Impact of network analytics in IT ops

Role of network analytics

Looking at the pace in which our world is moving towards digitization, one has to admit, that network analytics will play an important part in paving the way how IT would operate in future. Network analytics for an enterprise is complex, the AI and automation technologies in use help achieve intelligent and effective ways towards future IT operations.

Network analytics improves user experience in IT operations by analyzing network data. It compares and correlates data to address a problem or trend. It manages IT operations by channelizing the below mentioned data inputs.

  1. Real network traffic generated by client.
  2. Synthetic network traffic created by virtual clients.
  3. Metrics from infrastructure.
  4. System logs.
  5. Data flow.
  6. Application program interface (API) from application server.

Scope of network analytics

A user can face poor network performance or disruption in service due to either, OS problem, Wi-Fi or LAN issue, DHCP, WAN problem or application failure. To locate the actual cause for interruption is essential for smooth functioning of IT operations. Network analytics operate with the help of big data analytics along with cloud computing and machine learning to examine data and create a holistic perspective. Proactive IT Operations Led by predictive insights enhance 90% data accuracy. It can also interpret data in a visual format to develop an elaborate understanding. Here, network analytics plays an important role in redefining IT operations.

  • Network analytics uses proactive analytical tools such as; Sisense, Azure, R Open, GoodData etc. for a deeper understanding of issues and to locate the source of error which can make IT operation seamless. Sisense helps processing data 10 times faster, Azure’s 100 modules per experiment or 10 GB storage space is cost effective. GoodData allows 360-degree overview for customer insights.
  • Earlier, the task to fix a network issue was relatively simple, now, with the increasing usage of virtual and mobile devices and cloud computing, detecting an issue within a network and fixing the same has become complex. Without network analytics, IT Ops will not be able to sustain the wrath of disruption.
  • There has been huge diversification lately in the field of hardware, operating systems, application and services. Understanding network problems within these landscapes, can be challenging. Network analytics plays an important role here by easing the task through user performance management (UPM).
  • Network analytics also minimizes the issue with access network in IT operations starting from getting Wi-Fi access to authentications, obtaining IP addresses or resolving DHCP requests.
  • Network analytics tool can help reduce network traffic through alteration in facilities. It can use network event correlation to understand the impact on devices and customer’s experience on bandwidth latency.
  • Network analytics assists a great deal in network capacity planning and deployment opportunity for an improved network ROI by up to 15% as per market research.

Difference between monitoring and analytics network solution

To analyze the impact network analytics has on IT Ops, it is essential to understand the difference between monitoring and analytics solution. Monitoring refers to collecting and interpreting data in a passive form and sharing potentially actionable information to the network manager. Hence, it focuses on spotting problems without fixing them.

Analytics is more prescriptive where, recorded historical data is understood, learnt and analyzed paving a pattern to be followed. Data collected from Wi-Fi, devices, applications and WAN create trends that impact IT operations.

Advanced analytics

Along with pinpointing the area of concern, advanced analytics tries to automate new solutions to the detected problem. Advanced network analytics help to understand if the issue is with a client operating system, application, network services or Wi-Fi access. This enhances the scope of IT Ops by improving infrastructure by providing insights to take the overall operations to the next level. The new generation of network analytic tools and solutions can reduce outages, upgrade systems and applications, improve customer experience and simplify the process of operations in IT.

Benefits of network analytics in IT ops

  1. Network analytics can help IT Ops analyze the requirement and create a balance so that, the available resources can be optimally utilized to enhance network performance and lower the cost structure of IT Ops.
  2. Network analytics help with data mining insights for identification of revenue and enabling a data-driven and action-oriented IT operation.
  3. Network analytics can help in capacity planning where both resources and services can be calculated in advance for an apt provisioning.

Impact of network analytics in brief

Network analytics, with its analytics tool, can predict future down time, allowing necessary action to be taken on time. It also increases awareness of the root cause of the problem to remediate faster and eventually prevent and result in reducing MTTR by 95%. This can reduce organizational disruption and operational costs while increasing customer satisfaction.

