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Mature your data practices to achieve true data operationalization.
Survey methodology
Objectives
Organizations continue seeking new ways to capitalize on their ever-growing data to increase revenue, delight customers, and scale operations while meeting increased demands for security and operational resilience and regulatory requirements.
Priorities
These rising demands require increased investment in technology, practices, and skills. IT decision makers need insight about their unique data requirements to help guide when and where to invest.
Respondents
This report is based on a global survey of 1,100 professionals and executives in data, business, and IT roles across 11 countries, conducted by 451 Research, part of S&P Global Market Intelligence in Fall 2023 and commissioned by BMC. As part of a phased, multiyear analysis related to data management, DataOps, and data-driven business outcomes, the survey and report uncover insights into how organizations can assess and enhance their data maturity to help overcome challenges in how to use data for competitive advantage.
Data maturity
At this level, data management strategy is developing, but practices and architecture may not yet be closely linked to critical business outcomes. This maturity level is commonly associated with smaller organizations and businesses that are just beginning to establish and operationalize their practices.
Data maturity
Variables like leadership buy-in, internal skills availability, and regulatory history can influence where an organization sits on the data management or DataOps maturity curves, and shape how effectively it pursues improvement and optimization of its practices.
One of the strongest correlations in the survey was between an organization’s approach to DataOps methodology and an organization’s relative maturity in data management strategy.
Systematic data management
Organizations tend to ingest, land, and process the data that’s most familiar and that best supports and flows through mission-critical systems. These systems may be well-established or legacy in nature, indicating potentially untapped opportunities from newer and evolving data types.
Challenges in data-driven activities
In the organizational effort to continually provide high-quality data for consumption and analysis, people, process, and technology challenges can obstruct the flow of appropriate information to those who need it.
The top-reported technical challenge in providing high-quality data for consumption is “lack of technology automation” at 43 percent—a three percent increase from last year.
40%
Cited lack of automation
as a technical challenge in 2023
43%
Cited lack of automation
as a technical challenge in 2024
Data and emerging technologies
The explosive growth of AI, generative AI, and emerging technologies and environmental, social, and governance (ESG) initiatives, have organizations—particularly those with 5K+ employees, looking for ways to collect, manage, and optimize internal data resources to yield the most benefit in these areas.
To capitalize on new data-driven technologies and trends, organizations need to understand the flow of data throughout the organization in order to deliver relevant data to where it's needed most, yet data pipeline and application workflow orchestration remain difficult, even for mature organizations.
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