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Key insights from the IBM Data and AI Forum: DataOps - NYC

How DataOps adoption helps deliver a business-ready data pipeline

Portfolio Marketing Leader, WorldWide, IBM

What is DataOps? It brings best practices from DevOps, data management and data governance into a common framework. It’s a collaborative way of developing and maintaining data flows and pipelines across multiple stakeholders. DataOps has emerged as an agile methodology to improve the speed and accuracy of analytics from data quality to data integration. Although it began as a way to accelerate analytics operations, it now is essential to accelerate any business operations that have upstream or downstream dependency on data. 

So why adopt DataOps?

Implementing a DataOps methodology can help automate data operations to improve enterprise data management, data quality, data insights, and compliance. Automated data operations delivers operational excellence, differentiated insights, and enables privacy, compliance and collaboration between its data citizens.

DataOps adoption helps you build a business-ready data pipeline—resulting in competitive advantage. Automated data operations can help to unlock the value of your data in new ways—fueling journey to AI, finding new opportunities and business models while accelerating digital transformation.

Adoption of DataOps with IBM

At the IBM Data and AI Forum: DataOps in New York on September 10, IBM highlighted its commitment and investments in DataOps to help clients with their transformation in data operations. IBM created a DataOps Center of Excellence (CoE), an extension of the IBM Data Science and AI Elite team. It is comprised of experts that help measure the value DataOps can have on your data operations that can impact business outcomes. The DataOps CoE offers DataOps IBM Garage discovery workshops to pinpoint where you are, and where you need to be—all driven by prioritized business objectives and geared to help drive success.

The DataOps framework is a blueprint for better data automation so you can build a business-ready data pipeline. This means your data operations teams can spend more time on AI modeling, analytics reporting, iterating for new insights and business models and infusing them into operations throughout the company providing exponential business outcomes.

The IBM DataOps methodology and practice focuses on bringing agility, speed, and scale to analytics and operations through automation, data quality and governance.

Key insights on DataOps adoption

During the event, we hosted a breakout session with executives from various industries. The main topic of discussion was DataOps and the journey ahead to adopt it across their organizations. Common threads were found regarding their DataOps maturity and challenges. The top three insights:   

1. Most organizations are still focused on developing foundational cornerstones for DataOps. In recent years, governance has experienced a shift from being a reactionary, defensive measure to becoming an offensive strategic initiative. In the past, as regulations were announced, organizations were likely to scramble to meet the minimum compliance needs at the time, doing this over and over again as new regulatory measures hit the market. It’s now clear this method isn’t a sustainable business practice for something that’s so critical to your data consumption. With DataOps, practices like data quality, integration, cataloging and more become core to achieving your business goals.

Key insight: Organizations need to identify their challenges, obstacles and maturity in adopting DataOps.

Key insights from the IBM Data and AI Forum: DataOps - NYC

2. Help people adapt to change first, before adding technology. As with any methodology that includes people, process and technology, the people-related change management and engagement portion of the strategy can quickly become the most complex. Many executives expressed that in the past, one of the quickest pitfalls encountered was the lack of understanding in some roles—slowing down the organization’s adaptation to change.

If you don’t define the roles of the key focals that will be maintaining critical steps in your data pipeline, you compromise operational efficiency. You could quickly fall into the trap where the wrong people are doing the right things, or even worse, the wrong things. Ensure a more positive experience by clearly defining how your data strategy will support core business goals and the roles needed to support that strategy. Engaging people by appreciating what they do today, and exciting them with what they can do better with role clarity, are keys to success.

Key insight: Ensure your data strategy clearly defines the process and roles to be a data owner, a data engineer or a data steward unique to your needs. Keep the engagement up by appreciating people today and creating excitement for tomorrow.

3. Pick projects aligned with business objectives and define attainable goals are critical to maintain transformational change. The starting point is important. Pick a project that has strategic impact on business. Executives identified lost motivation as the number one factor in doomed projects.

Take an agile approach and define attainable goals in sprints. Many times, organizational leads set the goals very high—which, paired with the learning curve for new skills and changing behaviors, they can fail to easily see results or progress. This perception of failure over time causes projects to lose investment and resources. One key method to shift your project strategy is to set achievable goals in shorter time increments to achieve quick wins. Quick wins in every part of the organization mean exponential business results creating winning culture for all.

Key insight: Working on projects aligned with strategic business objectives motivates people working on them. Additionally, quick wins show that projects are moving in the right direction and that the end goal of the strategy is attainable. With a shorter validation runway, not only are key players more likely to stay motivated and solve problems more creatively, but also the team is able to iterate in a focused manner when new challenges arrive.

The IBM DataOps methodology, framework and best practices can help transform your organization to deliver a business-ready data pipeline. Learn more and register to be the first to access the whitepaper and project plan template: visit ibm.biz/DataOpsPaper. 

Want to dive deeper? Check out these related blogs posts: 6 DataOps essentials to deliver business-ready data andHow to Scale the AI ladder. If you’re ready to learn how to accelerate your journey to AI, join us in Miami at the Data and AI Forum.