Kubernetes observability encompasses the collection, analysis, and visualization of a Kubernetes cluster’s internal processes. It empowers developers and operators to:

• Swiftly identify and diagnose issues
• Optimize resource utilization and performance
• Boost application scalability and reliability
• Maintain high security and compliance standards

Essential Data Types for Kubernetes Observability

• Metrics: Numeric data representations used to gauge your Kubernetes cluster’s performance. Metrics can be collected using tools such as Prometheus or Datadog.
• Logs: Detailed event records within your cluster, essential for troubleshooting and pinpointing potential issues. Common log management tools include Fluentd, Elasticsearch, and Logstash.
• Traces: Tracing tracks request flows and communication between services in distributed systems. Jaeger, Zipkin, and OpenTracing are popular distributed tracing tools.

5 Reasons to Download Our Kubernetes Observability Guide

Take control of your Kubernetes environment! Gain valuable insights on:

1. Logs, metrics & tracing
2. Managing complex Kubernetes cluster
3. Leveraging popular observability tools
4. Practical implementation tips
5. Best practices for success

Learn with DevOps pros and download the guide!

Introducing Acure: The Unified Observability Platform

Acure is a versatile observability platform tailored for monitoring Kubernetes, AWS, Azure, GCP, and more. It offers an integrated solution for monitoring, logging, and tracing, enabling users to access metrics, events, and logs from a single location. Acure’s AIOps capabilities facilitate rapid incident identification and resolution, enhancing productivity and minimizing downtime. With Acure, you can eliminate the need for separate tools for monitoring observability.

Key Acure Features:

• Centralized dashboard: Acure’s unified dashboard consolidates metrics, logs, and traces for streamlined monitoring and troubleshooting of Kubernetes clusters.
• Real-time alerting: Acure’s customizable alerting system allows notifications to be sent through your preferred channels, enabling prompt responses to potential issues.
• AI-driven insights: Acure leverages artificial intelligence and machine learning for deeper insights into your cluster’s performance, facilitating resource optimization and cost reduction.
• Seamless integration: Acure effortlessly integrates with your existing Kubernetes infrastructure for a smooth transition to this comprehensive observability solution.

Monitoring with Prometheus, Grafana, and Acure

Prometheus, an open-source monitoring tool, is often paired with Grafana, a visualization platform, to monitor Kubernetes clusters effectively. Acure enhances this combination by seamlessly integrating with these tools and providing a unified dashboard for metrics, logs, and traces, making it easier to analyze your cluster’s health and set up real-time alerts.

Get Signal from the Noise: Kubernetes Observability vs. Monitoring Workshop

Join us on April 26 for 2 hours of engaging and informative sessions, where we will dive deep into the world of Kubernetes observability and monitoring.

The event will feature a keynote talk by a renowned expert in the field, who will provide insights into the latest trends and best practices for Kubernetes observability. Following the keynote, we will have a panel discussion with real-life cases from the audience, where experts will share their experiences and best practices for addressing common challenges in Kubernetes monitoring and observability.

Register now and save your seat!

Logging with Elasticsearch, Fluentd, Kibana (EFK Stack), and Acure

The EFK stack is a popular choice for Kubernetes log management. Fluentd collects and processes logs, Elasticsearch stores and indexes the data, and Kibana provides a powerful visualization and querying interface. Acure complements the EFK stack by offering a centralized platform to visualize and analyze logs alongside metrics and traces, ensuring a comprehensive and efficient approach to log management.

Tracing with Jaeger, Zipkin, and Acure

Distributed tracing is essential for enhancing Kubernetes observability. Jaeger and Zipkin are both open-source tracing systems with powerful features to trace requests and communications between microservices in your Kubernetes cluster. Acure streamlines the process by integrating with these tracing systems and presenting traces in a unified dashboard, making it easier to monitor and troubleshoot your cluster’s performance.

