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Tag: process improvement

Leveraging AI in DevOps for Non-Linear Scaleup
With technology evolving in leaps and bounds, AI is shaping the future of digital transformation for every business seeking speed, scalability, quality and flexibility.

Since DevOps is on the verge of delivering exceptional software development environments, DevOps in AI is undeniably the next breakthrough in technology. We’ve witnessed every industry racing toward the adoption of AI to transform their business. To support this transformation, smart automation solutions coupled with AI implementations is becoming the new normal.

So if you’re thinking about reinforcing your enterprise’s DNA by leveraging AI, DevOps principles for artificial intelligence are vital to these efforts. Here’s how businesses can unlock the true potential of AI and stay ahead of the curve.

DevOps and AI

DevOps has been s game-changer for numerous businesses. Applying AI can helps DevOps teams achieve the operational efficiency of machine learning (ML) models all the way through the journey from design to production.

Artificial intelligence undeniably enhances automation and fast-tracks the performance of DevOps. Since the digital transformation process is quite challenging for diverse businesses, DevOps and AI ensures the right processes are in place that can further bring flexibility throughout the journey.

DevOps enables continuous deployment and quick delivery of AI models via:
- Speed: Improved overall development and delivery time by reducing unnecessary activities
- Scalability: On demand and auto-scaling AI models
- Quality: Boosting cleaning datasets and eventually promotes consistent learning that further enhances AI
- Data monitoring: Decision making and computing helps handle huge amount of data with precise data monitoring
Benefits of Integrating AI in DevOps

There are endless benefits to integrating AI in DevOps.

Analysis

Data analysis is the key to business success, especially in times when there’s a huge list of competitors rushing to gain consumer trust. Since DevOps produces an enormous amount of data, it isn’t practical for humans to analyze the data without technological intervention.

Artificial intelligence’s smart analytics eases the process by identifying and solving problems in a
more systematic way, resulting in efficiency and increased consumer satisfaction.

Data Correlation

In the wider technology environment, development teams have their own set of issues and errors, which arrive while monitoring the processes. Since the communication scope is minimal and there’s a lack of mutual learning across teams, artificial intelligence can help to accelerate the learning cycle.

Artificial intelligence helps to improve data insights from multiple platforms, which, in turn promotes better communication and establishes better data correlation.

Failure Handling

Machine learning is paving the way to better error resolution based on data, and eventually artificial intelligence will be able to predict signs of failure by examining patterns. Since humans cannot match the observation capabilities of AI, certain indicators of failure can only be analyzed through AI. This further enhances the overall development processes, as quick identification helps identify issues before they can impact the software development life cycle.

Breaks Down Silos

DevOps with AI has the ability to streamline business processes and establish proper communication among diverse systems, removing barriers that slow down the development and deployment processes. Furthermore, the ability to automate, continuously integrate and enhance communication helps business leaders to focus more on other aspects that can further accelerate business growth.

Economic Growth

Since the basic function of an AI-based model is automation, it can help businesses save money and enhance human resources. For any business, eliminating repetitive human work is a tough nut to crack. but a necessity when businesses are demanding continuous innovations. Artificial intelligence could be the best way for enterprises to cut costs and eventually helps the organization to use human resources more creatively and productively.

Makes Software Intuitive

Besides endless business benefits, AI-based software is more intuitive and user-friendly, since it prioritizes the reduction of human error. While DevOps is a well-known, reliable, business-driven approach for delivering software, intermingling artificial intelligence could enhance the user experience by integrating technology more deeply into the system.

The perfect harmony of simplified interactions between humans and AI helps businesses achieve their desired goals of speed, efficiency, scalability and security.

