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How Parkar NexGen Platform Is Changing the Way we Approach AIOps?

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How Parkar NexGen Platform Is Changing the Way we Approach AIOps?

AIOps or Artificial Intelligence for IT operations, a term initially coined by Gartner, employs advanced analytics in the form of machine learning (ML) and Artificial Intelligence (AI) to automate operations in a way that enterprises can move forward towards their goals with agility and efficacy.

What it eventually does is bring about predictive outcomes that can lead to a faster root-cause analysis (RCA) and also speed up the meantime to repair (MTTR). The intelligent, actionable insights that AIOps offers helps enterprises attain a high level of automation as well as collaboration thereby helping them make huge savings in terms of resources and time.

AIOps bringing successful digital transformation

At Parkar, we understand the role of AIOps in bringing about a successful digital transformation where workloads and processes are handled with precision with lesser dependency on humans. There is so much riding on AIOps today, that we’ve curated a platform that gives enterprises greater agility and innovation to alleviate workload and create better user experiences.

There have been significant changes in distributed architectures, multi-cloud, containers, and microservices that have in turn increased the complexities of the IT infrastructure. The number of services and applications that rely on the infrastructure is large. Even the slightest changes to these services or applications can have a domino effect within the infrastructure to an extent that’s beyond the control of humans.

What we need to address this situation is a robust AIOps strategy to create real-time systems where context-rich data travels through the full application stack, thus curtailing noise and improving time to resolution through automation.

Parkar’s take on AIOps

The realms of data humans have to go through on a daily basis can be frustrating. We need good insights that can translate into data-driven decisions and help curtails costs by understanding hardware capabilities and factors that adversely impact cost savings.

Through a highly efficient NexGen platform, we also hope to eliminate the skills gap by ensuring better and easier access to data that helps experts focus on key decisions and improves the learning curve for new members.

We want businesses to effectively overcome customer frustration by addressing application slowdowns, particularly on busy, high transaction days. The rationale is to pull them out of fire fighting mode and give them a competitive edge in a thriving but aggressive IT environment.

The NexGen Platform

To address all the issues discussed above and to offer a world of benefits to customers, we created a platform that changed the business dynamics for ambitious enterprises.

It constantly captures important information that comes from various sources including operators’ experience and stores it for future reuse. It hugely relies on root cause analysis and algorithms to help organizations resolve incidents and perform smarter IT operations.

Parkar also depends on its proven track record of helping enterprises deploy smarter tools and solutions to monitor, integrate, perform and excel. The platform it offers delivers NexGen AIOps solutions with end-to-end capabilities in AIOps transformation through purpose-built Machine Learning algorithms. Unified alert management, root cause analysis, anomaly detection, and predictive capabilities are just a few of the many things the platform offers to aid organizations to map their digital transformation journey.

The platform is now helping organizations from different sectors including retail and healthcare, helping them work faster and smarter.

Case in point

A leading healthcare organization from the US known for its excellent services and quality care faced the challenge of data management. It has a large scale enterprise network it relies on for providing better services and creating pleasant user experiences. The expanding network brought along the challenge of monitoring and administering networks, managing traffic issues, and fixing application malfunctions.

The need for embracing emerging technologies was felt more than ever before since it was becoming increasingly difficult to monitor network segments while keeping a tab on traffic or application performance. A robust solution that could perfectly capture network operations data across the many application layers with relevant insights was immediately needed. Our platform was just what they needed.

Measures were immediately implemented to address issues and facilitate smoother data management. These were as follows:

  • Service and device attributes such as service name, service components, and topology were assigned to establish a correlation across service and infrastructure layers and enrich data.
  • Priorities were set based on business and service impact so as to help operators address issues based on the extent and gravity of the impact caused.
  • Automated service assurance through a model-driven approach was ensured.
  • Automatic noise reduction was achieved.
  • Patented algorithmic and machine learning techniques were leveraged to build algorithmic correlation through clusters of related alerts automatically. These helped identify unique situations without necessitating laborious development and time-intensive maintenance of rules, filters or inventory-based service maps.
  • The smart algorithms ensured efficient data processing since they can now expertly derive cognitive insights from raw data sets mitigating the risk of operator fatigue and maintenance issues and reducing metrics like the Mean Time to Detect and Mean Time to Repair by almost fifty percent.

What we achieved:

The figure below is a statistical representation of what we achieved for our customers within a short period of time. The numbers reflect the power and efficacy of our platform.

What we need is context-infused AIOps

We need to take important steps towards creating actionable, IT operational data with an AIOps strategy that functions at machine speed. Merely collecting data is not enough and what is actually needed is contextualizing it so as to enrich its quality and arrive at automated but dependable outcomes.

Fig: 5 Step towards achieving actionable AIOps insights

At Parkar, we address these needs as follows:

Data collection

Data is collected from various sources including agents, operators, devices, applications, and services based on the type of asset that needs to be assessed and monitored. The IT environment needs to be constantly observed for the same.

Data cleansing and preparation

This is achieved in stages and involves various aspects including data duplication, time synchronization, single data lake, etc. each playing a significant role in the process of cleaning and preparing data. No AIOps strategy will work unless the data is clean, precise and perfectly aligned with your objectives.

