Data Operations (DataOps): Streamlining Enterprise-Wide Healthcare Data Workflows

For the past few months, DataOps has been the buzzword at my workplace and in the health IT universe as a whole. The question on everyone’s minds right now is—what exactly is DataOps and how does it help me?

Data Operations, also known as DataOps, is a data management practice aimed at improving the efficiency, quality, and reliability of data processing. The process involves collaboration between data stewards, data owners, data scientists, and business stakeholders to streamline data workflows, automate data pipelines and ensure data governance to enable organizations to make timely and appropriate data-driven decisions.

Data is a critical asset for any organization today. Health organizations and programs use data to make informed policy decisions that can affect millions of people at a time. However, managing and using healthcare data is a complex and time-consuming process. The multiple sources of data, the volume of data generated, quality control of the data being processed, federal and state regulations, and the number of data analytics requests, add to the complexity of data management within organizations that is constantly evolving. This is where DataOps comes into play. As enterprise-level data organizations are set up in the healthcare industry, DataOps will be a crucial component of an organization's data management strategy to address all the challenges that may arise while setting up an efficient and long-term solution.

Components of DataOps

A successful DataOps practice has some critical components

1.     Collaboration: Perhaps the most important component of a successful DataOps practice is the collaboration between different teams within a healthcare organization. DataOps practice ensures that teams collaborate to collect, process, and store data consistently. In order to do so, communication is key so that everyone is on the same page and working towards the same goals.

2.     Data Governance: Managing data throughout its lifecycle is one of the key features of DataOps practice. Data governance involves establishing policies, procedures, and standards for data management and quality assurance ensuring that data is used responsibly, safely, and ethically.

3.     Monitoring and Metrics: Monitoring and metrics are important for ensuring the success of DataOps initiatives. DataOps practice will aim to track metrics like data quality, data request timelines, and overall efficiency through the data lifecycle process to identify key areas for improvement and make data-driven decisions.

4.     Automation: As we know, data operations in all major healthcare organizations are undergoing a huge transformation. This coupled with increasing data needs throughout the industry will require data processes to scale up significantly. The ultimate goal of DataOps practice will be to automate data workflows and pipelines to ease this process and speed up the transformation while reducing the risk of errors and omissions.

DataOps Key Roles

1.     Data Sponsor: Responsible for the DataOps practice and ensuring executive support

2.     DataOps Administrator: Responsible for day-to-day operations of the overall DataOps practice throughout a division.

3.     Data Ops Specialists: Coordinators of individual projects and initiatives. They, along with the DataOps Taskforce, define and deliver the data needs and act as the key communicators between DataOps groups and DataOps Administrators, and data teams.

4.     DataOps Group: Data governance team comprised of business and technical data stewards to assign DataOps specialists, prioritize work efforts and resolve project data issues

5.     DataOps Taskforce: Group of individuals including the DataOps Specialists, Project Manager, Vendors, business and technical data stewards, and data owners. They collectively define and deliver data needs and resolve project data issues. Participation in the task force is based on need which means that throughout the DataOps process for the particular initiative e, the DataOps task force can continually change.

DataOps Process:

In conclusion, DataOps is a crucial component of any successful data organization. Implementing DataOps practice requires collaboration, data governance, monitoring and metrics, and data process automation. With the success of DataOps practice, healthcare organizations will be able to unlock the full potential of healthcare data to provide accessible, timely, and quality healthcare to millions of people.