Unlocking Data Potential for AI and FinOps

In the rapidly evolving landscape of digital enterprises, data has become the cornerstone of innovation and operational efficiency. BMC, a global leader in software solutions for the Autonomous Digital Enterprise, recently released a comprehensive report titled "Putting the 'Ops' in DataOps: Success Factors for Operationalizing Data." This report sheds light on the current state of data maturity in enterprises and highlights the untapped potential of emergent data types, particularly for initiatives like generative AI, Large Language Models (LLMs), FinOps, and sustainability.

The report categorizes enterprises into four maturity levels based on their DataOps strategy and data management practices. Organizations with exceptional data management maturity are more likely to adopt DataOps methodologies, with 27% of such organizations using these methodologies across their operations. Despite the growing volume of data, only 17% of ingested data consists of emergent data types, and a mere 9% of this data is processed or analyzed. This presents a significant opportunity for enterprises to harness emergent data types to drive initiatives in AI, FinOps, and sustainability.

Enterprises face multiple challenges in managing data, including a lack of skills (48%), human errors (43%), scalability limitations (40%), and insufficient technology automation (43%). Addressing these data challenges through automation and skill development can significantly enhance data management and operational efficiency. Higher data management and DataOps maturity correlate with better business outcomes and higher adoption of data-driven activities.

DataOps, a methodology that emphasizes collaboration, automation, and data quality, is crucial for operationalizing data effectively. The BMC report highlights several benefits of implementing DataOps, including improved collaboration between data engineers, data scientists, and business stakeholders, ensuring that data initiatives align with business goals. By automating data workflows and maintaining high data quality, DataOps helps organizations streamline their operations and reduce manual intervention. Robust data pipeline orchestration systems enable enterprises to scale their data operations efficiently, accommodating the growing volume and complexity of data.

Emergent data types, such as unstructured data, sensor data, and real-time streaming data, hold immense potential for driving innovation in AI and FinOps. However, effectively leveraging these data types requires addressing several challenges, including data complexity, integration difficulties, and scalability issues. The BMC report underscores the importance of enhancing data maturity and adopting DataOps methodologies to unlock the full potential of emergent data types. By addressing the challenges in data management and leveraging advanced data processing techniques, enterprises can drive innovation and achieve better business outcomes in the era of AI and FinOps.

For more insights and detailed findings, you can read the full BMC report .

By leveraging the insights from BMC's report, enterprises can strategically enhance their data practices and capitalize on the opportunities presented by emergent data types. For further reading, check out our Rapid Innovation Blogs for more articles on AI, DataOps, and Blockchain Technology.