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Mastering Kubernetes observability is crucial for ensuring the smooth operation of containerized applications. By leveraging Acure, an all-in-one observability platform, you can gain valuable insights into your cluster’s performance and optimize your applications for maximum efficiency and reliability. Say goodbye to the hassle of using separate tools for Kubernetes observability – Acure has got you covered. Embrace a proactive approach to Kubernetes observability by implementing best practices with Acure’s advanced features.

Learn more about Acure’s Kubernetes Observability Solution

Сообщение Master Kubernetes Observability: The Ultimate Guide with Acure появились сначала на Acure AIOps Platform.

IT incident management is a critical component of IT service management (ITSM) that focuses on the prompt restoration of services after a disruption, while minimizing any adverse effects on business operations.

An IT incident is any event that disrupts or threatens to disrupt the regular operation of IT services. These events can range from technical failures, such as hardware or software malfunctions, to human errors, such as accidentally deleting important data. IT incident management aims to restore standard service as quickly as possible and minimize the impact on the organization.

What Is IT Incident Management?

IT incident management identifies, addresses, and resolves IT incidents as quickly as possible. It involves a systematic approach to incident resolution, with well-defined processes and procedures to ensure that incidents are dealt with efficiently and effectively.

Read our blog post: What Is Incident Management?

The incident management process typically involves the following steps:

1. Incident identification: The first step in the incident management process is identifying that an incident has occurred. This can be done through monitoring tools, user reports, or other means. 
2. Incident classification: Once an incident has been identified, it is classified based on its severity and impact on the organization. This helps prioritize the incident and determine the appropriate level of response.
3. Incident resolution: After an incident has been classified, it is passed to the appropriate team or individual for resolution. This may involve troubleshooting, repairs, or other actions to restore regular service.
4. Incident closure: Once an incident has been resolved, it is marked as closed, and any necessary incident documentation is completed. 

Why Is IT Incident Management Important?

Effective IT incident management is critical for minimizing the impact of IT incidents on an organization. When an IT incident occurs, it can cause disruptions to business operations and lead to lost productivity, customer dissatisfaction, and financial losses. By addressing incidents quickly and efficiently, organizations can minimize these negative impacts and ensure that their IT services run smoothly.

In addition, IT incident management helps organizations improve their overall IT service delivery. By tracking and analyzing incident data, organizations can identify patterns and trends and make changes to prevent similar incidents from occurring in the future. This helps improve the reliability and stability of IT services, leading to increased customer satisfaction and loyalty.

The Benefits of Effective IT Incident Management

Effective IT incident management has several benefits for organizations, including:

• Improved service delivery: By addressing IT incidents quickly and efficiently, organizations can ensure that their IT services are running smoothly, leading to improved service delivery.
• Increased productivity: When IT incidents occur, they can disrupt business operations and lead to lost productivity. Organizations can quickly resolve incidents and keep employees productive by minimizing these disruptions.
• Enhanced customer satisfaction: Customers expect IT services to be reliable and always available. By managing incidents effectively, organizations can meet these expectations and improve customer satisfaction.
• Cost savings: The longer an IT incident goes unaddressed, the greater the impact on the organization. By addressing incidents quickly, organizations can minimize the costs associated with downtime and lost productivity.

Challenges in IT Incident Management

Managing IT incidents can be challenging, as there are often many variables and a wide range of potential causes for an incident. Some common challenges in managing IT incidents include:

• Limited resources: IT incidents often require a quick response. Still, organizations may need more resources (such as staff or equipment) available to address the incident promptly.
• Complexity: IT systems can be complex, with multiple components and dependencies. This can make it challenging to identify the root cause of an incident and determine the best course of action for resolution.
• Limited visibility: With proper monitoring and reporting tools, organizations can quickly identify incidents as they occur and track their progress through the resolution process.
• Communication breakdowns: Effective communication is critical when multiple teams or individuals are involved in the incident resolution. However, communication breakdowns can occur, leading to delays and confusion.

