Enterprises Have Opportunities to Expand Use of Emergent Data Sources for AI and More

As organizations strive to capitalize on their ever-growing data trove to scale their operations and improve business outcomes, only 17% of data ingested or landed consists of emergent data types, and only 9% of that data is processed or analyzed, according to a new report from BMC, Putting the "Ops" in DataOps: Success factors for operationalizing data. This signals a significant opportunity to benefit from emergent data types critical for initiatives like generative AI, LLMs, FinOps, and sustainability. The study defined four maturity levels, including: 

■ Developing – discovery phase with strategies in their infancy, and practices and architecture not closely aligned to business outcomes. 

■ Functional – growth phase with strategies primarily developed and some high-priority practices and architecture linked to business outcomes. 

■ Proficient – adolescent phase representing a fully established strategy with nearly all practices and architecture linked to critical business outcomes.

 ■ Exceptional – innovation phase with a perpetually optimized strategy, practices, and architecture that generates competitive differentiation and business value. 

DataOps strategy is closely aligned with data management maturity. Of those respondents with exceptional data management maturity, 27% stated they use DataOps methodologies across their organization to support all data-driven activities. In comparison, those with proficient maturity levels reported 19%, and functional and developing levels stated 15% and 10%, respectively. Even among organizations with exceptional data maturity, only 41% report having "high maturity" for data pipeline and application workflow orchestration functions. Higher data management and DataOps maturity are linked to higher reported adoption and success with data-driven activities. 75% of those with mature practices have a Chief Data Officer, while only 54% with less mature practices do.

Challenges Obstruct Flow of Data

Multiple challenges continue to impact the flow of data in businesses, including those related to people, processes, and technology. These include a lack of skills (48%), human error and mistakes (43%), limitations on scalability (40%), and a lack of technology automation (43%). A lack of automation can exacerbate a lack of skills, while an appropriate use of automation can amplify skills already available. 

"AI and data are in a cosmic dance, and data challenges are increasing dramatically in the AI era," said Ram Chakravarti, chief technology officer at BMC. "This study highlights how organizations with mature data practices can achieve better business outcomes. Implementing DataOps methodologies to enhance collaboration and operational efficiency, maintaining high data quality through pragmatic investments, and developing robust data pipeline orchestration systems can help unlock value at scale." 

Methodology: BMC commissioned 451 Research, part of S&P Global Market Intelligence, to conduct the survey in late 2023, sourcing insights from 1,100 IT, data, and business professionals from large enterprises in diverse global regions across multiple industries in eleven countries.

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Enterprises Have Opportunities to Expand Use of Emergent Data Sources for AI and More

As organizations strive to capitalize on their ever-growing data trove to scale their operations and improve business outcomes, only 17% of data ingested or landed consists of emergent data types, and only 9% of that data is processed or analyzed, according to a new report from BMC, Putting the "Ops" in DataOps: Success factors for operationalizing data. This signals a significant opportunity to benefit from emergent data types critical for initiatives like generative AI, LLMs, FinOps, and sustainability. The study defined four maturity levels, including: 

■ Developing – discovery phase with strategies in their infancy, and practices and architecture not closely aligned to business outcomes. 

■ Functional – growth phase with strategies primarily developed and some high-priority practices and architecture linked to business outcomes. 

■ Proficient – adolescent phase representing a fully established strategy with nearly all practices and architecture linked to critical business outcomes.

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Challenges Obstruct Flow of Data

Multiple challenges continue to impact the flow of data in businesses, including those related to people, processes, and technology. These include a lack of skills (48%), human error and mistakes (43%), limitations on scalability (40%), and a lack of technology automation (43%). A lack of automation can exacerbate a lack of skills, while an appropriate use of automation can amplify skills already available. 

"AI and data are in a cosmic dance, and data challenges are increasing dramatically in the AI era," said Ram Chakravarti, chief technology officer at BMC. "This study highlights how organizations with mature data practices can achieve better business outcomes. Implementing DataOps methodologies to enhance collaboration and operational efficiency, maintaining high data quality through pragmatic investments, and developing robust data pipeline orchestration systems can help unlock value at scale." 

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