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.

Enterprises are evolving toward a connected data ecosystem

Adapting to market dynamics: the three horizons
Show all horizons
Velocity, Variety, Volume



Data the New Capital,
AI Transforms Life, Economy

Key Patterns

  • Ecosystem play
  • Marketplace
  • Embedded AI
  • AI-powered consumption
  • Connected data
  • Digital brain
  • Exascale data


  • Connected data across enterprises and ecosystem players
  • Consumerization and monetization of data – new business models
  • Semantic representation, pervasive intelligence, sentient enterprise
  • Augmented – AI engineering



Innovate, Transform,
Reimagine Business

Key Patterns

  • Big data and data platforms
  • Analytics
  • Digitize consumption
  • Hybrid cloud data strategy
  • Migration and modernization
  • Chatbots


  • Structured and unstructured data
  • Experimentation and innovation leveraging analytics



Better Decisions

Key Patterns

  • Legacy data arch
  • DW and appliances
  • MDM
  • Reports and dashboards


  • Structured data
  • Decision support systems - descriptive and diagnostic insights

Key trends across data subdomains

Databases and appliances

Movement from system of records to systems of intelligence

Trend 1

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.

Trend 2

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.

Trend 3

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.

Data pipeline and streams

Shift from batch-oriented ETL to event-driven, real-time ELT

Trend 4

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.

Trend 5

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.

Trend 6

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 consumption

Move from descriptive to prescriptive analytics

Trend 7

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.

Trend 8

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.

Data operations and governance

Rise of intelligent governance and operations

Trend 9

Intelligent operations

Digital disruption, customer experience and data explosion are the key drivers forcing businesses to reimagine their business processes and adopt intelligent operations.

Trend 10

Intelligent governance

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.

Data privacy and security

Rise of AI-driven proactive protection

Trend 11

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%.

Trend 12

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.

Trend 13

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.

Data assurance

Adoption of AI-driven data assurance

Trend 14

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.

Trend 15

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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Deepak P. N.

Deepak P. N.

Associate Vice President

Eggonu Vengal Reddy

Eggonu Vengal Reddy

Principal Product Architect

Jagadamba Krovvidi

Jagadamba Krovvidi

Associate Vice President

Jasdeep Singh Kaler

Jasdeep Singh Kaler

Associate Vice President

Rajeev Nayar

Rajeev Nayar

Vice President

Shashidhar Ramakrishnaiah

Shashidhar Ramakrishnaiah

Associate Vice President


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