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 & 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: Pyramid Consulting Solutions
Backed by domain expertise and years of experience, Pyramid Consulting Solutions offers the assistance that data-analytics leaders need for a profitable DataOps transformation.
As a part of our Advisory Services, Pyramid helps leaders pinpoint the scope for DataOps implementation, thus enhancing the overall ROI and relevance. Following this, end-to-end data services guide businesses through the entire modernization process, complete with managed DataOps implementation and monitoring.
In doing so, Pyramid partners with industry-best services, including Cambridge Semantics, Power BI, Snowflake, and more. DataOps entails knowing, trusting, and using data to build processes that are ready for an AI-driven future—a vision that Pyramid Consulting Solutions fulfills.
About the author
Practice Director, Data, Analytics and DevOps
Vandapalli, or “Sri,” as friends call him, helps clients harness the power of data with the latest and greatest analytic technologies. With a background in IT consulting and career guidance, Sri knows how to bring clients from where they are, to where they need to be. Always developing new skills and knowledge sets, Sri hopes to leave a legacy by teaching others to achieve their goals. An author of literature on Big Data and DevOps, and a yoga and meditation instructor, Sri finds joy in public speaking and mentoring peers in his community.