Businesses need to respond to all kinds of stimuli in real time to become a live enterprise. They learned this more than ever during the pandemic. Intelligent gathering, cleansing, storing, and using real-time data are crucial to ensure that technologies ranging from artificial intelligence (AI) to cybersecurity work seamlessly and deliver desired results.
Data platforms transforming into business growth enablers
Modernization and cloud adoption were earlier known to enhance cost-saving efficiency, but now they enable agility and connectivity to data as well. Platforms with packaged insights address specific business needs such as next-best recommendations, and off-the-shelf and/or custom-developed iterations are gaining popularity.
HTAP for faster insights
HTAP is an emerging application architecture that combines transaction processing and analytics within the same datastore. This trend has come in the spotlight with recent advances in research, hardware, in-memory, and cloud-native database technologies.
AI-driven data engineering to increase the velocity of innovation
Agility is critical to reduce time to market and remain competitive. While most organizations have adopted agility from a process perspective, data engineering techniques largely follow traditional ETL frameworks. They still rely on a requirement-driven approach, increasing the cycle time involved in provisioning new data for analytics needs.
Marketplaces helping businesses to democratize data consumption
Enterprises are leveraging marketplaces for the internal democratization of data consumption. This provides visibility on available enterprise data assets, enabling discoverability, collaboration, and consumption in a self-service manner. Marketplaces also enable a secure data exchange outside enterprise boundaries, critical for realizing the vision of a connected enterprise.
Sentient systems connecting AI/ML to business processes for better insights and actions
Sentient systems take AI/ML and insights generation to the next dimension by integrating with business processes to incite humans to action or drive autonomous decision-making. Real-time event sensing, contextual event processing, and intelligent decisions and actions are the key capabilities of a sentient system.
Proactive smart governance through AI-first
There is a shift from reactive and rule-based data governance to an end-to-end autonomous “no governance” ecosystem, leveraging the principles of AI-first, cognitive, and governance by exception. Smart data discovery, data tagging, DQ assessment, DQ rule discovery, relationship discovery, and automated cleansing enable smart governance.
Intelligent cloud-based data operations to increase operational efficiency
As increased cloud adoption has scaled up ondemand infrastructure, the focus is now around intelligent orchestration of infrastructure-ascode capabilities to drive operational efficiency. Capabilities such as leveraging ML-based techniques to predict capacity needs, identifying anomalies, and self-healing platforms will define the route of future data operations.
Growing data usage and privacy regulations call for unified rules
Growing data privacy regulations and data breaches are increasing the cost of privacy compliance, protection monitoring, and management. Advances in data-centric services have fueled the demand for better data privacy. As sentience and intelligence are increasingly embedded almost everywhere, enterprise and consumer advocacy groups have been asking for clearer rules to protect personal data and individual privacy.
Privacy-first modernization, driven by increasing cloud transformations
Cloud transformation and modernization offer significant opportunities for privacy-first app development. Organizations are looking to deliver high-quality applications at minimum cost. They need a test data management (TDM) strategy that supports waterfall and agile delivery models. With the rapid adoption of DevOps and increased focus on automation, the need for data privacy has grown immensely.
Cloud data validation for reliable data clouds and lakes
Data errors and inconsistencies accumulate, with data moving in or out of the cloud (or data lakes). Therefore, the lack of proper cloud data validation is an existential threat to data-sensitive organizations.
Developing end-to-end, self-service test data management
Organizations have shown increased interest in TDM in recent times, as they realize that proper test data can prevent financial losses caused by production defects. Test data has evolved from a few sample files to powerful test data sets with high coverage.
Securing data across the value chain, from origination to consumption
With enterprise boundaries fading, most of the enterprise data is either on public or private clouds. Further, with remote working, global teams, and increased cloud adoption, it is crucial to protect applications and data and the channels connecting to them. Digitization has also increased third-party and partner collaboration, leading to sharing of unstructured data.
Cloud access security brokers for enhanced data protection
Enterprises can now focus on core capabilities, with cloud adoption easing data storage concerns. Elevated concerns toward privacy and security breaches have increased the demand for cloud security solutions. That said, the prominence of cloud access security brokers (CASBs) is gaining traction. The global CASB market is estimated to expand at a compound annual growth rate of 18.2% during 2019-2025.
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