DevOps has emerged as a powerhouse approach for accelerating software delivery and availability while improving quality. However, the increasing complexity and scale of modern DevOps practices create challenges of efficiency, visibility, and governance. Here, the powerful combination of robotic process automation (RPA) and advanced analytics unlocks game-changing performance gains.
In this comprehensive guide, we‘ll explore leading applications of RPA in DevOps through an analytics lens – revealing key opportunities, implementation strategies, and the exciting future potential of these automated, insightful capabilities working hand in hand across the development lifecycle.
What is DevOps? A Quick Overview
First coined around 2009, the term DevOps refers to the increasingly popular set of principles, practices, and toolchains that bridge the traditional gap between software development teams and IT operations teams. Core goals include:
- Achieving faster time-to-market for new features and applications
- Enabling more frequent software releases and updates
- Boosting product quality through extensive testing and monitoring
- Creating robust infrastructure and applications capable of supporting rapid scaling or changes in demand
- Driving higher customer satisfaction through greater system availability, performance and security
By dismantling organizational silos and fostering tight collaboration across the entire software lifecycle – from requirements gathering to production monitoring – DevOps enables previously sluggish development cycles to accelerate tremendously.
DevOps combines development, QA, and IT ops to accelerate delivery
And companies are buying in to the enormous benefits:
- 80% of companies now embrace DevOps in some form, up from 66% in 2019 (TechBeacon State of DevOps Report)
- Top performing DevOps teams deploy code 208x more frequently with lead times 96x faster than low performers (DORA State of DevOps Report 2021)
- High performing DevOps teams spend 22% less time on unplanned work and rework (DORA State of DevOps Report 2021)
However, as DevOps toolchains grow more complex – with intricate orchestration required across CI/CD pipelines, infrastructure, security controls, and monitoring – inefficiencies can creep back in. Teams also struggle interpreting the firehose of fragmented data about system and code performance.
Here‘s where technologies like Robotic Process Automation (RPA) and advanced data analytics enter the scene – extending automation across under-optimized processes while providing unified visibility through insightful reports and dashboards.
What is Robotic Process Automation?
First surfacing around 2003, Robotic Process Automation (RPA) tools allow subject matter experts and developers to build "software robots" that automate repetitive digital tasks previously performed manually. Unlike traditional code-centric process automation, RPA provides a low code approach using simple graphical interfaces even non-programmers can navigate.
Some key characteristics of RPA systems like UiPath, Blue Prism, and Automation Anywhere include:
- User interface interaction – RPA bots automate processes by interacting with application UIs just as human users would
- Cross-platform capabilities – Bots connect across web apps, mobile apps, legacy systems, APIs and more
- Integrations – RPA seamlessly invokes capabilities of corporate systems like ERP, CRM and data warehouses
- Resiliency – Bot workflows incorporate error handling and self-healing capabilities
- Auditability – Bots extensively log activities for compliance reporting
With rapid time to production and strong ROI – typical RPA projects break even in under 6 months (Forrester) – RPA adoption has soared in recent years:
- 1 million+ RPA licenses already deployed globally
- RPA market projected to reach $13.74 billion by 2028 (Grand View Research)
RPA market poised for steep growth trajectory (Source: Grand View Research)
Now let‘s explore leading applications of RPA specifically in accelerating DevOps processes.
Top 5 RPA Use Cases to Streamline DevOps
RPA and DevOps are natural partners – with RPA optimizing productivity through automation while DevOps powers rapid updates that expand functionality.
Let‘s examine top use cases where RPA supercharges DevOps workflows:
1. Infrastructure Monitoring and Management
Like any complex 24/7 software system, DevOps toolchains rely on intricate infrastructure foundations spanning servers, networks, containers, APIs, cloud services and more. Carefully monitoring health status, usage metrics, warning signals and errors across all components is essential for preventing outages.
However, performing the required checks manually requires vast effort. Ops team members struggle interpreting disconnected monitoring dashboards and reports across diverse systems. RPA to the rescue!
DevOps teams employ RPA bots to:
- Log into servers, containers, cloud services to systematically check status and configurations
- Crawl through log files to identify anomalies
- Connect monitoring dashboards into unified views with drill-down capabilities
- Generate hourly/daily reports detailing infrastructure status sent directly to team inboxes
For example, leading gaming platform FanDuel automated over 70 daily server monitoring tasks using RPA, freeing up 8 hours per week in staff time.