Data enrichment

It’s impossible to enrich data without contextualizing it as it gives additional insights and perspective to raw data. Meta-data applied to a device, service metrics, application or infrastructure makes data more useful and insightful.

Data analysis

Operations teams are inundated with data. This puts a huge burden on them and also escalates analysis costs that result from staffing and data storage. AIOps analyzes, segregates and consolidates data by means of machine learning.


Context-rich data is always relevant and accurate facilitating better and fast decision making. It also helps organizations take automated actions to initiate changes, send notifications or make recommendations.


There is a seismic shift towards next-generation solutions including containerization, microservices, cloud, etc. and it’s hard to miss. It urges IT operations to revisit and recalibrate their monitoring and management tools and embrace an AIOps-enabled approach. It is the only way to close the gap between IT and business.

Says Padraig Byrne, Senior Director Analyst at Gartner, “IT operations are challenged by the rapid growth in data volumes generated by IT infrastructure and applications that must be captured, analyzed and acted on. Coupled with the reality that IT operations teams often work in disconnected silos, this makes it challenging to ensure that the most urgent incident at any given time is being addressed.”

Clearly, AIOps platforms are the answer to the perennial need for analyzing the deluge of data with respect to volume, variety, and velocity. It’s time enterprises embraced them with open arms.

In closing

Parkar has a prolific experience and capabilities to help enterprises with their long-term business goals. Our NexGen platform stands testimony to our constant endeavor to offer better and reliable solutions to all enterprises’ IT concerns. We strongly believe that the effect and impact of AIOps will be transformative. The question is- are you ready to adopt it?

Let us talk to assess your environment and discover a whole new world of possibilities.

The most important elements of AIOps

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With increasing efficiency and sophistication, the IT environment is becoming extremely complex too. The recent shift to microservices and containers has further added to the already large number of components that go into a single application, which means the challenge is equally big when it comes to orchestrating all of them.

The ability of IT Ops teams to handle such complexities is fairly limited and hiring more resources to configure, deploy and manage them is not very cost-effective.

This is where Artificial Intelligence for IT Operations (AIOps) comes into play. None come close to AIOps when it comes to leveraging Big Data, data analytics, and machine learning to offer a high level of customization along with invaluable insights necessary to cater to modern infrastructure.

Here’s what you should know if you are contemplating moving towards AIOps.

Understanding AIOps

As automated tools entered the scene, IT Ops teams realized that despite improved efficiency these tools were incapable of making automated decisions based on data, and therefore required considerable manual effort even then.

AIOps presented a more refined way of integrating data analytics into IT Ops, supporting more scalable workflows aligned with organizational goals.

AIOps Platform technology Components

Use cases for AIOps

Anomaly detection – This is definitely the most basic one since you can trigger a remedial action only after detecting anomalies within data.

Causal analysis – Root cause analysis is required for issues to be resolved quickly and effectively. AIOps plays a pivotal role here.

Prediction – Automated predictions about the future can be made using AIOps powered tools. For instance, you can find out how and when user traffic can possibly change and then react to address it.

Alarm management – Intelligent remediation, closed-loop remediation, is kicked in without necessitating human intervention.

Drawing parallels between AIOps and DevOps

DevOps had brought about a cultural shift in organizations and in that sense AIOps is pretty similar in effect and impact. AIOps is helping enterprises discover holistic insights from connected and disparate data to bring about decision-automation to make them better and more agile.

It is important for enterprises to break free from traditional silos as data should be generated and used keeping the ‘observability’ aspect in mind for the entire company, not just one department.

Thanks to AIOps, typical IT Ops admins are now transitioning into the role of Site Reliability Engineers helping them utilize information more efficiently and tackle issues in a more effective manner.

While both AIOps and DevOps share the same goal of making organizations better and more productive, AIOps can make DevOps practices more effective by reducing the noise that gets in the way of productivity. For example, AIOps streamlines the alerts and notifications from various platforms so that it becomes easier for DevOps engineers to address them. It would be safe to assume that AIOps complements the goals of DevOps engineers and enterprises effortlessly.

AIOps and time management

No matter what the team size, organizations will always struggle with the most common issue of having too much to do in too little time.

Luckily, there’s a lot AIOps can do for you in this regard. From helping you create a machine learning model to processing data to make it flexible enough to accommodate new information, AIOps can be just the value add-on you need.

Those who have been using AIOps would know the role of a well-trained machine learning algorithm in attaining and maintaining the high quality of data. Also, ‘real-time’ is the buzz word here since most use cases require real-time data processing.

So for instance, if the use case in question is detecting anomalies, then it is important to get information quickly so that you can prevent a security breach. The same applies for all use cases where the rationale is to get to a problem and resolve it in the fastest possible manner.

High-quality data, therefore, remains extremely important and AIOps makes it possible despite the complexities. Enterprises understand the importance of data analysis in principle, but find it difficult to trust and rely on it. As indicated by KPMG’s survey, 67% of CEOs agreed to have ignored the insights offered by computer-driven models or data analysis largely because they were not in line with their own thinking or experience.

The growing popularity of AIOps

Having data is one thing, and being able to be able to use it effectively is another. While machine learning holds a lot of promise, organizations need to employ resilient applications and stronger automation platforms.

