What IT Leaders Need to Know to Achieve DevOps Success

Software is crucial to every business and so is DevOps. It’s critical for high organizational performance as DevOps brings development and operations teams together to deliver with maximum speed, functionality and innovation.

To put things in perspective, here’s a side-by-side comparison of companies that leverage DevOps practices versus those that don’t.

Source: State of Devops 2018 report by DORA

As highlighted in DORA’s 2018 Accelerate: State of DevOps report, key development practices, such as database change management and data security, integrated early in the software development process drive high performance and are essential to successful technology transformations.

Filling in the Data Gap

With the growth in DevOps practices, not only will demand for data increase but data delivery remains a challenge. For instance, you could provision cloud infrastructure and the right software build in minutes, but it would most likely take days or weeks to deliver that data with the slow, glacial pace of a traditional, ticket-driven request-fulfill model.

Consequently, development teams are continuously held up by speed and the quality of data even as their organizations adopt faster development methodologies.

The solution here is to focus on the data by simplifying the complexity of data provisioning using a platform, like Delphix. The data platform should have features that enable developers to access secure fresh, realistic dev/test data in minutes while ensuring sensitive data is masked and anonymized. Using an automated approach, data environments can be provisioned as needed in a matter of minutes and developers can access the right sets of data nearly instantaneously.

By taking a data-first strategy, enterprise software teams can achieve faster time-to-market without compromising security and stifling innovation.

Security and security performance

Development and testing environments are often less scrutinized from a security perspective. However, designing risk mitigation measures into the software development process improves software delivery and operational (SDO) performance and security quality.

Findings from the DORA report show that low performers often take weeks to conduct security reviews and complete remediation while elite performers do the same thing in just a few days.

The vast majority of sensitive data in an enterprise exists in non-production environments used for development and testing. While realistic data is essential to test adequately, real data runs considerable data security risks.

An automated approach to masking your data can help provide secure access to data that flows across an organization to innovate faster and at scale, without compromising privacy and security. While various approaches, like encryption, are effective for securing data-in-motion or data residing in hard drives, they are ill-suited for protecting non-production environments that represent the largest surface area of risk in an enterprise. Compare data encryption vs. data masking in greater detail.

A security process shouldn’t slow down development process. Teams need seamlessly integrate masking with data delivery capabilities to ensure the security of sensitive data before that data is made available for development and testing - whether it’s on-prem or in the cloud.

Putting it all Together

Companies can win against data friction and accelerate software development with DataOps as DevOps did for infrastructure. Combining DevOps with a DataOps approach can further enhance individual and team outcomes to create an iterative agile flow.

With the explosion of data, the demand for data becomes greater, creating intense friction with the increasing cost, complexity and risk of managing, distributing and securing that data. DataOps brings two key audiences together as one team: giving data consumers access to and control over the right data in the right place while allowing data managers with the efficiency and oversight and confidence to support the business at scale.