2. Test Automation
Rigorously testing code changes at multiple levels – unit testing, integration testing, user journey testing, etc. – is fundamental to DevOps. However, executing test suites manually, fixing intermittent failures, analyzing results eat ridiculous amounts of QA and test engineer cycles.
RPA bots excel at tirelessly running test suites around the clock. By integrating with native testing tools like Selenium and Appium, RPA multiplies testing capacity while delivering consistent, rapid-fire test execution.
DevOps + RPA testing scenarios include:
- Automatically running full regression test suites with each code deployment
- Screenshot visual testing to catch UI rendering issues
- Generating analytics identifying flaky tests needing review
- Automating data setup/teardown and test environment refresh
For example, leading science publisher Elsevier achieved 90% faster test cycles using RPA to accelerate execution.
3. Compliance and Regulation Management
Strict regulations like SOC 2, ISO 27001, and regional data privacy rules require considerable effort documenting controls and demonstrating compliance. Non-compliance risks major fines and reputation damage.
Here RPA bots lend a hand by continually scanning for new regulations, identifying affected data and code, and auto-documenting required controls. Bots also compile detailed audit trails regulators can inspect.
Compliance assistance use cases include:
- Monitoring websites of regulatory bodies for new rulings
- Assessing systems storing protected data (e.g PII) to quantify risk
- Updating data classifications (e.g. PCI, PII) based on scanned content
- Generating reports demonstrating controls for security certifications
Top healthcare payer Highmark Health tapped RPA to validate PCI compliance across 350 applications – reducing labor needs by 75%.
4. Reporting and Analytics
Data insights are the lifeblood enabling DevOps teams to accelerate delivery while boosting quality. However, with metrics streaming in from dozens of systems – code repos, test tools, incident trackers, infrastructure dashboards and more – compiling holistic views is challenging.
RPA provides a secret reporting weapon here by aggregating scattered data into unified views. Bots auto-generate rich visual reports spotlighting trends, anomalies and emerging patterns team members can action.
DevOps analytics use cases powered by RPA include:
- Consolidated views of test coverage and pass rates per app version
- Dashboards revealing leading types/sources of production incidents
- Reports profiling infrastructure usage patterns to optimize capacities
- EmailDigests flagging stuck releases or code repos needing attention
For example, food services leader Aramark tapped RPA to extract scattered data into Tableau views revealing actionable insights that boosted worker productivity 7%.
5. Customer Support Functions
While just emerging, RPA also shows early promise helping DevOps teams directly assist customers. Bots handle common product support queries like order status, freeing staff. Bots also compile customer usage data and feedback offering product input.
Early customer support use cases include:
- Automating order/license lookup using CRM system APIs
- Answering common support questions via chatbots
- Analyzing quality rating trends to identify issues
- Generating usage reports detailing popular features
For example, UiPath‘s own customer support portal combines RPA with AI for enhanced self-service and automation.
While the above use cases showcase proven ways RPA multiplies productivity across the DevOps toolchain, emerging innovations promise even more revolutionary capabilities soon.
Cutting Edge RPA Innovations to Watch
Beyond the bread-and-butter use cases detailed above, RPA is a rapidly evolving technology unlocking ever-more-advanced automation opportunities.
Here we profile bleeding edge RPA capabilities beginning to penetrate DevOps workflows:
Enhanced Cognitive Capabilities Through AI Infusion
By combining RPA with artificial intelligence (AI) and machine learning algorithms, next generation intelligent process automation (IPA) tools emerging can handle far more complex workflows.
Capabilities like computer vision, natural language processing, and predictive analytics allow IPA bots to interpret semi-structured data, respond to language queries, automatically trigger interventions, and even improve patterns through self-learning.
Within DevOps, IPA unlocks scenarios like:
- Reading log files then diagnosing/dispatching solutions without human oversight
- Answering developer queries about code performance or best practices
- Monitoring infrastructure then auto-scaling capacities up/down in response to demand shifts
- Reviewing new code commits across multiple repos and flagging priority issues via ML
For example, leading RPA provider Automation Anywhere recently introduced an AI-powered DevOps offering called AutoDevOps specifically targeting such next-gen use cases.
End-to-End Process Discovery and Documentation
Understanding existing processes that cross application and toolchain boundaries can be hugely difficult for DevOps teams. Getting clear documentation is even harder.
Emerging process discovery capabilities built into tools like UiPath allow users to turn on recording while executing workflows. RPA then auto-generates full documentation detailing steps, screengrabs, data connections and more.
Such intelligent process mining shines light into previous black boxes – accelerating optimization. Usability testing is also streamlined by assessing holistic journeys spanning tools.