MarketsandMarkets predicts a 34% combined annual growth rate for AIOps platforms giving a sneak peek into its rising demand. The fact that AIOps helps businesses be more flexible and responsive without putting a burden on resources is fast making it a must-have in this highly digitized era.

Getting started with AIOps

As enterprises transition towards a state of enlightenment with respect to the incredible benefits of AIOps, the question that needs to be addressed is how to embrace it in a way that it aligns with your business needs. Here are a few things that should help you:

Understand the basics of artificial intelligence and machine learning so that you are better equipped to adopt it.

Identify the most time-consuming tasks that your people undertake and how AIOps intervention would help to alleviate this load. Particularly look for repetitive tasks that could be effectively dealt with automation.

Avoid taking on too many things at once. Start small and begin with high-priority tasks. Once you get good feedback, assess how this technology can be best leveraged to address other areas and tasks.

Employ AIOps for all kinds of data. No doubt this may take longer than you thought but you need to look at the bigger picture. Also, look at the metrics you want to evaluate and the parameters you want to define your success on. The rationale is to ensure that your efforts are aligned perfectly with your organizational objectives.

From the adoption and maturity perspective

IT leaders are keen on automating arduous tasks within incidents while bringing down costs of alerts which can be significant. Service disruptions and downtime costs have been major factors of concern for most organizations.

IT organizations can vary in their objectives when it comes to AIOps adoption but what they are looking for in general is overall visibility into their systems to get a better handle on operational efficiency and the production environment.

Let us look at a five-stage maturity model that can help organizations gauge where they stand in terms of their monitoring and automation journey.

Source: ScienceLogic

AIOps is for those who have long-term goals and perceive it as the change that is needed to drive modern applications using microservices. It will ensure a fluid flow of information and rather than merely improving processes may even change them to match the current perspectives and architectures of organizations.

They need to rethink how they are going to perceive the full stack rather than seeing it only from an application perspective or the perspective of a cloud team or architecture team. This is particularly important for applications that are built using microservices. Enterprises need to understand what the infrastructure does at the app layer by retooling the capabilities for operations thereby providing necessary insights to app developers with the right flow of data.

All you need is a willingness to look at it without prejudice and think of the myriad ways it can help augment your business goals.

In closing

Although AIOps is witnessing early adoption by enterprises, there are enterprises that are still unsure about the hype surrounding it and are wondering if it’s indeed wise to go the AIOps way. AIOps, however, is perhaps the only way to unlock your full potential. For more on AIOps and to leverage it perfectly for your organization, let’s talk and assess your IT operations to truly automate and transform your business.

Can Your PLM Solution Benefit from a DevOps Environment?

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Almost all products, including software and applications, have finite life spans and will go through various stages during their lifetimes. During each stage, IT executives must implement different strategies to overcome associated challenges as well as maximize sales and profits. Effectively managing these different stages and the various people and departments involved requires a comprehensive product lifecycle management (PLM) plan that connects all the relevant data, tools, processes, and people. But how do IT decision-makers manage all of this, and how do DevOps, containers, and microservices fit in to PLM?

The software product life cycle

Each software product’s life cycle involves four primary stages, and understanding the dynamics of each stage as the product advances through its cycle is imperative to optimal PLM. Although the length of time for each stage as well as the product’s total life span will vary according to the nature of the software or application, each stage has certain characteristics and challenges common across different products.

  1. Development and introduction — Software goes from concept to final product deployment through design, building, and testing. After, developers release the software and marketers introduce it to the public with promotional marketing.
  2. Growth — In this stage, demand increases, distribution expands, and competition grows. Developers may add new features according to customers’ needs. They must also make decisions whether to focus on further development to keep software relevant and keep up with competitors’ offerings.
  3. Maturity — The software or application is widely available, but competition is high. Increasing advertising spend no longer increases demand. Marketers during this phase should design and revise campaigns to protect market shares. The selling price begins to adjust downward.
  4. Decline — The software has peaked and begins to lose market share as demand tapers off. The software may become obsolete, with developers retiring it when it’s no longer profitable.

To successfully manage a software product from conception to retirement, IT executives need a clear, concise system to manage all the data, processes, and stakeholders throughout its lifecycle. Savvy developers have PLM solutions in place to apply different strategies for each phase to minimize time to market, improve quality, and maximize profits.

PLM and DevOps

When PLM follows a traditional linear model, little communication and collaboration exists among developers, operations staff, marketing personnel, and other stakeholders until the end of each stage. This reduces their abilities to detect problems early on and adjust quickly. When developers implement PLM using a more circular DevOps model, development and operations teams work closely with each other throughout the software lifecycle and, therefore, can quickly correct bugs, add new features, and adjust strategies according to each stage. This circular approach — combined with the benefits of continuous integration, continuous delivery (CD/CD) — is more efficient and flexible, reducing time to market, improving product quality, and reducing costs.

Adding containers and microservices

Two of the newest technologies that can help in agile PLM implementations are microservices and containers. Essentially, a microservice is one component of a larger application or application suite. These single-use services perform just one action each as part of a larger application. Containers, on the other hand, hold all the tools and methods microservices need to run, including code, runtime, tools, libraries, and settings. Each container keeps its microservice separated from any other software so different teams can work on separate microservices at the same time — without interrupting any other part of a software development project. Together, containers and microservices help provide an agile platform that allows IT executives to roll out new software products faster and more cost-effectively.