AI-driven systems are providing promising and scalable solutions for enterprises that are on a hunt
for innovative and rapid ways to scale business growth. AI in DevOps is undoubtedly the best solution for accelerating growth through precise monitoring, automation and process improvement. This is the right time for businesses to ensure they operationalize artificial intelligence as a core business objective to stay ahead of their competitors.
June 9, 2021
Demystifying Crucial Phases of Project Management
With competition becoming more intense for development companies, project management is becoming a more integral part of every project. It’s no longer limited to the actual creation of a new project. Now, it’s about planning, communicating, collaborating and analyzing. With so many moving parts, it’s difficult to understand all of the important phases of project management.

This guide will cover all the phases of project management so you can understand how they apply to your projects in DevOps. Even if you don’t have a background in project management, you’ll be a pro in no time.

Defining Project Management

First, we need to articulate exactly what project management is. It’s the discipline of initiating, planning and executing the work of a team to achieve specific goals within a time frame. It involves a lot of interteam collaboration, and it isn’t any single activity.

Project management experts draw knowledge from areas including human resources, communications, risk management and even accounting. That being said, there are a lot of moving parts that can make project management tricky, particularly with large projects or growing teams. Knowing all of the phases of a project process makes it possible to navigate these areas effectively.

A project process is any unique action that represents the entire project internal process within DevOps. This is usually decided by the project manager. It might include instructions, procedures, plans or organizational software.

Understanding the Phases of Project Management

When you’re talking about the project life cycle, you need to understand the project management phases. Throughout the project life cycle, you should be able to define clear goals. These will include a combination of the following:
- What will be accomplished?
- How will you measure what has been accomplished?
- Who will work on the project?
- How will you monitor progress?
Having these questions above in mind will direct the project along its life cycle. Without any clear initiative or direction, you can’t expect team members to know where to go with their tasks. That’s why clear goals are key in the world of DevOps, where every task matters.

Now that you know the importance of clear goal setting, it’s time to finally define the phases of project management. There are five in total, and they are:
1. Initiation
2. Planning
3. Execution
4. Performance monitoring
5. Closure
Phase 1: Initiation

This is the beginning of the project. In this phase, you’ll want to define your goals, what the project is exactly, and how it fits into your company goals. One of the biggest pitfalls in the world of development is choosing to start a new project for technology’s sake. While there’s always something to be gained in innovation, avoid falling for shiny-object syndrome. Just because you have new technology doesn’t mean it makes sense as part of your bigger business strategy.

To make sure you’re choosing the right project for your team, ask the following questions in this phase:
- Why are you choosing this project now?
- Is this project realistic?
- What does the end result look like?
From there, you’ll need to get approval for the project from higher-ups. This might take some time, so be sure to articulate your goals clearly at this stage so you don’t waste energy. Finally, you or your project manager will create a project initiation document (PID), which is a formal outline of the purpose and requirements of a new project.

It’s smart to include developers across teams in these initial meetings to ensure they can give guidance about the technical challenges of a new project. Remember, collaboration is key at every stage.

Phase 2: Planning

Now that you’ve gotten the green light, it’s time to create a plan of action. This will involve goal setting and job responsibilities for all team members of all levels. Don’t forget about the S.M.A.R.T. goal-setting process to make sure you’re creating goals that are realistic. Remember, smart goals are specific, measurable, attainable, realistic and time-bound by a deadline.

This is the most vital stage of project management. It’s when you’ll put pen to paper and work out any kinks. Poor planning will reflect itself in every stage after, so take the time to work with teams to decide the best plan of action. Limit your deadlines for every task to 10 days or les to ensure you don’t let obstacles take away from your final product.

Phase 3: Execution

The time for planning is over and now it’s time to get down to business. This is exciting because you’ll see your project start to shape itself. Your programmers are busy coding, designers are building graphics and managers are creating progress reports.

Don’t forget to update your higher-ups during this process, even with small changes. Regular updates are necessary to make sure everything is progressing according to schedule. Conduct weekly meetings to make sure everyone knows how the entire team is progressing as well as their own next steps. A project management software is valuable at this stage.

Phase 4: Project Performance

Now that the project is completed, it’s time to reflect on your success. Did everything align with your original goals? Did you meet your deadlines? Think about your objectives, your deliverables, your cost,and the performance of the project. How did all of these things measure up?

