more data than ever before and likely more than a user could ever produce. As a result, AI models that help to improve personalized services no longer need to be centralized on company servers, but can instead exist on the device itself.

Equally, the techniques used by federated ML ensure user data is kept on the device and not on a server, while still providing access to predictive AI modeling. Data isn’t shared in the same way either. The advanced ML models used in federated ML keep data in data owners’ hands, leading to greater privacy. This approach is a new take on data privacy and a growing megatrend. A good example of this is Siri running on your iPhone, but not sending all of your data to Apple’s servers.

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Federated ML and its principles are currently in use, but greater commercial applications in this area are on the horizon. The introduction of Apple’s M1 chip and the industry-leading neural engine, for example, was specifically designed for advanced ML processes. Federated ML will also provide increased use cases within the financial services sector in areas like loan risk prediction, while AI and ML applications will also advance many other industries.

2. Promising AI Applications Within The Health Sector 

The onset of Covid-19 was a catalyst for advancing technologies in pharma, medicine and the health sector, including a newly updated focus on nursing, patient care, remote patient monitoring and telehealth. 

The integration of AI technology promises use cases in terms of data aggregation, updating patients’ charts and analyzing tests and images to suggest possible diagnoses and more. The application of AI in health, in a supporting role, also frees up physicians’ workloads, allowing them to spend more time with patients and on actual patient care. Japan is already looking at augmenting their doctors with AI to combat their doctor shortage. 

AI technology is rapidly expanding into other healthcare areas, including early detection of diseases, treatment and research. The technology will only evolve in the year ahead and play a more prominent role, especially as the world continues to weather the effects of Covid-19.

3. Hyper-Personalization Within E-Commerce

It’s true many industries suffered under the weight of the pandemic. E-commerce, however, ballooned. Amazon saw over 5.2 billion visitors worldwide in June 2020 and even temporarily limited delivery to only essential items given an unprecedented flood of orders. Consumers will demand even more customized experiences in 2021, giving rise to hyper-personalization and greater customer experience within the e-commerce sector. 

“Algorithmic e-commerce” — or the smart, systemic digitization of business functions often handled manually — will usher in widespread adoption and utilization of AI and ML by enterprises in the e-commerce sector. For example, AI-powered natural language generation (NLG) content will produce an algorithmic e-commerce experience, where customers receive bespoke online shopping experiences through customized product and category descriptions that turn a product page into a personalized sales pitch. Ultimately, this burgeoning trend will lead to a market shift that delivers more value to consumers — where vendors take a more product-type approach to personalization and customer experience, versus a consulting-product approach, as they’ve done previously.

4. New AI And ML Innovations With NLG

Natural language from phonetics, understanding, processing and generation has seen significant advancements in the last few years. As a result, the combination of AI and ML technology with NLG is rapidly pushing the boundaries of what is possible. 

Large and small companies already utilize the technology across multiple sectors and industries. We’ve already seen consumer applications like Google phone calls and enterprise applications like business process automation based on unstructured data (i.e., text to voice). Facebook has also achieved impressive results in semi-supervised and self-supervised learning techniques utilizing AI and NLP.  

GPT-3, the brainchild of OpenAI, a San Francisco-based AI lab is the third in a series of autocomplete tools designed by the company that provides a “text-in-text-out” interface that provides automatic text completion. This area has gained commercial traction and we’ll see real-life innovations and advancing use cases in this area rapidly accelerate over the course of this year. Areas like CSS generation utilizing GPT-3 and applications similar to Google Smart Compose will gain ground in 2021.

IDC expects worldwide AI spending to reach $110 billion by 2024. Companies should review products and projects closely to capitalize on what AI and ML have to offer — and then laser focus on those that use existing first-party data instead of those that first require the setup and creation of large, complex big data sets and data crunching. 

AI and ML technologies are more than buzzwords or simple predictions: They offer businesses limitless possibilities to evolve use cases to improve productivity, expand their customer base, boost ROI and grow their bottom line.

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