But something felt heavier.

Developers were working harder to maintain pace, not improve it. Every sprint carried a hidden tax: triaging alerts from the last release, manually reviewing the same categories of defects, fixing integration issues that surprised no one except the part of the process that was supposed to catch them.

It is a systems problem, the kind that compounds quietly and only becomes visible when it’s expensive to fix.

This is the silent slowdown: a gradual erosion of your team’s capacity, quality, and motivation, one manual process at a time.

What the Silent Slowdown Actually Is

Software delivery is a compounding system. Every manual step that could be automated, every risk flagged too late, every post-release incident that cost two engineers three days to resolve. These don’t stay isolated. They accumulate.

The silent slowdown is what happens when a team’s operational overhead grows faster than its output. Sprint-by-sprint, it’s invisible. Zoom out six months, and the gap between effort and value delivered becomes undeniable.

It looks like this:

1. Release cycles that drift longer without a clear root cause
2. Defect clusters that resurface in the same architectural areas sprint after sprint
3. Senior engineers spending 35–45% of their week on review and triage, not design and architecture
4. Planning sessions driven by gut instinct rather than sprint history data
5. A technical debt figure no one can quantify, but everyone knows is growing

None of these are emergencies in isolation. Together, they represent hundreds of hours of lost capacity per quarter and a development culture that is increasingly reactive by design.

The 3 Places It’s Already Happening in Your Org

1. Code Review Is Your Biggest Unexamined Bottleneck

Code review, done well, improves quality. Done manually at scale, it becomes your single largest hidden time sink.

The average developer spends 4- 6 hours per week in code review. A significant portion of that time catches issues that should have been surfaced before a single human eye touched the PR: style violations, duplicated logic, test coverage gaps, dependency conflicts.

When review time is dominated by preventable issues, two things happen. First, reviewers get fatigued and miss the things that actually matter: architectural decisions, security implications, logical errors. Second, developers wait. PR queues back up. Deployment frequency drops. And your engineering leadership, watching velocity metrics, has no visibility into why.

2. Your Testing Strategy Is Built for Yesterday’s Codebase

Most QA processes were designed when codebases were smaller and release cycles were longer. As systems scale across more microservices, more third-party dependencies, and more edge cases, test suites built for simpler architectures become structurally inadequate.

The result is a lose-lose choice: release with lower confidence or invest exponentially more time in manual testing. Neither is sustainable beyond one or two team-growth cycles.

Predictive defect detection changes this equation. Instead of testing everything at equal priority, you concentrate effort on the highest-risk areas: the components statistically most likely to regress based on the specific nature of the changes made. Teams adopting this approach consistently report 30 to 50% reductions in post-release incidents without increasing testing time. The hours that testing previously consumed get redirected to feature work.

3. Leadership Is Making Strategic Decisions on Stale Data

Engineering leadership typically makes resourcing and prioritization decisions based on meeting notes, retrospective summaries, and developer feedback filtered through two or three layers of reporting. The issue is structural. Real-time, quantified data on delivery bottlenecks, defect distribution, and sprint predictability rarely reaches decision-making workflows.

The consequence: resource allocation that is consistently one step behind the actual problem. You hire for the issue from last quarter. You invest in the tool that solves last sprint’s pain. You run a retrospective on a cause that’s already evolved into something else. And by the time each decision takes effect, the problem has moved.

40-50%  faster defect resolution when issues are surfaced earlier in the cycle

30-35%  improvement in deployment frequency with pipeline intelligence

1,300+  developer-hours lost per quarter to manual overhead in a 20-person team

The Compounding Math Nobody Talks About

Here is a back-of-envelope calculation most engineering leaders should run but rarely do.

If your team of 20 developers each spends five hours per week on tasks that better tooling could handle (routine review feedback, manual test orchestration, deployment verification, documentation updates), that is 100 developer-hours per week in pure operational overhead.

Over a quarter: 1,300 hours. At an average fully-loaded developer cost of $75 per hour, that is $97,500 per quarter spent on work that does not require senior engineering judgment.

But the real cost is not the labor. It is the opportunity cost. What would those 1,300 hours have built? What technical debt would have been addressed? What product feature would have shipped a sprint earlier, gotten to market sooner, and closed a deal?

Why Teams Know This and Still Don’t Change

Three patterns show up consistently. They are more human than technical.

Pattern 1: The Pilot That Never Scaled

A team runs a proof of concept. It works. It gets celebrated in a retrospective. Then it sits in a single team’s workflow while the rest of the organization continues exactly as before.

The missing piece is never the technology. It is the operational playbook for scaling what worked: who owns the rollout, how results are measured, and how the case for the next step gets made. Without that, pilots become organizational trophies.

Pattern 2: The Complexity Excuse

Teams convince themselves that meaningful change requires data scientists, enterprise contracts, and a multi-year transformation programme. The belief: “we are not ready yet.”

