Introduction: Reading the Numbers

Interpreting the natural world through numbers, nowadays known as data analytics, is as old as human civilisation. Ancient Egyptians meticulously analysed the Nile River’s seasonal flooding to establish one of the greatest ancient civilisations based on agriculture. The Food and Agriculture Organisation of the United Nations states, “Egyptians are credited as being one of the first groups of people to practise agriculture on a large scale,” which was facilitated by data-driven planning. At that point, data-driven actions entailed observing patterns in the natural world and implementing necessary steps to reap the benefits – precisely what data analytics aims to achieve. Using technology isn’t vastly different in scope; it’s just on a different scale.

The Cambridge Dictionary defines analytics as “the process in which a computer examines information using mathematical models to find useful patterns.” Thus, at a fundamental level, analytics transforms our decision-making ability from being intuition-based to data-driven. Modern data analytics takes us one step further – we are no longer restricted to looking at a small sample of data. Instead, computers can now look at large quantities of data available to a business and help shot callers make well-informed decisions.

Projections show that analytics driven by Artificial Intelligence seem to be the future. In this blog, we are going to explore some of the applications of AI-integrated analytics and how these can shape the future of businesses.

Data Proliferation

With the incredible proliferation of data, we also have extraordinary tools to understand and interpret them. It has been estimated that around 328.77 million terabytes of data are generated every day. As of January 2024, there were around 5.35 billion internet users around the world. The storage capacity for data has also increased exponentially in the last decade.

For businesses, the benefits of using analytical solutions are quantifiable. Businesses can see how employing these solutions can help them increase their bottom line, satisfy their customers, and lead their businesses to higher growth. Companies, irrespective of the industry they are in, might use analytics to optimise their overall operations, improve business, solve specific problems, or find solutions to scale up their businesses.

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Artificial Intelligence and the Future of Data Analytics

Artificial Intelligence and Machine Learning Integration into analytics have changed the game for businesses operating around the world. We can now analyse data with increased accuracy and at an unthinkable speed. This would not have been possible without the growth in Artificial Intelligence technology.

However, we have only seen the tip of the iceberg.

In 2023 the global artificial intelligence market size was valued at $196.23 billion and it is expected to grow to $1.811 trillion by 2030, at a whopping 37.3% CAGR. That would imply incredible growth in AI-driven analytics as well. It is predicted that businesses around the world are going to invest heavily in these technologies to stay ahead in the race. According to predictions made by the International Data Corporation (IDC), by this year (2024), “organisations with greater enterprise intelligence will have 5x institutional reaction time, resulting in persistent first-mover advantage in capitalising on new opportunities.”

While some of these technologies have already created immense value for numerous companies, the future of business enterprises depends on how well end-users adapt to some of them. Let us explore some of the ways in which AI-integrated analytics is able to generate value for businesses:

a) Real-Time Decision-Making

Real-time decision-making entails analysing and acting on data the moment it is generated. Essentially, the time difference between data collection and decision-making is removed by this technology. This real-time decision-making could not have happened without the integration of advanced analytics, artificial intelligence, and machine learning with high-speed data processing frameworks.

These forms of data analytics are widely used in the financial markets, especially in high-frequency trading where these programmes analyse the markets, read trends and anomalies, and instantaneously make decisions. Real-time analytics is also used significantly in online retail businesses to help improve customer experience. This technology also has incredible potential to transform healthcare. During emergencies, real-time decision-making can save lives through the proper allocation of resources.

Perhaps the most tangible advantage of this technology for companies is the time and resources it can save them. Shell, the British multinational oil and gas company, used real-time analytics, and their time for doing a basic inventory analysis was reduced from 48 hours to 45 minutes. It is safe to say that all kinds of businesses employing real-time decision-making will provide extraordinary leverage over competition.

b) Predictive Analysis

Predictive Analysis technology has also led to incredible growth for early adopters. The most common everyday household name in this context is Netflix. Netflix’s Personalised Programming is based on Predictive Analysis. According to Ash Fenton of Netflix, “80% of what people play on Netflix comes from the recommendation algorithm.”  The technology analyses historical data to come up with possible predictions. These programmes can identify patterns and correlations that are often missed by human analysts. The predictions are also quite accurate and only grow accuracy over time.