Putting it all together

As technologies become more complicated, competition becomes fiercer, and customer demands intensify, software development and marketing are becoming more complex and difficult to manage. This requires an organized approach best handed with a PLM solution. But to get the best results, developers should run their PLM solutions in agile environments using a DevOps model combined with CI/CD, microservices, and container technologies. These can help streamline processes and bring products to market faster, which today’s global market requires, as the circular nature of DevOps speeds up the development process and produces higher quality applications with fewer bugs and at lower cost.

To further speed up the development process, IT decision-makers can deploy an automated testing solution such as TEAL — a comprehensive and extensible test automation solution. Its modular design makes it easy to use and integrate into an existing CI/CD development environment. It’s the perfect addition to round out an agile PLM solution.

If your PLM solution isn’t cutting it, it might be time to upgrade to a DevOps platform to achieve better collaboration between developers and operations professionals, streamline development and testing processes, reduce product lifecycles, and increase profits. Contact the experts at Parkar Consulting & Labs to discover how to raise your product lifecycle management to a new level.

3 Emerging Technologies Transforming the Health Care Landscape

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The rate at which technology is changing our everyday lives is truly remarkable, and one especially transformative area is health care. Emerging technologies are primed to disrupt many aspects of patient care in increasingly advanced ways, and they’re making their way into health care on a minor scale in mobile and wearable technologies.

These devices started as popular non-medical fitness monitors but are beginning to expand into medical-grade wearables, home health monitoring tools, and mobile care apps. Where else will technology take health care — and what do these advances mean for IT professionals?

1. Wearables, mobile apps, and big data

Researchers predict the wearable medical devices market will reach $14.41 billion by 2022, up from $6.22 billion in 2017. Until now, these devices had been limited to individual fitness trackers that connected to smartphone apps, but they are poised to offer real-time access to medical records as well as diagnostic and treatment functionalities. This could help empower patients to take control of their health, improving patient outcomes and saving health care providers and patients time.

To make this transition, medical device manufacturers, health care records system providers, IT developers, and health information regulators will need to learn how to integrate patient-generated data into their workflows and products. Privacy and security concerns, data relevancy to clinical situations, and big data handling are the biggest challenges facing the widespread adoption of clinically relevant personal medical-grade devices. Health IT managers and developers may need to look at Internet of Things (IoT) application programming interfaces (APIs) and standardization techniques to help handle this unstandardized user-generated data.

2. Machine learning and artificial intelligence (AI)

AI and cognitive computing technologies are able to integrate patient-generated and IoT big data. These technologies use algorithms to mine large datasets, recognize patterns, and make connections between disparate items in ways that mimic the human mind — but much faster and more comprehensively than any medical professional can. Savvy developers can tie these cognitive computing platforms to electronic health records to spot trends not only within a single patient’s records but also across patients to assist doctors in recognizing anomalies as well as diagnosing and treating patients with similar conditions.

AI is also likely to play an important role in researching and developing treatments for many health conditions. Using large centralized data repositories, these AI systems can store vast amounts of data generated through health care systems, the IoT, wearable medical devices, and more to gain deeper insights into some of the most impactful health issues such as heart disease, diabetes, Alzheimer’s, and autism. Health care providers, developers, and IT decision-makers alike will need to work together to develop big data gathering methods and analytical tools to best take advantage of the tremendous benefits machine learning and AI can offer health care industry insiders and their patients.

3. Blockchain

The third technology trend transforming health care today is blockchain. Blockchain is the technology behind Bitcoin, the cryptocurrency that’s shaking up the financial world. Blockchain is a massive distributed network of replicated databases containing records stored on an encrypted ledger. No central administrator exists, users can change only the blocks of records they have access to, and software time-stamps any entries or updates and syncs them across the other networked databases. Because of the massive amounts of data surrounding the health care industry as well as the need for security and adherence to privacy regulations, blockchain offers tremendous potential for many areas of the industry, including secure patient medical record storage, clinical trial data privacy, drug development, supply chain integrity, as well as medical billing and insurance claims. Although still in its infancy, blockchain will likely have a significant impact on the health care industry going forward.

As technology continues to disrupt the health care field, both patients and providers will likely benefit from improved diagnostic techniques, treatments, record keeping, research, security, and so much more. Only by staying abreast of these technological advancements will software developers and IT decision-makers find opportunities to optimize their health care software projects to integrate with and take advantage of blockchain, AI, and wearables, allowing them to offer the medical advantages this new technology enables to their patients and stay ahead of the competition.

Our experts at Parkar Consulting & Labs have the knowledge and expertise to help you make the most of emerging technologies so you can pass them and the value they bring to your customers. Contact us today to learn how we can help.

3 Big Ways Artificial Intelligence Is Changing Software Development

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Agile technologies such as DevOps and continuous integration, continuous delivery (CI/CD) practices have brought about positive changes for software developers over the past decade. From faster time to market and greater collaboration between development and operations teams to fewer end-user issues and improved testing, DevOps has changed the way developers work. In a similar vein, artificial intelligence (AI) is poised to change how organizations and their engineering teams approach software development.

