Presented by BMC
Today’s guiding principle is the autonomous digital enterprise, defined by three key traits: business agility, customer centricity, and the capacity to make data-driven decisions. These attributes rely heavily on quality data, elevating its value like never before. However, efficiently extracting value from an ever-growing and complex data landscape is increasingly challenging.
Ram Chakravarti, CTO of BMC Software, states, “While many organizations recognize the value of data, they still grapple with data management. This creates a significant competitive edge for those that excel in it and an existential threat for those that don’t. This issue—what I call the last-mile delivery challenge—is crucial for achieving data maturity.”
A BMC survey on global IT and business practices reinforces this: organizations with higher data maturity report better outcomes in strategic decision-making, customer satisfaction, cost savings, and product development.
Challenges to Achieving Data Maturity
In the AI era, traditional data challenges have intensified. The costs associated with mining, storing, and analyzing data, along with the need for skilled professionals, demand substantial investment. Additionally, the rapid generation of new data sources across devices, applications, and people complicates the landscape. Data silos often persist without strategic oversight, impeding necessary cultural shifts for streamlined data operations. Operationalizing data at the scale and sophistication expected by stakeholders remains a significant barrier. While automation and AI can enhance capabilities, their effectiveness is diminished without aligned data practices.
“Many organizations find it difficult to operationalize their data management and analytics beyond a few use cases,” Chakravarti notes. “It’s essential to rethink your operating model and processes. Traditional data management approaches fall short in the age of AI — you need DataOps.”
Understanding DataOps
DataOps, or data operations, is a comprehensive practice that applies DevOps principles, automation, and intelligence to democratize data and uncover business value. It bridges various roles within an organization—from analysts and data owners to engineers and risk management teams—facilitating collaboration to accelerate data-driven insights safely.
Chakravarti explains, “Collaboration among stakeholders is vital; without it, progress is hindered. It’s an agile process where data is treated as a shared asset, requiring end-to-end design thinking across teams to support high-value use cases.”
This includes identifying revenue opportunities, such as understanding customer behaviors that competitors may not recognize, thus enhancing loyalty and spending. Moreover, DataOps promotes productivity and efficiency through employee self-service, knowledge management, and effective risk mitigation. Utilizing data to adapt existing strategies may be challenging but becomes a competitive edge as data intelligence evolves.
Building a DataOps Foundation
Automation is crucial for enabling DataOps, streamlining complex data pipelines that manage information across traditional and emerging sources. This involves stages such as ingestion, integration, quality control, testing, deployment, and governance, all leading to actionable insights. Observability features allow real-time monitoring of data health and performance throughout these pipelines, underscoring the importance of oversight.
Maintaining high data quality is essential for the success of AI and analytics initiatives, addressing concerns like accuracy, consistency, and completeness. Organizations should implement robust tools for data assurance in analytics pipelines. However, improving data quality often requires a measured approach, as sudden initiatives can demand significant investment. Beyond technology, successful DataOps necessitates process changes and a cultural shift that may be transformative.
Embarking on the DataOps Journey
Implementing a DataOps strategy should begin with manageable goals rather than attempting to tackle all enterprise data management challenges at once. Focus on achieving higher-value, easily achievable outcomes while applying data quality best practices to initial use cases. When scaling efforts, consider the following:
1. Executive Support: Gaining leadership buy-in is essential for cross-functional collaboration.
2. Organizational Structure: Establishing a solid operational and stewardship framework ensures data is owned and managed across the organization.
3. Clear Objectives: Understanding desired outcomes helps identify and invest in high-value use cases. Successful projects align closely with tangible business benefits, such as improving customer retention or employee productivity.
4. Iterative Processes: Maintain high standards for data quality while implementing small, systematic improvements, ensuring to baseline and benchmark progress.
“Start small, demonstrate value quickly, and continually ask, ‘So what?’” Chakravarti advises. “Learn, build, scale, and refine practices. Introduce new strategies methodically, and you’ll achieve significant results.”
Learn more about unlocking your data's value at scale [here].
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