They sound like pretty cool and complicated job titles, don’t they? Well, that’s because they sort of are, in a way.
Whether browsing the feed on LinkedIn or reviewing the most “in-demand” jobs in 2021, it is highly likely that these titles will frequently pop up. Why is it so? Because of a combination of factors related to the increased importance of automation, data protection statutes, and corresponding corporate practices, DevOps, PrivacyOps, and AIOps have taken center stage amongst organizations’ strategic goals.
Some may argue that rather than being competitors, these are complementary frameworks that have helped organizations address varying issues in their operations. This has allowed organizations to evolve in their approach towards amalgamating the technical aspects of their operations with the other parts of their organization such as marketing, sales, and human resources.
But, coming back to the original question, what exactly are they? Why are more and more organizations adopting PrivacyOps? Why are DevOps-related jobs so well-paid? What does AIOps contribute within an organization? And perhaps most importantly, what is the difference between the three?
The juxtaposition below compares the three to make it easier to understand what each of these entails and what value they bring to an organization:
First things first, DevOps are a set of practices that combine the functions of software development (Dev) with IT operations (Ops). DevOps have become a cornerstone of any organization’s ability to develop software more efficiently in the past couple of years.
This is down to how DevOps promotes a cross-functional engagement between various teams involved in software development. Rather than working in their individual silos, business requirements, coding, quality assurance, networking, and database teams work simultaneously on a project. As a result, communication between teams is far more proactive, ambiguities are resolved early, and feedback.
AIOps is the amalgamation of DevOps with artificial intelligence and machine learning. It’s a relatively new framework, but the underlying idea and concept are fairly straightforward. Using the latest AI and machine learning tools, an organization can gather, categorize, analyze, and visualize data on an unprecedented scale. This can prove immensely helpful to DevOps teams in making proactive changes in their products and services while providing actionable insights to aid future improvements.
Additionally, AIOps can increase any organization’s ability to have an automated plan in place that can map out all possible results of a particular approach. Furthermore, DevOps engineers can fine-tune these automated responses and triggers to ensure they’re in line with the operational guidelines of their organization.
PrivacyOps is the need of the hour for almost every organization dealing with tranches of user data. Like DevOps, PrivacyOps’ primary function is to increase the collaborative functions between the IT department and legal team to ensure an organization’s products and services are fully compliant with various data protection laws globally.
An increasing number of organizations are promoting a cross-functional collaborative approach involving the data, cybersecurity, and marketing teams. However, PrivacyOps need not be limited to legal, and IT teams specifically. The better an organization’s workforce adapts the PrivacyOps framework, the better an organization’s ability to comply with data protection regulations.
The changing tech-landscape
At this point, it should be clear that any direct comparison between the three is a misnomer. Like other frameworks in the world of IT, these three approaches complement each other a lot more than compete against one another.
Each of these approaches is unique, just like every organization and its needs are unique. Hence, it is up to the organization to decide which of these approaches and frameworks would most effectively solve their needs and requirements. Depending on which requirements take top priority, an organization may find itself leaning on one framework more than the others.
PrivacyOps is rapidly becoming an incredibly vital framework for organizations globally. Owing to just how prolifically countries have adopted or are in the process of adapting data protection regulations, organizations have found the need to comply with these regulations both strategically and legally necessary.
PrivacyOps allows organizations to tailor their data protection mechanisms with operational frameworks comprehensively. This allows an organization to ensure it remains compliant with any data protection laws without making concessions on its operational effectiveness.
How can organizations adapt
By now, it should be clear that DevOps, AIOps, and PrivacyOps have had an incredible impact in changing how organizations view the integration of cross-channel collaboration within organizations. PrivacyOps allows a proactive approach within an organization where any practice or idea that may lead to non-compliance with data protection regulations is amended to avoid any breaches.
Similarly, DevOps allows for better coordination between various teams involved in software development, ensuring that both the technical and non-technical bases of a project are adequately covered throughout the organization. AIOps takes it a step further and automates that same exact process.
All of this leads to the question of how organizations can adapt each of these frameworks apropos to their needs. The answer is that there’s no magic formula for this. To reiterate something that’s been said repeatedly before, each organization’s needs are different. It stands to reason that the way DevOps, AIOps, and PrivacyOps would be adapted would be different as well.
However, there are some standard practices that organizations can undertake to ensure they’re on track towards including any of these approaches within their operations:
Implement metrics: Any organization can enforce any metric it feels reflects the viability and effectiveness of an approach to its own operations. It could be anything from average response time, bugs identified, non-compliance instances noted, etc. These metrics will give an organization a real-time view of how a framework performs and monitor any potential improvement.
Have a plan: Before you can implement a framework, you need to stipulate and understand what your organization wishes to achieve by adopting a particular framework. This should serve as an extension of the metrics mentioned above, where you chalk out exactly how you plan to implement a framework across the organization and contingency plans in case things don’t go as planned initially.
Regular testing: This is the key. Whether it’s to monitor whether your practices are compliant with the data protection regulation requirements or to see the average time from development to implementation, the key to continued success is to regularly test each stage to ensure efficiency on top of effectiveness.