5 critical issues solved by DataOps
What does the future of operations look like? This article examines the practical uses of DataOps, its advantages, and how it can solve critical issues. DataOps integrates development processes and data management functions, improves data-focused communication, and brings your business up to a higher level of efficiency.
Data Operations (DataOps) is an emergent approach to integrating data engineering into operations processes. It aims to ensure that DevOps teams have easy access to the insights and monitoring tools already used by data engineers and data scientists, and ultimately to put in place the organizational structures necessary for data-focused enterprises.
If you’re new to this concept, it can be confusing. In many businesses “operations”, as a discrete set of business functions, has already disappeared, or been subsumed within DevSecOps. In this context, adding yet another discipline into the mix may seem like overkill. Yet this is the way that business is moving. As we’ve previously argued, it may be that in the next few years operations are going to completely disappear as a separate department, and become indistinguishable from data management and security processes.
In this article, we’ll look at the practical implications of DataOps, and point out the specific advantages it can offer your business.
The basic principles of DataOps are simple enough. The discipline is informed by the agile methodology, and seeks to integrate continuous, real-time data analytics into the DevOps process. At a practical level, this means combining DevOps and data management staff into a fully collaborative team.
This way of working is also informed by technological advances in DevOps and data management over the past few years. The rise of fast, affordable cloud storage solutions alongside machine learning and AI tools in all parts of the product lifecycle means that many DataOps teams are built around supporting the end-to-end needs of Ai and ML systems. In fact, many of these teams come to see their analytic pipelines as analogous to lean or agile industrial production lines, with AI systems taking the place of machinery.
The hugely increased consciousness of cybersecurity has also been a driving force behind the development of DataOps approaches. Improving DevOps security has been one of the primary concerns for enterprises in the past few years, and the ability to access and respond to real-time analytics is a major asset in achieving this.
At a more practical level, DataOps offers a novel approach to long-standing issues with DevOps workflows. Let’s take a look at how it does that.
Issues that DataOps solves
Whilst DataOps cannot claim to completely “solve” issues that have longed plagued DevOps workflows, it can dramatically improve outcomes in a range of ways.
Implementing DataOps workflows increases collaboration between data-focused teams and Development-focused teams. At it’s best, in fact, DataOps aims to eliminate the distinction between these two business functions.
Critical to realizing this, though, is an underlying process of goal-setting. Both development staff and the data team need to collaboratively develop an overview of the data acquisition journey through your organization, so that both can see where the work of the other can be used to improve their own work.
While DataOps has generally been thought of as a way of improving the efficiency and agility of development processes, it also has great utility when it comes to incident management. Fixing bugs and defects in your products is likely to include input from both data and development specialists, and is also a time-critical business function.
With greater levels of communication and collaboration between these two staff groups, the timescale to respond to bugs and defects can be dramatically reduced. This is useful at a technical level – because data teams will be included in bug fixing processes from the earliest possible opportunity – but also when it comes to reputation management – because development teams can quickly use their position to point out that a bug has been fixed.
Perhaps one of the biggest challenges facing organizations today is responding to development requests – both from users and from higher management. Historically, requests to incorporate new features involved the same request being sent backward and forward between data scientists and the development team.
Because DataOps teams include both of these functions, staff can work together on new requests. This allows the development team to see what effect new features have on the data flow through the organization, and can also help data teams to better focus their processing on the actual goals of the enterprise.
When implemented correctly, DataOps can provide both development teams and management with real-time data on the performance of their data systems. These data are not useful only for monitoring success against business goals, though: if sufficiently adaptable business processes are in place, these data also allow management to adjust and update performance goals in real time.
All of the issues above reduce the efficiency of organizations. In the old DevOps model, each team would compile reports on their work, and these would be passed between multiple, hierarchical, vertically-organized structures.
With DataOps, data staff and development staff work alongside each other, and so the information flow is horizontal. Instead of comparing notes at monthly meetings, the exchange of information happens on an everyday basis. This greatly improves the efficiency of an organization.
The future of operations
All of these issues have long held back development teams from achieving agility and adaptability. DataOps, by integrating development processes and data management functions, promises to greatly improve communication between these two parts of a business, and thereby improve both.
Ultimately, DataOps rests on the realization that, for the modern business, data is king. Almost every process undertaken by contemporary firms is – or should be – informed by data analytics. As a result, it no longer makes sense for data teams to be siloed away, and only called on when they are absolutely needed. Instead, bring your data scientists into the heart of your business.