With today’s data-driven practices and AI demands, there has never been more pressure on data managers and professionals to deliver real-time, high-quality information when and where it is needed. Many organizations are struggling to achieve such a level of “data management maturity,” in which data is provided on an agile, automated basis.
This is one of the results of a recent survey, conducted in March 2024 by Unisphere Research and Radiant Advisors, which finds about 41% of respondents are taking a proactive approach to achieving data management maturity. At the same time, the survey exposes substantial gaps between strategic data usage aspirations and execution. In addition, only a handful (8%) indicate they are at the point of being able to leverage AI for insights, assistance, and recommendations.
There is now a flurry of activity to adopt and put cloud-based solutions and scalable data frameworks into production, as the survey shows. This year’s highest priorities are centered on improving data quality and governance, as well as focusing on integration platforms.
“Challenges persist, particularly with data integration complexities and stakeholder engagement, which are crucial for unifying data views and obtaining enterprise-wide support for data initiatives,” according to John O’Brien, principal advisor and industry analyst with Radiant Advisors and author of the survey report.
Respondents were asked to describe their current data management maturity, the results of which align with expected adoption curves. Most data managers and professionals feel they are in the early stages of basic data management maturity, either in a proactive or reactive way.
At least 62% of combined respondents indicate they are still in the early stages of developing more advanced data management capabilities.
A smaller fraction of organizations have reached stages at which data management is either repeatable or optimized, highlighting a potential market need for tools and strategies that enhance data governance and operational efficiency. The smallest percentage of organizations (8%) are at the highest maturity level, where AI is actively leveraged for insights and decision making, suggesting significant growth opportunities in AI adoption. Of course, financial resources are key to efforts to achieve data management maturity.
At this point, only one in four organizations has a working budget to achieve the data modernization needed to carry this forward. Close to 25% of respondents have submitted budgets, with another 15% having approved budgets for these initiatives. This combined 39% of respondents mirrors the percent who regard their data management initiatives as having achieved mature levels.
Overall, annual budget allocations show a balanced distribution, with the $100,001–$500,000 and more than $1 million categories each capturing 27% of the budgeting preference. These investments underscore the criticality of robust data management systems in driving business efficiency and insights.
The goals of these data management maturity initiatives reflect the challenges of today’s digital business environments.
Operational efficiency is the main goal of data modernization efforts, which is critical for companies looking to streamline processes and reduce costs. Cultural transformation and compliance also rank as important goals, as significant attention to cultivating a data-driven culture and regulatory compliance suggests ongoing challenges and opportunities and the need to manage data responsibly and effectively.
Lower on the list is facilitating AI development, a ranking which aligns with the relatively few organizations that currently have active AI systems in production.
Data modernization mechanisms such as data fabric, master data management (MDM), and data catalogs are also garnering interest, the survey finds. About a third of data managers and professionals (32%) are embracing data fabric. At the same time, 52% are taking a wait-and-see attitude toward the technology. “The implementation faces hurdles, like transitioning from legacy systems and addressing data governance,” O’Brien pointed out, “yet the optimism surrounding data fabric’s potential to innovate data management remains strong.”
MDM is recognized as providing a comprehensive view across domains, but its integration complexity and the need for enhanced stakeholder involvement remain prominent challenges, the survey reports.
About 45% see MDM as providing comprehensive views of critical data areas such as customers, products, and suppliers. Another 40% are looking to MDM to master multi-domain and cross-domain relationships.
This “[r]eflects the importance of MDM in managing complex data relationships across different business areas, enhancing data consistency and reliability,” O’Brien stated.