5 things that keep data management leaders up at night

Gartner predicts that by 2026, data management leaders teams guided by DataOps practices and tools will be 10x more productive than teams that do not use DataOps.

DataOps, or data operations, is a modern practice in data management at the crossroads of DevOps and data science. This practice, which is critical to digital transformation and the growth of data-driven companies, provides better data lifecycle management to optimize and improve data quality.

Keep reading to discover five Gartner recommendations to get a better idea of a DataOps strategic roadmap to enhance data maturity, secure executive buy-in, and create a clear, measurable value chain.

The top 5 questions & considerations of data management leaders

Here are the top five DataOps questions that are top of mind for data management leaders today:

How do I cope with and scale to address the increasing demand in my team?

Drive operational excellence

Since we know that data operation drives efficiency, data management teams should first strive to drive operational excellence. To scale effectively, data leaders should focus on streamlining, automating, and augmenting their data pipelines and platform operations with advanced technology to achieve new levels of efficiency.

Automation can reduce manual, repetitive tasks in data ingestion, transformation, and quality checks, ensuring data flows seamlessly across platforms. Implement monitoring tools that provide visibility into workload costs so you can measure and align spending with the value generated by your data.

This approach allows you to balance performance with cost-effectiveness, ensuring your data management operations can meet rising demands sustainably.

How can we turn information silos into proper collaboration?

Involve your business stakeholders

To break down information silos and foster cross-functional collaboration, data leaders can follow a comprehensive, step-by-step approach. Here's a look at each stage of the strategy for turning information silos into collaborative information-sharing practices:

How do we build the foundation for scaling and leveraging the market dynamic changes?

Begin considering a data fabric or data mesh architecture

Data fabric is a new type of data management system that uses automated data integration or data engineering to create a categorized environment. This environment then secures human work with artificial intelligence and metadata automation.

The data fabric architecture is designed to manage various levels of diversity, distribution, and complexity of data resources. It provides better visibility into data and actionable information. Data access, control, and security are also improved, thanks to metadata and centralized data engineering.

On the other hand, data mesh focuses on data filtering, organization, and accessibility with a domain-oriented architecture. Each domain manages its data pipeline and is responsible for processing the data. Data is organized by business area and jointly owned by data owners. This approach enables teams to source actionable data based on their specific requirements, avoiding duplication of effort and providing autonomy.

The data mesh architecture is also designed to promote organizational change. It leverages team expertise to create and design a business-oriented data product. The process of creating a data mesh requires breaking down silos between teams and adopting a culture of data ownership and governance.

Data mesh and data fabric offer complementary solutions for data leaders aiming to scale data infrastructure and quickly respond to market changes. Together, these architectures enable both flexible, domain-specific control and seamless, holistic insights, creating a scalable, responsive foundation for leveraging dynamic market shifts effectively.

How do we ensure data management practices are safe, trustworthy, and reusable?

Establish effective governance practices

Effective data governance ensures that data management is safe, trustworthy, and reusable by establishing structured oversight. Proper data governance enforces:

• Security protocols like access controls and data masking to protect sensitive information, boosting safety and compliance
• Data quality through standards that ensure accuracy, consistency, and reliability, building trust among users
• Reusability with standardization and documentation practices, making data more accessible and understandable across teams.

Together, these practices create a solid foundation for secure, reliable, and easily repurposed data throughout the organization.

However, it is important to note that data governance must always be adaptive in your operating model. This can include making slight changes in governance requirements or integrating new AI models and information as the need arises.

Everybody talks about genAI. What does this mean for my team?

Ask yourself: Is our data AI-ready?

Don’t worry - In the end, genAI tools will benefit your teams and your processes! However, it is extremely important data leaders prepare their teams to begin working with these tools. To be sure their teams are ready for the oncoming wave of AI, data leaders must have confidence in everything from their metadata to the final products and use cases.

Here are a few first steps data leaders can take when deciding if their teams are ready to integrate genAI tools:

Recommendations for data management leaders

To address these key issues haunting data leaders, Gartner suggested the following recommendations:

• Improve your overall maturity by addressing the five key issues listed above: Have a deep look at the points raised in response to all of the main questions asked above. This should give you a good idea of your organization’s data maturity level and how to raise it.
• Involve all C-level data managers as early as possible in the process: Gaining executive support is crucial for having momentum and financial backing for all your data project efforts.
• Define your data management value chain and measure the cost: Clarify each step involved in creating, managing, and delivering data to ensure that all activities align with business goals. Measuring the cost of this value chain helps identify inefficiencies, optimize resource allocation, and demonstrate the tangible value of data management efforts to stakeholders.

Conclusion

In conclusion, data management leaders face complex challenges that demand a blend of strategy, innovation, and cross-functional alignment to tackle them effectively. Follow these Gartner recommendations to get a better idea of a strategic roadmap to enhance data maturity, secure executive buy-in, and create a clear, measurable value chain.