Top 10 Big Data Trends For 2024

Today, we live in a new digital age where things like Artificial Intelligence and Machine Learning have changed how businesses and society work. Big data trends have become important for understanding what’s happening in the market and making decisions for businesses. With so much data available, companies want to find better ways to use it. Big data trends have been especially helpful during the COVID-19 pandemic, boosting sectors like healthcare and online shopping.

Top 10 Big Data Trends For 2024

It is expected that the big data market is going to shoot up to 200 USD Billion by 2025. So, let’s check out the Top 10 Big Data Trends for 2024.

Top 10 Big Data Trends in 2024

So now, if you want to know what’s coming up in the future of big data, check out these top 10 big data trends!

1. Growing IoT Networks

The Internet of Things (IoT) is growing fast, especially with the introduction of 5G. 5G is like a superhighway for data, making connections faster and stronger. This helps industries like healthcare, self-driving cars, and smart cities get real-time information quickly, improving how things work.

Iot Networks

In simple terms, cities are getting smarter by using new technology called the Internet of Things (IoT). This tech helps change how traffic, energy use, trash, and healthcare work in cities. In 2024, we’re seeing a mix of 5G, AI, edge computing, and smart city stuff creating cool chances. This means that in the future, things like being connected, working smoothly, and staying safe will be super important as IoT grows. This is also part of what we call the future of big data.

Real-time applications of IoT include:

1. Smart Homes: IoT-enabled devices control home appliances, lighting, and temperature to boost energy efficiency and cut costs.
2. Smart Healthcare: IoT-enabled devices monitor patients’ health, offering real-time feedback to healthcare providers.
3. Smart Cities: IoT-enabled devices monitor traffic, reduce energy consumption, and enhance public safety.

2. More Approachable Artificial Intelligence

In the future, analyzing data will be easier for businesses, big or small. This is because new tools and platforms are being made that make it simpler to use artificial intelligence (AI). AI helps in understanding and using data better. This trend of making AI accessible to everyone is expected to continue in 2024. It means AI will be easier to get and cheaper, especially for small and medium-sized businesses.

One big change is the rise of low-code and no-code AI platforms. This means businesses can make and use AI models without needing to be experts in coding. This shift is a big deal because it means more businesses can benefit from AI without needing a lot of technical know-how.

Growing Global Artificial Intelligence Market

AI is becoming more human-like with the rise of natural language processing (NLP) and conversational AI, making it user-friendly for businesses, while Forbes Advisor predicts the AI market to reach $407 billion by 2027, with a projected 21% GDP increase by 2030 and 64% of businesses expecting enhanced productivity, based on a Forbes Advisor survey.

Real-time applications of AI:

1. Chatbots: Smart computer programs called chatbots help customers online. They can answer common questions, give support, and even help with buying things.
2. Predictive Maintenance: Computers use AI to guess when machines might stop working. This helps businesses fix them before they break, avoiding problems.
3. Fraud Detection: AI is used to find fake transactions. It helps stop people from stealing money, protecting businesses from losing cash.

3. The Rise of Predictive Analytics

The adoption of predictive analytics is on the rise as businesses seek ways to gain a competitive advantage. Employing machine learning algorithms, predictive analytics analyzes data to make informed predictions about future events. The accessibility of predictive analytics is poised to increase for businesses of all sizes in 2024, owing to the development of new tools and platforms that simplify the creation and deployment of predictive models.

Rise of Predictive Analytics – Big Data Trends

Predictive analytics is used in many different industries to make predictions about the future using data analysis. In healthcare, it helps predict what might happen to patients and who might get certain health problems. In finance, it can spot fake transactions to stop money from being lost. And in retail, it helps predict how much of a product will be needed and set prices effectively.

Real-time applications of predictive analytics:

1. Fraud Detection: Using advanced math to find fake transactions and stop money from being lost.
2. Predictive Maintenance: Using smart math to guess when machines might break, so we can fix them before they do.
3. Healthcare: Using fancy math to guess what might happen to patients and figure out who might get sick.

4. Cloud Migration of Dark Data

In simple words, “dark data” means information that’s collected but not used. In 2024, it’s expected that more businesses will start moving this unused data to the cloud. Why? Because the cloud offers benefits like being able to easily expand, saving money, and making data easier to get to and analyze, unlike keeping it on their own computers. By doing this, businesses can find important insights from the data, which can help them run better. This shift is part of what people call the “future of big data.”

Cloud Migration of Dark Data

As per MarketsandMarkets, the worldwide predictive analytics market is projected to experience significant growth, increasing from $7.2 billion in 2019 to $21.5 billion by 2024, exhibiting a compound annual growth rate (CAGR) of 24.5% throughout the forecast period. Notably, the banking, financial services, and insurance (BFSI) sector are anticipated to dominate the market size during this forecast period.