You’ll base your future project plans on the success of your analysis at this stage. Even though the project is officially done, that doesn’t mean there isn’t still more to learn. Also, don’t forget about preparing maintenance for your software going forward. You might need a program like Amazon CloudWatch – monitoring and logging service to continue to learn about the success of your project into the future.

Phase 5: Closure

Your project is completed and your analysis is coming to a close. It’s time to follow-up on any final pieces and close the door on your project for good. This is also known as the post-mortem phase because it’s when you’ll decide your key takeaways and stop progressing.

Project managers will meet together to decide key players, project performance, and deadline analysis. How can you learn from the events of the past few days or weeks to grow during your next project? Don’t overlook this stage. It’s important to reflect on what worked and what didn’t. If something didn’t work out the way it was planning, talk about it.

Finally, host a team meeting to talk about how the project went as a group. You might have a different perspective than those under you, and it’s important that everyone feels heard. Take team member feedback seriously so you can learn from it for next time.

Manage Projects Successfully

In the world of DevOps, project management plays a lead role. Don’t overlook any stage in the process as not being necessary. It’s important to have a well-rounded, thoughtful approach to leadership. There is no perfect way to lead a group in big projects, but you can use every phrase to carefully tackle new challenges and grow your team strengths.

When you practice these stages above, they’ll come naturally. You’ll notice each stage leading gently into the next, and that’s how you make a business that’s ready for anything. Every project manager will experience some level of failure, but learn how to roll with the punches and keep moving forward.

— Wendy Dessler
December 10, 2018
The Automation of Compliance

Consider how much money your enterprise spends on audits. Smaller firms not subject to external regulation should consider how much money is spent on quality assurance—or the lack of it. A business that suffers from a customer-facing quality incident will, at minimum tarnish the brand of its services or at the high end, lose market share altogether. Larger firms that fail audits can suffer from brand tarnishing as well as high fees, not to mention further scrutiny by regulatory agencies.

Failure to perform or failure to follow proscribed processes inevitably leads to a black eye in front of customers. The entire discipline of governance is premised on avoiding both. Whether your governance practices are efficient or not—and whether your “process” as it sits today leads to quality or not—is not the subject of this review; it is how DevOps and automation can shine a spotlight on both and radically reduce the costs you spend today to achieve them.

The Spotlight of DevOps

The spotlight of DevOps begins with consistency that leads to predictability. Automating any given kind of process or technology the same way every time begins to give insight on any “holes” from things that do not work (or work well). If all that changes in your deployment process, for example, are pointers inside a configuration file and the kind of testing executed against this version of the code, you have achieved a build-once, deploy-many state of deployment automation. Operations staff who support the deployment of change or maintain the environments in the software development life cycle (SDLC) begin to isolate any issues faster because they become familiar with “how” a given kind of technology is automated to deploy. This reduces the total number of deployment errors and, in tandem, reduces the length of time it takes to fix them. Any “process” problems or weaknesses in your governance model become apparent quickly and can be fixed quickly. Limiting variation is the medicine to keep them away in future.

The spotlight of DevOps next turns to transparency. Offering your entire enterprise visibility or “read only” access to your entire DevOps tooling suite has a profound impact on the transparency of change. As an example, consider that I am a humble accountant in your firm who has submitted an agile story to improve our billing system. I may or may not, given the nature of my job, be co-located near the programming team who will fulfill this request. If our firm specializes in accounting software then perhaps I will co-locate, but if I am only requesting a change to an ancillary or support system, likely not. Without direct access to the programmers through traditional agile co-location, my visibility into the progress of change is limited to the tracking system I submitted the request within (assuming you have one, and don’t use email to do this).

