For decades, IT has relied on conventional business intelligence and data warehousing, with well-defined requirements and pre-defined reports.
In the new world of big data analytics, discovery is part of the process, so objectives shift as new insights emerge. This requires an infrastructure and process that can quickly and seamlessly go from data exploration to business insight to actionable information.
To swiftly transform data into business value, a big data architecture should be seen as a supply chain that can manage and process the volume, variety, and velocity of data. To get started, every company needs a big data process. That process is divided into three steps:
1. Identify business goals
No one should deploy big data without an overall vision for what will be gained. The foundation for developing these goals is your data science and analytics team working closely with subject matter experts. Data scientists, analysts, and developers must collaborate to prioritize business goals, generate insights, and validate hypotheses and analytic models.