How strategic data management impacts your business returns
Investing systematically in data management infrastructure and processes can leads to large returns for organizations. This is becoming more evident as companies that prioritize data management often outperform their competitors in data-driven initiatives.
Despite the clear benefits, many organizations face hurdles in fully capitalizing on their data. Large volumes of data often remain untapped, not due to a lack of data but because of the complexities involved in managing it.
Challenges such as data silos, lack of integration, and inadequate real-time data processing capabilities prevent organizations from turning their data into actionable insights—reinforcing the importance of investing in data management, but doing so with a clear strategy that addresses these barriers.
A focused approach to data management makes sure data assets are leveraged effectively, leading to more informed decision-making and, ultimately, a stronger competitive edge.
Effective data management demands a holistic approach, incorporating everything from data governance frameworks to the latest in AI and machine learning tools that can automate and optimize data processes.
Companies that excel here report a higher return on investment in their data initiatives, pointing out the direct correlation between systematic data management and business performance.
What you need to know about BMC’s DataOps report
The BMC report, titled “Putting the ‘Ops’ in DataOps: Success factors for operationalizing data”, offers a comprehensive look at how organizations are managing and operationalizing their data in today’s fast-paced business environment. Released on July 24, 2023, the report provides a detailed analysis based on research conducted by 451 Research, a well-known firm in the tech industry.
The research draws from the experiences and insights of 1,100 IT, data, and business professionals working in large enterprises across 11 countries—painting a broad yet detailed picture of the current state of DataOps and data management practices.
One of the key findings of the report is the absence of a one-size-fits-all approach to data-driven practices.
Organizations differ widely in their strategies, shaped by factors such as company size, geographic location, and the maturity of their data management and DataOps practices.
Whether an organization centralizes or decentralizes its data management functions—and how it incorporates AI and machine learning into its operations—greatly impacts its data strategies and outcomes.
Why maturity in DataOps determines success in a data-driven world
How mature dataops practices are the key to winning in data-driven business
Organizations with mature DataOps practices report higher success rates in their data-driven activities. The BMC report points out that 75% of companies with advanced DataOps practices have appointed a chief data officer (CDO), compared to only 54% of companies with less-developed DataOps practices.
Mature DataOps practices let organizations handle data more effectively and efficiently, leading to better integration of data insights into business strategies. These practices typically include automating data pipelines, data processing in real time, and deploying AI-driven analytics, all of which contribute to more agile and responsive decision-making processes.
Why agile data management is key for business success
DataOps, as defined by the BMC report, covers the application of agile and automated methodologies to data management—supporting data-driven business outcomes by making sure data is readily available, accurate, and actionable across the organization.
DataOps bridges the gap between data management and data usage, letting companies rapidly respond to market changes and customer needs with data-driven insights.
From DIY to blended Models: How organizations approach DataOps
Small vs. large companies: DataOps strategies that work
The size of an organization greatly influences its DataOps strategy. Smaller companies, typically with 5,000 employees or fewer, often adopt a do-it-yourself (DIY) approach to DataOps.
These organizations tend to build their AI models in-house to support data management, leveraging internal expertise and resources to customize solutions that fit their specific needs. This can be cost-effective and highly tailored but may also stretch resources thin, particularly if the organization lacks the necessary talent or technology infrastructure.
Larger organizations and those with more mature DataOps practices are more likely to adopt a blended approach—combining internally developed AI models with commercially available AI-enabled technologies. This then helps them benefit from the innovation and efficiency of external tools while still tailoring their data management practices to meet their unique business requirements.
Blended approaches typically result in a more scalable and flexible data management framework, which can adapt to changing business needs and technological advancements.
Regional leaders and organizational trends shaping the future of DataOps
Larger organizations, particularly those based in Europe, are at the leading edge of active data management to support emerging technologies like generative AI. European companies, in particular, have shown leadership in integrating AI into their data strategies, driven by both regulatory environments and a strong emphasis on innovation.
DataOps responsibilities in these organizations are typically distributed across many different roles, making sure data management is a collaborative effort that spans multiple departments.
Distributing responsibilities here helps mitigate risks while promoting a more holistic approach to data governance, ultimately making sure data management practices are aligned with broader business goals.
Overcoming the biggest data management challenges facing companies today
The missed potential of underused data in your organization
A large amount of organizational data remains underused, typically due to challenges in active data management. Despite the growing emphasis on data-driven decision-making, many companies struggle to harness the full potential of their data assets.
Underutilization can be attributed to many different factors, including data silos, lack of integration between systems, and difficulties in processing and analyzing large datasets in real time.
Without effective data management, companies miss out on valuable insights that could drive innovation, improve customer experiences, and improve operational efficiency.
To unlock this potential, organizations must invest in technologies and practices that let them fully leverage their data, turning raw information into actionable insights that guide strategic decisions.
Breaking down the barriers to effective data management
Data quality remains one of the most pressing issues, with inconsistent, incomplete, or inaccurate data leading to flawed insights and poor decision-making.
Deploying and orchestrating data pipelines is another major challenge, as companies strive to move data efficiently across their systems while maintaining its integrity and security.
Automation, or the lack thereof, is a common obstacle in data management efforts. Technical challenges, such as outdated infrastructure, combined with cultural resistance to change, often prevent organizations from implementing the automation necessary to guarantee timely and consistent data delivery to stakeholders.
Overcoming these challenges demands strategic initiative that typically include investing in modern data management tools, developing a culture that embraces change, and continuously refining data governance practices.
Why prescriptive and predictive analytics are the future of data consumption
Prescriptive and predictive analytics are rapidly becoming the go-to of enterprise data consumption. These advanced analytics techniques let organizations understand what has happened and why, as well as to anticipate future trends and recommend actions to optimize outcomes.
As businesses increasingly rely on data to drive their strategies, the demand for prescriptive and predictive analytics is set to grow.
These tools offer a potential competitive advantage by providing deeper insights into customer behaviors, market trends, and operational efficiencies. Organizations can use these insights to move from reactive to proactive decision-making, better positioning themselves to capitalize on emerging opportunities and proactively limit potential risks.
Final thoughts
As you look at your data practices, consider this: Are you addressing the key challenges like data quality, automation, and pipeline orchestration? Solving these issues is a must if you’re to transform your data into actionable insights. What steps will you take to overcome these barriers?