How to Overcome These Challenges

To overcome these challenges and ensure effective IT incident management, organizations can implement the following best practices:

• Implement a robust incident management process: A well-defined incident management process can help organizations respond to incidents quickly and efficiently.
• Invest in the right tools and resources: To manage incidents effectively, organizations need the right tools and resources, such as monitoring and reporting tools, knowledgeable staff, and the necessary equipment.
• Foster assertive communication and collaboration: Effective communication and collaboration are critical for incident resolution. Organizations should encourage open communication and ensure that all relevant parties are informed and involved in the resolution process.
• Regularly review and improve processes: To continually enhance incident management processes, organizations should periodically review and analyze incident data to identify patterns and trends and make necessary changes.

Steps to Take When Managing IT Incidents

When an IT incident occurs, it is essential to take a systematic approach to address and resolve the issue. Here are some steps to take when managing IT incidents:

1. Identify the Incident 

The first step in the incident management process is identifying that an incident has occurred. This can be done through monitoring tools, user reports, or other means.

2. Classify the Incident 

Once an incident has been identified, it is essential to classify it based on its severity and impact on the organization. This helps prioritize the incident and determine the appropriate level of response.

3. Assign the Incident

After an incident has been classified, it should be passed to the appropriate team or individual for resolution. This may involve troubleshooting, repairs, or other actions to restore regular service.

4. Communicate the Incident

Keeping all relevant parties informed about the status of an incident is essential for effective incident management. This includes updating users on the quality of the incident and any steps being taken to resolve it.

5. Document the Incident

It is essential to document the incident, including details such as the time it occurred, its impact on the organization, and the steps taken to resolve it. This information can be used to analyze the incident and identify ways to prevent similar incidents.

6. Resolve the Incident

Once the root cause has been identified, the appropriate actions should be taken to resolve the issue and restore standard service.

7. Close the Incident

After an incident has been resolved, it is essential to mark it as closed and complete any necessary documentation. This can help ensure that the concerned authorities adequately document the incident management process and take note of any lessons learned from the incident.

Summing Up

IT incident management is critical to ensuring the smooth operation of IT services within an organization. Organizations can reduce disruptions and improve service delivery by addressing incidents quickly and efficiently. IT Service Management (ITSM) plays a crucial role in effective incident management, providing a framework for designing, delivering, managing, and improving IT services.

Effective IT incident management also requires overcoming common challenges, such as limited resources, complexity, and communication breakdowns. By implementing best practices, such as a robust incident management process, investing in the right tools and resources, fostering strong communication and collaboration, and regularly reviewing and improving processes, organizations can ensure that they prepare well to handle any IT incident that may arise. 

Therefore, IT incident management is a vital component of effective IT service delivery, and organizations should prioritize it to ensure the smooth operation of their IT systems.

Сообщение A Complete Guide to IT Incident Management появились сначала на Acure AIOps Platform.

Because IT infrastructures encompasses software, hardware, and human power, they interact at varying levels. With business impact analysis, you can understand each of those levels, understand the health of your system, and plan for business continuity if part of the system goes down.

What Is Business Impact Analysis in ITIL?

Business impact analysis in the IT Infrastructure Library demonstrates how IT systems integrate within a business. 

With many dynamic components in an IT infrastructure, it can be tricky to understand the entire system and how each subsystem impacts business operations and application and server performance. 

Business impact analysis gives a clear picture of the IT infrastructure, its topography, and its weaknesses to show where the company needs improvements.

Why Is IT Business Impact Analysis Important for Your Business?

Businesses always face risk, and the main purpose of business impact analysis for IT infrastructure is risk management. Understanding risks and potential bottlenecks in the IT system allows companies to evaluate, minimize, and eliminate threats. 

Addressing specific potential risks helps businesses take action ahead of time to mitigate risks should circumstances affect performance. 

By using business impact analysis for IT infrastructure with dynamic components, you will understand the following about your company:

• How to avoid system outages and what initiatives are in place to prevent them
• The maximum amount of downtime allowed for critical systems
• The most important IT systems and processes critical to business operations and performance
• The recovery order of critical IT systems and processes if a failure occurs

When tackling the complexity of analysis, it’s best to start at micro levels, with small implementations and examinations. By understanding small, individual failures or successes, you will notice interdependencies and how they affect your overall infrastructure.

