In 2023, a staggering 91.9% of organizations said that they achieved measurable value from data and analytics investments. But it’s not just about reaping benefits; it’s also about driving innovation. About 3 in 5 organizations are using data analytics to fuel business innovation.

Enterprise’s inclination towards using data analytics to their benefit is also paving the way for new analytics models. For example, a study forecasts that the Analytics as a Service (AaaS) sector will reach $68.9 billion by 2028, up from $18.9 billion in 2022.

As enterprises look to further their data analytics investments, it’s important for them to be aware of the trends in the space and how they can help drive greater innovation and transformation. To that end, here are five trends to watch for in the enterprise data analytics space:


1. AI in Data Analytics

For a while, Artificial Intelligence (AI) has loomed on the horizon; however, its integration into data analytics is now becoming more apparent. “AI Analytics” can bring automation and predictive capabilities to data analytics, enabling businesses to gain deeper insights from their data. These technologies can unearth patterns and trends that would be otherwise difficult for humans to detect, leading to more accurate forecasts and improved decision-making.

AI in analytics lays the groundwork for “Augmented Analytics.” Augmented analytics is devoid of the need for constant data scientist intervention. Instead, it scours data for patterns and uncovers valuable insights through the power of artificial intelligence and machine learning.

The surge in generative AI solutions is also lending itself well to the enterprise data analytics space. Generative AI streamlines the process of data cleaning and organizing datasets and serves to democratize access to data for more informed decision-making.


2. The Increasing Importance of Real-Time Data Analytics

Real-time data analytics is now a requirement for most enterprises as it allows them to respond promptly to changing market conditions, customer preferences, and emerging trends. It also helps them resolve business challenges in real-time.

For instance, financial institutions leverage real-time analytics to detect fraudulent transactions as they unfold. Similarly, eCommerce companies employ real-time analytics to fine-tune pricing and inventory management in response to fluctuating customer demands.

It also helps in bridging the gap between business and IT environments. For example, a real-time analytics model can streamline IT operations by instantly alerting system administrators to any issues that occur. It can even help prevent costly incidents before they happen.


3. The Shift to Cloud-Based Data Analytics Platforms

As Milind Govekar from Gartner puts it, “You can’t really have a business strategy without a cloud strategy.” The cloud has become an indispensable foundation for strategic innovation and growth for enterprises. It’s projected that by 2025, a staggering 85% of organizations will adopt a “cloud-first” approach.

Cloud-based data analytics platforms have become increasingly popular as they allow customers to access a wide range of analytics capabilities from a single interface. Moreover, they are accessible from anywhere and everywhere on any device.

The shift to the cloud for running analytics initiatives proves immensely viable for enterprises as they can:

  • Efficiently consolidate disparate data for more accurate insights
  • Alleviate departmental fragmentation and allow people from different departments to extract insights
  • Reduce the costs that are otherwise related to the on-premises setup
  • Scale their analytics workloads as needed


4. Self-Service Analytics

The idea of democratizing access to data and toning down the dependence on core IT or BI experts seems enthralling to enterprises, and rightly so. In a 2020 study, about 90% of organizations affirmed that their success was dependent on data-driven decisions taken by frontline employees.

Now, with approaches like citizen data science becoming more pervasive than ever, it’s highly prudent that enterprises drive self-service analytics across departments and democratize knowledge across the board.


5. The Growing Focus on Data Governance & Compliance

Data governance takes precedence for 60% of data leaders, especially as data governance and compliance gain major importance amid growing data privacy regulations and concerns.

Companies need to recognize the need to establish robust data governance frameworks to manage data effectively. This includes defining data ownership, implementing data security measures, and ensuring compliance with the applicable data protection regulations like GDPR and CCPA.

Data governance not only mitigates legal and reputational risks but also enhances data quality and trustworthiness. Businesses prioritizing data governance will be better positioned to utilize the full potential of their data assets while preserving customer trust.


Wrapping Up

Technological advancements and shifting business needs drive the evolving world of enterprise data analytics. As we move forward, businesses that adapt to the aforementioned trends will be better positioned to unlock the full potential of their data, drive innovation, and gain a competitive edge in their respective industries.

At Trinus, we help enterprises harness the power of data analytics to create new value and enhance their competitive position. With our suite of analytics services, including dashboard development, data visualization, data science, machine learning, and more, we can position you to gain actionable insights and take steps for more effective decision-making.

Don’t miss out on the transformative power of data analytics. Reach out to us today to learn more!