The goal of XOps (data, machine learning, model, platform) is to achieve efficiencies and economies of scale using DevOps best practices - and to ensure reliability, reusability and repeatability while reducing the duplication of technology and processes and enabling automation. Small data, as the name implies, is able to use data models that require less data but still offer useful insights. Wide data - leveraging “X analytics” techniques - enables the analysis and synergy of a variety of small and varied (wide), unstructured and structured data sources to enhance contextual awareness and decisions. Small and wide data, as opposed to big data, solves a number of problems for organizations dealing with increasingly complex questions on AI and challenges with scarce data use cases. Plus, data fabrics can leverage existing skills and technologies from data hubs, data lakes and data warehouses while also introducing new approaches and tools for the future. 3: Data fabric as the foundationĪs data becomes increasingly complex and digital business accelerates, data fabric is the architecture that will support composable data and analytics and its various components.ĭata fabric reduces time for integration design by 30%, deployment by 30% and maintenance by 70% because the technology designs draw on the ability to use/reuse and combine different data integration styles. Not only will composable data and analytics encourage collaboration and evolve the analytics capabilities of the organization, it increases access to analytics.ĭownload IT roadmap: Data and Analytics Trend No. Gartner client inquiries suggest that most large organizations have more than one “enterprise standard” analytics and business intelligence tool.Ĭomposing new applications from the packaged business capabilities of each promotes productivity and agility. The goal of composable data and analytics is to use components from multiple data, analytics and AI solutions for a flexible, user-friendly and usable experience that will enable leaders to connect data insights to business actions. These AI systems must also protect privacy, comply with federal regulations and minimize bias to support an ethical AI. This means that AI technology must be able to operate with less data via “small data” techniques and adaptive machine learning. Organizations will begin to require a lot more from AI systems, and they’ll need to figure out how to scale the technologies - something that up to this point has been challenging.Īlthough traditional AI techniques may rely heavily on historical data, given how COVID-19 has changed the business landscape, historical data may no longer be relevant. Smarter, more responsible, scalable AI will enable better learning algorithms, interpretable systems and shorter time to value. ![]() 1: Smarter, more responsible, scalable AI
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