AI’s promising potential

Software engineers are tasked with creating solutions for the myriad problems, challenges, and everyday organizational tasks in most every industry imaginable. Ironically, they even develop the tools that make their development processes easier. AI is well-suited to helping software engineers develop these intelligent software tools because it can learn from and replicate human behaviors. Because of this, AI and machine learning algorithms can impact nearly all areas of software development.

Best uses for AI in software development

AI and machine learning have already made big impacts in software development. Here are three of the most important ways it is changing the development landscape and the evolving role of software engineers.

  1. Estimating delivery schedules — When development teams work together for long periods of time, they become fairly adept at estimating delivery times, although they may still encounter challenges due to a variety of influencing factors, including flawed code and changing user demands. AI can help development teams make more accurate estimates, even with the numerous and diverse factors that come into play. And as the AI programs gather more data and learn from other development projects, the accuracy of those estimates is likely to continue to improve.
  2. Project management — AI systems can take over daily project management tasks without the need for human input, according to The Next Web article. Over time, they can understand project performance and use that knowledge to form insights, complete complex tasks, and help human project managers make improved decisions.
  3. Testing and quality assurance — Developers are creating tools that use AI to detect flaws in code and automatically fix them, according to the Forbes article. This is the logical next step after testing automation and will likely lead to higher-quality software and improved time to market. Software engineers could have less involvement in testing mechanics but would shift their roles to approving and acting on test findings. In other words, AI could streamline software testing by providing the right data to the human engineers who can then make better decisions.

evolution of the programmer

Best practices and AI’s future

Based on these changes alone, it seems AI and machine-based learning are primed to disrupt the software development field. What does that mean for development company leaders, software engineers, and software development in general?

Overall, AI will likely help software development become better, faster, and less expensive. However, for this to happen, engineers would have to learn a different skill set so they could build AI into their development toolboxes. They’d need more data science skills and a better understanding of deep learning principles to reap the full benefits of machine-based learning. Also, instead of turning to logging and debugging tools to find and fix bugs, engineers would need tools that allow them to question the AI to find out how and why it reached a particular conclusion. In addition, AI could allow more tasks to run autonomously and require fewer daily management tasks. Finally, developers could use AI for routine tasks so humans can focus on what makes them human: thinking creatively according to the problem’s context, something that AI has not yet mastered.

Will AI eventually replace the human element in software engineering? Not likely, but it certainly has the potential to make development faster, more efficient, more effective, and less costly all while letting engineers and other development personnel focus on honing their skills to make better use of AI in their processes.

For the best in thought leadership on emerging technologies, look to the software development experts at Parkar Consulting & Labs. Contact the Parkar professionals today.

7 Metrics for Improving Software Engineering Efficiency and Productivity

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In today’s fast-paced technological landscape, tomorrow’s products are already at risk of being “old news.” To overcome lengthy production cycles, successful software developers are focusing on efficiency and productivity. Greater efficiency leads to greater productivity, accelerated product life cycles, shorter time to market and, ultimately, an increased bottom line. Engineering team managers can use many metrics to gauge efficiency and productivity along the way. But of course, the ultimate measure of a software application’s success is whether it meets end users’ needs and improves the organization’s business results.

Tracking the metrics

Keeping track of certain metrics is an important step in making sure your company’s software engineering team is performing as efficiently and productively as possible. Here are some of the most essential metrics for developers to track:

  1. Lead time — Lead time is a general measurement of the amount of time that passes from product concept to final delivery. It will depend on project complexity and the number of engineers working on the project, both of which will, in turn, affect project cost. By tracking project lead times, developers can better predict time to market for current and similar future projects.
  2. Impact — Impact measures how much changes to the code affect the overall project. It is also a measurement of how those changes impact the engineers who make them. This is an important metric to measure as code changes can lengthen the product’s time to market, making it costlier. In addition, developers could be dedicating too much time to making certain changes, negatively affecting their productivity and adding to such delays. Therefore, large changes, such as those that involve more code or files, and changes that are more difficult to make have higher impact scores.
  3. Churn — Churn is the percentage of time developers spend editing, adding to, or deleting their own code. A high code churn indicates rework and may mean something is wrong in the development process.
  4. Efficiency — Efficiency measures the amount of each engineer’s “productive” code, or code that provides business value. An engineer creating a whole new solution or implementing sweeping code changes will likely deal with lots of trial and error with a low efficiency rate. An engineer who is making a lot of small changes with a low churn rate, however, is likely to have a higher efficiency rate.
  5. Cycle time — Cycle time, part of lead time, is how long it takes to make a desired change to the software and put it into production. When a team is using DevOps and employing continuous integration, continuous delivery (CI/CD) practices, they can often measure cycle time in minutes rather than months.
  6. Open and close rates — The open and close rate is the number of issues or problems developers report and fix or close over a period of time.
  7. Production — Production is a downtime analysis and includes mean time between failures (MTBF) and mean time to recover/repair (MTTR). These measure how well software performs in a production environment.

TEAL to the Rescue

What does improving software engineering efficiency and productivity look like in a real-life situation? The professionals at Parkar Consulting & Labs used the TEAL test automation solution to solve a problem in the Kronos development process. Kronos is a workforce management software and services company whose leaders needed to test a large set of applications on various frameworks. With such large loads, they needed a more efficient way to test their products. The Parkar experts created a flexible, scalable test solution that was 100% automated as well as reduced defect leakage by 85% and execution time by 90%. The single test solution removes the need for other tool sets, reducing management headaches and enhancing the user experience.

