Data Analytics Trends To Watch In 2025

Coming back to the Russo brothers’ primordial success, Captain America: The Winter Soldier, the plot consisted of a brilliant albeit negative use of the powers of data analytics. If you haven’t seen the movie, beware of spoilers in the next two paragraphs.

Hydra, an antagonistic organization focused on world domination, wanted to eliminate anyone who would be a threat to it, in the present or future.

Wait – how did they know who would be a threat in the future? They did this by applying an algorithm against data such as bank records, medical histories, voting patterns, e-mails, phone calls, SAT scores and more to evaluate people's past to predict their future.

While their intentions remained nefarious, their use of data analytics was outstanding. Also, you just know that they followed the latest data analytics trends or were quite possibly trendsetters, staying ahead of the curve on trends in data analytics!

Either way, it’s important to keep up with the trends to maximize its effectiveness and just like last year, this year too we shall explore the top 5 trends in data analytics. However, before we get into data analytics trends 2025, let’s do a quick recap of the Data Analytics Trends Of 2024 and where we stand with those this year.

Operationalizing AI was key throughout 2024 and will continue to be a vital process for years to come. Real-time data is key to efficient data analytics and edge-computing is a growing market for that, courtesy of IoT (Internet of Things). Security is crucial and data mesh architecture is one to look out for in the coming years, while synthetic data and data literacy will be enhanced by artificial intelligence (AI).

So, let’s get into the latest Data Analytics Trends, explore the current trends in data analytics, and see what’s in store for 2025!
 

Trend 1: A Blend Of AI And ML Will Be The Main Ingredient

Is there any industry that artificial intelligence hasn’t affected or transformed? AI has changed things forever and to paraphrase Director Nicholas J. Fury, “It’s time you get with the program.”

AI comes into the data analytics sector along with the addition of GenAI (generative artificial intelligence) and ML (machine learning). Stepping up from the process of manually sifting through data to gather often slow-burning and inaccurate insights, AI and ML are here to speed up processes. This will be done by automating routine tasks, decrypting customer behavior, predicting future business trends, identifying patterns and more. Machine learning enables data scientists to improve their insights, while GenAI tools have made it possible for them to interact with their data and get answers to specific questions through simple text prompts.

“A range of AI technologies are used across the industry,” said Martin Harris, Head of Digital at Tank, a digital marketing and PR agency, and added, “ChatGPT/Gemini is mostly for starting points for copywriting, such as writing hundreds of variations of meta descriptions or ad variations. It’s great for data analysis, where we can pull up a large data file and ask it to find trends, opportunities or any issues we need to fix.” Tank also found such tools helpful “for complicated formulas in Excel,” and laborious tasks, while also providing, “better customer insights through analysis and refined customer segmentation, making marketing efforts more effective.”

A report by Exploding Topics states that around 90% of the world's data was generated in the last two years alone and that number is set to increase by 150% in 2025. That’s a lot for businesses to handle without AI to make routine tasks easy, right?
 

TechDogs Takeaway:
 

• Implement unbiased, ethical, fair, transparent and accountable AI And ML practices to avoid skewed results and maintain trust.

• Leverage advanced AI features that smoothen operations and data processes, such as automating routine and time-consuming tasks, freeing up your skilled workforce and data scientists to focus on more strategic tasks.

• Audit your current infrastructure and upgrade its assets, network and architecture to ensure the increasing data loads can be handled by various AI-powered tools without compromising on performance, quality and accuracy.

Trend 2: Things Are About To Get Real With Real-Time Analytics

Unlike the Winter Soldier who was kept a secret, everyone knows that today’s commercial world is run by data. From websites visited to posts liked and seconds spent on applications, everything is data for businesses. According to Cybersecurity Ventures, the total global data storage is poised to exceed 200 zettabytes by 2025, up from 30 zettabytes in 2018. The best way to leverage all these different types of data is in real time!