In practice, the highest-ROI improvements in software delivery are surgical, not systemic. Automating a specific part of your PR review process. Introducing defect prediction for your highest-risk service. Neither requires a transformation programme. Both can return measurable value within 90 days. The readiness question is not “is the organisation ready?” It is “what is the smallest intervention that delivers a measurable result?”

Pattern 3: The Misread Threat

Some developers interpret any tool that surfaces code quality issues or flags risks as a threat to their professional judgment. It is not. It is a redistribution of where that judgment gets applied.

The developers best positioned for the next decade are the ones who use better tooling to operate above the noise: reviewing architectural decisions instead of style violations, focusing on user-facing impact instead of routine regressions. That is a career expansion, not a contraction.

5-Step Audit: Find Your Silent Slowdown

Run this against your current delivery process. The output is a clear map of where capacity is being lost and what to address first.

What Fixing It Actually Looks Like

Teams that successfully shift from reactive to predictive development share a few consistent behaviors. None of them started with a large-scale transformation.

1. Start with a friction audit: Map your delivery cycle before introducing anything. Identify the three highest-cost manual processes: where time is being lost, where defects recur, and where decisions are made on insufficient data. That map becomes your implementation priority list.
2. Measure before and after: Vague improvements don’t sustain organizational change. Track specific metrics: PR review time, post-release incident rate, sprint predictability, mean time to resolve. When numbers move, the next leadership conversation becomes simple.
3. Treat tooling adoption as a product problem: Developer adoption of internal tools follows the same logic as user adoption of any product. If onboarding is painful, usage drops off. If feedback loops are slow, trust doesn’t build. Treat your rollout with the same rigor you apply to a customer-facing release.
4. Scale from a single win: Pick one high-friction process, reduce it measurably in 90 days, document the result, and use it to build the case for the next intervention. Compounding starts with a single data point.

What the Data Shows from Early Movers

The results from teams that have made this shift are consistent enough to be instructive.

Teams using predictive defect analysis resolve issues 40-50% faster, because problems surface earlier when they are cheaper and simpler to fix.

Organisations that introduced pipeline intelligence into their CI/CD workflows report 25-35% improvements in deployment frequency without proportional increases in release incidents. The engineering effort previously consumed by manual verification gets redirected to feature delivery.

On retention: engineers who move from firefighting-heavy environments to higher-leverage work stay longer. The correlation between meaningful work and engineer retention is well-documented. What is less discussed is how much attrition is driven by the quiet drain of operational overhead that accumulates, unchecked, over 12-18 months.

The One Decision That Separates High-Performing Teams

The leaders who close the gap are not the ones who wait for organisational readiness, a better budget cycle, or a transformation initiative to land.

They identify one high-friction process in their current delivery cycle. They reduce it, measurably and with documented results, in the next 90 days. And they use that result to build the case for the next intervention.

That is not a strategy. That is a discipline. And it is the only thing that separates teams compounding their advantage from teams compounding their overhead.

The slowdown is silent. The decision to stop it does not have to be.

The post The Silent Slowdown: The hidden overhead draining your software delivery and how to find it. appeared first on Sandhata.

There’s nothing quite as frustrating as hearing “We’re almost there” when it comes to DevOps automation.

For most enterprises using PEGA, especially in complex, regulated environments like telecom, “almost automated” really means:

• Partial scripts that break mid-way.
• Teams are stuck doing sunrise and sunset checks manually.
• Rollbacks that aren’t rollbacks at all (just a rushed “fix forward” scramble).
• A deployment process so fragile it feels like defusing a bomb with oven mitts.

our client was no different. Their PEGA AOM and VADR systems had become a labyrinth of partial automations, manual file drops, and endless approvals.

When 14+ engineers are stuck pushing buttons, no one’s innovating

Let’s put some numbers on it.

At one point, our client had 17 separate pipelines in its PEGA deployment chain: 12 for AOM and 5 for VADR. But only PEGA artefacts were automated; SQL scripts and CSV files still needed manual intervention.

14+ people were directly involved every time they pushed an update. That meant:

• Long hours on calls coordinating manual steps.
• Higher chance of human error.
• Slow response to production issues.
• Engineers who should be building value are stuck babysitting deployments.

They needed a system that didn’t just look good in a slide deck but actually worked reliably in the real world.

Phase 1: Proving the concept (but still chained to manual tasks)

The first push was to introduce PEGA Deployment Manager (PDM).

Code deployment? Automated.
Configuration files and database updates? Still manual.

This partial fix did reduce some of the friction, but didn’t break free from the core problem: too many manual dependencies.

It was like adding an electric starter to a car that still had flat tires. Better, but not good enough to win any races.

Phase 2: The real leap , End-to-end Azure DevOps automation

Our client knew they had to go further.

