devopsy.kinsta.cloud

Author: Chandra Shekhar

How AI is Changing DevOps for the Better
Managing a DevOps environment involves a high degree of complexity. The rapid proliferation of data has made it difficult for DevOps teams to effectively onboard, ingest and unlock data and implement information to make good business decisions and deliver delightful experiences. Imagine a team poring over troves of information to identify critical factors that triggered an event—they would end up investing long hours to interpret and analyze the information.

The future of DevOps will be AI-driven. It’s fair to say that it’s challenging for human intelligence to harness large volumes of highly complex data. Consequently, AI-powered solutions will become the number one choice for integrating and analyzing data and transforming how teams develop, deliver, deploy and manage applications.

By 2023, 40% of DevOps teams will leverage application and infrastructure monitoring solutions with built-in AI capabilities, Gartner noted. Currently, in times of disruption, Gartner predicts the AIOps market will grow at breakneck speed, between $300 million and $500 million per year.

Let us explore how AI and DevOps are intertwined before we explore how AI is transforming DevOps.

DevOps and AI are Intertwined

AI and DevOps are intertwined. Integrating AI in a DevOps environment boosts the overall performance and efficiency of an organization.

AI and ML-powered solutions enable DevOps teams to onboard, map and integrate large volumes of customer or partner data with accuracy and speed. That is to say, business users find it easier to drive processes like data onboarding, data mapping and data integration, thus improving ease of doing business and delivering improved customer experience (CX).

Organizations that rely on AI-powered solutions no longer need to devote long hours to custom coding and EDI mapping to analyze large volumes of data. The time saved can be used to focus on more important, innovation-driven tasks.

AI also boosts automation, allows users to address and resolve issues in both data and applications and facilitates better cooperation among teams.

4 Ways AI is Transforming DevOps

The efficacy of DevOps correlates with an upward shift in AI. It can empower even non-technical business users to create connections and drive transactions at the speed of business. As a result, it brings non-technical business users to the forefront of business, thus freeing IT and developer teams to focus on more important tasks. Business users can leverage machine learning algorithms to collect data from different parts of DevOps systems and then map and integrate it in minutes through predictions.

Here are four ways AI is transforming DevOps:
1. Improved data access: The lack of free access to complex data streams is a challenge for DevOps teams. AI enables business users to access all the siloed data and harness its true potential for better insights delivery and decision-making. AI-powered tools can gather data from various sources for consistent and repeatable analysis.
1. Faster data integration: AI and ML-enabled technologies enable non-technical business users to execute data mapping and data integration in minutes instead of months. As non-technical users drive operations, IT becomes free to take up governance and instead focus on more high-value tasks. Thus, not only can AI help business users integrate data faster to deliver experiences and value but also enables IT teams to drive innovation and growth.
1. Higher implementation efficiency: With the support of AI, businesses can perform more tasks with less human intervention and minimal coding. This reduces the burden laid on IT or developer teams, enabling them to concentrate more on innovation and creativity.
1. Better security: As security is essential to facilitate a successful software implementation, DevSecOps is a fundamental aspect of software development. Organizations must bolster their security systems to guard against an increase in threats. AI can play a vital role here. It can enhance DevSecOps and boost security by recording threats and executing ML-based anomaly detection through a central logging architecture. By combining AI and DevOps, business users can maximize performance and prevent breaches and thefts.
Accelerating development cycles while making sure data is interpreted and analyzed properly is a challenge faced by DevOps teams today. Artificial intelligence-powered solutions are helping organizations transform every stage of DevOps building cycles without burdening IT teams or adding costs. Ergo, AI can enable users to transform the DevOps environment, deliver delightful experiences and grow revenue.
November 2, 2021
5 Reasons Why DevOps Needs AI
AI and DevOps together make a powerful combination of efficiency and intelligence

Supervising and managing a DevOps environment can be complex. The proliferation of data has made it challenging for DevOps teams to effectively absorb and implement information to evaluate and tackle customer issues. Imagine a team navigating through data in exabytes to search for important events that triggered an event; they would end up investing hundreds of hours in identifying the issue.

A lot of such critical issues can be resolved with artificial intelligence (AI)-powered technologies. Organizations can transform their DevOps environment by deploying AI systems. Since it is difficult for humans to handle large volumes of data, AI has gained traction as the solution that transforms the way teams develop, deploy, deliver and manage applications. Before understanding the role of AI in transforming DevOps, however, we first must understand how AI and DevOps are interrelated.

DevOps and AI Work Together

DevOps takes a business-driven approach to deliver, and AI can be integrated into the system for improved functionality. By using AI-powered technologies, DevOps teams can utilize volumes of information to the best of their ability, which in turn helps streamline testing, coding, releasing and monitoring products with precision and efficiency. It also plays a central role in improving automation, quickly identifying and resolving issues and enhancing collaboration amidst teams.

AI provides a major boost to DevOps efficiency. Various AI tools enable users to map and integrate data at the speed of business, driving business decision-making and delivering delightful customer experiences. Machine learning systems can simplify the way DevOps systems work with data to deliver value.

