The majority of collected data, over 60%, remains untapped for analysis. This occurs because the IT department faces overwhelming demands to manage data requests while also troubleshooting and providing ongoing maintenance.
Implementing self-service analytics can mitigate this issue, enabling employees to leverage data effectively by facilitating analytics across all departments within the organization. This self-service approach is integral to executing big data projects.
Business Requirements
Transforming business requirements into actionable use cases is essential for establishing the analytical framework in an organization. This process aids in developing a smooth data flow, ensuring uninterrupted analytics and valuable insights.
Data Framework
The architecture for big data must be in alignment with the organizational needs and long-term objectives. It should remain adaptable, scalable, and secure to support growth.
Application Integration
Identifying which current applications are critical for the organization is necessary. It’s important to evaluate how these applications utilize the insights generated from the big data framework. Integrating these systems will optimize workflow efficiency.
Data Integrity
Eliminate low-quality and redundant data through the establishment of data governance policies. This step will facilitate the extraction of more accurate and valuable insights.
Development
Now is the moment to convert the established design into actual code, constructing the big data pipeline within the organization, whether that be on-site or utilizing cloud-based servers.
Employee Development
Ultimately, it is crucial to train and empower employees so that they can effectively use data analytics and visualization tools, allowing them to gain insights independently from IT support.
