In this 3-part blog series, we will understand the current data management challenges and the need for DataOps. Further, we’ll look at the DataOps practices, strategies to implement DataOps, and benefits that can be gained.

Data Management

Data has gained more significance in the business landscape for fact-driven decision-making and organizations aspiring for AI-first strategy. In fact, more and more data is getting sourced from within the organization, user interactions, and external agencies and funneled through data lakes, warehouses, and data marts for driving sales, analytics, and research. With the rise of AI, the quality and timeliness of data have assumed greater importance. The outcome of machine learning models is directly related to the quality of information. With more stringent data privacy regulations, the security of information and the protection of data assets is vital.

Data and Analytics initiatives need to adapt quickly to business needs for additional features and ensure good quality in the data pipeline at all times. The Data Pipeline represents the flow of data across the various stages and platforms from the source of creation to where it is used. An organization typically uses multiple tools and technologies to build the data pipeline covering storing, processing, analyzing, and securing data as a valuable resource for the organization.

Also, collaboration between the stakeholders like data owners, data engineers, business operations, business users, data scientists and Chief Data Officer (CDO) is essential. Managing these in traditional data programs is a challenge and organizations are looking at approaches to help derive value from their data estate.

This is where DataOps can help achieve the desired speed in the development of new capabilities while ensuring overall data quality and security.

DataOps

DataOps takes the DevOps practices used for building applications and extends those to cover the unique challenges of data programs. DataOps achieves this with a combination of Agile practices, DevSecOps automation, and Statistical Process Control.

Agile provides for interactions, customer collaboration, working features, iterations, and cross-functional teams.

DevSecOps enable continuous integration and delivery of software components ensuring fast time to market yet reliable systems. This allows new ideas and features to be delivered at speed.

Statistical process control is used to monitor the data pipeline. Data flowing through the system is constantly monitored and verified to be working by ensuring that statistics for quality, volume, and performance remain within acceptable ranges.

There are two dimensions in which DataOps improves the outcome.

• It introduces structure and automation into the feature addition process so that enhancements can be moved quickly from requirement to implementation.

• DataOps also streamlines and monitors the movement of data across the data pipeline which improves the overall data quality, security, and timeliness.

DataOps needs to be incorporated into the People, Process, and Technology spheres of the organization.

People: Clarity of data stakeholders, roles, and responsibilities, and aligning people to the common objective are important for the success of DataOps. Large data programs in organizations need extensive coordination across multiple systems and groups. To streamline the effort and ensure that various groups are prioritizing as per the program goals, it is advisable to establish a Data Steering Committee

Process: Process improvements follow the agile principles of frequent small releases with fail-fast feedback. It also involves monitoring the data pipeline and eliminating wasteful processes.

Technology: While numerous technology practices can be put in place to achieve a high level of DataOps maturity the adoption would vary based on the organization’s priorities, use cases, and selection of tools.

In the next blog, we will understand these DataOps technology practices in detail.