Phase 2 wasn’t about tinkering; it was about rethinking deployments from the ground up:

• Code and package deployments are now fully handled by Azure DevOps pipelines, integrated tightly with PEGA’s PRPCServiceUtils.
• CSV and file deployments? Automated. Files pushed from SharePoint to S3 are picked up and deployed instantly, with no human in the loop.
• SQL and database scripts? Automated and version-controlled. No more manual approvals at midnight or rushed fixes.
• Rollbacks? Properly integrated, tested, and reliable. 

Even restarts and sanity checks have been moved to automated pipelines. Python scripts run server restarts and log cleanups seamlessly. Automated sanity reports pull data from CloudWatch, AppDynamics, and PEGA; then roll it up in one clear, consolidated email.

The final piece? A transparent Azure DevOps dashboard that gave leaders a real-time, no-excuses view into every pipeline, every environment, every deployment.

How our client Quietly Rewired Its Entire Software Delivery Model, By Ditching Manual PEGA Deployments

This isn’t a story about shaving a few hours off a release cycle. It’s about removing friction that teams had accepted as normal for far too long.

When our client moved to a fully automated, end-to-end deployment model for PEGA, it forced a shift that went beyond engineering. It reshaped how product, operations, and delivery teams worked together,and what they expected from their own processes.

Here’s what actually changed.

1. No More Crowds Around the Button

Before automation, deployments looked like this:

• Fourteen engineers involved
• CSV files passed around like hot potatoes
• SQL scripts manually reviewed
• People chasing approvals
• Artefacts manually pushed
• Everyone hoping it wouldn’t break in production

That wasn’t resilience. It was ritual.  Every extra person added more surface area for errors,and more chances for something to fall through the cracks. With a fully automated pipeline, those tasks didn’t get reassigned. They got removed.

No one had to “own the deployment” anymore.  It ran in the background. Quietly. Predictably.
The engineering team got their time back,to write code, fix real problems, and stop treating Friday releases like a dare.

2. Rollback = One Click, Not One Crisis

Ask any team what “rollback” really means and you’ll usually get some version of:

“We just fix it forward and pray.”

our client changed that by baking rollback into their deployment pipeline,not as a last-minute patch, but as a normal, tested, version-controlled step.

When something went wrong, the response wasn’t:

“Okay, everyone get on a call.”

It was:

“Just hit revert.”

No scavenging through old backups. No rewriting scripts. No late-night war rooms.Just one confident step back to a known-good state.

3. The Speed Boost That Didn’t Come at the Cost of Sleep

It’s easy to talk about velocity. It’s harder to show how that speed actually helped.

For our client, deployment lead times dropped by over 60%. But that wasn’t the headline.

The real impact showed up when:

• A critical update reached customers in days, not weeks
• A partner integration was fixed before it even became a problem
• A new feature went live while competitors were still gathering approvals

Speed mattered because it was sustainable. It didn’t rely on heroics. It didn’t come at the cost of sleep.  It was baked into the system.

4. From “Let’s Hope We’re Covered” to Actual Audit Readiness

In regulated industries, manual processes aren’t just inefficient.
They’re dangerous.

Every undocumented fix, every email-based approval, every untracked config change is a liability waiting to surface during an audit,or worse, a breach investigation.

Before automation, our client’s compliance trail was scattered:

• Half in spreadsheets
• Some in email chains
• Some just… lost

Now?

Every deployment step is logged.
Every change is version-controlled.
Every approval has a timestamp and an owner.

If the auditors show up tomorrow, the answer isn’t.

“Let us pull together some reports.”

It’s:

“Here’s the full record.”

Risk didn’t vanish, but it became visible and manageable.
And that changed how everyone,from compliance officers to security teams,slept at night.

5. Finally, Everyone’s Looking at the Same Dashboard

Here’s how it used to work:

The engineering lead sent a Slack update.
The release manager shared a spreadsheet.
The PM forwarded an email.
And the exec still had no idea what stage the deployment was in.

Now, our client has a single source of truth.

With Azure DevOps dashboards and real-time deployment tracking, leaders can:

• See what’s going live today
• Check which releases passed quality gates
• Spot where something’s stuck, before it becomes a blocker

No more piecing together the truth from four different channels.
No more surprises in Monday standups.

This kind of visibility builds trust , because everyone’s reading from the same page.
Operations isn’t chasing updates. Business isn’t left in the dark.
And engineers don’t waste time explaining what’s already visible on the board.

The bigger win: unlocking engineering focus

When you remove repetitive manual work from talented engineers, you’re not just freeing up a few hours; you’re fundamentally changing the trajectory of what your teams can accomplish.