Examples of Business Impact Analysis for IT

An example of business impact analysis for IT infrastructure is examining storage as a service. With most businesses relying heavily on the cloud, storage is essential for business functions such as email, file systems, document creation, communication services, enterprise resource planning, and more. 

If a company’s cloud storage service, with servers located in one region, experiences failure, divisions worldwide may face downtime, disruptions, or other impacts. 

Some measurement examples of business impact analysis for IT infrastructure include: 

• Creating a system to determine an activity’s impact on operations
• Categorizing IT systems and processes by profitability
• Ranking IT systems and processes by how necessary their recovery is to operations; critical, vital, important, non-essential, etc. 
• Analysis to determine how combinations of failures can factor into operations

Through this business impact analysis, a company can devise a plan for restoring business operations if its cloud storage fails. 

How Acure Can Help with Business Impact Analysis for IT Infrastructure

Impact analysis in Acure helps you understand how much the problem affects the business and which incident takes priority.

After collecting the data and correlation setup, you can see the status of the entire IT infrastructure and information about all incidents on a single screen.

Topology shows the whole IT infrastructure like a tree with the relationships between configuration items. You can click on an affected CI to see the health, weight and dependecies and easily identify the source of the problem using the connections on the graph. Then, via log analyzing, you can easily identify the impact of the problem on a particular service.

Try the root cause and business impact analysis approach by yourself in Acure Userspace.

Сообщение Examples of Business Impact Analysis for IT Infrastructure появились сначала на Acure AIOps Platform.

For those who are unfamiliar, event correlation is the practice of interpreting and evaluating the thousands of events that can be generated by various monitoring tools if infrastructure or an application fails. Instead of missing or disregarding information because they are overwhelmed by “alert storms,” IT Operations teams may use these tools and the insights they offer to understand and handle issues quickly.

Understanding the connections between events that take place in the IT environment is done through the process of event correlation. It enables IT operations management to understand the numerous events that occur and to pinpoint those that call for immediate ITOps team action or further inquiry.

An infrastructure monitoring tool would undoubtedly overlook higher-level connections that an AIOps tool can find by analyzing unstructured data. Some go even further than IT Ops and absorb data from streams like social media, allowing businesses to comprehend when their consumers or brand are harmed fully.

Events are just the occurrences that take place throughout everyday routines. Most incidents are normal. A virtual machine turning on in response to an increase in workload; a parent device talking with devices or apps downstream; a user logging into a workstation. Events in IT operations can be divided into three groups:

• Informational or regular events are normal and only demonstrate that your network’s infrastructure is operating as intended;
• Exceptions happen when a device, service, or application operates outside of its intended parameters, suggesting serious performance deterioration or failure. 
• Warnings are events produced when a service or device exhibits warning indications or unusual behavior.

Every event has additional information to it that can be used to assess the condition of a network, and event correlation is the method used to assess that condition. A big company’s IT estate can produce thousands of events at any given time thanks to today’s broad, sophisticated networks.  This presents a challenge for IT operations teams if they lack the tools with the pace and intelligence to keep up.

The ability to differentiate between occurrences that are actually related and those that are just concurrent or coincidental is required for event correlation to be truly successful. AIOps capabilities are needed for that kind of data analysis to handle the task’s complexity and quantity.

Learn more about the event correlation in this guide:

• Event Correlation in Integrated Service Management
• The Process of Event Correlation
• Types of Events in Event Correlation
• Event Correlation KPI
• Event Correlation Use Cases
• Event Correlation Approaches and Techniques
• Importance of Event Correlation
• Benefits of Event Correlation
• AI-Driven Event Correlation
• Event Correlation in AIOps
• Analyst Recommendations on AIOps
• Misconceptions about Event Correlation
• How to Choose the Ideal Event Correlation Tool for Your Business
• Why Companies Choose Acure For Event Correlation

Event Correlation in Integrated Service Management

Event correlation is growing to become an important part of integrated service management. An integral part of integrated service management is event correlation. It’s a well-known way to use a collection of standardized techniques to manage IT operations as a service.

