Analytics

Data Lakes
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.

Data Grid
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
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.

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.

Machine Learning
Machine learning involves self-learning models that can provide recommendations on decisions based on the data. This technology also uncovers trends and patterns that are significant for business decisions without forcing the decision-maker to construct specific questions.

Analytical Workbench
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
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.

Data Governance & 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.

Data Architecture
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 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.

Prescriptive & 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.