# The AI-Powered Evolution of Big Data
## 4 Materials and Methods
### Proposed Components
#### 4.3.4 Viability  
Reliable and accessible data sources are crucial to building more credibility and transparency in data management. This is an important concept, as data must remain useable and accessible over time (data viability). Platforms such as Talend and Google Cloud have advanced capabilities in terms of automating data hygiene procedures, ensuring that the data are scrubbed and consistent. If data are not handled with the best data viability practices to ensure automated data pipeline management, data are inconsistent and unreliable, leading to shoddy analyses and unhealthy decisions.  
To address the issue of tracking data origins and changes, data provenance and lineage monitoring is crucial. Tools like SAP HANA and Alation help organizations to trace the lifecycle of their data, ensuring transparency from the point of ingestion to final use. For example, in banking, consolidating data from multiple sources for fraud detection is only effective if the bank can trace where the data originated from and how they have been transformed. Ensuring continuous data integrity requires automated data pipeline management, which tools like Apache NiFi [69] and Google Dataflow excel at. These tools streamline the process of moving data from the source to their destination while maintaining their quality and structure. This prevents data loss or corruption and ensures that data flow seamlessly through the system, ready for real-time analysis and use. As data flow through the pipeline, continuous data monitoring and real-time issue resolution become essential for detecting and addressing issues as they arise. Platforms like Splunk and Datadog provide real-time insights into data processes, ensuring problems are identified and resolved immediately before they affect data-driven decisions.  
Sustainable data infrastructures, such as AWS S3 and Google BigQuery, provide scalable and cost-effective solutions for sustainable data infrastructure and storage solutions. These infrastructures ensure that organizations have the resources to store, manage, and retrieve data efficiently as data grow. Such a system is crucial for maintaining a robust and future-proof data strategy. Lastly, reliable data-driven applications and business intelligence rely on the properly implementation of all the preceding steps. With solutions  
Figure 13: AI-enhanced data validity.  
like Snowflake, Datameer, and RapidMiner, organizations can transform raw data into actionable insights that inform business strategies, optimize operations, and drive innovation. Reliability and accessibility throughout the data lifecycle guarantee that applications and intelligence platforms operate efficiently, providing accurate and timely insights for critical business decisions.  
As illustrated in Figure 14, the data viability framework component integrates tools such as Talend and Google Cloud for automated data pipeline management and viability assurance, SAP HANA and Alation for data provenance and lineage monitoring, and Snowflake for continuous data monitoring and real-time issue resolution. These tools collectively ensure the sustainability and reliability of data infrastructure, enabling robust business intelligence and decision-making processes.