Introduction
What made Men in Black so incredibly cool? Their coordinated costumes? Or the fact that they helped aliens visiting Earth from numerous planets blend in properly here? Well, above all, the most amazing thing was their ability to monitor Data Trends, track, and control all kinds of aliens so well.
So, how did they cope with all this? Well, first of all, they recorded all the possible data about every possible alien from every possible planet they encountered. However, all this BIG amount of data can’t be managed on its own, right?
That’s why they had the latest and most excellent tools and technology to help them. Just like MIB, companies also process a lot of big data. The question is, are they handling it correctly?
Well, to stay on top of the latest and greatest big data news, check out these top five significant data trends that will shape your data-driven 2025! Dive in now!
MIB and real business are not so different in terms of data. The more information they have, the better they can function.
In today’s business world, data is gold, and companies spend vast amounts of money acquiring, processing, and using it. This data comes in different forms: structured, unstructured and semi-structured.
A wide range of technologies come into play to help store, manage and process data trends. Every year, the most widely used technologies change depending on what they bring to the table.
Last year, we reviewed the top significant data trends for 2024, where we saw an emphasis on data masking to improve compliance, stream processing to gain real-time insights, increased investment in data warehouses, AI and ML driving streamlined automation, and DataOps simplifying extensive data deployment.
Most of these technologies will continue to impact businesses this year, and some new trends will join them.
So, let’s take a look at the latest significant data trends and see what’s in store for us in 2025.
AI and machine learning will help sift through mountains of data.
Artificial intelligence (AI) and machine learning (ML) are here to stay. By 2025, these technologies will become more powerful, sophisticated, and complex. This will provide businesses with access to real-time data, powerful predictive analytics, accurate, actionable insights, automated decision-making processes, and other benefits.
Of course, this is in addition to their data processing, filtering, cleansing, and analysis capabilities that businesses already use. Additionally, since AI models rely on large amounts of data to grow and learn, big data will enable the creation of specialized AI tools that can better help businesses.
With the global AI market expected to grow to $1.8 trillion by 2030, more than 60% of IT executives plan to increase their investments in AI and ML technologies, according to Grand View Research. AI and ML solutions are already transforming processes across a range of industries, from healthcare to real estate.
For example, in the medical sector, AI/ML algorithms can analyze a 30-minute video for specific neural activity in less than 30 minutes, compared to 4 to 24 hours for humans. In real estate, the use of AI on big data enables companies to understand the business landscape better, anticipate changes in the industry, and flexibly shift their strategies accordingly.
According to Harish Fabiani, President of India and Group (part of Americorp Group), “The convergence of AI and big data has opened new horizons for market understanding, enabling previously unimaginable levels of decision-making accuracy. Real estate professionals now leverage massive data sets, analyzing real-time trends and preferences to navigate the complexities of the market. As we explore the changing landscape of commercial real estate, the symbiosis between AI, big data, and human expertise charts the future.”
TechDogs Conclusions:
- Ensure your data is accurate, clean, and well-structured before using AI/ML to gain insights or train custom AI/ML models.
- Implement robust data governance practices, such as data governance, compliance, standardization, and verification, while reducing algorithmic bias.
- Choose AI/ML tools that easily integrate with existing significant data ecosystems, are compatible with current systems, and can scale to cloud environments.
Edge computing will enable faster data processing with real-time analytics.
Just as MIB agents were trained to handle situations on their own without having to transmit information back to their base, edge computing and IoT (Internet of Things) devices are enabling businesses to process data closer to where it is generated. Because this technology reduces latency and bandwidth usage, it allows faster decision-making, operational efficiency, real-time analysis, and faster generation of actionable insights. In addition to this, the power of AI and processing speed have become even more rapid, more accurate, and more efficient.
This will lead to adoption in 2025 and beyond, especially in areas such as healthcare, manufacturing, and automotive.Fortune Business Insights reports that the global edge computing market size was valued at $15.96 billion in 2023 and is expected to grow from $21.41 billion in 2024 to $216.76 billion in 2032 at a CAGR of 33.6%.
“By enabling data processing earlier to the source of data generation, edge computing reduces latency, improves data security, and supports real-time analytics, which is critical for time-sensitive applications in manufacturing and autonomous vehicles,” says Rahul Pradhan, vice president of product and strategy at Couchbase. According to Dan DeBacker, senior vice president of product at Extreme Networks.
“When organizations can turn all of this available data into actionable insights, they can gain deeper business intelligence about user activity, which can help them make more knowledgeable decisions about investments and improvements. “user experience”.
This was the case for IT consulting and services company Atos, which was offering predictive maintenance-enabled services for a theme park in Orlando. Together with highly targeted predictive maintenance and reduced wait times in queues for attractions, these devices have enabled the theme park operator to minimize downtime, lower costs, and improve the customer experience. Yes, edge computing and big data can be used for entertainment, too!
TechDogs Conclusions:
- Ensure that data transferred from edge devices is encrypted and protected to prevent data leaks.
- Use applications that can generate real-time data analytics to gain insights quickly, scale to meet your data needs, and offer interoperability.
- Consider your budget, plan and manage your investments carefully, taking into account both upfront costs and long-term ROI.
Data Lakehouses will provide the best views.
Collecting as much data as possible and storing it is the mission of the hour, and data warehouses are every company’s best ally in this mission. A data warehouse (a mix of data lake and data store) is a data architecture of unlimited size that allows it to store structured, unstructured, and semi-structured data.
These platforms can extract data from any system at any speed and enable companies to perform deep analysis of their data at a later stage.
