Shaping Tomorrow’s Analytics: Trends For 2024 And Beyond
Explore 2024 analytics trends: AI-first, developer-centric ecosystems, and AI-analytics convergence.
Ayala Michelson, chief product officer at Sisense, writes about the transformative trends in analytics for 2024 and beyond, featuring AI-first strategies, developer-centric ecosystems, and the convergence of AI and analytics.
The realm of analytics is undergoing a profound metamorphosis, transitioning from data-first strategies to AI-first experiences based on a large data foundation as its component. GenAI and other disruptive technologies are catalysts for a reimagined future, pushing the boundaries of what can be achieved with data. Amid this transformative landscape, developers emerge as creators, building the real-time applications of tomorrow and accelerating innovation for businesses.
Developer-centric Ecosystem: Shaping the Analytics Future
Today, developers yearn for a resilient and adaptable framework that nurtures creativity. The industry is undergoing a shift towards modular, composable solutions, placing a strong emphasis on API-first functionalities. This transformation is driven by the necessity to simplify intricate processes, empowering developers to concentrate on creating analytics and data experiences in the context of any product or business workflow.
Efforts to leverage data to create meaningful end-user experiences are nothing new. Yet perceptions in the market are lagging behind a little, and many haven’t yet caught up to the fact that most of these end-user experiences are, in fact, analytics-driven. People still associate analytics with dashboards and widgets and don’t realize that the same technology also powers well-loved features, such as their streaming service’s personalized recommendations.
But it’s only a matter of time before the market catches up. Many organizations are starting to embrace a holistic approach to embedded analytics, recognizing analytics as a core rather than a secondary feature. Solutions should cater to various development teams, offering diverse coding approaches, including low/no-code, light/limited-code, and full-code, aligning seamlessly with modern application development practices.
API Economy Growth
The surge in the API economy has led to this escalating desire for developer-first analytics. Companies actively seek solutions capable of simplifying complexity, mirroring the transformative impacts of platforms like Twilio and Stripe on communication and payments. This trend highlights the essential requirement for robust solutions customized to meet the specific needs of developers, leveraging cutting-edge technologies, including Generative AI.
Gartner’s foresight echoes the industry’s trajectory, projecting a preference for modular applications delivering functionality through API/event-first business components. By 2025, a significant shift is expected, with 60% of new SaaS designs supporting UI-first and API-first access, highlighting the growing importance of composability in cloud applications. Gartner predicts that 60% of new custom business applications will leverage reusable business services via curated component catalogs or marketplaces.
AI-first Strategies
On the heels of GenAI and LLM disruptions, what can now be achieved with data is being completely reimagined. New technologies are arising, and every company worldwide is sprinting to build AI-first data strategies and experiences.
Leading companies that have already implemented this shift are accelerating their innovation cycles and are already ahead. But things are not as simple as they seem: With all eyes on GenAI, most companies still struggle to adopt AI-first experiences and haven’t yet bridged the gap that would allow them to rely on data, insights, and analytics that are deployable and deliverable at scale and integrated into the context and flow of applications and business workflows. When it comes to AI-readiness, are seeing a bit of the-cart-before-the-horse situation, which rarely plays out well long-term.
AI-First in Action
In 2024 and beyond, the analytics narrative unfolds as a convergence of AI-first experiences and a developer-centric approach. The future is not just about data; it’s about unlocking limitless possibilities through AI, analytics, and the ingenuity of developers. As we stand on the precipice of this new era, one truth becomes evident: the journey forward is as much about the developers shaping it as the technology propelling it.
Conversational AI: Bridging the Gap in Data Literacy
As we explore the transformative alliance of AI and analytics, a standout innovation is Conversational AI. The integration of natural language processing makes AI an integral and seamless part of daily workflows.
Enabling end-users to interact with data in their language and obtain immediate and explainable insights without needing specialized data literacy or understanding complex query languages is a game-changer. This democratizes the use of AI, making it accessible and usable for everyone, irrespective of their technical background. Integrating AI into applications and tools empowers decision-making processes with data-driven insights, making AI a ubiquitous and invaluable asset in everyday tasks.
