Big data is a core component of modern analytics. Every data-driven organization finds that they must embrace big data. Yet big data is a complex topic and the road to value is fraught with potholes and potential wrong turns. Successful big data projects that deliver real business value are challenged by continuously evolving architectures and technologies. Achieving good return on your big data investment requires strategy that focuses on purpose, people, and process before exploring data and technologies. Strategy drives planning and architecture to ensure that big data extends your existing BI and analytics capabilities without risk of competing or conflicting solutions. To prepare for success with big data, start by understanding all of the pieces and how they fit together.
You Will Learn
- Common definitions of big data and the implications of each
- Key characteristics of big data and why size is not among the top five
- The structures that can be found in “unstructured” data
- Types of big data sources—streaming data, social data, sensor data, etc.
- Value opportunities and common applications for big data
- Considerations when adapting architectures, organizations, and cultures to incorporate big data
- The scope of big data processes, tools, and technologies
- Business and data analysts; data scientists, BI and analytics program and project managers; BI and analytics architects, designers, and developers; data governance and data quality professionals getting started with big data; anyone seeking to understand the opportunities, challenges, and realities of the big data phenomenon