https://neovisionconsulting.blogspot.com/2023/01/devopsaiops.html
AIOps is one of the tools that cannot be ignored in modern society where IT and DX are recommended in Japan. It is predicted that AIOps will continue to increase the trend to solve the shortage of human resources and improve the efficiency of operations. However, AIOps is still not well known,
"I don't know what will change with the introduction of AIOps"
"I don't know what AIOps can do"
The reality is that there are many people who say. So this time
- What is AIOps
- Uses of AIOps
- Benefits of AIOps
- Procedures up to introduction
I will explain.
If you want to advance DX or digitalization, or if you want to achieve operational efficiency with AIOps, please read to the end and use it as a reference.
What is AIOps
AIOps was proposed by Gartner in 2018,An IT operation method that uses AI to perform machine learning on the large amounts of data held by companies to automate operations and streamline operations managementis. AIOps makes it easier to automate complex system management and make effective decisions using data.
AIOps can handle IT operation management not only for general companies but also for a wide range of industries such as manufacturing and medical sites. A major feature of AIOps is the efficient management of increasingly complex data and systems.
What is an AIOps platform?
There is a word similar to AIOps, "AIOps platform", but there is no big difference, it is a tool with AIOps functions.Diversification to solve the management issues of each company, such as IT operation and security managementAdvanced AIOps tools are being developed.
Difference from MLOps
MLOps and AIOps are similar words, but they have completely different meanings.
MLOps areA system for machine learning development and operations teams to smoothly advance the process from machine learning implementation to operationrefers to
AIOps can simplify complex system management and problem solving by machine learning, so development with MLOps will also lead to reduction of man-hours and efficiency of work.
What are the 5 uses of AIOps?
AIOps can be used in a variety of ways, but among them, the following five are representative.
- Business automation
- Performance analysis with advanced AI
- Outlier detection
- Analyzing warnings and suggesting remediation
- IT service management
We will explain each below.
Use 1: Automation of business
In AIOpsAutomatic collection of data and automation of work by machine learning of AI is possibleis. Traditional data collection requires manual collection of information from various sources, which consumes human resources.
However, AI machine learning improves operational efficiency,Increased productivityYou will also be able to connect to For example, with AIOps, you can automate alerts when problems occur. Since the monitoring system can be automated by learning when to issue an alert,Quick response to troubleis possible.
Application 2: Performance analysis with advanced AI
Due to the increase and diversification of data, it is becoming difficult for people to analyze the performance of the complex marketing as a whole. However, AI machine learning enables performance analysis quickly and efficiently.
With ever-more-advanced AI technology, you can automatically and efficiently analyze large datasets to quickly respond to existing data analytics challenges.
also,By predicting and perceiving issues and problems that may occur in the future, you can also learn so that you can quickly investigate and solve the cause.
Application 3: Abnormal value detection
AIOps, which uses AI algorithms to detect abnormal values, is also a typical use. Software anomalies other than obvious anomalies such as system failures are difficult to detect, and there were cases where detection was delayed in existing systems.
In AIOpsCreate an algorithm based on past data history for abnormal values, detect abnormal values in the current KPI score, and issue alerts for quick responseis possible.
Use 4: Analyzing warnings and suggesting remediation
AI OpsBy automatically presenting analysis and repair methods when an error occurs, the cause of the problem can be quickly identified and dealt with.make it possible. Conventional systems only detect errors and issue alerts, but cannot analyze data and propose corrections.
AIOps can analyze data information related to errors instantly and sort out those that need to be dealt with and those that do not need to be dealt with, so error handling can be performed efficiently.
Application 5: IT service management
IT service management with AIOpsComprehensive management from service design and construction to supportcan. IT services must consider what can be a good service for users, so it is also necessary to improve IT services. Therefore, it is necessary to efficiently extract and improve problems in IT services through data analysis of user information.
With AIOps, it is possible to manage users and businesses instead of managing traditional computer systems.
3 benefits of introducing AIOps
There are three main benefits of introducing AIOps:
- Increased productivity through automation
- Reduced burden of big data operation work
- Improving customer satisfaction by speeding up error recovery
We will explain each below.
Increased productivity through automation
AI OpsMachine learning using advanced AI can automate simple tasks to complex data analysis, leading to improved productivity.In particular, the analysis of complex big data has been a typical task that requires a lot of human resources, so the introduction of AIOps to improve the efficiency of data analysis alone will be a great advantage.
In addition, since work such as service construction and testing requires accuracy, it can be said that human error can occur. but,AI-based automation also ensures accuracy, reducing human errorsI can do it.
Reduced burden of big data operation work
Analyzing the collected data, which increases on a daily basis, also causes a large amount of human resources to be used. However, due to operation by AIOps,Enables fast and effective data analysis, and problems can be presented in real time.
Especially for big IT companies with big data, it will be possible to analyze big data, which has become a complicated black box, and to operate IT efficiently.
