In today’s fast-paced world, organizations of all sizes have recognized the importance of data in driving success. With the rise of data analytics technologies such as predictive analytics, organizations can harness the power of data to gain valuable insights and make data-driven decisions. According to research by industry experts, the global predictive analytics market is expected to reach $2,245.33 million by 2028, growing at a CAGR of 2.34% from 2022 onwards (Source: industryresearch.co).
The Power of X-Analytics:
At the core of predictive analytics is the concept of X-analytics, a term introduced by Gartner to collectively describe small, wide, and big data. X-analytics highlights the importance of leveraging all types of data to build a comprehensive picture and make informed decisions. By 2025, Gartner predicts that 70% of organizations will be compelled to leverage available data more effectively, either by reducing the required volume or by extracting more value from unstructured and diverse data sources (Source: Gartner). This shift towards X-analytics highlights the growing need to extract insights from different types of data, allowing organizations to make better decisions, with a more holistic view of information.
Augmented Analytics: Unlocking the Potential for All
Another trend that is gaining momentum in the data analytics space is augmented analytics. Augmented analytics combines AI with human intelligence to provide advanced analytics capabilities that are easier for non-technical users to access complex data. This democratization of data ensures that everyone within an organization has the ability to access and understand the insights hidden within the data. As such, experts predict that the augmented analytics market is set to reach $32.64 billion by 2027, with a CAGR of 25.71% (Source: globenewswire.com).
The Role of Data Fabric in Enhancing Data Management:
The use of data fabric is set to become increasingly important for organizations aiming to improve their data management. Data fabric is an intelligent metadata-driven approach to optimizing data management processes within organizations. It allows for the effective listening, learning, and acting on metadata, allowing for the reduction of various data management tasks by up to 70% (Source: Gartner). Consequently, organizations that utilize data fabric can build robust data pipelines, leverage innovative data analytics techniques and use AI cloud services to process different data types in order to gain actionable insights.
Context-driven Analytics and AI: Looking to the Future:
As more types of data become available, it is important to capture and utilize contextual data effectively. By 2025, it is anticipated that context-driven analytics and AI models will replace 60% of existing models that are built on traditional data (Source: Gartner). Organizations need to leverage X-analytics techniques and AI cloud services to process and analyze different data types in real-time to gain accurate insights. This approach enables the development of more precise models that enhance decision-making capabilities based on real-time insights.
Embracing Decision Intelligence:
Organizations gaining insights from data analytics must understand the importance of decision intelligence. By 2023, more than 33% of large organizations will have analysts practicing decision intelligence, including decision modeling, predicts Gartner (Source: Gartner). Decision intelligence is the process of combining advanced analytics, AI algorithms, and domain expertise to optimize decision-making processes across an enterprise. This approach enables organizations to make more informed and effective decisions, driving growth and opportunity.
Building Trustworthy and Purpose-driven AI:
As progress in AI technology continues, trust is becoming increasingly important for successful implementation. By 2026, Gartner estimates that organizations that develop trustworthy and purpose-driven AI models will see over 75% success rate in their AI projects (Source: Gartner). Building transparent, explainable, and ethical AI models is now essential to gain trust and maximize the potential of AI technologies.
The Rise of Edge Computing:
As digital processes continue to persist in more areas of the enterprise, Gartner analysts predict that more than 50% of enterprise-critical data will be created and processed outside traditional data centers or the cloud by 2025 (Source: Gartner). Edge computing enables real-time analytics at the edge of the network, reducing latency and enhancing decision-making capabilities. Organizations need to adapt to this shift by leveraging edge computing technologies and developing robust data strategies that encompass both centralized and decentralized data processing.
Conclusion:
To drive growth, organizations need to embrace predictive analytics guided by humans. Organizations should adapt to emerging technologies and trends like X-analytics, augmented analytics, and decision intelligence to form better and more informed decisions. Building trustworthy AI models and capitalizing on the capabilities of edge computing will further enhance the power of predictive analytics. The potential of data and analytics can be optimized by following these trends and embracing the future of data-driven decision-making—a future guided by humans.
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