BIG DATA STRATEGY
BIG DATA CONSULTING
Holistic thinking and proactive planning so you can start smart, deliver value quickly and scale seamlessly. That’s what strategy is all about – or should be. Yes, we believe in blue-sky and green-field thinking, but only if they produce clear and detailed roadmaps to reach those excellent destinations.
The most effective strategies establish the foundation for long-term success, but also clarify specific objectives and generate value quickly. It’s about prioritizing objectives, balancing competing imperatives and thinking through architecture, infrastructure and toolset decisions within the broader context of business goals.
Resource needs and organizational impacts must also be accounted for. And the right business stakeholders engaged. That’s how strategy sets the stage for successful execution.
Think Big helps you prioritize and plan your implementation while considering business impact, and existing data, technology and skills. Benefits include:
- IT and operational cost reductions
- Improved insight into customer behavioral data
- Adoption of analytics as a competitive weapon
- Increased yield, productivity or profitability
- Clearer visibility into emerging threats and risks
Plus, our extensive industry expertise means we have specific use cases for high-tech manufacturing, financial services, healthcare, media and advertising, transportation, retail, telecommunications and more.
Specific deliverables include:
- Use case definitions: identify and prioritize area where big data can drive the biggest business value
- Architecture design: gap analysis, readiness assessments and ecosystem blueprints
- Capabilities plan: recommended organizational structure, governance models and resource needs
- Big Data roadmap: 12-month plan and recommendations for top initiatives, with key milestones, detailed timelines, investment models and visual prototypes
A PROVEN APPROACH
What’s different about Think Big’s approach to Big Data strategy?
- Collaborative, agile and creative – focused on high-value use cases
- Action-oriented – so data and analytics are acted on and operationalized
- Holistic and inclusive – with a bias toward integrating existing data sets and tools
- Results-driven – mapping technology assets to specific business goals