Real-time analytics will allow businesses to respond quickly to changing market conditions, make informed decisions on the fly and adjust their marketing strategies based on current scenarios.  IoT (Internet of Things) devices and edge computing have further enhanced the capability of businesses to harness and analyze data in real-time. As such, real-time analytics will be a big hit in healthcare, finance and e-commerce. It will help monitor health equipment, identify disease patterns, reduce fraudulent transactions, discover key performance indicators (KPIs), design hyper-personalized campaigns, measure marketing performance and more.

A key area this technology enables is risk analytics. Global F&B leader Kraft Heinz engaged Ernst & Young to transform their risk analytics program by garnering better insights from real-time data, to gather better actionable insights. A move that led to Fernando Garcia Bueno, Head of Internal Audit at Kraft Heinz, saying “Now we have risk analytics in real-time, these analytics can pull the latest 12 months’ data at any point in time.” The result also allowed the company to make the auditing process more efficient, to which Corrado Azzarita, Global CIO, Kraft Heinz, said, “I firmly believe that data-driven decision making can become a reality in several business domains, improving both efficiency and effectiveness.”
 

TechDogs Takeaway:
 

• Invest in scalable infrastructure and technologies that can handle large datasets as data volumes continue to grow, without compromising on quality or performance.

• Deploy effective data governance and security practices and implement robust data governance frameworks that can uphold data integrity, compliance and cybersecurity measures.

• Conduct thorough tests to ensure compatibility with data lakes, data warehouses, automation tools, AI/ML platforms and other existing systems, to gain real-time actionable insights.

 

Trend 3: Data Democratization Will Make Data Of The People, By The People, For The People

With loads of data generated across a wide range of verticals, different departments can perform better if they get access to data pertaining to their process. This is where Data Democratization comes in. Businesses have come to realize that making data available to all employees and providing them with the right tools enables them to make informed data-driven decisions. Moreover, it will help them target the right KPIs. Platforms like Tableau and Microsoft PowerBI can be leveraged to enable non-technical staff to generate insights by exploring data through user-friendly interfaces. With the introduction of AI, ML and automation into the mix, leveraging data and generating actionable insights will only get easier.

Dresner Advisory Services finds that 73% of organizations believe data democratization has improved decision-making capabilities, while 69% observed a rise in organizational agility. Furthermore, the data democratization market is forecasted to grow at a 25% CAGR through 2025.

Even large conglomerates such as Google and Microsoft have benefited from implementing data democratization and have seen benefits in the form of increased innovation, efficiency and employee engagement. Damon Buono, the Head of Enterprise Governance for Microsoft, said, “Our data was in silos. Various parts of the organization were managing their data in different ways, and our data wasn’t connected. We needed a shared data catalog to democratize data responsibly across the company.” This is where the company brought in Microsoft Fabric, which unifies data across the organization and “connects enterprise data across multiple data sources and internal organizations to create a comprehensive perspective on enterprise data.”

So, don’t be like Hydra and keep people on a need-to-know basis, share the data around!
 

TechDogs Takeaway:
 

• Implement robust data governance frameworks, strict security measures, role-based access controls and compliance protocols to protect sensitive data and prevent unauthorized use.

• Promote a culture of collaboration within the organization, especially between data scientists, IT teams and other business units, as well as encourage cross-functional teamwork – to move to a democratized environment.

• Ensure that your data infrastructure is scalable and flexible enough to handle the collaborative environment along with efficient data storage solutions, high-performance analytics tools – preferably on cloud-based platforms.

 

Trend 4: Data Visualization Will Help Tell The Story Of Data

One of the most admired qualities of the movies made by the Russo brothers was its visual treatment. How a particular scene looks on screen can influence a viewer’s mood. Similarly, how employees or executives view data can make a big difference in their decision-making capabilities and how the information resonates with them.

