Top 10 Analytics Trends for 2024

Where 2023 saw the emergence of many new technologies, 2024 will be instrumental in testing their market-proven gains. Natural Language Processing (NLP) became a household technology, thanks to ChatGPT. Large Language Models (LLMs) enjoyed their time in the limelight. Generative AI took center stage enabling everyone with content creation skills. The rush to leverage these technologies also gave rise to important discussions on AI bias, ethical and responsible AI, data security, proprietary data, sustainability, and high costs of training models. After a year of running proof of concepts and low hanging use cases, 2024 will actually determine which of these newer technologies bring in significant gains in terms of insights, productivity, and economy.

Here are the top 10 trends that we predict will influence data analytics and business intelligence in 2024.

1.Analytical platforms to provide actionable insights

AI has boosted the capabilities of business intelligence (BI) platforms to process data, extract insights, and present them in an easily understandable manner, in line with the prevailing analytics trends for 2024. Organizations will start rationalizing platforms that provides integrated capabilities of business intelligence along with the advantages of AI. Look for integrated platforms that offer advanced AI-powered insights along with AI capabilities such as automation, deep exploration, natural language processing, and generative AI content.

2. Business user at the center of decision making

The rise of business technologists, low code/no code platforms, and intuitive search interfaces have contributed to empowering business users. More and more organizations will start keeping business users at the center of their technology investment decisions. Efforts will be made to bring business users closer to data, drive insight-driven decision making, and make data warehouses and data stores consumable for them with newer age technologies

3. Self-service analytics with NLP will promote data consumption

Natural Language Processing (NLP) has facilitated the inclusion of a diverse range of users – whether technical or non-technical, analysts or business decision-makers, boardroom executives or in-store staff – within the realm of data analytics and business intelligence. This underscores the significance of self-service analytics with NLP. With its ability to interpret human language, NLP has enhanced access to insights and streamlined interactions with data. User-friendly interfaces and self-service analytics capabilities further empower users to explore data and consume insights in an intuitive manner.

4. Dashboards to take a backseat

The role of dashboard will remain limited to periodic reporting for slow changing use cases. AI-based technologies are improving data conversations and making answers and actionable insights readily available in real time. Business decision makers are no longer dependent solely on dashboards to gain insights. Dashboards will serve more as a medium to view high-level statuses, instead of a means to answer crucial business questions and make strategic decisions.

5. Cloud is table stakes in a data ecosystem

This trend has been topping the charts consistently in the recent years and will continue to do so in 2024. According to an IDC forecast, worldwide spending on the whole cloud opportunity (offerings, infrastructure, and services) will surpass $1.0 trillion in 2024 while sustaining a double-digit compound annual growth rate (CAGR) of 15.7%. Rapid development and adoption of cloud-based solutions, increasing role of automation, and the cutting-edge advances in AL and ML technologies are driving the movement of data to the cloud. Cloud will become the default choice to collect, store, process, and analyze data. By 2024, Gartner expects 50% of new system deployments in the cloud will be based on a cohesive cloud data ecosystem rather than on manually integrated point solutions.

6. Embedding of Insights in Workflows

The embedded analytics market is expected to grow at a compound annual growth rate (CAGR) of 14.70% by 2030, is at the forefront of embedded analytics trends for 2024. Through rich embedding of intelligent search with NLP and AI-powered insights, business users get insights at the point of decision making in their existing enterprise workflows such as emails, ERP, CRM, or Operations. A McKinsey report predicts that by 2025, data will be embedded in every decision, interaction, and process. Daily business workflows like tracking sales leads, optimizing inventory levels, reviewing marketing plans, or verifying credit ratings will come equipped with seamlessly integrated insights and promote data-driven decision making.

7. Separation of myth vs. reality about LLMs and Generative AI

In 2023, the hype around Large Language Models (LLMs) forced many organizations to join the mad rush for leveraging LLMs for their business workflows. After a year of experimenting and encountering hallucinating behaviors, the myths about LLMs and generative AI will clear out to reveal their actual applicability. Organizations will realize the limitations of applying LLMs in their functional use cases. NLP based search will emerge as the better technology in terms of accuracy, usability, and efficiency for search-based business intelligence and data analytics.

8. Focus on specific LLM use cases

Considering the shortcomings of LLMs in terms of cost, risks, and customizability, organizations will start testing proof of concepts on defined and limited LLM use cases, aligning with current data analytics trends. Typical low hanging use cases may include workflows in customer service and contact centers, followed by marketing, content creation, and knowledge management. The rise of domain-specific and fine-tuned small language models promise better efficiency and customizations to handle unique use cases specific to an organization.

9. A multi-cloud strategy is the best bet for the future

Organizations continue to explore multiple cloud to deploy and distribute their workload. According to a Forbes report, the number of large organizations with a multi-cloud strategy is predicted to rise from 76% to 85% during 2024. Multi-cloud strategy provides flexibility to manage and secure data over multiple public, private, and sovereign clouds. It helps address the concerns of regulatory compliance and prevent being locked in with a single cloud provider. A Virtana research found that more than 80% of enterprises have a multi-cloud strategy and 78% use more than three public clouds. As businesses requirements from data evolve, a multi-cloud approach allows organizations to explore and assess different cloud infrastructures that match their industry-specific requirements.

10. Deeper technical focus on newer technologies

With NLP search technology poised to become the common interface for all data analytics platforms, organizations will focus on granular functional and technological differences while evaluating competing products. Data analytics platforms offering advanced analytics capabilities, seamless data connections, automated data catalogs, integrated actionable insights, data quality assurance, AI-powered data visualizations, generative content capabilities, and secure data governance practices will emerge as winners.

It is definitely an exciting era for data analytics as newer technologies improve the access to data, democratize insights, and simplify analytics for everyone. As we step into 2024, we hope these trends guide you into making effective decisions and creating optimal strategies for adopting data analytics in your organization.

MachEye integrates the powers of Generative AI, Natural Language Processing (NLP), and Machine Learning (ML) technologies to understand simple language search queries, identify user’s intent behind a query, and provide instant contextual answers with actionable insights. With its intelligent search, interactive audio-visuals, and actionable insights, MachEye offers a modern analytics experience that simplifies analytics, gives more control to the users, reduces time-to-insights, promotes a data-driven decision making culture, and integrates seamlessly in existing applications.