At our client, DevOps engineers had become the last line of defense in a system overloaded with manual checks and fragile processes. Every deployment cycle meant long calls, manual CSV file pushes, cross-team sign-offs, and the constant anxiety of “what if this breaks production?” Instead of building new capabilities or optimizing services for end users, they were stuck running playbooks that felt more like insurance policies than engineering work.

By automating the entire PEGA deployment lifecycle from code and package promotion to database and file configurations, including restarts and sanity checks, the engineers could finally shift their focus.

Here’s what that looked like in practice:

Proactive reliability engineering: Instead of reacting to incidents after each release, the team began investing in improving system resilience, strengthening rollback strategies, and tightening monitoring for early detection.

Accelerating innovation: Engineers were able to dedicate time to refining pipelines further, integrating advanced automated tests, and contributing to platform improvements that previously sat at the bottom of the backlog.

Enhanced cross-team collaboration: Freed from the grind of manual approvals and handoffs, DevOps engineers became strategic partners to development and product teams, influencing architecture decisions and delivery timelines from the start, not just at the deployment gate.

Talent retention and morale boost: Top engineers don’t want to be button-pushers. By eliminating repetitive tasks, our client reduced burnout and increased satisfaction, turning DevOps roles into high-leverage, intellectually rewarding positions.

Faster customer-facing improvements: With operational friction removed, teams could push meaningful updates and new features to production faster and more confidently, directly impacting customer satisfaction and business agility.

In short, automation didn’t just make deployments faster; it fundamentally redefined the role of engineering within the business.

The DevOps team went from being perceived as “the deployment team” –  always fixing, always patching –  to becoming force multipliers who empower the entire organization to move at the speed of market demands.

That’s the real value: unlocking the strategic potential of your most skilled people, and finally letting them do the work they were hired (and want) to do.

What does this mean for you

If your team is still stuck in “almost automated” deployments, here’s what you’re paying for:

• Slow feature rollouts that frustrate business stakeholders.
• Higher operational costs from wasted engineering hours.
• Increased risk of errors, outages, and compliance violations.
• Talent attrition, as top engineers tire of being “click monkeys.”

What our client achieved with Sandhata wasn’t a magic overnight fix. It was a systematic, phased transformation that replaced manual drudgery with automation precision and turned deployment from a dreaded bottleneck into a competitive advantage.

Want to see where your real bottlenecks are hiding?

Most organisations think they know where their delays come from; they’re almost always wrong.

We’ve helped large enterprises (like our client) expose their hidden inefficiencies and rebuild pipelines that don’t just work, but work brilliantly.

If you’re tired of “almost automated” and ready for deployments that actually deliver, let’s talk.

One last thought

You don’t need more “best practices” slides. You need a concrete, step-by-step plan that actually sticks.

We know how to build it. Let’s make your deployments as fast, reliable, and invisible as they should be: https://www.sandhata.com/contact-us

The post How our client Transformed Their PEGA Deployments From Manual Headaches to Fully Automated Wins (Without Burning Out Their Ops Team) appeared first on Sandhata.

The solution? AI-powered fraud detection. Sandhata’s ClaimsMadeEasy, built on the Agentic AI framework, is redefining how insurance companies handle claims by eliminating fraud in real-time—without human intervention.

The Problem: Why Traditional Fraud Detection Fails

Detecting insurance fraud has historically been like finding a needle in a haystack. Human investigators spend weeks analyzing claims, cross-checking data across multiple sources, and relying on subjective judgment. The lack of real-time insights makes it easy for fraudulent claims to slip through the cracks. Different teams use different methods, leading to inconsistencies in fraud assessments. Worse, data silos prevent insurers from identifying patterns across claims, which means fraudsters can repeatedly exploit weaknesses in the system.

Most insurers operate reactively, detecting fraud only after payouts have been made. By then, the damage is done. The need for an automated, intelligent, and real-time fraud detection system has never been more urgent.

The AI Revolution: How ClaimsMadeEasy Detects Fraud in Real-Time

Agentic AI is a self-learning, autonomous system that leverages machine learning, natural language processing (NLP), and vector databases to identify fraudulent claims instantly. By continuously analyzing vast datasets and learning from historical fraud cases, it can detect anomalies and flag suspicious claims before they are paid out.

Instant Claim Submission & AI-Powered Data Extraction

The claims process begins the moment a customer submits their request, whether through email or a web portal. Instead of relying on manual data entry, ClaimsMadeEasy leverages advanced Optical Character Recognition (OCR) technology to scan and extract key details from claim documents, receipts, and supporting evidence. The AI identifies critical information such as policy numbers, dates, and monetary values, automatically pre-filling claim details into the processing pipeline. This automation not only accelerates claims processing but also ensures accuracy by minimizing human errors. Any discrepancies, such as mismatched details or altered documents, are flagged instantly for further review.

