We live in a world governed by data and AI technologies. Yet, in the next five years, big data and machine learning will grow exponentially, especially in the following areas: Marketing, operations, finance, and healthcare. Expert Hassan Shuman explains why the union of cloud and big data is important, and how it’s already being used to drive value.

In today’s world, there isn’t a day that goes by without a mass of data being collected. Valuable information is produced any time we use an app on our phones or scour the web through our computers. Organizations capable of efficiently collecting, curating, storing, and analyzing as much of this data as possible, are better placed to drive innovative solutions for end-users.

But with the amount of data currently generated every minute, how can companies effectively leverage this information to benefit their business and customers?

The union of big data and cloud computing

Cloud and big data are experiencing accelerated adoption rates by enterprises of all types – to power growth, operational efficiencies and deep business insights.

The cloud computing space itself is projected to grow to more than $830 billion by 2025 while the global big data market is projected to reach $230 billion by 2025. It is also estimated that approximately 90% of organizations are already utilizing cloud services on some level, and we can expect this to inch closer towards full enterprise adoption in the coming years.

But how can their union benefit companies?

While big data platforms are designed to handle high volumes and varieties of data, cloud can store, collect, create, clean up, transform, and analyze this data for a variety of scenarios – such as marketing, operations, finance, and healthcare.

Its capacity to scale on demand is invaluable for hosting big data, and the ability to rapidly provide IT resources on the cloud reduces the time-to-market for data driven solutions.

Both big data and cloud computing, therefore, are vital for organizations to succeed. Not only does this union allow companies to remain relevant in a highly competitive market, but it also enables them to benefit from the wealth of data to stay on top and thrive.

Moreover, the benefit of this combined power is not limited to the private sector. As we have seen over the past several years, governments have also been tapping into the strategic advantages of the predictive and prescriptive insights generated by big data using the power of cloud computing.

As such, the following decade will belong to those who can harness the power of cloud computing to rapidly extract value from big data resources into an ever-growing range of insights that would put them ahead of their competition in the market.

Specifically, there are four areas which will experience exponential growth in the use of big data and machine learning (ML) over the next five years – although some areas in marketing and finance are already ahead of the game.


Businesses can learn about customer preferences, sentiment, and trends by analyzing vast amounts of customer data, such as online behavior, social media activity, and transaction history.

This data can be used to personalize marketing messages, increase client retention, and create customized product recommendations. Examples include:

Personalization: By analyzing customer data, businesses can gain insights into individual preferences and tailor their marketing messages accordingly. For example: Netflix – The platform has been able to predict demand through ML, helping them understand how to release series and which to recommend to a specific viewer, helping them save $1 billion year-on-year.

Social media analysis: Businesses can follow customer sentiment on social media and spot new trends by keeping an eye on these platforms. For example: Amazon Lex – In 2017, Amazon introduced Amazon Lex, which has enabled the company to understand what their consumers want or need before they do themselves. Among many of its features is its ability to detect sentiment in responses, which has allowed the company to comprehend how customers feel about its goods and services.

Customer retention: Businesses can spot customers who are at risk of leaving and take proactive measures to keep them by analyzing customer data. For example: Vodafone – Chatbots, virtual assistants, and ML algorithms have enabled Vodafone to analyze customer utilization trends, deliver targeted promotions and a better service. As such, the company improved customer satisfaction by 68%.


Machine learning can be applied to operational data, such as supply chain, logistics, and production data, to identify patterns and optimize processes. This can result in cost savings, improved efficiency, and faster delivery times.

Examples include:

Predictive maintenance: By analyzing sensor data from equipment and machinery, businesses can predict when maintenance is required and take proactive steps to prevent downtime. For example: Delta – Through its partnership with Airbus in 2018, Delta has been able to use the Skywise Core Platform and Skywise Predictive Maintenance App to improve reliability. The result? It decreased flight cancellations due to maintenance faults from over 5,600 in 2010 to just 55 in 2018.

Supply chain optimization: Businesses can anticipate when maintenance is necessary and take preventative action to avoid downtime by analyzing sensor data from equipment and machines. For example: United State Cold Storage – This cold chain major saved $1.2 million in operations through ML and predictive AI solutions.

Quality control: Businesses can identify patterns and take corrective action. For example: Knorr – The German food and beverage major, used a ML and AI-powered vision system from US company Cognex to inspect its products. This technology helped Knorr achieve its goal of zero defects globally.


Financial organizations can enhance risk management, fraud detection, and compliance with the aid of big data and machine learning. Businesses are better able to spot abnormalities and reduce risks by analyzing vast amounts of transaction data and other pertinent information. Examples include:

Fraud detection: Businesses can spot patterns of fraudulent activity and take preventive steps by analyzing transaction data. For example: Danske Bank – One of Denmark’s largest banks increased its ability to detect fraud by 50% after implementing an ML-driven fraud detection system.

Risk management: By studying data on market trends and economic indicators, businesses can assess their exposure to risk and act appropriately. For example: AXA – AXA used ML to forecast high-loss scenarios, aiming to reduce costs and improve pricing. Its model contains 70 risk indicators and it achieved a 78% accuracy rate, which greatly optimized its pricing strategies.

Compliance: Businesses can guarantee compliance with relevant laws and regulations by investigating regulatory data. For example: Bank of Marin – This US-based bank uses the compliance.ai platform to ensure that its employees have access to all relevant regulatory content in one place. This has reduced time wasted over finding such documents, as well as increased accuracy.


Cloud-based platforms that combine big data and machine learning can help healthcare organizations improve patient outcomes, reduce costs, and streamline operations. By analyzing electronic health records, clinical data, and other sources of information, healthcare providers can identify patterns and develop more effective treatment plans. Examples include:

Diagnostics: Healthcare providers can make more accurate diagnoses and create better treatment plans by analyzing medical images and other data. For example: UK-based research – A UK-based multi-center study, including institutions WMG and the University of Warwick, discovered a non-invasive way of identifying tumors in children. By using the diffusion of water molecules to obtain contrast in MRI scans, they were able to feed that map into an AI model that can identify tumors.

Personalized medicine: Genomic data provides doctors with information to personalize treatment plans. For example: The PERSEPHONE project – This project aims to deliver a personalized approach to optimally care for hemophilic patients. Through ML, the project can detect joint bleeding early and, therefore, not over- or undertreat patients.

Population health management: Healthcare providers can identify patterns of disease and develop preventive measures. For example: ProMED-mail – This company is a reporting platform which monitors evolving and emerging diseases in certain areas, providing outbreak reports in real-time.

Embrace cloud and big data

Through big data and cloud computing, businesses can now gain deeper insights into their data. They can also spot patterns and make more informed choices thanks to the development of advanced analytics. Coupled with the Infosys Cobalt suite of tools, our accelerators are enabling a number of big brands to accelerate their investments and capitalize on this rich inventory of data in ways that were previously unimaginable.

Clearly, from the examples above, 4.0 technologies, such as ML, IoT, big data and robotics, can assist businesses in automating repetitive chores, detecting anomalies, and streamlining operations. As such, companies can increase productivity, cut expenses, and provide better customer experiences – all of which is made possible by implementing big data platforms and cloud computing technologies as one.

Hassan Shuman

Hassan Shuman

Senior Principal

Dr Hassan Shuman is a multi-certified cloud architect, AI specialist and trusted advisor in the industry. With 22 years in IT consultancy, he helps clients transform how their businesses operate with an open, extensible data and AI platform that runs on any cloud. Hassan is also a regular speaker at events including AWS summits and IDC conferences on topics including cloud migrations, serverless technologies, AWS machine learning and data science.

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