In the early 1980s, several approaches developed by the artificial intelligence and database industries were deployed to network element management to analyze alarms when event correlation was first utilized in integrated management. Today, it is used for various purposes, including identifying wireless network faults, monitoring the performance of non-self-aware devices in network systems, identifying firewall intrusions, etc.

The six main processes that make up integrated service management include change management, service level management, operations management, configuration management,  incident management, and quality management. Event correlation is a part of incident management but affects all six processes somehow.

A system’s monitoring generates data regarding events that occur. The volume of event data increases along with the complexity of an enterprise’s IT systems, making it tougher to make sense of this information stream. Problems arise because of:

• Changes in the arrangement of networks, devices, and connections, their relationships with each other;
• Combining software, computer resources, and cloud services;
• Practicing concepts like virtualized computing, decentralization, and processing of growing data volumes;
• Application addition, removal, updating, and integration with legacy systems.

The number of notifications is too high for IT operations employees, DevOps teams, and network operations center (NOC) administrators to keep up with, making it impossible to identify issues and outages before they impact important back-end systems or revenue-generating apps and services. These elements increase the chance that mishaps and outages will negatively affect the company’s bottom line.

The event correlation software will solve this problem for them. Automation and software tools are called event correlators. Event correlators accept a stream of event management data automatically created from all over the controlled environment, are key components of IT event correlation. 

The correlator examines these monitoring alerts using AI algorithms to arrange events into groups that may be compared to information about system modifications and network architecture to determine the root of the issue and the best course of action. 

The Process of Event Correlation

In addition to monitoring events, you should implement automated procedures to identify connections between various complex occurrences. The steps that make up the full event correlation process are usually as follows:

1. Aggregation: Infrastructure monitoring data is gathered and delivered to the correlator through various devices, programs, instruments for monitoring, and trouble-ticketing systems.

2. Filtering: User-defined parameters, such as source, timeframe, or event level, are used to filter events. Alternatively, this step could be carried out before aggregation.

3. Deduplication: The technology locates repeated events brought by a single problem. Duplication can occur for various reasons (e.g., 1000 individuals get the same error message, generating 1000 different alerts). Despite numerous notifications, there is usually just one problem that needs to be solved.

4. Normalization: Normalization uniformizes the data’s format so that the event correlation software’s AI algorithm can understand all the data’s sources similarly.

5. Root cause analysis: Event connections are finally examined to identify the root cause of the event in the most complicated step of the process, the root cause analysis. (For instance, events on one device are investigated to identify their effect on all other devices in the network.)

The number of events that originally needed to be correlated will now only be a small subset of those that do. This will result in a response from some event correlation tools, such as a suggestion for additional investigation, escalation, or automatic remediation, enabling IT managers to perform troubleshooting duties more effectively.

Types of Events in Event Correlation

There are a few basic types of event correlations, even though many organizations correlate various types of events depending on their unique IT environments and business requirements:

System events: These events reflect unusual shifts in the condition of the system’s resources. System events include things like a full disk or a heavy CPU load.

Network events: Network events show the functionality and health of switches, routers, terminals, and other network elements. They also show network traffic when it exceeds certain thresholds.

Operating system events: These events, which represent changes in the interaction between hardware and software, are produced by operating systems like Windows, Linux, Android, and iOS.

Database events: These events explain to analysts and administrators how data is read, saved, and changed in databases.

Application events: Events produced by software applications can shed light on how well an application is doing.

Web server events: Events related to the hardware and software that provide web page content are referred to as web server events.

User events: These are produced by synthetic tracking or real-user monitoring systems and show infrastructure performance as seen by the user.

Other Events: Synthetic checks, also known as probes, that examine functioning from the outside, are another type of event. Client telemetry and real-user monitoring produce particular events as users engage with the service.

Event Correlation KPI

Compression is the main key performance indicator (KPI) in event correlation. The KPI measures the proportion of events linked to fewer incidents and is expressed as a percentage.