AI-Driven Fraud Detection & Risk Analysis

Once the claim data is extracted, it is cross-referenced against historical claims, police reports, and financial transactions. ClaimsMadeEasy employs machine learning algorithms to detect fraud patterns, including duplicate claims filed across multiple insurers, falsified medical reports, and digitally manipulated receipts. The system continuously learns from new fraud cases, refining its ability to detect emerging fraud tactics. Unlike rule-based systems, which rely on static conditions, AI evolves dynamically, identifying even the most sophisticated fraudulent schemes.

AI-Based Decision Making

After risk analysis, ClaimsMadeEasy categorizes claims into three buckets: Legitimate, Potential Fraud, or Definite Fraud. If a claim is deemed fraudulent, it is automatically rejected with a detailed AI-generated explanation, reducing the burden on human investigators. In cases where the AI detects uncertainty, the claim is flagged for human review. Investigators receive AI-generated risk reports that provide a comprehensive breakdown of detected anomalies, recommended actions, and a confidence score for each decision. This hybrid approach ensures that legitimate claims are processed quickly while fraudulent claims are stopped in their tracks.

Automated Communication & Compliance

Transparency is critical in fraud detection. ClaimsMadeEasy ensures that all stakeholders—customers, insurers, and regulators—are kept informed throughout the process. Customers receive real-time claim status updates, eliminating frustration and uncertainty. Investigators are provided with AI-generated fraud reports that include a summary of detected risks and suggested next steps. Every event in the claim lifecycle is logged for regulatory compliance and audit purposes, ensuring that insurers remain compliant with industry standards and legal requirements.

Real-World AI Success Stories in Insurance

Several insurance giants have already adopted AI-first fraud detection models, with game-changing results.

Lemonade, an insurtech pioneer, has demonstrated the power of AI-driven claims processing by approving claims in seconds. Their AI-powered chatbot, Jim, once processed a claim in just three seconds, thanks to real-time fraud detection and instant verification. This level of automation not only reduces fraud but also enhances customer satisfaction.

Allstate has taken a hybrid approach by integrating AI-driven predictive analytics with human expertise. Their fraud detection model continuously learns from human investigators, improving fraud detection precision while reducing false positives. This approach has saved millions in fraudulent payouts while maintaining accuracy.

GEICO leverages AI not only for fraud detection but also for risk profiling based on customer behavior. By analyzing past claims, transaction histories, and behavioral patterns, AI enables GEICO to ensure fair claims processing while identifying potential fraud with greater efficiency.

Why Agentic AI is a Game-Changer for Insurance Fraud Prevention

Insurance fraud is evolving. So should fraud detection. AI-powered systems like ClaimsMadeEasy offer unparalleled advantages:

• Zero Manual Errors: AI eliminates human oversight issues, ensuring consistent and objective fraud assessments.
• Lightning-Fast Processing: Claims are processed in minutes, dramatically reducing wait times for customers.
• Massive Cost Savings: AI-driven fraud detection prevents millions in fraudulent payouts annually.
• Continuous Learning: Agentic AI adapts and evolves with every claim, refining its ability to detect fraud over time.
• Regulatory Compliance: AI ensures full documentation, audit trails, and adherence to compliance laws, mitigating legal risks for insurers.

The Cost of NOT Using AI

Ignoring AI-driven fraud detection comes at a steep cost. Without AI, insurers continue to hemorrhage revenue due to fraudulent claims. Slow, inefficient manual claims processing frustrates customers and leads to higher operational costs. Inconsistent fraud assessments increase regulatory risks, exposing insurers to potential compliance penalties and reputational damage. The longer insurers rely on outdated methods, the more they risk falling behind in an industry that is rapidly embracing AI-first solutions.

The Future of Insurance Fraud Detection: AI as the Ultimate Guardian

Fraudsters are getting smarter. AI is getting smarter, faster. With every claim processed, Agentic AI learns, evolves, and becomes more ruthless in identifying deception. Insurers who embrace AI-first fraud detection aren’t just preventing fraud—they are shaping the future of insurance.

Our CTO of the Hyperautomation Business Unit wrote this blog, bringing deep insights from the AI trenches. He will be traveling to the UK soon—a great opportunity to catch up and discuss how AI is revolutionizing insurance fraud detection.

Will your company lead the charge—or be left behind? Now is the time to evolve. Now is the time for Agentic AI.

Curious how AI can cut fraudulent payouts for your company? Get in touch with us. 

The post From Sherlock Holmes to Supercomputers: How AI is Cracking the Code on Fraud appeared first on Sandhata.

When a single throw event is triggered, the data is processed without any issues. However, if multiple message events are thrown simultaneously, only the first one is successfully processed, while the others fail.  

This is because the message catch event is busy handling the first message event. 

We recently spotted this issue with Camunda 7’s message catch event. 