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AIOps – IT Infrastructure Services for the Digital Age

The IT infrastructure services landscape is undergoing a significant shift, driven by digitalization. As focus shifts from cost efficiency to digital enablement, organizations need to re-imagine the IT infrastructure services model to deliver the necessary back-end agility, flexibility, and fluidity. Automation, analytics, and Artificial Intelligence (AI) – comprising the “codifying elements” for driving AIOps – help drive this desired level of adaptability within IT infrastructure services. Automation, analytics, and AI – which together comprise the “codifying elements” for driving AIOps– help drive the desired level of adaptiveness within IT infrastructure services. Intelligent automation, leveraging analytics and ML, embeds powerful, real-time business and user context and autonomy into IT infrastructure services. Intelligent automation has made inroads in enterprises in the last two to three years, backed by a rapid proliferation and maturation of solutions in the market.

Artificial Intelligence Operations (AIOps) . Everest Group 2018 Report . IT Infrastructure

Benefits of codification of IT infrastructure services

Progressive leverage of analytics and AI, to drive an AIOps strategy, enables the introduction of a broader and more complex set of operational use cases into IT infrastructure services automation. As adoption levels scale and processes become orchestrated, the benefits potentially expand beyond cost savings to offer exponential value around user experience enrichment, services agility and availability, and operations resilience. Intelligent automation helps maximize value from IT infrastructure services by:

  1. Improving the end-user experience through contextual and personalized support
  2. Driving faster resolution of known/identified incidents leveraging existing knowledge, intelligent diagnosis, and reusable, automated workflows
  3. Avoiding potential incidents and improving business systems performance through contextual learning (i.e., based on relationships among systems), proactive health monitoring and anomaly detection, and preemptive healing

Although the benefits of intelligent automation are manifold, enterprises are yet to realize commensurate advantage from investments in infrastructure services codification. Siloed adoption, lack of well-defined change management processes, and poor governance are some of the key barriers to achieving the expected value.  The design should involve an optimal level of human effort/intervention targeted primarily at training, governing, and enhancing the system, rather than executing routine, voluminous tasks.  A phased adoption of automation, analytics, and AI within IT infrastructure services has the potential to offer exponential business value. However, to realize the full potential of codification, enterprises need to embrace a lean operating model, underpinned by a technology-agnostic platform. The platform should embed the codifying elements within a tightly integrated infrastructure services ecosystem with end-to-end workflow orchestration and resolution.

The market today has a wide choice of AIOps solutions, but the onus is on enterprises to select the right set of tools / technologies that align with their overall codification strategy.

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Can automation manage system alerts?

System alerts and critical alerts

One of the most important and critical roles of an IT professional is to handle incoming alerts efficiently and effectively. This will ensure a threat-free environment and reduce the chances of system outages. Now, not all incoming alerts are critical; an alert can pop up on a window screen for a user to act on, blocking the underlying webpage. One can configure the setting to automatic alert resolution where an alert will be closed automatically after a number of days.

Can automation manage system alerts?

Gradually, many companies are incorporating automation in the field of managing system alerts. The age-old technology of monitoring system for both, internal and external alerts is not effective in streamlining the actual process of managing these incoming alerts. Here, IT process automation (ITPA) can take incident management to a whole new level. Automation in collaboration with monitoring tools can identify, analyze and finally prioritize incoming alerts while sending notification to fix the issue. Such notifications can be customized depending on the selected mode of preference. Also, it is worth mentioning here that automated workflows can be created to open, update and close tickets in the service desk, minimizing human intervention while electronically resolving issues.

Integration of a monitoring system with automation

Automation of system alerts happen with the following workflow. It highly improved the incident management system, reducing human intervention and refining the quality of monitoring.

  1. The monitoring system detects an incident within the IT infrastructure and triggers an alert.
  2. The alert is addressed by automation software and a trouble ticket is generated thereafter in service desk.
  3. Then the affected lot is notified via preferred method of communication.
  4. Network admin is then notified by ITPA to address the issue and recover.
  5. The service ticket is accordingly updated through implementation of automation.

Benefits of automation to manage system alerts

Relying on a process that is manually performed especially, while dealing with critical information in a workflow can be difficult. In such a scenario, automation of monitoring critical data in business systems like accounting, CRM, ERP or warehousing can improve on consistency. It can also recognize significant or critical data changes immediately triggering notification for the same. With this 360-degree visibility of critical information, decision making can happen a lot faster which in the long run can forestall serious crisis. It also improves the overall performance of the company and customer service and reduces financial risk due to anomalies and security threats. Hence, it can be aptly mentioned that automation of system alerts can effectively reduce response and resolution time. It can also lessen system downtime and improve MTTR.