Here are five reasons why implementing artificial intelligence-based solutions in a DevOps culture is necessary:
1. Increased Data Access
The lack of unfettered data access is one of the most important issues that surfaced in the DevOps environment. It inhibits business users’ capability to leverage data for making decisions, ultimately making organizations difficult to do business with. AI-powered data mapping technologies enable them to deal with data silos that can help them get the right information at the right time so they can make good decisions. AI-powered data mapping, for example, helps users map troves of fragmented data faster, which can then be integrated into a unified database. They can employ machine learning algorithms to map data faster without succumbing to inaccuracies or errors.

In short, machine learning data mapping tools empower users to map big data with ease and precision. Moreover, they can integrate the data silos present across the business ecosystems to gain accurate customer insights to improve data quality and efficiency. So, AI can tackle organizational silos to help users make decisions without compromising speed or accuracy. AI can combine data from multiple sources and get it ready for consistent and repeatable analysis.
1. Timely Notifications and Alerts
DevOps teams must have good alerts and notifications systems to identify flaws without delay. There are times when notifications come in enormous numbers, all marked with the same severity. Such huge numbers can have a negative impact on the response rate of teams. AI can help prioritize the responses based on the information derived from past behavior, the intensity of the alert and the source of the alerts. As a result, teams can manage the tsunami of data with confidence.
1. Improved Execution Efficiency
The transition from a rule-based, human management of analysis to self-governed systems can be realized through AI. This helps business users alleviate the complexity of analysis and enable a level of change adaptation for digital transformation.
1. Anomaly Detection Capabilities
For any business, protecting security systems is extremely important. AI and machine learning can help enhance security by enabling an encrypted data environment wherein, only authorized personnel have the authentication to access data (AI-powered data integration tools play a vital role). Machine learning algorithms help users streamline anomaly detection to boot. Simply put, combining AI and DevOps will facilitate performance and stop breaches or thefts.
1. Efficient Collaboration
The minimum the disruption is, the maximum is productivity and collaboration. And AI mechanisms help developers release code at high velocity to minimize the disruption. AI can create a collaborative DevOps environment that helps companies deliver maximum value, maximizing ROI and ultimately making them easier to do business with.

AI-powered systems can support the teams by offering a single, unified view into data and applications across the chain of DevOps. Along with that, it can improve the accuracy of data analysis as anomalies can be detected and rectified right away.
December 23, 2020
The Role of AI, ML-Powered DevOps in Digital Transformation

Global businesses are in the midst of an enormous digital transformation wave. As we step into the realm of industry 4.0, exponential technologies are becoming a fundamental segment of traditional business settings.

Despite having access to transformative technologies, however, many organizations fail to embrace disruption and harness their potential to stay ahead of the curve. Much of this stems from their inability to foster a collaborative environment wherein myriad teams and operating IT systems work in unison to achieve their goals. Without a strong foundation of infrastructure automation and well-chosen team collaborations, embarking on the digital transformation journey is difficult. And to do that, adopting a DevOps mindset is necessary. In essence, organizations need to incorporate DevOps practices to transform, compete and protect their new value pools without sacrificing quality, stability and productivity.

Digital Transformation and DevOps

DevOps helps enterprises succeed with digital transformation by triggering a paradigm shift in the cultural mindset of the business, breaking down silos and driving continuous change and rapid experimentation.

There are multiple benefits of merging DevOps with a digital transformation strategy. First, this methodology breaks down the silos between IT and business operations, thereby ensuring consistent levels of productivity, efficiency and service delivery. This has a direct impact on companies’ time-to-market. Second, bug fixes are much easier due to the automation of toolchains. Third, the automation that DevOps introduces improves developer/IT productivity as they can focus more on innovation rather than being caught in a cycle of bug-fixing and firefighting.

Despite DevOps’ contribution to digital transformation, its adoption in many industries is still slow due mainly to scarcity of skills and the right toolset. Complete automation remains elusive. Additionally, patchy adoption has given rise to a two-paced overall technology environment—one part is fully integrated with DevOps and the other part is users still mulling over the “how” of tightly integrating it in the overall process.

Advanced technologies such as artificial intelligence and machine learning could resolve these issues. They can alleviate operational complexities in DevOps to help organizations transform at a faster pace. The following are some of the benefits offered by these technologies.

Better Automation

The need to rely on manual processes can be reduced with AI- and ML-based technologies, which has an enormous impact on data analysis. Organizations can use powerful AI data mapping techniques to automate data transformation processes with ease and speed. They can make use of the predictive ability to accelerate the process, increasing accuracy by leaps and bounds. This transformed data can be easily integrated using another solution AI integration, and so it becomes easier for users to work on data for extracting business intelligence and streamlining decision-making. This entire process takes only a few minutes to complete. Plus, since the need for manual intervention is curtailed, developers who previously were mired in handling transformation operations can move on to more strategic tasks such as governance, control and resource planning.

Better Security Through Intelligent Anomaly Detection

AI and ML-powered technologies, with their highly precise features, augment system security to deliver better performance. With a centralized logging architecture, users can record and root out suspicious activity on the network. These technologies togehter with DevOps can help mitigate the impact of threats and hacker attacks to help companies drive their digital transformation initiatives with precision.

