By: Divyanshu Singh, Senior Quality Engineer, ACE, LTIMindtree

Introduction

In the dynamic landscape of contemporary business, the use of automated data analytics has appeared as a transformative force, offering unparalleled advantages to organizations and their stakeholders. There are multifaceted benefits of automated data analytics, emphasizing its pivotal role in informed decision-making, strategic solutioning, and enhancing the capabilities of board members and stakeholders. Shifting the paradigm from the traditional IT Service Management (ITSM) industry to a data-driven approach is the need of the hour, presented with visually enhanced results.

Need for automation in data analytics

As we have seen the explosion of data, we may overlook processing data that may affect attaining useful information due to time-consuming processes and lack of understanding. As data itself may need a huge number of analysts to work fast and effectively that every organization cannot afford and often not opt for the same thing. Almost everyone just wants to see a clear output without going through a lengthy process of examining, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making.

Process of automated data analytics

Automated data analytics is a continuous, systematic, and sequential process that covers the combination of data received from sources (there might also be one source). After data ingestion, it is cleaned and made into a structured format for use in an Artificial Intelligence (AI)-Machine Learning (ML) engine to gain insight. The AI-ML engine mostly doesn’t create ready data, but it creates baseline or intent-based data that is then controlled by a rule or logic-based process. Keeping output for the end user, the data is structured, or the engine is finetuned, which sums up the high-level sequential process. Although there will be some iteration in the process, the sequence is made as per the propagation of data. Overall, the process is supported with tools, Infrastructure, and its assisted process.

Application of automated data analytics in ITSM and ITOps

There can be a number of effective use cases and applications for automated data analytics, but the scope is covered by keeping the traditional market of IT Operations (ITOps) and ITSM industry. Companies can harness the power of automated data analytics to derive significant advantages in business solutioning and decision-making for stakeholders and board members.

• Incident management: Early detection and resolution where automated analytics can identify patterns in historical incident data to predict and proactively address potential issues before they escalate, minimizing downtime and service disruptions. Also, root cause analysis, where analyzing incident data automatically helps identify the root causes of recurring issues, allowing for targeted and permanent solutions.
• Service-level management: Automated analytics tools can continuously monitor service levels and performance metrics, providing real-time insights to ensure that services meet or exceed predefined standards to provide accurate performance monitoring. Keeping in mind SLA compliance where, tracking and reporting on the overall health of IT services will be an added advantage.
• Change management: It covers the risk assessment and change success prediction by checking the potential impact of proposed changes by analyzing historical data to make informed decisions about change implementation and predictive analytics can forecast the likelihood of successful changes, aiding in risk mitigation and resource allocation.
• Capacity planning: It has to be the most discussed aspect when discussing an operation. Thus, resource optimization by effective resource allocation ensures that IT infrastructure meets demand without unnecessary costs with the scalability analysis with the help of forecasting future capacity requirements, allowing for proactive adjustments to meet changing business needs.
• Problem management: Identifies patterns and trends in recurring problems, aiding in developing long-term solutions and preventing chronic issues, keeping a good knowledge base to analyze ticket and documentation data to continuously improve the knowledge base, facilitating faster problem resolution.
• Security and compliance: Anomaly detection can identify unusual patterns in network traffic or user behavior, signaling potential security threats and vulnerabilities. This will provide an upper hand in compliance auditing as per industry regulations, providing a transparent view for stakeholders and board members.
• Continuous improvement: Need feedback analysis by gathering feedback from users, helping IT teams identify areas for improvement in service delivery, and enabling regular analysis and tracking of Key Performance Indicators (KPIs) and performance metrics by managers to maintain the effectiveness of operations.
• Cost management: Expense tracking and budget forecasting can accountably be done by keeping track of IT-related expenses, providing insights into cost distribution and opportunities for cost optimization, forecasting future IT expenses, and supporting budget planning and financial decision-making.
• Dashboard and reporting: Automated reporting tools generate executive-level dashboards, providing board members with a high-level overview and a deep-dive view that can help save time and cost in the long run. Tailored reporting for required information that might be helpful to slice or collate information as per the usage of different end users.
• Automation orchestration: Edging with automation and optimizing IT workflows by identifying bottlenecks and areas for process improvement, reducing manual efforts of daily mundane tasks. This helps provide efficiency gains in routine tasks and workflows, improves operational efficiency, frees up resources for more strategic initiatives, and reduces human error.

Conclusion

Automated data analytics is a cornerstone in revolutionizing ITSM and ITOps industries, as well as any solution involving data. It offers numerous advantages for businesses and stakeholders, including board members. With automation, organizations can derive actionable insights from large datasets, enhancing decision-making, optimizing resources, and strengthening the resilience of IT services. The reach of automated data analytics spans critical areas from incident management to security and compliance, turning challenges into strategic opportunities. It empowers IT professionals to proactively resolve issues, predict and mitigate risks, and continually improve service delivery. Stakeholders and board members, equipped with real-time and predictive insights, can better align IT initiatives with organizational goals, ensure industry standard compliance, and make informed decisions impacting the entire enterprise. As the ITSM and ITOps sectors evolve, automated data analytics is set for further growth. The integration of advanced machine learning algorithms and the ongoing drive for automation promise to enhance the role of analytics in shaping IT management’s future. This journey towards a data-driven, automated IT environment can boost efficiency, security, and foster a culture of continuous improvement and innovation.