With each passing day, organizations are realizing the meaning of ‘big’ in big data – and more acutely aware that high volumes of data mean nothing without analytics.
Here are five key trends CIOs should follow now to keep up with the industry:
1. Self-service data analytics
The industry-wide awakening to the need for analyzing relevant data compelled many companies to invest in software that included self-service data analytics capabilities. Such software includes low-code or no-code application builders and data visualization features. Enterprises are using tools, such as Qlikview, Tableau, and Power BI, to democratize their data, foster transparency, and empower decision-makers across the organization.
2. Solutions embedded with AI
Enterprises are increasingly shifting from proof-of-concept AI projects to operationalized AI. AI is helping teams handle complex situations that require analyzing historical data and identifying demand patterns. Chip innovations such as neuromorphic hardware are also enabling IT teams to embed AI/ML capabilities in edge devices. The reduced processing reliance on centralized systems is helping teams reduce latency and accelerate computations and natural language processing (NLP).
3. Decision intelligence
Enterprises are investing in ever more advanced AI capabilities, particularly to augment data science with decision theory, managerial science, and social science. Such decision intelligence leverages the feedback cycle with a framework that designs, develops, operates, evaluates, and manages decisions in a way that improves business outcomes. Such decision intelligence is expected to become a major decision-making tool for 33% of large organizations by 2023, Gartner predicts.
4. Event-driven architecture
Event-driven architecture is a software framework for application design. The programming approach orchestrates the production, detection, analysis, and experience of events. It limits the need for coupling, making it easier to support the modern, distributed application architectures that requires flexibility to adapt to changes and make real-time decisions.
IT teams facing the complex challenge of handling high volumes of data are adopting platforms that automate their IT operations. Such AI-powered platforms are helping teams to monitor their operational and application performance, analyze system events, and troubleshooting. AIOps allow IT teams to remove siloed architecture, simplify configuration complexities, and proactively respond to IT challenges all in near-real-time.