Across various industries, enterprises are realizing they must become digital natives to survive and thrive in the data economy. AI-powered data is driving new possibilities for the digitization of businesses, new types of experiences (collapsing digital or physical boundaries) and new products or services. Data and AI are defining the characteristics of the future-ready enterprise: hyperpersonalization, real-time context, event-driven and intelligence at the edge.
Accelerated migration from proprietary databases and appliances to open source and cloud platforms
With digital transformation of businesses comes huge capital and time investment in new, innovative technologies. Cloud computing is a major trend in digital transformation, as it makes business applications and infrastructure easily accessible.
Increasing adoption of varieties of NoSQL databases
NoSQL, or nonrelational database, adoption is rising as enterprises develop a need to access and analyze large amounts of unstructured data or the data stored in multiple virtual servers in the cloud.
Convergence of transactional and analytical data
Enterprises generally have transactional, analytical and operational workloads across separate data warehouses, data lakes and databases. This leads to data silos, making it difficult to provide real-time analytics and insights without movement of data across these systems.
Shift to streaming data from batch processing
In today’s ever-connected society, enterprises are being bombarded with huge amounts of data from sensors, machines, tracking devices, banking and trading sources, smartphones, social media content, and other “internet of things” devices.
Event processing of device or sensor data
Streaming data provides opportunities for interesting future use cases with AI and event-driven applications, most notably giving rise to various tools and frameworks for building and running scalable event stream processing.
Metadata driven ETL Pipelines fueling agility
Enterprises have countless data sources and need scalable and robust pipelines to maintain data integrity. The ETL development tools generally require expertise with the tool set and can be time consuming and error prone.
Data marketplaces enabling data democratization
In the information age, data experts mainly have been responsible for unleashing the power of data within organizations, as others lacked training to handle the humongous amount of data effectively.
Digital brain to drive hyperpersonalization
According to the Customer Experience Impact survey, 86% of customers are willing to pay more for a better customer experience. This provides organizations an opportunity to stand out from their competitors by delivering hyperpersonalization.
Digital disruption, customer experience and data explosion are the key drivers forcing businesses to reimagine their business processes and adopt intelligent operations.
To become data-driven, enterprises need to apply automation, intelligence and self-service across the entire organization to accelerate business processes and empower all business units.
Regulatory compliance in key domains and across new geographies
Security compliance has become a hot topic following the rise of various regulations including the European Union (EU) General Data Protection Regulation (GDPR), Basel Committee on Banking Supervision’s standard (BCBS) 239 and the California Consumer Privacy Act (CCPA). As shared in a Capgemini report from 2019, the percentage of organizations that are fully GDPR-compliant stands at less than 66%.
Privacy by design and default for applications
Privacy by design includes privacy at the inception stage of new devices, networked infrastructure, IT systems and even corporate policies. Developing and integrating privacy solutions in the initial project phases proactively identifies problems and helps prevent them in the long run.
Cloud Access Security Broker (CASB)
Enterprises now can focus on core capabilities because data storage concerns have been eased by cloud adoption. Behind the increased need for cloud security solutions are major concerns about privacy and security breaches.
Cloud data validation
As data moves in or out of the cloud (or data lakes), data errors and inconsistencies accumulate. This causes less than 40% of data clouds (and lakes) to be reliable and usable. Lack of cloud data validation is an existential threat to data-sensitive organizations.
End to End Self Service Test Data Management (TDM)
Major financial losses caused by production defects have led to a rapid rise of interest in test data management (TDM) in the testing industry. This is because the losses could have been prevented by detection via testing with proper test data. Test data has evolved from a few sample files to powerful test data sets with high coverage.
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