Preface:
AIOps is a technology that combines the use of Artificial Intelligence (AI) in (ITOps) Operations. Automation in IT Operations helps improve the primary level of business processes. Delay to adopt these proven technologies conservatively is an offense. It hampers the organization’s growth. Competitive advantage goes to companies that can demonstrate proficiency. Fetch the culture of standardization with modern technology.
What is the need of AIOps?
For every business, operational data and organizational data are most important. Multi-layered platforms enhance IT operations. Support of IT operations brings stability of infrastructure to support all applications of the organization. Automation can be a success if the infrastructure is stable. Automation eliminates human errs and reduces efforts.
Why is there a need for AIOps now than ever before?
- Getting end-to-end visibility of a business is difficult
- Traditional IT management solutions cannot keep pace with a higher number of transactions
- Digital Transformation can add value to your business
- Integrate data received from assorted sources
- If you want to apply Machine Learning and Data Science to IT operations
- Machine learning analyzes voluminous data
- Data categorization is faster
- Precise documentation of data
- Exceeds human ability to manage complexities
- Surpasses the performance monitoring of mobile applications, IoT devices, and APIs
- Supports the ChatOps – chat-based IT operations
- Need for faster response by IT operations team
- Apply to the applications on iOS, MacOS, Android, and Windows
Market trends show AIOPs as the future of DevOps, exponential growth of data, and IP Traffic. New technologies offer promising growth for IT companies. Create and make the data valuable for the organization with automated IT operations.
Benefits of AIOps for the organization:
- For Business:
- Improves business processes and tasks
- Delivers AI-based reliable automation
- Continuous improvement and revised solutions
- Enhances decision-making abilities and get actionable insights
- Apply analytics to a broader range of data
- Identify the controllable factors
- Helps to meet service level agreements
- Reduces operational bottleneck, operational costs, and operational errors
- Prompts the problems even before they happen
- Data-driven recommendations helps to speed up service
- Automatically detects and solves issues with dynamism
- Improved resource utilization
- Reduction in overheads of IT staff
- Predictive Analytics, Service Analytics, and Capacity analytics
- Automation assists in taking decisions even if data is complex
- Adoption of the cloud for higher data security
- Brings visibility to business as a whole with an improved understanding
- Saves you from mundane work and think innovative IT solutions
- For Applications:
- Modern applications with cloud-native architecture
- Scalability of systems
- Anytime access to data with inbuilt lake pool
- Identifies service level issues
- Data-driven automated processes and recommendations
- Real-time data correlation
- Accurate root cause analysis
- ML learns from data using algorithms
- Get consistent automation architecture
- Prioritize query and issue resolution
- Tremendous reduction in MTTD for speedy MTTR
- Monitor performance and security of the applications on-premise or cloud
- For the IT Teams & other employees:
- Monitoring systems becomes easy
- Real-time data ensures root cause analysis to propose solutions
- Reserve the efforts for the strategic tasks
- Better engagements and contributions to the company’s projects
- Provides visibility to each service by automated responses
- Improves both IT processes and the workflow
- Quality tasks for IT professionals is satisfying
- Increased efficiency of problem analysis and problem-solving
- Separate the significant and general alerts for defining actions
- Proactive resolution with a deeper analysis of the problem
- Inbuilt asset intelligence that helps in continuous learning
- Improved IT service lifecycle
Being an IT organization if you are in a struggle to keep pace with the world; explore the possibilities that the latest technologies offer.
Cautions before implementing AIOps:
- Consider the impact in the long run
- Identification of skills and experience
- Set your priorities and capabilities
- Transformation can be challenging
- Applying algorithms on data sources
- Implementation in various network conditions
- Proper documentation of IT processes
Along with the approvals, there are certain objections to this technology.
Objections to AIOps:
- Inadequacy of skilled Operations Team
- Organizational cultural clash
- Application of emerging technologies requires additional skills
- Questionable ability to adapt to the change in IT operations
- Readiness of the team to take on modified responsibilities
- Failing to cope up to the technology affects the career
- Need for trainings on Machine Learning and deep learning algorithms
What should you propose while drawing the advantages of AIOps?
- Expecting the change to bring improvement to IT operations
- Plan for success should include understanding the need for business
- Set clear expectations including what change does your business need
- Focus on goals, reduced workload and data requirements
- Handling IT solutions with AI to what extent should it be automated
- Data processing strategies and addressing concerns
- Measures to reduce the operational costs
- Actual function of algorithms for improved interaction
- Handshake of IT and AIOps helps to build a better business
- Identification of latent service level issues
- Introduce the prevention and resolution of IT-operational issues
- Evaluate automated IT operations using Opensource and Machine Learning software
- Evolve products around AIOps for knowledge management, and task automation
- Software performance or risk analysis, historical analysis,
- Establish correlation between real-time and historic data
- Contextualization increases the capability to observe for planning actions
- Reduce both the Mean time to detect and repair
- Adopt best practices to improve system capabilities
- Standard responses to ensure minimum response time
- Deploying a thoroughly tested application on production
- Observe transparency within applications
- Define alert and notification management process
- Validate the algorithmic decisions by the platform is must
- Incident management converted to learning’s for the software
- Automated system health check
- Accurate predictions about the infrastructure issues
- Reduced efforts and improved results of digital transformation
- Set measurable for success of automated IT operations
- Addressing future problems and finding probable solutions
- Program for post-implementation of automation in ITOps
What is the after implementation challenges?
- Multiple monitoring tools is a challenge for analytics
- Ability to manage the alerts and actionable
- Applications do fail to perform if the data quality is compromised
- Response is not satisfactory if data is missing or inconsistent
- Design level issues get highlighted
- Business processes and approach is not defined accurately
- Establishing a correlation between customer information and transaction processing
- Digital data cannot be completely unsupervised
- Maintaining automated IT operations requires domain knowledge and expertise
- Selection of tools for AIOps is a critical decision
Top Free Software for AIOps Platform:
- Dynatrace
- Instana
- Netreo
- Logic Monitor
- AppDynamics
Review:
Reduce implementation costs of AIOps with the use of open source Machine Learning software. Automation of operations increases the speed to achieve a higher customer response rate. Avoid superfluous bottlenecks to a great extent to provide a superior user experience.
Market Analysis points towards the continuous growth of the AIOPs platform, expected to touch 9.2 billion USD by the year 2025.
AIOps is the future of IT operations. Get future ready; invest in it now.