Real-time applications of cloud migration of Dark Data:

1. Predictive Analytics: Moving hidden data to the cloud lets businesses use predictive analytics to guess what might happen next.
2. Data Mining: Shifting dark data to the cloud helps companies employ data mining techniques to find patterns and trends within their data.
3. Business Intelligence: Migration of dark data to the cloud allows businesses to use business intelligence tools, gaining insights into their operations and making data-driven decisions.
4. Future of Big Data: This shift towards the cloud is shaping the future of big data, enabling more advanced analysis and decision-making processes.

5. The Rise of Chief Data Officers

In the future of data, as businesses understand more and more how important data is, we expect to see more Chief Data Officers (CDOs) being hired. These CDOs are responsible for making sure that a company’s data is correct, safe, and easy to use. They create rules about how data should be handled, make sure the data is good quality, and make sure the company follows the rules about data.

The Rise of Chief Data Officer

According to IDC, the number of Chief Data Officers (CDOs) in the United States is expected to go up from 1,000 in 2019 to 5,000 by 2024. This increase is because data is becoming more and more important for businesses, so they’re hiring someone specifically to handle their data strategies.

• At JPMorgan Chase: Jacqueline Comey is the Chief Data Officer. She’s in charge of coming up with new ways to use data to make the company better, like figuring out innovative ideas and understanding what customers want.
• At Unilever: Arif Karsan is the Chief Data Officer. He uses data to make marketing campaigns work better, make customers feel special, and make the company’s supply chain run smoother. They’re both examples of how companies are using new data technology to get ahead.

Real-time applications of CDOs :

1. Data Governance: CDOs are instrumental in developing data governance policies and ensuring adherence to data regulations.
2. Data Quality Management: CDOs are tasked with managing data quality, guaranteeing the accuracy and currency of organizational data.
3. Data Security: CDOs are responsible for ensuring the security of organizational data, protecting it from unauthorized access.

6. Quantum Computing

Quantum computing, is a new kind of computing that’s still in its early stages, but it has the potential to change how we handle data. In 2024, we expect more businesses to start looking into quantum computing.

Instead of using regular bits like in normal computers, quantum computers use something called qubits. These qubits can be both 0 and 1 at the same time, unlike regular bits which can only be one or the other. Because of this, quantum computers can do certain tasks much faster than regular computers.

Quantum Computing

According to a company called MarketsandMarkets, the global market for something called quantum computing is expected to get bigger. They say it was worth $472 million in 2021 and could grow to $1.7 billion by 2026. That’s a pretty big increase, about 29% every year. They also say that industries like banking, finance, and insurance will be the biggest users of this technology.

Another report by McKinsey, from 2022, thinks even bigger. They say that by 2030, this market could be worth a whopping $1 trillion! This shows that quantum computing, a new data technology, has a lot of potential.

Real-time applications of quantum computing:

1. Drug Discovery: Quantum computing helps find new medicines faster by quickly simulating how molecules interact.
2. Optimization: Quantum computing is great at making complicated systems, like supply chains, work better.
3. Cryptography: Quantum computing might be able to crack regular codes, so we’re working on new ones that it can’t break.

7. Smarter and Tighter Cybersecurity

As the volume of generated data continues to soar, the significance of cybersecurity is poised to intensify. In 2024, an upward trend is anticipated, with more businesses channeling investments into enhanced and more sophisticated cybersecurity measures. The ascendancy of artificial intelligence (AI) and machine learning (ML) is set to play a pivotal role in fortifying cybersecurity, empowering businesses to swiftly and accurately detect and respond to emerging threats.

According to BCG, cyberattacks are projected to surge by 50% by 2024, underscoring the imperative for bolstered cybersecurity initiatives. The financial implications of cybercrime are also expected to rise, with estimates hinting at a potential escalation to $10.5 trillion by 2025.

Endpoint Security

Safeguarding devices connected to a network, such as laptops, smartphones, and tablets. Enhanced endpoint security measures act as a deterrent against cyberattacks, safeguarding sensitive data.

Endpoint Security

Cloud Security

Ensuring the security of data stored in the cloud. Strengthened cloud security measures serve as a defense against data breaches, guaranteeing protection from unauthorized access.

Cloud Security

Real-time applications of cybersecurity:

1. Crowdstrike Falcon XDR: This platform leverages AI and machine learning to detect and respond to threats across endpoints, workloads, and cloud environments.
2. Palo Alto Networks Cortex XDR: This solution combines behavioral analytics with network traffic analysis to provide a comprehensive view of security threats and risks.
3. McAfee MVISION ePOch: This cloud-based platform utilizes big data analytics to deliver automated threat detection, endpoint protection, and vulnerability management.

8. Open Source Data

The popularity of open source data is on the rise as businesses seek collaborative ways to share information. In 2024, an increasing number of businesses are expected to embrace open source data for gaining insights and enhancing their operations. Open source data entails information made freely available to the public, allowing anyone to utilize, modify, and distribute it.