But which metrics are the most important for helping you meet your goals?

Making use of metrics

With so many metrics available to track, it’s important developers don’t get carried away. Only track metrics directly tied to your business goals. Typically, you’ll want to use metrics to judge general trends to see where problems might exist, then do more in-depth research to discover more specific causes for inefficiencies or low productivity. For example, a high churn rate could mean a problem with the development process, such as lack of clarity over requirements or an issue with a specific engineer who might not have the best skills for developing a particular set of code. An engineer with a low churn rate, however, may have developed highly efficient code, or the low churn could indicate that he or she already resolved major issues.

Metrics are great to show the management team, but those who don’t know how to interpret or use them to drive improvements might as well not track them at all. If productivity is not up to expectations or efficiency is lagging, it might be time to take a hard look at the above seven metrics to decipher where problems exist. Developers can then use this information to make positive business changes. Once they understand their strengths and opportunities for improvement, software developers can take action. When they increase software development efficiency and productivity, they can increase product ROI, improve the development process, reduce costs, and better manage workloads.

Ready to make your software development and testing processes more efficient?  Contact the professionals at Parkar Consulting & Labs today.

Shorten Your Product Life Cycles with DevOps

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Technology has benefited society in many ways; however, it has also created an expectation of instant gratification. This presents challenges for business leaders to develop products that meet customer demands — before the competition does. Being first to market as well as being able to ramp-up production quickly to meet demand can mean the difference between product success or failure — or even company success or failure. But traditional linear product development methodology, in which developers wait until the end of the product development process to test and make changes, is quickly giving way to the more agile DevOps approach. This development process integrates testing and improvements into every step, which can accelerate time to market and reduce development costs. If you have not yet moved your company from a traditional software development approach to DevOps, you’re likely losing out on some tremendous benefits.

The DevOps advantage

As today’s savvy IT professionals know, operating under a DevOps model can significantly accelerate product life cycles. What they may not truly understand, however, is why acceleration is important. By the time a new software project approaches completion and release in today’s rapidly advancing technological climate, that “new” software may have already become antiquated. Or perhaps users’ needs will have changed. That can require scrapping or drastically altering entire projects, creating significant delays in time to market, wasted resources, and low-quality finished products.

On the other hand, when developers work closely with operations team members to continually test and monitor, they can correct software mistakes and improve upon defects well before project completion. With the DevOps model, customers can offer insights and request changes at any time during development. It’s a collaborative approach that fosters increased customer satisfaction and reduced frustration among development and operations teams. DevOps also promotes healthier inclusion among team members, improves business agility, and produces better results through continuous testing as well as continuous release and deployment cycles.

Implementing DevOps

Implementing DevOps for your software development operations can provide great benefits, but it is not an easy task. One of the major obstacles most company leaders face is building an effective test environment. It’s also one of the most important keys to success. DevOps personnel require a framework in which they can simulate and test applications in real-world scenarios. The experts at Parkar Consulting & Labs understand that need. They have developed a test automation platform to help company decision-makers trial diverse applications to optimize and bring them to market quickly while keeping costs low. The result was the TEAL Test Automation Solution, an extensive plug-and-play solution that automates manual testing processes.

What Is TEAL?

TEAL is a DevOps test automation suite backed by Parkar Consultants’ years of experience. An open-source plug-and-play tool, TEAL allows developers to complete projects in weeks, rather than months or years, significantly accelerating time to market. And, since developers can integrate TEAL into their current ecosystems for use with their existing continuous integration (CI) tools, return on investment can be impressive.

One Parkar client, a leading DNS provider for many Fortune 500 companies, is responsible for answering more than 25 billion queries per day. Their problem was, because they’d built their infrastructure over time, they relied upon many interdependent modules and disparate technologies. This meant that adding any new features or improvements required lengthy testing. Using TEAL, Parkar experts were able to take the DNS client’s DevOps strategy to the next level, adding automation at every stage of the development cycle, reducing feedback loops, adding back-end scripts for testing some of their services, and integrating automation with their continuous integration pipeline.

In addition to accelerating product life cycles and decreasing time to market by 400% while effectively increasing product return on investment, TEAL helped this Parkar client:

  • Reduce costs greater than 80% by using automation testing, DevOps, and a global delivery model.
  • Improve quality and increase defect detection.
  • Decrease the number of customer tickets.
  • Reduce application downtime.
  • Replace 15 manual product testers with four automation engineers.

As the DNS provider case shows, TEAL can be a tremendous tool for adding to or improving DevOps processes. It easily integrates into nearly any existing development environment. Parkar experts can customize TEAL to meet any company’s needs, allowing for a faster, more agile software development process. By accelerating product life cycles, developers can detect flaws quickly and address them earlier in the process, resulting in higher-quality products. Decreasing costs and increasing speed to market can keep savvy IT executives ahead of the competition and help them make positive contributions to their companies’ bottom lines.

At Parkar Consulting and Labs, we’ve reduced time to market for several clients and successfully completed six DevOps projects. Connect with the DevOps experts at Parkar Consulting & Labs to learn more today!

