The DataOps approach is an agile development practice that unites existing DevOps teams, data engineers, and data scientists. Created in the mid-2010s to help organizations in their data-driven journeys, DataOps aims to:

  • Provide organizations with real-time insights 
  • Unify formerly disparate teams to avoid duplicated work and save time
  • Allow all stakeholders to work towards a common goal 

If this sounds good to you, you’re not alone. IDC predicts that 60 percent of organizations will have begun implementing DataOps programs to reduce data and analytics errors by 80 percent within the next three years. 

With that in mind, the following are three important steps to bringing DataOps into your organization: 

  1. Make Industrial Data Available 

As we’ve written about before at the APEX of Innovation, locking critical data in siloes is a surefire way to negatively impact its quality and keep it from reaching its full potential. As such, it follows that the first step to successfully implementing DataOps is to get this information out and permanently eliminate data silos. From there, companies can begin the integration process to ensure that data from multiple sources across the organization is available to all stakeholders in one location. 

  1. Make Data Useful 

Once the data is integrated, the next step is contextualizing it and making it available to people while still keeping it secure. All stakeholders require access to this information, but their individual data needs and level of technical abilities will vary greatly. As such, it’s important that companies consider the best way to grant data access to stakeholders so that they can easily understand its value and how to act on it. 

  1. Make Data Valuable 

The ultimate goal of DataOps is to help companies better extract, unify and act on their existing information. In order to realize this, DataOps-driven companies must apply advanced data models to uncover key insights, which will, in turn, guide people to decisions that will yield the greatest benefit or ROI. 

Because DataOps is a practice based on people, processes, and technology, the importance of executive buy-in cannot be overstated. Leadership must be supportive of DataOps efforts and encourage data and domain experts to collaborate and uncover new and more effective ways to use existing information.

For more on DataOps and its role in digital transformation, check out this eWeek article.