Better Team Collaboration

In any organization, two teams function in parallel: the developer team, which has the responsibility to code fast, and the operations team, which ensures minimal disruption. While maintaining this balance can be difficult, transformative technologies such as AI and ML can help these two teams work in unison, improving collaboration by helping them have a single, unified view into system issues across the business ecosystem.

Better Customer Experience

AI-based technologies can have a direct impact on organizational productivity and performance. They can develop and launch their products faster (keeping customer expectations in mind) with lower TCO. Plus, DevOps-inspired service teams are able to provide a higher level of service, resulting in more satisfied customers

AI-powered technologies are critical to kickstart DevOps performance and ultimately drive digital transformation initiatives. Infusion of AI not only improves enabling faster development and deployment cycles through automation, but also enables better collaboration and uncovers and remediates key issues, helping businesses unlock digital transformation value.

August 24, 2020
Data Mapping and GDPR: How Are They Related?
Every business enterprise receives data from an array of diverse data points. The volume of data is growing, and businesses understand the importance of leveraging data and converting them into actionable insights. Owing to data diversity, it becomes important to integrate them in a standard format. This is where data mapping takes a precedence. It helps establish relationships between different data models. Although data mapping offers several business benefits, it is also an integral aspect of the General Data Protection Regulation (GDPR) compliance. This post discusses the concept of data mapping and how it helps you adhere to GDPR.

What is Data Mapping?

As the name suggests, data mapping is a term for mapping the journey of data in an organization. A data map answers questions ranging from the origin of data to its journey through the organization. Today, most business enterprises are using various types of data mapping software, including ETL data mapping tools, to achieve their data mapping targets. Depending on the capabilities of these software applications, data mapping is used to perform a wide range of data management tasks such as data integration, data migration, data warehousing and data transformation.

How Does a Typical Data Map Look Like?

Typically, a data map includes the following information:
- Type of data collected
- Basis of data processing (personal or sensitive)
- Purpose of data collection
- Storage period of data
- Place of storage
- Conditions of data storage
- Various data transfer destinations
- Location of third party vendors (especially, international third party vendors)
- Protocols followed during the data transfer (to international third party vendors)
- Legal basis for data processing
GDPR Compliance and Data Mapping

The GDPR mandates business enterprises to ensure fair handling and security of the information they own. To assure the security, it is important to track the data journey from its point of origin to a conclusion. Without a detailed insight into data lifecycle, it is difficult to implement any security. Thus, data mapping becomes an essential step to achieve GDPR compliance. The following GDPR articles and their requirements will help you understand the importance of data mapping.
- Article 30: Maintaining a written record of data processing activities in an organization is mandatory. A data map serves as a best written record, which organizations can easily retrieve and present at the time of GDPR audit.
- Article 25: Data mapping helps organizations showcase their commitment towards data privacy.
- Article 6: This article governs personal data processing under a lawful basis. Data mapping helps organizations to show the basis of processing–personal or sensitive.
- Article 28: Data maps enable GDPR auditors to understand the extent of third party access to organizational data. All organizations are required to maintain a list of third-parties with whom they might be sharing their business data for various reasons.
- Article 35: This article discusses Data Protection Impact Assessment (DPIA), and data maps enable business enterprises to meet this requirement effectively. To analyze the risk of a particular process, an organization needs to understand the data flow across an organization–how the data is being collected, where it is stored, who uses the information, how long it will be retained and so on.
How an Efficient Data Mapping Software Helps in Achieving GDPR Compliance

Now, what is an efficient data mapping software? It can be any application that aids a business organization to exploit the benefits of data mapping while adhering to the GDPR regulations. The following are a few important features of such GDPR compliant data mapping tools, which helps distinguish them from others.
- Ability to Identify and Map Personal Data: Advanced data mapping software applications, which may or may not involve ETL data mapping software, allows users to identify and map personal data flow in an organization. They are designed to handle a large influx of data. These solutions utilize metadata to identify various data elements. The metadata helps in creating data maps that answer questions ranging from the origin of data, type of data collected, data subjects involved in process, service level agreements, among others. Thus, advanced data mapping software applications enable users to maintain, manage and monitor data maps for business efficiency and GDPR compliance.
- Helps Ensure Data Security: Professional-grade data mapping software solutions are equipped with various security features that enable users to secure their database while providing them access to data, whenever required. They also allow organizations to conduct risk analysis of their data and ensure adherence to DPIA.
- Assures a Better Control: Earlier reference to GDPR articles suggest how GDPR mandates enterprises to ensure data protection while handling personal data. An efficient data mapping software will help them address this need. They enable users to modify their applications to establish control, as well as safeguard personal data effectively.
Achieving GDPR compliance is not an easy task. However, you can easily address it at various stages and it will help your business adhere to privacy standards while effectively meeting customer demands. You can take advantage of various data mapping software applications including advanced ETL data mapping tools to create effective data maps, stay compliant and, above all, drive your business forward.

— Chandra Shekhar
July 2, 2019