Adopting DataOps for Agile Data Management Processes
DataOps optimizes and streamlines the data value-Innovation pipeline to ensure agility and resilience in data preparation, analysis, and implementation.
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As businesses become AI-ready, efficient data management has acquired an unprecedented role in ensuring their success. Bottlenecks in the data pipeline can cause massive revenue loss while having a negative impact on reputation and brand value. Consequently, there’s a growing need for agility and resilience in data preparation, analysis, and implementation.
On the one hand, data-analytics teams extract value from incoming data, preparing and organizing it for the production cycle. On the other, they facilitate feedback loops that enable continuous integration and deployment (CI/CD) of new ideas.
By applying DevOps principles to data management, DataOps optimizes this Value-Innovation pipeline, streamlining it to ensure better ROI.
Data Management Challenges and DataOps Solutions
First, data-analytics verticals involve multiple roles, making internal data-centric communications and transfer extremely complex. Moreover, teams are unable to achieve the speed, durability, and flexibility required to meet the rapidly-changing demands for personalization and specialization.
Second, traditional data-management processes are highly siloed and have low interoperability. Alongside hindering seamless collaboration between key roles, this also hampers innovations at the company level, primarily due to bottlenecks and inertia.
Preparing businesses for Big Data, AI, and ML integration, three broad categories of DataOps solutions solve these problems.
- Orchestration solutions like Apache Airflow or Saagie break the barriers to multi-environment and multi-platform interactions.
- Automated Testing and Monitoring solutions like iCEDQ to ensure continuous QA and feedback.
- Environment and Deployment solutions like Git, Docker, and Jenkins, which facilitate version control, modularization, and CI/CD.
The Steps for Implementing DataOps
Incremental implementation is crucial to ensure effective DataOps transformations. Data-analytics leaders must start by identifying the aspects of the business that are the most susceptible to bottlenecks.
The below figure gives you the steps to jumpstart your DataOps practice so you can start delivering business-ready data fast.
The Outcomes of Implementing DataOps
There are some immediate and practical benefits. Primarily, these are achieved by automating data curation, meta-data management, and core governance.
- It makes data-analytics teams more reliable, adaptable, and fast. Continuous QA, feedback, testing, integration, and deployment imparts time-appropriate agility to data management. Moreover, it makes these processes more scalable and intuitive.
- It enhances collaboration across verticals, by enabling seamless data-flow across all of the major roles. In turn, this imparts greater resilience to teams, shortens production cycles, lengthens products’ lifecycle, and above all, removes bottlenecks.
Identifying the Scope for Intervention
The success of DataOps transformation depends upon the effectiveness of the execution process.
- Data-analytics leaders must work with a thorough understanding of the state of their business and the scope for intervention. Rather than aiming for holistic change at once, DataOps implementation must be approached at the level of individual processes.
- Planning, Creation, and Monitoring can be considered the three broad phases of DataOps implementation.
- The planning phase involves a precise identification of the business’s goals. Based on this, data-analytics leaders can isolate aspects of the value-innovation chain that slow down the rest of the processes.
- Any continuous process entails stringent monitoring and DataOps is no exception. Setting up robust monitoring checkpoints is pivotal to the cyclical process.
Integrating Dataops With Expertise
It's important to choose the right IT partner who can guide your business through the entire modernization process, complete with managed DataOps implementation and monitoring that enhance the overall ROI and relevance.
Published at DZone with permission of Ramesh M. See the original article here.
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