Data operations and governance

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.

With adoption of intelligent operations at the core, a company becomes more agile, flexible and responsive; generates value faster; and achieves sustainable competitive advantage. The four vital ingredients of intelligent operations include utilizing cloud, applied intelligence, innovative talent and smart partnership ecosystems for long-term business process transformation. Intelligent operations provide the flexibility, responsiveness and agility that businesses need to swiftly change and to navigate a new course confidently.

For an operations example, Infosys partnered with a top financial services company, to implement a big data cluster monitoring using Splunk and improved operational efficiency by 30%. The company used predictive analytics to detect anomalies and optimize its capacity planning, and it set up real-time alerts that would trigger self-healing of the servers when an alarm was raised.

Data operations and governance

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.

Intelligent governance is an important trend, as it is required both for conforming to industry regulations and for best practices in security. In meeting demands of AI or ML initiatives, according to ESG research, data security or compliance and governance have frequently been reported as the weakest link in technology stacks.

Conformance is the most important requirement at the organizational level. Improper governance leads to improper management and protection of growing data volumes. When it comes to being audit-ready and compliant, this harms the organization’s ability in both regards. By ensuring proper access control, businesses can potentially generate faster, more valuable insights — shortening time to value and accelerating innovation.

Without holistic data governance, data becomes chaotic. Enterprises expend great energy struggling to document and implement data governance. This requires taking data governance to the next level. Data across the enterprise needs to be easy to access, understand and use. Intelligent data governance fuels this process and accelerates data-driven digital transformation . An ML-driven engine drives automated quality and security tools to collaborate with business in IT, making information accessible to everyone.

Enterprises should enable end-to-end process and governance framework for creating, controlling, enhancing, attributing, defining and managing a metadata schema, model to cover, data flow from source to target, data flow dependencies, operational metadata (load execution, run stat, end to end monitoring of data pipelines, etc). Enterprises should establish a metadata repository to provide a clear and consistent definition of the data across the enterprise and importing the business and technical metadata for creating the lineage and business glossary. Collate the information specific to the business, technical, and operation metadata for the identified business elements and identify the metadata store for centralized management and astute governance including a single version of the truth.

Turning to intelligent governance, Infosys partnered with a prominent fast-moving consumer goods company. When it receives incomplete sales data from its point-ofsale systems, it now leverages an MLbased data-cleansing solution to fill in those missing values.