If I know a programmer in MIS, perhaps I ask him at lunch how my change is coming along. But if I have read-only access to the testing systems, or to the release orchestration (RO) tooling, I may get a much better up-to-the-minute view of exactly where my change is, how it is doing from a quality assurance (QA) perspective, and when it is scheduled for release into production. Based on this news, I may make decisions about how we bill our customers in what time frame to increase overall revenue to the company. I would notify my boss of same (who also has access, but pays less attention), and of any changes that impact the new timeline I create. The marketing team (who also has access) begins to work with public relations (PR) and the advertising guys on customer announcements, etc. etc.

The net impact of having universal transparency is that audiences who are not traditionally notified or interested in the inner workings of change movement now make decisions based on it. Consider that prior to this systemic access, and the reliability of DevOps to ensure delivery, the only news you got before was from “the IT guy,” who was often wrong. It does not take long before IT loses credibility from being wrong about delivery timelines, and these other audiences begin to pay less attention (if at all). At that point, an “I’ll believe it when I see it,” attitude emerges. And this same attitude infects internal auditors because of what they hear and see from disaffected internal customers who have been misled repeatedly by the failed manual methods of the past (pre-DevOps).

Processes that are disinfected over time by the consistency of DevOps begin to change the internal company thinking about the delivery of innovation—namely that it can be counted on as accurate. Transparency offered to all audiences of a company increases the visibility into all aspects of innovation delivery and better ideas emerge, both about what to do with completed efforts and how to improve the innovation process itself. And before you know it, your governance and QA processes become efficient, reliable and predictable. Variation is eliminated or greatly reduced, and the costs of operation and innovation begin to decline.

The Forgotten Stepchild of Automation

What is rarely a matter of focus in most companies is the secondary ability to automate the compliance history of a given application. Most firms are content (translate, only able to) work with their testing tools to produce logs about a given version of an application now residing in production. A test log does show an auditor the majority of the quality related information about a given version of an application. But the log is a highly limited view of what has taken place. What is needed is the full context of innovation. Enter compliance automation.

Consider for a moment, that requirements drive the train. Why are we doing the innovation we are doing? Is this a bug-fix, a new feature, a client request, a regulatory requirement? Being able to trace the code itself back to the requirements system that drove the need adds significantly more context than is possible from mere test logs. Next, think about the number of attempts that finally resulted in this version of the application. An automated build tool (part of our DevOps suite) has this information. At this point you could evaluate how many builds and deployments took place in development before a worthwhile candidate emerged to traverse the remainder of the SDLC. An automated deployment tool (part of our DevOps suite) can trace the number of environments this version of the app was deployed into for testing and how long the overall process took to get something moved from development to production.

And finally, testing is often done from a variety of tools, with results compiled in a variety of result repositories. This information may be available from a solid RO tool that tracks the gated progress of this version of the application, but if it’s not, it still should be sitting in each of the testing tools used throughout the process. Depending on the level of inherent DevOps tool integration (i.e., using a vendor suite) much of this information may “flow” from one tool to the next. But if not, the information does still exist in the logs produced by each of the DevOps tools.

It may be well worth the application of internal programming resources to put together a tool that gathers all this information (automates the collection and presentation of) for audit purposes. Imagine the reduction in cost of supporting an audit that can gather a simple push-button report (or dashboard) of the entire life cycle of a given version of change. At that point, you have the entire context for a piece of innovation. You know what drove it (requirements). You know what it took to create it (build and deploy). You know the entire history of testing it (testing tools). And, finally, you have the entire governance process used in its deployment to production (RO tooling). But instead of examining multiple tools to collect and present it, you have created a push-button system to do so.

Impacts to the Bottom Line

The investment in adding automation to the compliance history of innovation pays off in more than one way. It is not just the ease with which history can be recited. It follows the same principles as DevOps itself. Automation introduces consistency in how audit information is gathered and presented (reducing variability from one audit to the next). Automation introduces transparency if the entire company is given “read only” access to this new system. Automation shows auditors we are not afraid to learn from our mistakes and proud of our accomplishments. And in effect, it reduces the need for audit altogether.

