“Our clients are increasingly looking for deeper levels of technical expertise in data management, analytics and EIM to turn their data assets into business value. The breakthrough Big Data methodologies, architectures and design patterns that we have developed deliver immediate results and create a lasting foundation.”
Big Data technologies and platforms have matured enough to be enterprise-ready and are already transforming the speed at which companies are turning data into value, while dramatically reducing the costs of traditional business processing. Big Data is enabling Fortune 500 enterprises to:
- Answer critical business questions in seconds rather than days, days rather than months, by accelerating Time-to-Answer (TTA)
- Reduce business costs by migrating processes from traditional high-cost platforms to low-cost Big Data platforms to maximize process efficiency
- Accelerate the business processes used to produce and deliver products and services: client on-boarding, fraud detection, anti-money laundering, threat identification, dynamic pricing, client and fiduciary reporting, market risk models, portfolio valuations
- Innovate through rapid exploration and discovery; cost-reduction through transparency and rationalization of critical business processes, and faster time-to-market
How NewVantage Partners Can Help
NewVantage brings a practitioner’s perspective to Big Data, guiding our clients NewVantage from strategy through delivery of Big Data solutions, and pioneering efforts focused on Design Patterns, Target Operating Models, and Governance of Big Data for the Enterprise. We recognize that enterprises may be at different stages of maturity — some may be in the aspiring stage, some in the nascent stage and some who already are heavily invested but need further assistance. Our service offerings are aligned to the stages of maturity of the firms we support
“Our mainframe migration solution reduces cost and accelerates processing by a factor of 10. Similarly, our Big Data Hub solution allows new enterprise data sources to be connected, cleansed, and delivered in weeks, rather than the months that are typical with complex data warehouses. This is critical to finance, risk and marketing executives who need to respond to rapidly changing market conditions and regulatory requirements.”
As practitioners in Big Data we have crafted a “Framework of Response” that focuses on 4 core areas to enable our clients to realize the full potential of Big Data within the enterprise.
Vision, Strategy and Governance:
- Understand capability and readiness – information maturity and culture change
- Align Big Data to the business model – value levers, usage scenarios, priorities
- Create the vision after business “statement of actions” around Big Data are available — with visibility to business and technology
- Develop the strategy — approach, roadmap, and implementation plan
- Design a governance model and detailed operating model
Architecture and Technology:
- Assess current architecture and technologies
- Identify technology gap/fit for Big Data implementation with a detailed evaluation model
- Detail design drivers, criterion, principles and architecture of Big Data and the coexistence model with existing enterprise technology investments and standards
- Detail information provisioning and distribution architecture at the departmental and organizational level [information integration architecture]
- Detail architecture for integration of streaming information
- Assess non-functional needs and the capability and capacity to meet them
Enterprise Content Integration:
- Integrate sources of structured, unstructured and semi-structured content inside and outside enterprise boundaries that fits business model
- Ensure architecture aligns well to integrate, store, process, retrieve and analyze content
- Include event processing and business processes that interact with content to support deep analytics
- Design and integrate meta data, taxonomies, classification, ontologies for information exploration, discovery, access and retrieval
Deep Analytics:
- Define a framework for collaborative analytics modeling that aligns with lines of business’ needs
- User and business friendly tools, technologies and user interfaces selection, design and implementation
- Integration of hard and soft data for Deep Analytics
- Ensure the integration architecture can integrate with real time decision applications, event engines and business processes to deliver deep analytics from Big Data
- Ensure the architecture supports historical retention of models and meta data of data sets