The impact of data analytics as a powerful decision-making tool has increased across industry domains in recent times. Enterprises that are not using data-driven insights are losing their competitive advantage. The global market for Big Data analytics is poised to reach $349.56 billion in 2024.

According to Gartner, by 2026, chief data and analytics officers (CDAO) will partner with CFOs to drive extended business value in their enterprises. Business leaders in data and analytics are embracing AI-enabled tools to automate and improve their business productivity. 

Here’s a look at 6 technology trends in the data and analytics space:


  • AI-powered data analytics

It’s not surprising to see the growing role of AI and machine learning in data analytics. While traditional analytics require the expertise of a data scientist or engineer, AI-powered analytics can extract insights from data streams without any human intervention.

Among the popular use cases, AI in data analytics enables enterprises to understand customer behavior and personalize their offerings. Product companies are optimizing features based on inputs from AI-powered analytics. 

With more sophistication, AI-powered analytics can be used across use cases like:

  • Predictive analytics
  • Anomaly detection
  • Customer segmentation


  • Edge computing

Edge computing is another exciting trend that is accelerating the pace of data analytics. Gartner estimates that by 2025, over 50% of enterprise data will be generated outside their data centers and the cloud. 

With this technology, data processing is coming closer to smart devices,  thus reducing latency. Besides security, edge computing also speeds up data analysis. In data-intensive industries like healthcare, edge computing can facilitate real-time analytics for immediate decisions.

Similarly, as IoT devices generate more data, edge computing can help process this data in milliseconds. Here’s an example of how the U.S. postal service is leveraging edge computing to generate analytics and locate missing packages.


  • Augmented analytics

In 2024, data analytics is combining the power of AI, machine learning, and natural language processing (NLP) to deliver augmented analytics. With this technology, non-technical users can also work with data using natural language. 

With augmented analytics, companies can use AI platforms to make sense of unstructured data. For example, phone calls. Here’s how Ohio-based Universal Hospitals are using augmented analytics to monitor and understand over 400,000 customer calls each month.

Augmented analytics can lead to a wide variety of use cases including:

  • Price optimization
  • Demand forecasting
  • Customer behavior


  • Data observability

More enterprises rely on data-driven insights to improve their decision-making. 64% of teams believe data-driven marketing is critical in the modern business landscape. 

For real-time insights, companies need to monitor and access data across the entire lifecycle. Data observability is the latest trend that can monitor data quality, reliability, and performance. It also ensures compliance with industry regulations about data security and privacy.

With effective data observability, companies can obtain relevant insights and improve their decision-making process. It also enables teams to quickly identify data errors through anomaly detection and real-time monitoring.


  • Data-as-a-Service (DaaS)

With more generated data, enterprises must utilize this data for their competitive advantage. However, the challenge is that every enterprise cannot store and analyze data. This is why cloud-enabled Data-as-a-Service is becoming popular. 

The DaaS business model enables on-demand data whenever needed by the company, irrespective of their location. Hosted on the cloud, DaaS provides real-time data services to customers on a subscription basis.

An example of a DaaS provider is Snowflake which allows its customers to store and analyze data on its cloud platform. The DaaS model is suited for enterprises with:

  • Multiple business units or functions
  • Distributed geographical locations


  • Data fabric

More companies are now moving to hybrid multi-cloud environments, thus adding to data complexity and variety. This makes it more challenging for them to extract real-time data insights. To address these challenges, data fabric can connect data across multiple systems and create a unified view.

According to Gartner, data fabric can reduce:

  • Data integration time by 30%
  • Data deployment time by 30%
  • Data maintenance time by 70%

Among other benefits, a data fabric can:

  • Meet the business need for real-time data insights.
  • Ensure proper data governance and management.
  • Prepare data and train models for implementing AI and machine learning algorithms. 

Data Analytics Challenges in 2024

Companies continue to adopt data analytics thanks to its ability to extract actionable insights from raw unstructured data. They are also keen to keep improving their data analytics for future requirements. Having said that, it’s not easy to implement data analytics.

Here are some of the major data-related challenges in 2024:

  • Rising data volumes in industries like financial services and healthcare – This makes it challenging to extract insights from massive datasets and data formats including customer data, phone calls, and transactional data.
  • The presence of legacy and siloed systems – this hampers data integration from disparate sources, thus requiring a cohesive strategy to eliminate data silos.
  • High data latency – causes delays in data-driven decision-making, thus limiting companies from taking immediate action on the data insights.
  • Growing concerns about data security – Modern enterprises continue to face the threat of cyberattacks and data breaches in 2024, thus requiring them to implement effective cybersecurity practices.



Data and analytics continue to influence business decision-making in 2024. We are also seeing the emergence of technology trends like AI-powered analytics and edge computing. Despite the recent advancements, organizations face data management and governance challenges.

With its data management service, Trinus enables its customers to effectively manage data in a multi-cloud environment. With our services, you can simplify and optimize your data for efficient analytics. Our Business Intelligence & Analytics service can power your company to leverage data analytics.

If you are looking for a reliable technology partner, contact us now.