Unlocking The Solution To This Challenge 

Our team of experts fabricated a groundbreaking “Generic Message Buffering” logic, with which we’ve revolutionised the way messages are processed asynchronously, effectively resolving any scenario you may encounter.   

Fig. 1.1 unveils the powerful list of message catch events eagerly awaiting the trigger. 

Fig1.1

 In Figure 2.1, we present the logic for Generic Message Buffering. By sending a POST request, we can activate the BPM process described below. Additionally, we have the ability to specify the retry count and retry delay as request parameters. Once the request is triggered, we conduct a basic validation to ensure all necessary information, such as message name, payload, process instance id, and business key, is present.  

If all the required data is available, we proceed to send the message to its destination within the “Message Send Task” depicted in Figure 2.1.  

If the message is successfully picked up and processed by the message catch event, we encounter no issues.  

Fig2.1

In a situation where the message catch event is already occupied processing another message, throwing our own message will result in failure. This is a common occurrence in real-time scenarios, and it is up to the developers to determine how the flow should be handled in such cases. 

In our specific case, the failed message will be caught by the boundary event. The error message will then be examined in detail within the “Read Error Message” process (Fig 2.1). 

In the event of an error, we can:  

• Distinguish between errors that require retry logic.  
• Determine if it is a business error or a technical error.  
• Necessitate the involvement of technical experts, if it happens to be a business error 

When encountering a retriable error, we make an attempt to retry the operation. The number of retries and the delay between retries is determined by the input request. 

Potential Scenarios In Re-try

In the context of re-try logic, there are two potential scenarios to consider: 

• In one of the re-try attempts, the message can be successfully delivered to the end system. 

Fig 3.1

• Alternatively, if the maximum number of re-try attempts is reached without success, the message will be marked as an error. 

 If the maximum re-try attempts have been exceeded and the message still hasn’t been delivered successfully, it will require manual re-processing (see Fig 3.2). 

Fig 3.2

  Key Advantages  

• Streamlined Error Handling: By effectively managing busy message catch events, we can significantly mitigate potential errors during data processing. 

• Efficient Data Re-processing: Our approach allows for a substantial reduction in the amount of data that needs to undergo re-processing. 

• Versatile Solution: Utilizing the same bpmn models, we can easily adapt and apply them to various similar use cases simply by modifying the message name. 

 Optimizing Efficiency 

 In order to optimize efficiency, it is vital that both the target BPM and the message buffering BPM are connected to a centralized database. This will allow for seamless integration and streamlined performance. Maintaining a unified database instance is key to achieving optimal results. 

The post Generic Message Buffering | Camunda 7 appeared first on Sandhata.

Initially the DevOps movement stemmed from the agile, innovative, born-on-the-web companies who were able to change their way of working quickly. However, the principles and practices are completely transferrable to all organisation types. This includes those at the other end of the spectrum – where processes are very tightly controlled and regulation dictates a significant part of the business investment.

.

Why highly regulated companies initially resisted DevOps

When DevOps first emerged, the major concerns for companies following strict regulations were to maintain their security controls and governance processes. Deploying changes more frequently was viewed as a risk to security and the governance controls which were firmly in place.
Another reason for not moving towards a DevOps way of working was the “segregation of duties” requirement, making it impossible for developers to deploy into production – thereby not being able to “do” DevOps.

.

‘Is DevOps really worth it?’

There were established, formalised processes in place which satisfied all the regulatory needs. For some organisations, it seemed like making any changes to these processes would be more work than what would be saved by automation. This meant that the processes stagnated and hindered future change.

Despite this, the majority of executives now believe that DevOps is a crucial ingredient in making sure that their company can adhere to new regulations and stay ahead of their competitors in compliance adoption.

.

Why DevOps is actually better for highly regulated industries

DevOps can be your biggest ally when it comes to regulation and compliance – as long as you follow a few simple rules:

.

1. View auditors (regulators) as stakeholders in the DevOps journey.

By collaborating with the auditors in the same way as other stakeholders, you can engage with them early in the process and ensure details get agreed upon without costly change further down the line.Getting buy-in from auditors on any technical solutions at an early stage reduces the likelihood of change requests, missed deadlines, and non-compliance. By working closely with auditors, you will ensure that the company has a strong understanding of the level of compliance needed for each different aspect or component of their systems. This helps avoid the waste of being compliant in areas which are not necessary.

Sometimes you will only be able to clearly interpret a regulation through close collaboration with auditors. This avoids misunderstanding and unnecessary rework. In some cases, working closely with the regulatory body can highlight aspects which had not previously been addressed, developing a feedback loop with them. This evolving feedback means that the company will end up with a solution which is satisfactory.

Close collaboration with auditors increases trust in the processes which have been developed in your organisation.

.