Emerging Technology: How Could Blockchain Benefit Your Business?

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The buzz around bitcoin has embedded cryptocurrency into the minds of company leaders who deal in global finance. However, blockchain, the technology behind bitcoin and other cryptocurrencies, has the potential to disrupt nearly every aspect of business and industry as we know it. Over the coming decade, more tech-savvy company decision-makers will be turning to blockchain to simplify and secure everything from pre-employment screenings to supply chain accountability. So, what are some of the other blockchain applications that exist? And how can they benefit your company?

What is blockchain?

To understand blockchain applications, you must first understand its basics. Blockchain is a massive, shared, decentralized database that logs completed transactions and updates automatically. Because the database updates and maintains the chain of records within each transaction, no central authority confirms these transactions. Instead, all computers connected to the database have identical copies of the blockchain record. Today, users employ blockchain primarily for financial transactions but, as Mike Almeida, president of Empire ATM Group, stated in a Business News Daily interview, “There is no end to the list of applications blockchain can be used for. Blockchain technology can make many aspects of our life easier on a mathematically provable platform.”

Here are a few examples of potentially beneficial applications for blockchain technology:

  • Smart contract — Computer programs automatically carry out the terms of a contract across different organizations in a blockchain network without divulging sensitive information. Business leaders can bypass regulations for common transactions, lowering costs and making contracts unbreakable.
  • Digital identity With ID, account login, birth certificate, and passport applications, blockchain can help fight fraud and identity theft problems. According to an UpWork article, Ameer Rosic, founder of Blocgeeks, said, “Blockchain-based authentication systems are based on irrefutable identity verification using digital signatures based on public key cryptography.” One huge bonus: No more forgotten passwords!
  • Supply chain — Blockchain can help determine the location of a product, offer transparency into its condition, identify who owns it, where it’s been, and more. According to an article in The Economist, blockchain can produce permanent digital records that contain timestamps and increase visibility into the product’s state through every step of the supply chain. This can assist those along the supply chain in predicting when each shipment will arrive, better managing inventory, reducing waste and spoilage, providing better quality control, and resolving disputes.
  • Financial services — Blockchain enables inexpensive, near-instant secure funds transfers because there are no intermediaries. The technology also allows users to streamline securities and commodities trading on the stock market, according to the Business News Daily article, allowing those who record and confirm transactions to finalize them within minutes rather than days.

Another blockchain application lies within the often lengthy and expensive employee recruitment process. The experts at Parkar Consulting & Labs demonstrated such a solution when they created a blockchain application concept for the professionals at xScion, a consulting firm that helps health care and financial services leaders improve their business processes. The professionals at xScion wanted a method their executives could use to perform faster and less costly background checks on job candidates without involving a third-party provider.

Parkar experts experimented with Hyperledger Fabric, an open-source blockchain framework, to demonstrate a modular plug-and-play concept to enable real-time applicant information and background verification. In similar applications, blockchain can ensure verified data security, cannot be tampered with, and is not controlled by a single entity. With the blockchain solution, xScion professionals would be able to quickly perform background checks and reliably verify candidate information, saving significant time and costs in the hiring process.

Additional blockchain services

The consultants at Parkar offer much more than concept solutions for employee information verification. In fact, they offer a variety of blockchain services. For example, Parkar experts can help with smart contract applications to facilitate, verify, and automatically execute contract terms. They can also provide digital ID authentication records that integrate all stages of employee work history from hiring and contractual agreements to payroll and other types of transactions. With experience providing solutions for the health care industry, Parkar experts can help ensure clinical trial authenticity and the privacy of clinical trial participants. From insurance fraud risk prevention to asset tokenization and more, blockchain applications offer cost-effective, secure, cloud-based solutions with permission-based access. Is it time you reap the benefits?

What are your blockchain needs? At Parkar Consulting & Labs, we can help you identify potential use cases for your business. We provide blockchain advisory services that include feasibility studies, business propositions, as well as cost-effectiveness, security, and scalability studies. We will conduct proof-of-concept and pilot programs to prove business value and technology impacts for your business as well as provide an implementation strategy showing your path forward. If you don’t want to wait to take advantage of the many benefits blockchain technology offers, contact us today.

What Could Improved ETL Processing Do for Your Business?

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Extract, transform, and load (ETL) processes are at the heart of business data integration and warehousing operations. ETL processes are vital to those who need to access strategic business intelligence (BI) data. However, the growing volume and diversity of data is making managing these ETL processes increasingly difficult, time-consuming, and costly. How can savvy IT leaders ensure their processes keep up with demands — and how can they benefit?

Benefits of an improved ETL process

As leaders begin to scale up their company data processing efforts, they often begin to see problems in ETL performance. Complaints begin coming in from staff members who rely on the processed data for their reports, decision-making, and daily operations. Most ETL operations run overnight, and staff members expect and need processed results when they arrive for work in the morning. Everything slows down when these staffers don’t have the information they need to perform their jobs. By implementing improvements to ETL processing, you can improve performance, reduce bottlenecks, as well as provide better support for end users immediately as your business continues to scale up.

