Infosys helps organizations transform themselves into analytics-driven enterprises. Our proven expertise and extensive experience enables you to monetize data by leveraging the three pillars that support the elements of process, people and technology in your organization. These pillars also constitute three distinct Infosys offerings:
The boundaryless information platform removes barriers within data, processes and technologies to make the right information available to the right people at the right time. The Infosys approach is driven by data provisioning and begins with the objective of building an inventory of information assets with internal master, transactional and external data. Real-time data streaming enables enterprises to generate accurate and actionable insights about customers and operations. The platform integrates data from internal and external sources, stores the data in a data lake, makes it available for consumption using the right data grid, and effectively leverages internal infrastructure and external cloud environments.
The Infosys Boundaryless Information Platform offering comprises of various technology components:
Data lakes enable cost-effective and scalable storage of unlimited data in any format, schema and type. The data lake expands existing data warehouses by capturing data at lower grain and higher diversity. It supports agility in analytics by allowing
discovery and exploration on raw data to identify correlations between seemingly unrelated data streams.
Master data management
Master data management enables universal definition of data domains such as customer, product and market. It also ensures seamless interoperability between domains and applications with the relevant business-centric semantics model to deliver a 360-degree view of customers, products, services, suppliers, and employees.
The data grid breaks the physical data boundaries by integrating data from a variety of sources such as on-premises/cloud in real-time using integration technologies such as Extract, Transform, Load (ETL) and virtualization. It also enables data democratization by using a catalog of raw and enriched data that allows seamless and secure data consumption through a metadata-driven semantic layer.
Real-time processing captures various types of data including clickstream, machine-generated and streaming data that is generated in real-time by various channels. Once acquired, this data is made accessible through the data grid and stored in the data lake, allowing business users to create real-time insights.
Platforms and data-analytics-on-the-cloud
Platforms and data-analytics-on-the-cloud leverage the on-premises and/or cloud-base deployment models to reduce cost. They also provide a seamless and integrated data platform to create a boundaryless data fabric and deliver a data and analytics services-based delivery model.
A progressive organization is the glue that binds, builds and delivers capabilities of the boundaryless information platform and pervasive analytics. Progressive organizations introduce the right structure, processes and culture to embrace new paradigms. The success of a progressive organization depends on establishing the right data and analytics strategy along with the appropriate operating model to execute the strategy. By focusing on data governance, change management and strategic organizational design, enterprises can align business and technology and enable business speed and responsiveness. Data governance ensures that data is accurate, complete, secure, available – anytime and anywhere – and that it enables the relevant architecture, policies and procedures in the information value chain.
Infosys delivers key capabilities to build a progressive organization:
Strategy and target operating model
Strategy and target operating model assesses and defines the transformational strategy and roadmap to build and operate a boundaryless information platform. It also provides analytical capabilities while prioritizing high-value functional assets in high-impact domains. The model enables organizations to unlearn old ways and discover new ways to be analytics-driven. It is the key enabler for new transformations while ensuring business continuity.
Change management aligns people and the organization with the transformational change. This strategy identifies the right capability across the data value chain to meet organizational objectives and improve decision-making. It also ensures that change is seamless and widely-accepted across all stakeholders through continuous communication, training and development.
Landscape simplification and modernization
Landscape simplification and modernization simplifies and modernizes the existing landscape, making it agile and efficient. The adoption of new technologies and best-practices in performance measures as well as the consolidation of systems and technologies make this a viable initiative for enterprises.
Architecture and engineering
Architecture and engineering design future-state architecture that is scalable, flexible and robust. It handles all aspects of business, data, applications, and technology architecture to address ever-changing business paradigms and compliance with regulatory needs.
Data governance and management
Data governance and management enhances data governance capabilities with the right data strategy, policies and guidelines to maintain high quality and secure data for consumption and compliance. It also provides data lineage for internal use, regulatory queries and archived data with the right retention strategy.
An analytics-driven enterprise is defined by how proactively it uses available data to make decisions across levels, i.e., how mature it is in terms of analytics and the features that are enabled. While strategic decision-makers need predictive analytics, field sales and operations need real-time analytics to address changing customer preferences, predictive maintenance issues, etc. Pervasive analytics focuses on data consumption and ensures that analytics is used across all organizational levels and for all important business decisions.
The Infosys Pervasive Analytics offering includes the following:
Self-service delivers an interactive user interface (UI) for analytics, visualization and reporting. Most organizations seeking to become data-driven want self-service capabilities to view and analyze data. This offering can be consumed on interactive screens that allow users to run pre-created models, iterate on the same with changed parameters and also create personal views of relevant data.
Analytical workbench uses self-service analytical platforms for model creation and modification for diagnostic and predictive analytics. Analysts can trigger these using the workbench along with model combination workflows, leveraging pre-built analytical models and new model creation and plug-in/refresh mechanisms. Post creation of models, they can be published for business user consumption using self-service interfaces.
Responsive enterprise is about understanding user behaviour and being responsive to their need for analytics anywhere, anytime and on any channel. It is important for an organization to be responsive and enable business users to leverage analytics. The combination of the right data and analytics strategy, future-state technology components and architecture along with relevant governance of data availability and access enables organizations to become responsive enterprises.
Machine learning involves self-learning models that can provide recommendations on decisions based on past or user-driven decisions. This technology also uncovers trends and patterns that are significant for business decisions without forcing the decision-maker to construct specific questions.
Prescriptive and optimization
Prescriptive and optimization analytics enable automated decision-making where possible. For example, a pricing analytics output can display recommended prices where only those prices over a certain threshold will need exception approval. This enables automated price optimization and is a key driver in making analytics pervasive across the organization.
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