Top 3 Data and Analytics Trends to Watch in 2024

The ubiquity of data, analytics, and a knowledge-driven work ethic has been the hallmark of business since the turn of this millennium. It coincided with the rise of the internet. Increasingly, businesses and organizations are finding great value in the data they generate and collect. So much so that many use the phrase: data is the new oil. Data and applications are becoming the lifeblood of business, and rapid innovation in IT technologies is helping enterprises evolve and thrive in the digital age.

A growing majority of today’s business leaders believe in the power of data-driven insights to steer decision-making and drive profits. Most have or are implementing data-focused organizational changes, with 98.2% projecting that their data initiatives will deliver a return on investment in 2023. With data analytics and artificial intelligence (AI) becoming more and more central to enterprise operations, 69.4% of those organizations assert that data analytics is now part of their Chief Data Officer roles.

However, even as data and analytics are more infused with decision-making across all levels of an organization, few businesses seem to accurately understand the stakes involved, as 90% of IT officers believe leaders in the boardroom compromise data security and best practices for the pursuit of other goals.

Looking at the big picture, it’s clear that data and analytics have transformed the way business is done. A recent McKinsey study titled The Data-Driven Enterprise of 2025 contends that, in the very near future, “smart workflows and seamless interactions among humans and machines will likely be as standard as the corporate balance sheet, and most employees will use data to optimize nearly every aspect of their work.”

Data makes the world go round. Powerful advances in data collection technologies and business intelligence platforms help companies keep better track of market variables. Insights from this kind of data help leaders make informed decisions and grow their businesses. There are three critical trends in data and analytics that businesses will be keeping an eye on in 2024 and beyond:

1. Industry-Wide Readiness for AI and Machine Learning

It seems like yesterday when marketing automation burst into the market and simplified large swathes of branding and promotion activities. Similarly, most analytics tasks are now automated, leaving data scientists free to interpret and apply their learnings to offer more value to their organizations.

Recent Gartner studies show that by 2025, AI-based data models will replace 60% of existing traditional models. Additionally, infrastructure design will reflect these changes as well, as 50% of enterprises –up from 10% in 2021 – will develop capabilities for infrastructure automation in hybrid and multi-cloud environments.

Increasingly, organizations will offload to AIs and automate a host of data tasks such as data cleansing, generating weekly reports, multivariate testing, and applying predictive analytics. Many businesses are already using AI-powered tools like Tableau for complex data analysis and data visualization.

Brady Brim-DeForest, CEO of TheoremOne, explained in an article for Forbes that, “Companies across industries are exploring ways to leverage AI to enhance their operations, increase productivity and gain a competitive edge.” Brim-DeForest goes on to say, however, that not every company is ready to take advantage of AI and machine learning (ML).

While AI/ML readiness is still in its early stages for many organizations, the coming years will see a trend in entire industries becoming pervasively ready to seamlessly adopt and implement AI tools.

Brim-DeForest asserts that “The rise of generative artificial intelligence is transforming the business landscape.” This is true not only in regard to data analysis tools but also in general-use AI software like ChatGPT that any individual or organization can utilize for a wide range of tasks. “AI readiness requires a combination of factors, each as important as the next,” Brim-DeForest goes on to say.

The specific factors that Brim-DeForest recommends that enterprise leaders evaluate to determine AI readiness are:

• Data integrity and accuracy
• Implementation of the right tools
• A healthy cloud ecosystem
• Liability and data protection for AI-specific risks
• A strategy for building an AI stack

With each passing month, IT teams become more skillful in utilizing AI tools and third-party service providers become more competent in supporting AI/ML initiatives for their enterprise partners. It will soon be the case that when organizations require more efficient data analytics solutions, they will be ready to pivot toward AI/ML as the natural course of action. 

2. The Shift to AI Computing at the Edge

Edge computing is an emerging trend in its own right, and it entails the placement of computational processes closer to the end user and the actual sources of data. This can encompass the placement of AI computing as well.

NVIDIA defines edge AI as, “the deployment of AI applications in devices throughout the physical world.” The edge of a network can refer to practically any location, but the benefit of performing AI computing at the edge stems from the proximity of an edge location to the user compared to an organization’s on-premises datacenter or a distant remote cloud. 