Remote Execution and Control of Physical Tasks
While RPA centers on digital process automation, new solutions like UiPath‘s Task Capture allow users to directly observe and remotely control physical tasks by field staff via mobile feeds.
In DevOps this unlocks fresh use cases like:
- Virtual walkthroughs of server rooms or data centers to diagnose hardware issues
- Augmenting technicians installing/upgrading equipment with remote expert guidance
- Confirming hardware changes/reboots executed properly without needing eyes on site
Integrated Third Party Service Orchestration
Many RPA platforms now integrate directly with common DevOps tools – like Jenkins, Selenium and Jira – via plugins and connectors. This reduces friction invoking toolchain apps from bot automations.
We‘ll also likely see high level orchestration capabilities emerge allowing bot developers to link together multi-step workflows spanning various tools through easy to define process logic – no coding required.
The Vital Role of Analytics in RPA Success
Given the firehose of valuable automation opportunities, how can DevOps data analytics pros help identify and size the best RPA prospects?
And as use cases launch, how can analytics guarantee RPA projects continue optimizing processes and delivering demonstrated value?
Finding Your Automation Sweet Spot with Data
When assessing workflow automation potential, analytics helps:
- Quantify costs of current manual processes using time tracking data
- Profile process value regarding release velocity, system uptime and quality gains
- Estimate automation potential considering input variability, error rates and tool integration complexity factors
- Rank opportunities via data-driven criteria scoring integration difficulty, costs, risks and process criticality dimensions
This drives data-backed automation roadmaps staff readily support.
Monitoring RPA Performance with Actionable Intelligence
Once use cases launch, analytics is crucial for continual tuning by:
- Tracking productivity gains using before/after time consumption, system performance and quality metrics
- Monitoring bot health via execution logs assessing reliability, exceptions and maintenance needs
- Identifying new optimization opportunities like additional handy integrations based on manual workaround tracking
analytics aligns RPA outcomes to business KPIs like release frequency, system robustness and customer satisfaction.
Combining RPA and AI for Next-Gen Automation IQ
Emerging RPA platforms fusing robotic automation with artificial intelligence like machine learning, computer vision and natural language open exciting new vistas for supersmart DevOps automation.
- Natural language interfaces allowing conversational access to code and system state data
- Predictive models identifying brewing issues like clusters of unstable unit tests or impending disk space shortages days before they risk progress
- Image and signal analysis detecting problems from infrastructure camera feeds before outages occur
- Anomaly detection across log streams highlighting unseen issues early
Here analytics both informs the machine learning models powering these innovative capabilities and measures their impact.
Best Practices for Smooth RPA Adoption
Given the enormous potential, how can DevOps leads confidently get started with RPA then expand initiatives across the organization?
Follow these pro tips and frameworks for ensuring automation success:
Accelerate Adoption with an RPA Center of Excellence
Centralized RPA expertise accelerates tooling mastery and best practice sharing across ops teams. RPA CoEs also offer vital change management guidance helping familiarize staff.
Extensively Pilot Before Broad Initiatives
Start small with targeted quick win automation opportunities before tackling trickier mission critical processes. This allows building internal capability while demonstrating benefits.
Plan End-to-End Governance Up Front
Auditability, access controls and dependency tracking are crucial for smooth bot performance. Analytics dashboards tracking automation health are also vital for ongoing oversight.
Involve Staff Early for Requirements Gathering
Operator input ensures designed automations adequately handle real world complexities while gaining user buy-in to changes.
Combine Innovation Sprints with Ongoing Improvement Cycles
Locking in big leap ahead initiatives while also incrementally enhancing and extending existing automations balances innovation with scale.
Analyze Benefits Religiously Using Staff Time Saved and Cycle Time Reduced
Quantifying before and after productivity gains and monitoring trends proves value, highlights opportunities, and builds support for further RPA funding.
The Exciting Outlook: Surging RPA Assisted DevOps Productivity
With IDC predicting that 60% of enterprises will be on board with AI-enabled RPA by 2024 as tools grow smarter and easier to implement in diverse apps (IDC Link), the writing is on the wall:
The combination of DevOps and RPA is set to drive previously unthinkable acceleration in software delivery speed, robustness and flexibility over the coming decade.
And advanced analytics will provide the fuel powering this revolution under the hood – guiding teams towards automation opportunities then guaranteeing initiatives realize their full, game-changing potential.
Are you looking to assess or launch RPA assisted DevOps initiatives? Reach out to our team for in-depth guidance.