BPA platform’s role to manage system alerts

The business process automation (BPA) platform enables multi-recipient capabilities so that notification can be sent to employees across different verticals. This will increase their visibility on real-time information that is relevant to their organizational role. This platform also provides escalation capabilities where notification will be sent to higher management if an alert is not addressed on time.

Conclusion

For large-scale organizations, the number of alerts spotted by detection tools are growing in number with time. This inspired IT enterprises to automate security control configurations and implement responsive security analysis tasks. Through automation of security control and processes, a new firewall rule can be automatically created or deleted based on alerts. Once a threat is detected, automated response is created. We can conclude that automation can manage system alerts efficiently and effectively. And a pre-built workflow often helps to jump-start an automation process of addressing a system alert.

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AIOps Trends in 2019

Adoption of AIOps by organizations

Artificial Intelligence in IT operations (AIOps) is rapidly pacing up with digital transformation. Over the years, there has been a paradigm shift of enterprise application and IT infrastructure. With a mindset to enhance flexibility and agility of business processes, organizations are readily adopting cloud platforms to provision their on-premise software. Implementation of technologies like AIOps and hybrid environment has facilitated organizations to gauge the operational challenges and reduced their operational costs considerably. It helps enterprises in:

  • Resource utilization
  • Capacity planning
  • Anomaly detection
  • Threat detection
  • Storage management
  • Cognitive analysis

Infact, if we look at Gartner’s prediction, by 2022, 40% of medium and large-scale enterprises will adopt artificial intelligence (AI) to increase IT productivity.

AIOps Market forecast

According to Infoholic Research, the AIOps market is expected to reach approximately $14 billion by 2024, growing at a CAGR of 33.08% between 2018–2024. The companies that will provide AIOps solutions to enhance IT operations management in 2019 include BMC Software, IBM, GAVS Technologies, Splunk, Fix Stream, Loom System and Micro Focus. By end of 2019, US alone is expected to contribute over 30% of growth in AIOps and it will also help the global IT industry reach over $5,000 billion by the end of this year. Research conducted by Infoholic also confirmed that AIOps has been implemented by 60% of the organizations to reduce noise alerts and identify real-time root cause analysis.

Changes initiated by enterprises to adopt AIOps

2019 will be the year to reveal the true value of AIOps through its applications. By now, organizations have realized that context and efficient integrations with existing systems are essential to successfully implement AIOps.

1. Data storage

Since AIOps need to operate on a large amount of data, it is essential that enterprises absorb data from reliable and disparate sources which, then, can be contextualized for use in AI and ML applications. For this process to work seamlessly, data must be stored in modern data lakes so that it can be free from traditional silos.

2. Technology partnership

Maintaining data accuracy is a constant struggle and in order to overcome such complexity, in 2019, there will be technology partnership between companies to deal with customer demands for better application program interface (APIs).

3. Automation of menial tasks

Organizations are trying to automate menial tasks to increase agility by freeing up resources. Through automation, organizations can explore a wide range of opportunities in AIOps that will increase their efficiency.

4. Streamling of people, process and tools

Although multi-cloud solutions provide flexibility and cost-efficiency, however, without proper tools to monitor, it can be challenging to manage them. Hence, enterprises are trying to streamline their people, process and tools to create a single, siloed-free overview to benefit from AIOps.

5. Use of real-time data

Enterprises are trying to ingest and use real-time data for event correlation and immediate anomaly detection since, with the current industrial pace, old data is useless to the market.

6. Usage of self-discovery tools

Organizations are trying to induce self-discovery tools in order to overcome the challenge of lack of data scientists in the market or IT personnel with coding skills to monitor the process. The self-discovery tools can operate without human intervention.

Conclusion

Between 2018 to 2024, the global AIOps market value of real time analytics and application performance management is expected to grow at a rapid pace. Also, it is observed that currently only 5% of large IT firms have adopted AIOps platforms due to lack of knowledge and assumption about the cost-effectiveness. However, this percentage is expected to reach 40% by 2022. Companies like CA Technologies, GAVS Technologies, Loom Systems and ScienceLogic has designed tools to simplify AIOps deployment and it is anticipated that over the next three years, there will be sizable progress in the AIOps market.