“The move to edge computing presents a significant shift in the way businesses deal with data and how decisions are made,” said Peter van der Made, founder and CTO of BrainChip Ltd. “Reduced latency, eliminating annoying wait times, higher power efficiency, interacting directly with locally generated data and increased security are [factors in edge computing], as well as availability in areas with no or limited internet availability.”

The question remains as to why the shift to AI computing at the edge is occurring now. According to NVIDIA, this is due to advances and innovations in AI technology that only emerged recently:

• Maturation of neural networks
• Advances in compute infrastructure
• Widespread adoption of IoT devices

This all goes back to the importance of data analytics in business. Data itself has the power to shift a business’s center of gravity. Data exists where the user is, so it is only natural that business will shift to the edge if it is capable of doing so.

“Over the next few years, many new products will emerge equipped with edge AI to enable advanced functionality in portable and battery-operated handhelds and wearables,” according to van der Made. In regard to data analytics, though, van der Made expresses concern for challenges that organizations must overcome. “Is training data available, and what data engineering tasks must be undertaken to make the data suitable for neural network training and verification?”

With proper preparation, enterprise leaders can be ready to fully utilize edge AI for data analytics, even if trends soar beyond expectations. “the effort is well worth it when it strengthens the company's competitive position in the market and leads to increased sales.”

3. New Application Development Closer to Data Sources

While edge AI brings the latest ML-powered computational solutions closer to data sources, there is also a demand for more and more of the application development process to occur near the source as well. This enables increasingly data-driven software development in which observable data informs decisions such as user experience design, quality assurance processes, and more.

“Depending on a startup’s needs, one possible way to adapt to the times is to consider shifting application development to edge computing,” said Rafael Umann, CEO of Azion. “If organizations are struggling with security, customer satisfaction, cost or achieving the flexibility needed to make big changes, edge infrastructure may have a transformational impact.”

Umann proposes that one key consideration when shifting application development is to adopt a multi-vendor strategy. “ If a provider relies heavily on proprietary services and doesn’t support open standards, moving your applications to another platform will be expensive and time-consuming.”

This is where the amalgamation of AI and hybrid cloud can make a noticeable difference in bringing development closer to the data source. 

“Leveraging AI to more efficiently and effectively help our customers is a top priority for us but, as a regulated financial services organisation, maintaining full control over our data is necessary," sand Jon Cosson, CISO at JM Finn in a recent press release.

With the efficiency of artificial intelligence and the flexibility of the hybrid cloud, organizations can quickly and seamlessly extend application development capabilities from an on-premises datacenter to the public cloud. From there, those capabilities can shift to other clouds or edge locations where the relevant data exists.

Application development in the hybrid cloud benefits from agility, elasticity, and cost control measures that are not as easily accessible in traditional environments. Additionally, developers can gain access to crucial infrastructure and service resources through cloud-based self-service portals powered by the latest innovations in AI and automation.

Umann also proposes that moving to the edge by way of a multicloud setup can help with latency, outages, and regulatory compliance concerns. 

“While the edge is not a panacea that will fix every challenge companies are currently facing, it could be an important strategy to ease certain common pain points,” Umann explains. “And because edge computing is a prerequisite for technologies like modern facial recognition and cashier-less point-of-sale systems, embracing the edge now could offer new benefits over time.”

Staying Ahead of the Data Analytics Curve

Technology evolves fast in today’s IT landscape, but data grows even faster. There is an explosion of data in the world that decision-makers can harness and utilize if only they have the means to optimize, manage, and present that data meaningfully.

With all the effort that goes into capturing, storing, analyzing, and interpreting data, companies look for best-in-class analytics because they understand that business analytics is a critical enterprise asset.

That’s more than can be said for most business processes. As the world rapidly moves towards a data-first approach, it helps to know that smart bets on data and analytics can keep businesses ahead of the curve, the competition, and the threat of obsolescence.

Learn more about the role data analytics can play in IT sustainability.

This is an updated version of the article originally published on September 21, 2022. 

Michael Brenner is a keynote speaker, author and CEO of Marketing Insider Group. Michael has written hundreds of articles on sites such as Forbes, Entrepreneur Magazine, and The Guardian and he speaks at dozens of leadership conferences each year covering topics such as marketing, leadership, technology and business strategy.

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