2. Codify compliance requirements and policies.

Good DevOps processes will increase the amount of audit trails and governance in the process, while enabling fine-grained traceability throughout the entire delivery process. Compliance documentation can be enforced more easily as part of projects. There is generally less need for process documentation as so many tools now automatically generate any documentation required. By using collaborative sharing tools we can also reduce the problem of different document versions.

.

3. Automate your delivery pipeline end-to-end.

Automation gives reliability, repeatability, and traceability. Automation means we have a predictable outcome and fewer manual processes, which is good for auditing and tight control.By having fewer manual tasks, we can also reduce the possibility of manual error and missed processes. Automated environment provisioning improves the quality of testing and gives a high level of confidence in the change.

With end-to-end automation and process orchestration, controls are written in and embedded to processes and systems. This helps to reduce liability. Systems are designed to reduce the risk of individuals making errors, forgetting processes intricacies, or even taking malicious action. This also helps to develop a blameless culture. Bullet-proof processes means that security protocols now become built-in. Rather than see DevOps as a threat to security, it is now being viewed as the best way to mitigate risk. It also becomes a way of enforcing security, audit and compliance requirements.

Automation in all areas allows you to quickly evolve with changing regulation requirements and last-minute additions, helping you to stay ahead of your competitors who aren’t able to do that. Future regulation is likely to require reporting on the lowest-level processes, and the only way to ensure that processes are being followed reliably every time is to introduce automation, and DevOps – a culture of ongoing improvement.

.

What is happening now

Some of these companies in highly regulated industries are actually starting to lead the curve in terms of innovation and DevOps adoption now. They have seen just how powerful DevOps can be and are creating an agile business which can meet regulatory requirements and evolve with customer needs – making their business much more competitive.

.

Learn More

To discover more about how Sandhata can help you achieve DevOps success, take a look at our DevOps brochure.

The post Does DevOps Work in Highly Regulated Industries? appeared first on Sandhata.

What DevOps is NOT

I would like to share with you a few of my thoughts on what DevOps is NOT. Hopefully this will help you to keep focused on the primary message of DevOps and result in better ROI throughout your journey.

1. It is not an end goal.

There is no logical end state to DevOps as each organisation is operating in an evolving customer market. For businesses to stay competitive, they need to grow and change. DevOps is a great way to make your business more agile and seize opportunities, but tools and processes will always need to change and improve to remain fit for purpose.

2. It is not a body of knowledge.

There is no “right way” to implement DevOps in your organisation. DevOps provides the principles and highlights the goal – Business Value – which everyone should focus on. Each organisation has different challenges and takes a different path along their DevOps journey, which can all be valid.

3. A DevOps solution cannot be bought off the shelf from a vendor.

There are vendors now who are offering a full DevOps toolset, and that is great because the tools will likely integrate well together and can help to give you a joined up pipeline. However, just incorporating the tools does not mean that you are doing DevOps, it is just the first step. To really reap benefits of DevOps in the long term, you need to embrace collaboration and ensure that the tools you have chosen are fit for purpose and adding efficiency to your processes. It is very easy to use the right toolset in the wrong way, or simply to not get the maximum benefit out of it!

4. It is not sacrificing governance and compliance.

In fact, DevOps can be a real enabler for more efficient, more effective governance and compliance. The automation and streamlining processes which are part of DevOps help to focus the governance and compliance requirements and usually enable more thorough auditing.

5. It is not just automation.

If you have automated part of your delivery pipeline then that’s great, and its (probably) going to add value to your team, however there are so many aspects of DevOps which work with the automation to multiply the benefits. It is quite easy (and common) to have automated processes, but some of those processes will not have been streamlined and are actually wasteful / unnecessary. In cases like this, automation just hides the waste more effectively, but it is still there.

6. It is not just a toolset.

Using Jenkins to do CI is great, but that doesn’t mean that you have got DevOps. It’s a small part of a big journey, and you need to remember that continuous improvement is key to DevOps, so your CI strategy needs to evolve to make sure that you are continuing to meet your business goals and deliver for your teams. This means that stagnating with an embedded tool generally in not in line with DevOps.

7. It is not “using Cloud”.

Cloud is a great thing which has the power to transform your IT capability, but just using the cloud doesn’t mean that you are realising any of the potential benefits of proper DevOps.

8. It does not mean that anyone can release any change to production at any time.

Releases should be entirely in line with the business needs. Having a streamlined, highly automated delivery pipeline enables you to release changes quickly. Releases can then be easily aligned with business needs to provide maximum business value and reduce unnecessary work and wastage.

9. It is not a separate team or organisational unit.

DevOps, at its heart, is about removing silos not creating additional ones. Having a separate team to kick-start the process, come up with a strategy and adoption guidelines is a valid way of embarking on this journey. However, the “DevOps team” should be a short term measure with a view of dismantling the team once the goals have been defined and progress is being made across the organisation.