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Cost effective solutions on AIOps platforms

Digital transformation in IT operations

The global market value of AIOps is predicted to increase from $2.24 billion in 2017 to $9.90 billion by 2023, as per industry reports. IT organizations, globally, are focusing on digital transformation aggressively. Technologies like AI, Big Data, ML are compelling IT operations’ platforms to modify and adapt to multi-cloud infrastructure. With a vision to explore new arena of opportunities, AIOps can monitor, analyze, correlate and automate, easing IT operations. The focus areas where AIOps plays a key role in enabling digital transformation includes:

  1. Open data access, where data can be recorded from various authentic sources and can be freed from organizational silos for repetitive analysis
  2. Big data was initially thought to increase efficiency and decision-making capabilities of enterprises. However, with the expansion of data, things became complex. Here the intervention of AIOps improved the ability to handle huge data thus, expanding the scope of data analysis
  3. ML can access data from various sources and can modify or create new algorithms without human intervention. AIOps enhances ML’s ability to handle enormous data and at the same time stay aligned to organizational goals
  4. Data analytics can solve major data related problems in IT domain and on top of that, AIOps could leverage competitive advantage by promising richer business context, short response time and ability to predict potential risk

Scope of AI in IT Ops – are they cost effective?

  • With an intent to study time and labor management, any organization will end up spending massively on both, time and money. For that matter, an application programming interface (API), can help a company complete, its reports in no time. This can ramp up the pace of report creation, thus opening a scope for real-time analysis of compliance. Now, that is definitely cost-effective.
  • A global recruitment firm increased its hiring ratio by about 8%, through implementation of AI. It helped the firm to identify and match the right skill set along with the prediction for attrition per resource. This proved cost effective since attrition costed the organization up to $25,000 per resource.
  • From the operational perspective, in a 24/7 environment, if there is an outage, it will result in a series of logged complaints, which then will become difficult for an individual to manually transcribe. This is where AI plays an important part in identifying the main issues through log analytics.
  • Technology like cognitive insight, creates a data pool of wide range of solutions on critical issues. AI bridges the gap between big data and humans through operational intelligence, accuracy and speed, thus making it cost-effective to a great extent.
  • Enterprises like Dyn and British Airways suffered Distributed Denial of Service (DDoS) attacks post which they implemented cognitive insight which secured their operations.

Cost effective solution of AIOps

Analyzing and managing cost is essential. Doing a cost analysis of cloud with components like IOPs, VMs, storage capacity, bandwidth, API can be tricky and complex. AI implementation can help here to segregate the cost of securing a more accurate IT budget.

  • AI and root-cause analysis
    AI is very effective in the area of root-cause analysis. It is efficient in locating an issue and creating a remediation for the same, thus solving complex problems in a short span. AIOps helped a US Bank to automate root cause correlation to gather data on customer dissatisfaction and thus, enhancing customer experience.
  • Threat detection is now a cakewalk
    Through machine learning algorithms, AIOps can learn to detect anomalies and critical issues. GAVS’ security division designed a remedial platform combining ML algorithms and AI’s self-learning capabilities to reduce risk and predict future anomalies on an IT platform, ensuring a secured environment for GAVS’ customers.
  • AIOps and its outage forecasting competences
    AIOps can forecast outages through data prediction and also increase resource utilization through identifying areas of cross training. The market of forecasting outages through AIOps, is expected to grow from $493.7 million in 2016 to $1.14 billion by 2021, as per industry reports.
  • Combining tools for an innovative future
    Automation and collaboration of tools can enhance productivity and accuracy. AIOps powered with big data and ML helps in process automation and is used more as a strategic than operational tool. With this merger, data could be analyzed, optimized, and transformed efficiently. In GAVS, the focus is on a “Zero Incident” platform where GAVS can help enterprises to reach Zero Incident state through the above-mentioned collaboration of tools. This will definitely prove cost-effective and enhance the end-user experience.

Solutions built with innovation and cost-efficiency is the key

In their zeal to enter the digitized innovation area, organizations are aggressively trying to locate cost-effective and reliable solutions. Although many companies still rely on age old machines and processes which require constant monitoring and human intervention, however, automation of IT operations is a boon, ensuring cost-efficiency across levels.