Other benefits include

  • the ability to store uniform, complete data in one place, simplifying management and reducing redundancies;
  • access to historical data and comparison reporting;
  • improved security;
  • back-end processing that can handle data from acquired or merged companies; and more.

Those in production, sales, or customer service — any and all areas reliant on data analytics — can experience these improvements.

Improve your ETL process

When your ETL process isn’t keeping up with your growing data warehouse and analytical demands, it’s time to act. Here are some tips for optimizing your ETL operations.

  • Correct bottlenecks. Determine which ETL operations use the most resources, then rewrite code for greater efficiency.
  • Consolidate indexes. Database administrators often try to solve performance slowdowns by creating additional indexes, but this actually increases load times.
  • Use set logic instead of cursors. Change a row-based cursor loop in your ETL code into a set-based SQL statement to make ETL processes run faster. Many ETL tools run load jobs in set-based ELT mode.
  • Offload table joins to your database. This is more efficient than using an ETL tool to read and join the data as it frees up your ETL tool for processing.
  • Divide large tables. Partition large tables into smaller ones. This speeds up ETL because multiple small tables with fewer rows are easier to process than enormous data sets made of many rows.
  • Run parallel threads. Running parallel instead of serial threads when possible can optimize processing.
  • Use only relevant data. Collect as much data as possible, but only use the most relevant data. This cuts down on processing time and allows leaders to scale as their businesses grow.

The best tip for ensuring your ETL processes don’t struggle with the data load as your business grows is to plan for scaling during the design phase. Use the above tips when planning your ETL operations and writing corresponding code. Through constant warehousing and data processing performance monitoring, IT decision-makers can ensure long-term success in their ETL implementations.

If you’re ready to optimize your data warehouse operations and improve the worker productivity of those who depend on them, the experts at Parkar Consulting & Labs can provide expertise and custom ETL tools to get you to the next level. Contact the Parkar professionals today.

DevOps: Unify Your Software Development and Operations Teams

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You’re involved in software development, but are you stuck in the past? If your development and operations professionals use older models, they likely work in two separate teams with little integration or interaction until development hands projects off to operations. Without constant communication, your operations teams test products and send changes back to development teams to incorporate. That’s a lot of information to pass back and forth — not to mention a lot of rework. It’s not only difficult to estimate time and costs while utilizing this method but goals and technologies can also change during the project cycle.

If you haven’t heard of DevOps, your product development process likely sounds a lot like the example above — and it might be costing you more time and frustration than necessary. But other than being a software development buzzword, do you know what it is and what it means — or could mean — to your project management processes? If not, it’s time you found out.

What is DevOps?

DevOps, a portmanteau of development and operations, is an organizational structure and culture with a continuous development and testing cycle. That means a lot less back and forth between development and operations teams as well as less rework, making for a quicker time to market. In fact, DevOps projects allow developers to produce fully functional, high-quality final products more quickly and cost-efficiently than other development processes. This is made possible through the DevOps continuous integration, continuous delivery (CI/CD) project management model that makes extensive use of automation.

CI/CD practices

In a DevOps organization, development team members work closely with operations personnel throughout project development from the initial build through release. CI allows two or more different developers to write code, which they continually integrate with code from other developers — at least daily — in an area known as the source repository. With the code in a single location, developers can then test it. Code will behave differently in different environments, so if developers continually test in different environments, they can be fairly certain it will perform as expected in most situations. Developers usually use a tool such as a CI server for the coding, integration, and testing so each piece of code and all changes are implemented, tested, and verified before moving on.

This leads to the CD component of CI/CD. Through close collaboration between the development and operations teams, developers can discover and correct problems earlier in the development process. In a CD environment, developers not only integrate and test code after they make changes but they also test in environments similar to production. This helps ensure their code works as intended in the real world and reduces product failures and rollbacks.

DevOps model advantages

DevOps streamlines the development process through such cross-team collaboration and a circular approach. Instead of development team members fighting to release new features and operations team members worrying about new code stability, both teams work together to deliver new features and stabilize code at the same time. Through a shared code base, continuous integration, and constant testing, they can discover problems and fix them earlier in the development process.

In non-DevOps environments, developers write all code then hand it off to the operations team in a slow, cumbersome linear process that often results in building new features on top of poor or untested base code. In some cases, development teams need to scrap entire projects and restart using alternative development methods.

With DevOps, you can achieve benefits such as:

  • Shorter time to market
  • Greater agility in responding to problems or changing specifications
  • More frequent product update delivery
  • Greater collaboration across your development and operations teams, resulting in better end products with greater usability
  • Fewer deployment problems and failures
  • Greater alignment between IT and other areas of your business

Some benefits are intangible, such as greater collaboration, communication, and trust among team your members, which can lead to more brainstorming and innovation. Working within the DevOps model, your development and operations team members can cultivate more confidence, empowerment, and job satisfaction.

Fast forward into the future! If you’re ready to help those in your organization experience the benefits of a DevOps life cycle management environment, it’s important you work with an expert team to coordinate the process and reap the most value.

The team at Parkar Consulting & Labs has streamlined product life cycle development for multiple clients using DevOps CI/CD best practices — including reducing the incumbent cost by a fifth and time to deployment by half for one client. Ready to improve your product management process with DevOps? The experts at Parkar Consulting & Labs are ready to help you get there. Contact us today.

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