Empowering Developers: The Democratization of Data Analytics
Through natural language processing (NLP) and no-code interfaces, AI enables users to construct queries, generate reports, and even explore data without deep technical expertise. This democratization of data analytics allows a broader range of professionals to create, customize, and utilize data-driven insights, fostering innovation across various organizational levels.
Moreover, AI is a game-changer for application developers, particularly those with limited experience in data modeling. It automates complex data modeling aspects, enabling easier analytics integration into applications. This approach reduces the learning curve for developers and accelerates the inclusion of data-driven features in applications, streamlining the development process and enhancing efficiency.
Forecasting the Future: Predictive Analytics and Real-Time Business Adaptation
Integrating AI with analytics is about understanding the present and foreseeing the future. By leveraging historical and real-time data, predictive modeling is more accurate and efficient, enabling businesses to anticipate various outcomes, from consumer behavior to equipment failures.
For example, organizations can accurately predict equipment failures by analyzing real-time sensor data and comparing it with historical maintenance records. This predictive insight facilitates timely interventions, reducing downtime and maintenance costs.
Furthermore, AI’s role in real-time data analytics allows businesses to adapt to market dynamics swiftly and internal process changes, ensuring smoother operations and heightened responsiveness. AI in areas like supply chain analytics has become a cornerstone for strategic decision-making, enabling businesses to stay ahead in an ever-evolving landscape.
Prioritizing Responsible AI
As organizations eagerly embrace AI to enhance decision-making processes, the importance of responsible AI cannot be overstated. Responsible AI practices ensure that algorithms and models used in analytics software are fair, transparent, and unbiased. This is crucial in avoiding unintended consequences and maintaining ethical standards. Incorporating responsible AI safeguards against potential risks and fosters trust among users, stakeholders, and customers.
Striking the right balance between innovation and ethical considerations is paramount to building sustainable and impactful analytics solutions. As the capabilities of AI continue to expand, businesses must prioritize responsible AI practices, thus paving the way for a future where technology not only delivers insights but does so in a manner that aligns with ethical principles and societal values.
See more: AI Unleashed: A Guide to Responsible Implementation
A Timeline for Change
Over the next year, businesses will increasingly demand quicker insights, propelling the industry toward a heightened need for real-time analytics. Simultaneously, the acceleration of the merging of artificial intelligence (AI) and machine learning with analytics systems will gain significant momentum. These augmented analytics gain traction and make data analysis more accessible, setting the stage for realistic industry advancements.
Looking ahead over the next three years, quicker insights and augmented analytics trends will serve as the building blocks for more sophisticated developments. The focus will shift towards embedded analytics, poised to transcend its current state and become the new Business Intelligence (BI). This shift signifies a redefinition of BI, integrating analytics seamlessly into daily applications and workflows, fostering a more data-driven culture, and enhancing organizational decision-making efficiency. Simultaneously, a convergence between BI and AI will unfold, driven by the emphasis on real-time data, empowering AI for more proactive and predictive analytics.
The subsequent five years will mark a major industry shift influenced by the impact of quantum computing on analytics. This transformative trend promises to significantly accelerate data processing and complex analysis, unlocking new frontiers for the industry. Each of these progressive phases builds upon its predecessor, emphasizing the necessity of a structured and planned approach for the industry to maximize accessibility and benefits for everyone involved.
An Exciting and Transformative Journey
In the dynamic realm of analytics, the trajectory into 2024 and beyond encapsulates a compelling narrative marked by AI-first experiences and a developer-first environment. This paradigm shift transcends a mere data-first strategy, unfolding as a tale of unlocking limitless possibilities through the fusion of AI, analytics, and the creative ingenuity of developers.
Developers will continue to unleash their creativity to give their customers decision-making confidence through insights into when and where they need them. It’s an exciting journey of innovation and limitless potential for the future.
Have you explored the limitless possibilities at the intersection of AI and analytics? What are your experiences? Let us know on Facebook, X, and LinkedIn. We’d love to hear from you!
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