Instead of being bogged down, confused and overwhelmed by excessive numbers and charts, employees can get a better understanding of actionable insights by transforming raw data into visually appealing, interactive, animated, memorable and compelling narratives. Furthermore, data visualization tools are now designed to process data in real time and offer contextual insights. As per a report by Maximize Market Research, the data visualization tools market is expected to grow at a CAGR of 11.6% and will reach a valuation of $15.8 billion by 2030. According to the report, its demand has been growing rapidly across sectors such as finance, healthcare and retail.

Even companies in the manufacturing sector have found great potential in data visualization. Fastenal, a logistics and technology company, partnered with MachineMetrics, an IIoT-analytics platform, to provide real-time visualizations of production data for the entire production floor in eight of its facilities across multiple continents. Joe Garteski, operations manager at Fastenal, said, “Getting the data out of our previous system wasn’t easy, and the dashboards weren’t especially legible. That was what started us on MachineMetrics. There was no clutter. The ease of reporting was a huge factor. Our previous system was just barcodes, whereas MachineMetrics visualizes the data in a way that makes it so easy to use, it’s just plug and play.”
 

TechDogs Takeaway:
 

• Ensure your data visualization solutions are tailored to your audience’s needs, enabling even the most complex data to be communicated in a clear, concise and accessible manner.

• Adopt scalable and flexible data visualization tools that can handle and process vast and diverse amounts of data without losing clarity, quality or performance.

• Go for solutions that can evolve with diverse data and changing processes. It's vital to leverage AI-driven and automation-capable tools to access real-time insights, anomaly detection and predictive analytics.

 

Trend 5: Cloud-Native Analytics Will Garner Business Success

Spoilers: While cloud architecture became Hydra’s Achilles heel which allowed Captain America to defeat them (in the literal clouds), the technology was also instrumental in propelling them to the forefront of innovation.


Today, the cloud has become an integral part not just of data analytics processes but also of modern business operations. As per a study by Exploding Topics, around 60% of corporate data is stored in the cloud and has doubled since 2015. Cloud environments offer many benefits including scalability, flexibility, accessibility and cost-savings by reducing the need for expensive on-premises infrastructure. As data analytics finds it revolutionized by AI, ML, real-time analytics, data democratization and other technologies, cloud-native platforms will become the norm, as they simplify and enhance real-time collaboration, innovation and productivity. Furthermore, they offer better interoperability with edge and IoT devices to process and leverage data in real time.

Research by MarketsandMarkets finds that the Cloud Native Applications market size is forecasted to grow at a CAGR of 23.7% from 2023-2028 and reach a valuation of $17 billion by 2028. These include platforms such as Amazon’s AWS, Google Cloud and Microsoft Azure, which offer comprehensive data analytics, storage, processing, visualization and other services, enabling businesses to use the full potential of their data.

Such was the case with a major company in city gas distribution in India, which was facing issues with its data management framework. Consultancy firm Ernst & Young (EY) was hired to help them improve their data science operations, which included using cloud-native Microsoft Azure for data mining and analytics. The improvements led to a senior executive at the company saying, “With the help of the solution on the Azure platform we will now be able to Integrate IT and OT data, derive insights easily and have access to real-time insights to respond swiftly and achieve superior business results.”
 

TechDogs Takeaway:
 

• Choose cloud computing platforms that can easily scale up or down based on your requirements, cater to varying workloads.

• Pick data analytics solutions that support diverse data environments (multi-cloud, hybrid cloud) and can easily connect with existing platforms as well as emerging technologies.

• Ensure that your cloud-native analytics platform offers robust security features, encryption, access controls and compliance with industry-specific regulations.

 

To Sum It Up

If data is the engine that runs today’s corporate world, then data analytics is the fuel that keeps that engine running. If businesses wish to compete at the highest levels, they must ensure they adapt to the latest data analytics future trends that will dictate the future of the industry. From the wonderful capabilities that AI and ML will bring into the process, to the effectiveness of real-time analytics and the added enhancements of data democratization, data visualization and cloud infrastructure, these trends will play a major role in the successful use of data analytics.