Improving customer satisfaction by speeding up error recovery
Real-time display through big data analysis enables quick response even when an error occurs, and even multiple errors can be prioritized and dealt with efficiently.
AlsoWhen AIOps issues an alert, it presents the cause of the error and how to fix it, so the process from problem detection to repair can be done smoothly.
Explain how to introduce AIOps without failure and 3 steps
You can't expect a high effect just by introducing AIOps. By following the steps below, you will be able to implement effective IT operations.
- Step 1: Prioritize system operations challenges
- Step 2: Narrow down the fields to be introduced
- Step 3: Expand the range of data handled
We will explain each step individually.
Step 1: Prioritize system operations challenges
First, identify issues in IT system operation,Investigate challenges to adapt to AIOpsTo do.
AIOps does not automate or streamline all tasks in IT operations immediately after its introduction. Let AIOps learn the cause and trigger of the problem occurrence, and test how accurately it can actually detect it.
Step 2: Narrow down the fields to be introduced
AIOps can capture a variety of data information. However, if you try to import all the data, not only will it take a long time to load, but it may also lead to inefficient results such as incorrect machine learning.
Therefore, AIOpsIn what areas is it most effective to apply?Let's start by considering .
Step 3: Expand the range of data handled
If you have narrowed down the field and obtained the introduction effect, let's expand the data range that handles AIOps.
AI OpsFind and associate correlated or similar data from different data sourcesis characterized by Therefore, it is not effective to use only within the same data range, so we recommend using different data ranges.
4 typical AIOps tools
Various AIOps tools have been developed around the world. Among them, we will introduce about four representative AIOps tools.
Splunk
Splunk is an AIOps tool developed by Splunk, an American company founded in 2003. It is used in a wide variety of industries such as medical sites, financial services, and public institutions.
We are especially good at log analysis and can analyze system logs from different data groups to automate IT operation management and visualize system operation status.
It has been selected as one of the 100 fastest growing companies published by the American business magazine "Fortune", so it can be said that it is a highly reliable and representative AIOps tool.
OpsRamp
OpsRamp is a SaaS-type AIOps developed by American company OpsRamp. It is one of the representative AIOps along with Splunk, with more than 1,000 installation results.
Since all necessary functions for IT operation services are provided, companies can select and use only the functions they need. It is possible to automate infrastructure environment operation, monitoring, error detection, and countermeasures.
Since it is provided as a cloud service on the Internet, AIOps is easy to introduce and is used by a wide range of users from small to large companies.
Watson OpenScale
Watson OpenScale is an AIOps tool provided by IBM, which operates in over 170 countries around the world. One of the features of Watson OpenScale is that anyone can correctly judge the operational status of AI.
Expert knowledge is required to determine whether AI is operating correctly, but Watson OpenScale can trace back the data used by AI for judgment in each process.
Since it is an AIOps tool for other AI tools to make correct judgments, introducing it will enable more accurate and efficient IT operations.
SysTrack AIOps
SysTrack AIOps is an AIOps tool provided by the American global company "Lakeside Software".
SysTrack AIOps can collect various data such as data performance information and system logs.
In addition, AIOps has excellent scalability, with a simple layered architecture that enables IT operations with a minimal number of servers.
Data management by AIOps is the key to domestic DX
You can't expect a high effect just by introducing AIOps. By following the steps below, you will be able to implement effective IT operations.
- Step 1: Prioritize system operations challenges
- Step 2: Narrow down the fields to be introduced
- Step 3: Expand the range of data handled
We will explain each step individually.
Step 1: Prioritize system operations challenges
First, identify issues in IT system operation,Investigate challenges to adapt to AIOpsTo do.
AIOps does not automate or streamline all tasks in IT operations immediately after its introduction. Let AIOps learn the cause and trigger of the problem occurrence, and test how accurately it can actually detect it.
Step 2: Narrow down the fields to be introduced
AIOps can capture a variety of data information. However, if you try to import all the data, not only will it take a long time to load, but it may also lead to inefficient results such as incorrect machine learning.
Therefore, AIOpsIn what areas is it most effective to apply?Let's start by considering .
Step 3: Expand the range of data handled
If you have narrowed down the field and obtained the introduction effect, let's expand the data range that handles AIOps.
AI OpsFind and associate correlated or similar data from different data sourcesis characterized by Therefore, it is not effective to use only within the same data range, so we recommend using different data ranges.
4 typical AIOps tools
Various AIOps tools have been developed around the world. Among them, we will introduce about four representative AIOps tools.
Splunk
Splunk is an AIOps tool developed by Splunk, an American company founded in 2003. It is used in a wide variety of industries such as medical sites, financial services, and public institutions.
We are especially good at log analysis and can analyze system logs from different data groups to automate IT operation management and visualize system operation status.