Typically, regulatory bodies investigate and audit firms where they are uncertain of how the results were obtained. An automated system like this one fully eliminates that need. Transparency as could be offered here, may convince auditors that attention is not warranted here. Keeping in mind the spotlight of DevOps has already improved quality and streamlined process, add to that full transparency of audit materials and the confidence of auditors regarding your company may increase significantly. Routine becomes the only reason to audit, rather than suspicion.

Invariably the “new” will sometimes result in an error that your current systems and processes (automated or not) will not catch. But this truism is recognized, and accounted for, in how DevOps enables innovation. And if you have this kind of automation in place, it can be demonstrated in how we fixed what we fixed (whether governance or QA). Your strategies in DevOps should help you achieve this level of confidence. (If you need help, feel free to contact me, I am currently looking.)

To continue the conversation, feel free to contact me.

May 26, 2016
An Engineering Think Tank to Change the World

In his famous book “Mythical Man Month,” Fred Brooks says, “Techniques proven and routine in other engineering disciplines are considered radical innovations in software engineering.”

While being very fond of engineering on the big scale, such as building millions of high-end and yet individually configured cars or double-deck aircraft or space ships, for example, I have always asked myself, why the most promising industry—software engineering—is so often not capable of doing engineering on that scale, too? Or, why are so many big enterprise IT projects painful in so many ways?

With these questions that nobody seemed to be really interested in on my mind, one day I met some higher corporate management, who wondered why the IT department would be so reluctant to follow their lean transformation advice and instead talked about some agile and continuous integration thingy.

From that moment, I understood that everything necessary to radically upgrade software engineering was already there! The biggest problem was that we do not communicate enough—and furthermore, there is no holistic view on the complete IT value chain.

By that I mean we need to create awareness for the value chain, starting with digitized formal models of business processes and written business requirements.

That was how the following chart came to existence, and I still love it. Apparently, most awesome things happen on the borders of separated knowledge domains; you need just to talk to colleagues from departments you’d never talk to! What if we would mix knowledge from big industry, best available process improvement and quality assurance methodology, and apply all this to how we design, build and deliver software?

At first I thought I had found a new perspective on DevOps, but now I believe there could be more to it. Well, now the door to the radical innovation is open! Just imagine: What if we would apply the Internet of Things approach to our industry? Maybe we could close the loop of process improvement in software development and test, moving it to real time and running optimization not by ambiguous frameworks but by hard facts coming from virtual sensors, with unprecedented levels of transparency?

This was how a couple of friends and I came to the idea of our foundation we call SIGSPL (Special Interest Group for Software Production Lines). This public organization is a non-profit think tank, built around three generic domains—Labor Automation, Analysis and Advanced Visualization—which we consider to be the three pillars of the upcoming convergent software engineering in the post DevOps era.

We are sure this sort of thing inevitably will happen, but our mission is to catalyze these processes to make radical innovation happen intentionally and faster, so we all can build information technology that rocks!

We are a small team of six with some infrastructure, publications at our blog and a well-established yet growing knowledge base. We would like to reach out to more like-minded people to let this idea grow and change the world. Our plan is to foster further exchange and discussions between DevOps experts and other specialists “unexpected” in this context, such as production lines and supply chain management experts, Six Sigma Black Belts, 3D artists, hackers, venture capitalists and many, many others.

Our proof that we are not ponding to nowhere is our magic triangle diagram, showing some well-known tools we map to the three generic domains, which are also of high interest for venture capital. Our question to the world is, What would appear in the middle of this triangle in, say, five years?

About the Author/Peter Muryshkin

Peter studied M.Sc. in Applied Computer Science and works in management consulting roles focusing on complex analysis and customer satisfaction, including IT Business Analyst and Change Manager. His current scientific research focus is digital transformation applied to the field of software engineering in large distributed organizations.

LinkedIn: https://de.linkedin.com/in/muryshkin
Team blog: http://sigspl.org/blog/

February 25, 2016