Open Source Data

Features:

• Apache Hadoop and Spark ecosystems: These established platforms remain foundational, offering robust frameworks for data storage, processing, and analysis.
• Cloud-based open-source solutions: Companies like Google and Amazon are increasingly making their big data tools available in open-source versions, lowering barriers to entry.
• Machine learning & AI libraries: Frameworks like TensorFlow and PyTorch are leading the charge, democratizing access to the tools needed to build intelligent data applications.

Real-time applications of open source data:

1. Using free data to analyze helps us understand how a business works.
2. Free data is really important for teaching computers to learn better.
3. Making pictures with free data helps businesses understand their information better so they can make smart choices. It’s the future of big data.

9. Data Democratization

In the future of big data, more companies will start sharing data with everyone in the company. This is called data democratization. It helps people make better decisions. By doing this, businesses can help their workers use data better and get more out of it.

Data Democratization

Features:

• Self-service analytics platforms: These are easy-to-use tools that let people who aren’t experts in tech look at and understand data on their own. They don’t need to rely only on experts to help them.
• Data visualization tools: These tools make data easier to understand by turning it into pictures and charts. That way, everyone can quickly see the most important information.
• Data governance and collaboration frameworks: These are systems that make sure data is used responsibly and kept private. They also help experts and non-experts work together and share what they know.

Real-time applications of data democratization :

1. Understanding Data: Making data easier for everyone to use helps more people understand it better. This means even if you’re not a data expert, you can learn to use data well.
2. Using Data for Decisions: When businesses let all their workers access and understand data, they can make smarter choices based on that information. This is called data-driven decision-making.
3. Working Together: Sharing data among employees helps them work together better. When people can easily share what they know about data, it leads to better choices and helps the business grow. This is one of the current data trends.

10. Data Ethics

In the world of big data trends, more and more data is being created all the time. This makes businesses really think about how they handle data. In 2024, we expect to see more businesses using ethical practices when dealing with data. This means they’ll be more careful about keeping their customers’ information private and safe. Data ethics is all about following the right moral rules when collecting, using, and sharing data.

Data Etics

Features:

• Privacy concerns: More people are becoming aware of data breaches, profiling, and unfairness caused by hidden biases in computer programs. This worries the public because they fear their personal information might be misused. Rules like GDPR and CCPA say that collecting and using data must be done in a fair and ethical way.
• Algorithmic bias: Sometimes, computers unintentionally favor certain groups over others because of hidden biases in the data they use. This can lead to unfair results, like some people getting loans or jobs easier than others. It’s important to fix these biases to make sure everyone is treated fairly.
• Transparency and accountability: As we rely more on data to make important decisions, like in healthcare and finance, it’s crucial to be clear about how data is used. We need to know who is responsible if something goes wrong.

Real-time applications of data ethics:

Big Data Trends: Ethical ways of handling data are important for three main things: Data Privacy, Data Security, and Data Governance.

1. Data Privacy: This means making sure people’s personal information is kept safe. It’s about collecting, using, and sharing data in a way that respects people’s privacy.
2. Data Security: This is all about keeping data safe from bad guys who shouldn’t have access to it. We need to protect data from hackers and other security threats.
3. Data Governance: This is about managing data well. It’s making sure data is accurate, complete, and reliable, and that it’s handled in a fair and responsible way.

Conclusion

It’s not that difficult to understand how the world is shifting towards a digital world, surrounded by advanced technology. Thus, implementing big data trends for your business can be and will be definitely a charm. The only thing here is, you need to figure out your purpose of applying them in your business. The sooner you’ll identify – it will become easier for you to pick any trend. So, these are the Top 10 big data trends for 2024 that are going to dominate the market one-sided for sure!

Top 10 Big Data Trends For 2024 – FAQs

What are the five 5 of big data?

1. Volume
2. Velocity
3. Variety
4. Veracity
5. Value

What is the big data statistics for 2024?

Global predictive analytics market to hit $21.5B by 2024, growing at 24.5% CAGR. BFSI sector leads. Big data analytics market to soar from $307.52B in 2023 to $745.15B by 2030, with a 13.5% CAGR. Data generation to spike 150 times by 2027, projected to reach 300 times by 2032, defining big data trends for 2024 and beyond.

What is the next big thing in big data in 2024 ?

In 2024, the big thing in big data is democratizing data—making it accessible to all in the organization. This empowers employees to use data for added business value. Other trends include quantum computing, stronger cybersecurity, and the rise of Chief Data Officers.

Will AI replace data analysts?

AI transforms business operations, automating repetitive tasks in data analysis workflows. Yet, it won’t fully replace human data analysts. Instead, it empowers them for more efficient and accurate roles.

What are the data engineering trends in 2024?

In 2024, data trends include the transformative impact of large language models, data teams adopting software team practices, and software teams becoming adept data practitioners.