It has been selected as one of the 100 fastest growing companies published by the American business magazine "Fortune", so it can be said that it is a highly reliable and representative AIOps tool.
OpsRamp
OpsRamp is a SaaS-type AIOps developed by American company OpsRamp. It is one of the representative AIOps along with Splunk, with more than 1,000 installation results.
Since all necessary functions for IT operation services are provided, companies can select and use only the functions they need. It is possible to automate infrastructure environment operation, monitoring, error detection, and countermeasures.
Since it is provided as a cloud service on the Internet, AIOps is easy to introduce and is used by a wide range of users from small to large companies.
Watson OpenScale
Watson OpenScale is an AIOps tool provided by IBM, which operates in over 170 countries around the world. One of the features of Watson OpenScale is that anyone can correctly judge the operational status of AI.
Expert knowledge is required to determine whether AI is operating correctly, but Watson OpenScale can trace back the data used by AI for judgment in each process.
Since it is an AIOps tool for other AI tools to make correct judgments, introducing it will enable more accurate and efficient IT operations.
SysTrack AIOps
SysTrack AIOps is an AIOps tool provided by the American global company "Lakeside Software".
SysTrack AIOps can collect various data such as data performance information and system logs.
In addition, AIOps has excellent scalability, with a simple layered architecture that enables IT operations with a minimal number of servers.
Data management by AIOps is the key to domestic DX
You can't expect a high effect just by introducing AIOps. By following the steps below, you will be able to implement effective IT operations.
- Step 1: Prioritize system operations challenges
- Step 2: Narrow down the fields to be introduced
- Step 3: Expand the range of data handled
We will explain each step individually.
Step 1: Prioritize system operations challenges
First, identify issues in IT system operation,Investigate challenges to adapt to AIOpsTo do.
AIOps does not automate or streamline all tasks in IT operations immediately after its introduction. Let AIOps learn the cause and trigger of the problem occurrence, and test how accurately it can actually detect it.
Step 2: Narrow down the fields to be introduced
AIOps can capture a variety of data information. However, if you try to import all the data, not only will it take a long time to load, but it may also lead to inefficient results such as incorrect machine learning.
Therefore, AIOpsIn what areas is it most effective to apply?Let's start by considering .
Step 3: Expand the range of data handled
If you have narrowed down the field and obtained the introduction effect, let's expand the data range that handles AIOps.
AI OpsFind and associate correlated or similar data from different data sourcesis characterized by Therefore, it is not effective to use only within the same data range, so we recommend using different data ranges.
4 typical AIOps tools
Various AIOps tools have been developed around the world. Among them, we will introduce about four representative AIOps tools.
Splunk
Splunk is an AIOps tool developed by Splunk, an American company founded in 2003. It is used in a wide variety of industries such as medical sites, financial services, and public institutions.
We are especially good at log analysis and can analyze system logs from different data groups to automate IT operation management and visualize system operation status.
It has been selected as one of the 100 fastest growing companies published by the American business magazine "Fortune", so it can be said that it is a highly reliable and representative AIOps tool.
OpsRamp
OpsRamp is a SaaS-type AIOps developed by American company OpsRamp. It is one of the representative AIOps along with Splunk, with more than 1,000 installation results.
Since all necessary functions for IT operation services are provided, companies can select and use only the functions they need. It is possible to automate infrastructure environment operation, monitoring, error detection, and countermeasures.
Since it is provided as a cloud service on the Internet, AIOps is easy to introduce and is used by a wide range of users from small to large companies.
Watson OpenScale
Watson OpenScale is an AIOps tool provided by IBM, which operates in over 170 countries around the world. One of the features of Watson OpenScale is that anyone can correctly judge the operational status of AI.
Expert knowledge is required to determine whether AI is operating correctly, but Watson OpenScale can trace back the data used by AI for judgment in each process.
Since it is an AIOps tool for other AI tools to make correct judgments, introducing it will enable more accurate and efficient IT operations.
SysTrack AIOps
SysTrack AIOps is an AIOps tool provided by the American global company "Lakeside Software".
SysTrack AIOps can collect various data such as data performance information and system logs.
In addition, AIOps has excellent scalability, with a simple layered architecture that enables IT operations with a minimal number of servers.
Data management by AIOps is the key to domestic DX
AIOps is an important tool in data management that makes IT operations more efficient and effective.
By automating the data analysis work, which is becoming more complicated due to the daily increase in data, it is possible to not only increase human resources but also reduce human errors, so relative productivity can be expected to improve.
The analysis and utilization of big data is one of the core issues of DX for both small and medium-sized companies and large companies.Let's promote DX by introducing AIOps and evolve into a business style that can grow further.If you have any worries or problems with IT operation management, you may be able to solve them with "Watson AIOps". By collectively managing multiple data sources, you can solve problems efficiently and quickly, such as processing a huge amount of data and alerts, and prolonging troubleshooting.
Please feel free to contact us for a free consultation.
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