10. It is not combining the Development and Operations team and leaving it at that.

Real value won’t materialise unless changes are implemented in ways of working on the ground. A thorough and interesting article about how teams can be structured when embracing DevOps is featured on the DevOps Topologies website.

11. It is not replacing or removing Ops.

Similarly, DevOps does not mean that developers are suddenly responsible for supporting their own code in production. Operations do continue to have a very big role: they also take principles from DevOps to implement Lean practices, streamline their work tasks with an eye on business value, as well as automating tasks where appropriate. Successful DevOps means that the Dev and Ops teams have THE SAME BUSINESS GOALS. This is such an important point as traditionally the objectives of the Dev and Ops teams were at odds with each other, but they need to be aligned for the journey to be successful.

Summary

To finish on a positive note, let’s bear in mind the key things that DevOps IS:

It challenges the status quo.

By adopting a change in mindset, you are able to analyse what really adds value for your business. The pieces that are left over are simply waste.

It is a culture shift.

DevOps brings a culture shift that spreads across the whole organisation. It radically changes the way we work and engage with each other, to become more collaborative and responsive to change.

I hope this has helped your understanding of the DevOps principles and how they can improve your organisation.

Good luck on your journey!

The post What DevOps is NOT appeared first on Sandhata.

Want to know more?

Discover the Sandhata approach to DevOps and how we can support you by improving your ability to innovate and stay competitive.

The post DevOps Myths – Debunked! appeared first on Sandhata.

Life without DevOps

Most large enterprises live by formal governance processes. These are in place to police the software delivery and ensure that all deliverables follow the organisation’s standards in terms of security, design, architecture, and reusability.
Although these processes are designed to safeguard the business and keep the delivery on track, they do present a series of hoops to jump through – requiring additional time and work for each delivery.

These formal governance processes often include:

• Checklists to be completed at different stages of the project
• Workflow approval on documents – often using collaborative platforms
• Meeting with participants from various teams to discuss one aspect of the delivery in detail
• SOA governance meetings to get formal architect sign-off on the design

Why are these processes needed?
Without any kind of DevOps strategy, businesses need processes like these in order to catch any deviations from slipping through the net. The checkpoints are used to maintain stakeholders’ trust in the delivery process. However, even the strictest governance processes can’t fully prevent issues from occurring.

The illusion of collaboration
In a typical business, collaboration only happens when people from different teams sit down together in a meeting. All other collaboration outside of the meeting room becomes optional. This means that people often rely on these meetings to the point where they are seen as the only opportunity for collaboration and communication across the departmental borders. This can in turn cause team members to treat the meetings as a safety net where any issues get picked up, which means they become less focused on proactive improvement.

How DevOps can help

DevOps often involves automation of large parts of the value chain. This helps the organisation to improve the speed and quality of software delivery while also automating the required governance and control processes along the way.

DevOps makes the right thing to do the easy thing to do.

Automation is a powerful way to reduce risk, ensure compliance, and maintain governance. It can restrict access to certain systems through ‘known-good processes,’ to ensure all activity follows the standards and compliance requirements. It also provides an audit trail for governance and reporting.

With the help of DevOps you will also be able to eliminate any unnecessary processes in the business so that you don’t waste time or effort on governance you don’t need.

Life after DevOps Adoption

Once you have put DevOps processes into practice and established a collaborative culture across the business, your team members will naturally have a much higher level of trust in the delivery process and quality. This will in turn lead to less resistance to change from within and outside the team. The shared goal of all team members becomes the same: to deliver more business value faster.

Fewer hoops means faster delivery
Once you have implemented your DevOps strategy and your value chain has been streamlined, there is very little or no benefit to having the same set of formal governance processes which were previously in place. Any of these extra tasks and meetings would just mean unnecessary admin and overheads, slowing down the overall delivery. With an active DevOps culture, many of the manual, labour intensive governance tasks – such as checklists and formal SOA governance meetings – are simply no longer needed.

The culture shift
You will be able to see a cultural difference in the teams implementing DevOps, compared to those who are not. Teams following DevOps practices generally take on an attitude of proactivity and ownership, without relying on formal processes and checklists to catch any issues. Although this puts more responsibility in the hands of the individual, you will also see that an established DevOps-minded team will have standards in place which make the right thing the easy thing to do.

The cultural change is an important factor. It’s crucial that all team members feel comfortable with the new responsibility and the new processes. To the outside observer, there will be fewer controls, fewer formal approval meetings and fewer hoops to jump through. However, the governance is still very much alive – it has simply been moved under the surface.

Learn more
Want to know more about how your organisation can remain fully compliant with industry regulations, while adopting a DevOps strategy for faster and better delivery? Download our service brief: The Sandhata DevOps Approach.

The post How DevOps can improve your Governance appeared first on Sandhata.