Companies operating in the financial sector often use Predictive Analysis to forecast stock market trends. It is also useful in assessing credit risks. In healthcare, Predictive Analysis can be used to predict disease outbreaks as well as in personalising treatment plans. This technology is also helpful in customer segmentation, targeted advertising, sales forecasting, enhancing customer engagement, and optimising marketing strategies for businesses. It is a given that, with time, more and more companies will adopt Predictive Analysis into their analytics framework.

c) Edge Computing

Edge Computing is one of the newer technologies that processes data at the very source of data generation, and it has become a necessary function of data analysis.

“But why is it necessary?”, you may ask.

For this, we will need to understand the sheer volume of data that is generated by the Internet of Things (IoT). IoT refers to billions of physical objects around the world that have sensors, software, and other technologies embedded in them. These are non-standard computing devices that connect wirelessly to the internet. By 2030, it is estimated that the total number of IoT devices will reach 29.42 billion.

Statistics of IoT Connected Devices Worldwide 2019 to 2023

According to Fortune Business Insights, the Internet of Things (IoT) market size will grow from $714.48 billion in 2024 to $4,062.34 billion by 2032. The amount of data that almost 30 billion IoT devices can generate is beyond the storage capacity of traditional cloud computing. Hence, to cater to real-time data processing, decentralised edge computing will increase with time. By processing data locally, it will save time and resources for companies. This would be exceptionally important for the overall operational efficiency of businesses as well. Additionally, edge computing contributes to enhanced data security and privacy. By processing data locally, sensitive information can be analysed and filtered at the source, reducing the need to transmit large volumes of data to the cloud or a central data centre.

d) Augmented Analytics

Augmented analytics also uses machine learning and artificial intelligence. This simplifies data analysis process, making data more understandable for non-experts. Augmented analytics automates the data preparation and analysis steps. It saves time and resources and helps companies gain insightful ideas and enhance decision making process.

Augmented analytics have made data more accessible to a wider range of businesses, enabling them to engage directly with data. Businesses may increase customer engagement and satisfaction by providing personalised recommendations, content, and services by analysing consumer data in real-time. This customised strategy can result in better outcomes and lend a competitive edge in sectors including retail, finance, and healthcare.

e) Adaptive and Continuous Learning Models

While traditional static models are trained on fixed datasets and require manual updates, the adaptive and continuous learning models learn from new data without human intervention. These models can therefore adapt to changing environments and refine themselves in the process. These are often used by financial sectors to detect fraud. The nature of fraudulent activities might change with time and that is where adaptive models are particularly helpful. As fraudulent activities change, the adaptive learning models also change, making it possible for them to detect these activities.

Continuous learning models go one step further. These models not only adapt to new data but also learn from their interactions with the environment. One of the most advanced applications of this is Tesla’s Self-Driving cars, which use cameras, radars, and other sensors to gather information about their surroundings. The cars navigate, detect obstacles, and change lanes, using big data analytics and continuously improve their performance over time.


No discussion on the impact of data analytics on the future of business can be addressed without talking about unstructured data. Unstructured data is defined as the data that doesn’t have a fixed form or structure. Images, videos, audio files, text files, social media data, geospatial data, data from IoT devices, and surveillance data are examples of unstructured data. According to estimates, about 80% of data is unstructured. The value of AI and ML in analysing this data cannot be overstated. The future of businesses would rely on how well they can make sense of some of these data points that are still untapped.

Needless to say, the advancement of data analytics has ushered a new era for businesses around the world. Data Analytics Solutions built on some of these technologies may help businesses adapt to changing market dynamics and stay ahead in the game. It is important to note, however, that adopting to these technologies is not enough, companies also need to do it well. One of BCG’s studies found that “70% of digital transformations fall short of their objectives.” Hence, expertise-led analytics is the way forward.

Find out how AKW Consultants’ Software Development Solutions will help you stay ahead in this game by creating unique data analytics software designed to fit your business model.