They also simplify data management, improve security, reduce resources, speed up data analysis, save costs, and offer the scalability and elasticity of data lakes with the data management prowess of data warehouses. The multifaceted capabilities of data lakes will allow them to expand their use by 2025.
According to MarketResearch.biz, the global data lake market size is predictable to grow from $8.9 billion in 2023 to $66.4 billion in 2033, at a CAGR of 22.9%.
This powerful combination allows us to connect and enrich vast amounts of customer data quickly. By democratizing access to data, we empower our non-technical users to make data-driven decisions easily. Amperity has allowed us to maximize the value of our data while focusing on delivering exceptional travel experiences.”
TechDogs Conclusions:
- Ensure your data warehouse architecture is optimized for hybrid and multi-cloud environments to ensure data availability, scalability, and cost-effectiveness.
- Leverage open source platforms or APIs to implement a more comprehensive data strategy and use AI and machine learning tools to automate data processing and analysis.
- Invest generously in robust data security, compliance, and governance to guarantee compliance with regulatory requirements while maintaining data quality, compliance, and security.
Blockchain technology will open a new horizon for big data
For years, the business world has speculated that blockchain technology would become the new standard for exchanging money and data. Although this technology has gained a strong position in the financial system of many countries, its involvement in the data management and sharing sector has been quite limited.
However, with recent advancements in Web3 technology, the adoption of blockchain technology in data management and analysis will increase. The main reason for this is its ability to make ransomware obsolete while also offering greater resilience and transparency.
Its decentralized nature will promote better accountability, longevity, and stability, improving data stored in data lakes and warehouses as it allows companies to trace the provenance of their data and identify anomalies, ensuring data integrity. In the future, companies will be able to use intelligent contracts to execute automatic data-sharing agreements and manage the privacy policies of individual instances.
Fortune Business Insights estimates that the global blockchain technology market, valued at $17.57 billion in 2023, is expected to produce from $27.84 billion in 2024 to $825.93 billion in 2032, growing at a CAGR of 52.8%.
“Blockchain technology is being accepted by 80% of the world’s public companies, which are home to transaction and customer data. At various stages of adoption such as research, piloting, development, and production, companies are making their mark with this growing technology,” said Vijay Praveen Maharajan.
Founder and CEO of its crunch, an AI-enabled decentralized blockchain data platform. He added, “For functions such as real-time data analysis and traceability, blockchain packs a punch when combined with big data analytics and is here to stay if the infrastructure becomes more cost-effective in the near future.”
TechDogs Conclusions:
- Prioritize data integrity and security with an immutable blockchain ledger to ensure data accuracy, prevent tampering, increase trust, and remain interoperable.
- Adopt blockchain-based solutions that enable interoperability, reduce bottlenecks, and increase scalability.
- Deploy smart contracts to automate and optimize key data processes such as data validation, access control, and transactions.
The Rise of Quantum Computing
According to Fortune Business Insights, the global quantum computing market was valued at $885.4 million in 2023. It is projected to grow from $1.1 billion in 2024 to $12.6 billion by 2032, with a CAGR of 34.8%. However, a report by McKinsey & Company predicts that the market will reach $1 trillion by 2035.
“Using blind quantum computing, clients can access remote quantum computers to process sensitive data with secret algorithms and even verify the correctness of the results without revealing any useful information. Realizing this concept is a major step forward in both quantum computing and in keeping our information secure online,” said study leader Dr Peter Drmota, from the University of Oxford’s Physics Department.
TechDogs Conclusions:
- Invest in a quantum-ready architecture that supports hybrid quantum-classical environments and facilitates a smooth transition to realise their full potential.
- Develop your employees to have quantum computing skills and hire quantum computing experts to use quantum algorithms.
- Implement quantum-resistant encryption along with comprehensive regulatory, compliance, and security requirements to protect against quantum threats.
To Sum Up
MIB was not harmed by the aliens threatening the planet with extinction. Mainly because they had enough information to overcome their problems. Likewise, companies seeking leadership must incorporate the latest trends into their operations. In 2025, big data will enable AI and machine learning to enable better decision-making.
While edge computing will allow the generation of real-time insights without taxing enterprise bandwidth resources. Data lakes will allow for the storage of vast amounts of data. While blockchain technology will enable better data management and security, and quantum computing will improve the entire system. If big data is the foundation of modern business, then these trends are shaping the innovations that will strengthen it.
Frequently Asked Questions
What are the top five significant data trends that will impact businesses in 2025?
In 2025, five critical big data trends will have a significant impact on businesses. AI and machine learning will improve data processing and decision-making; edge computing will enable faster. Real-time data analysis; data lakes will provide efficient data storage and management. Blockchain technology will enhance data security and management; and quantum computing will revolutionize data processing capabilities.
How will AI and machine learning, edge computing, data lakes, blockchain, and quantum computing shape the future of big data?
AI and ML will facilitate data analysis and provide predictive insights. Edge computing will enable real-time data processing close to the source, reducing latency and improving decision-making. Data lakes will allow you to store and analyze structured, semi-structured, and unstructured data. Blockchain technology will improve data integrity and security. Quantum computing will enable faster and more complex data analysis, solving problems beyond the scope of traditional computing.
What should companies do to prepare for these significant data trends in 2025?
Companies should focus on ensuring data accuracy and security by implementing strong data management practices and governance. They should also invest in technologies that enable real-time analytics, scalable data storage, and secure data sharing. Additionally, preparing for the integration of quantum computing will be critical to leveraging advanced data processing capabilities in the future.