Finding all events connected to a specific issue is the main objective of event correlation. Events that result from the primary issue will occur, with symptoms as the primary failure has an effect on secondary components. When operators completely comprehend the relationship, they can handle both the cause and the symptoms.

A compression percentage that is close to 100 percent is ideal. However, in practice, this is unachievable because as incidents approach that threshold, accuracy is sacrificed by the compression rates. This implies they misinterpret events as coming from the same problem or fail to see how one issue is connected to another. On the other hand, putting accuracy first causes the compression rate to drop.

Software for event correlation does not compute the accuracy. For instance, firm A might have events that are extremely different from firm B’s, and each organization may value various things more than the other. Therefore, it is practically difficult to determine the accuracy of the link between the two. Instead, customers evaluate accuracy as a soft, qualitative KPI using spot checks and business value analysis.

Event correlation professionals advise that businesses making this decision should aim for the highest compression rate possible without compromising accuracy and business value. This often results in a compression rate of between 70 and 85 percent. However, in other circumstances, higher correlation values of 85% or even 95% are not uncommon.

You may improve the efficiency of your company’s event management efforts by using analytics to gain insights from event correlation software into other event-driven metrics. You must consider raw event quantities and improvements brought on by deduplication and filtering in order to do this. Analyze false-positive rates, signal-to-noise ratios, and enhancement statistics. To be more proactive in avoiding problems, you can also consider event frequency in terms of the most prevalent cause of hardware and software failures.

Good event correlation can lead to other metrics. These metrics, often found in IT service management, are designed to assess how engineers, DevOps workers, service teams, and automated repairs manage these situations. One of them is a set of KPIs known as MTTx since they all begin with the acronym MTT, which stands for “mean time to.” These consist of:

• MTTR: Mean Time to Recovery, Mean Time to Respond, Mean Time to Restore, Mean Time to Repair, and Mean Time to Resolve. The average length of time it takes from the time an incident report is first filed until the event is resolved and marked closed is known as MTTR. The measurement considers both the time spent testing and fixing the problem.
• MTTA (Mean Time To Acknowledge): MTTA is the period of time between the activation of an alarm and the start of problem-solving. This indicator is helpful for monitoring the response of your staff and the performance of your alert system.
• MTTF (Mean Time To Failure): It is the average time between a technology product’s irreparable breakdowns. The computation is used to evaluate how long a system will normally last, whether the latest version of a system is functioning better than the previous version, and to advise clients about predicted lifetimes and the best times to arrange system check-ups.
• MTTD (Mean Time To Detect): The mean time to detect metric measures the time an IT deployment has a problem before the relevant stakeholders are aware of it. Users experience IT disruptions for a shorter period of time when the MTTD is shorter.
• MTBF (Mean Time Before Failure): The average time between system failures is the mean time between failures. MTBF is an essential maintenance parameter to assess equipment design, performance, and security, particularly for important assets. 
• MTTK (Mean Time To Know): The mean time to fix an issue is the interval between issue detection and cause identification. In other words, MTTK is the amount of time required to determine the cause of a problem.

Examine the raw event volume for event management metrics, then take note of the reductions brought about by deduplication and filtering. Use the percentage of alerts that were enhanced and the level of enhancement, signal-to-noise ratio, or false-positive percentage for event enrichment data. For locating noise and enhancing actionability, accurate event frequency is helpful. It is also beneficial to consider the overall monitoring coverage in terms of the proportion of events started by monitoring.

Event Correlation Use Cases

Event correlation is essentially a method that links different events to recognizable patterns. If certain patterns pose a security risk, a response can be ordered. Once the data is indexed, event correlation can also be done. Among the most significant use cases of event correlation are:

Leading Airline

A major American airline was aware that even a small service interruption may result in millions of dollars in fuel wastage and lost revenue. The business employed numerous monitoring systems in an effort to maintain high uptime. However, they were fragmented, and the processes for incident detection and resolution were manual. The carrier introduced an AI-driven event correlation solution after first rationalizing and modernizing its monitoring technologies. Centralized monitoring, fewer incident escalations, and a 40% reduction in MTTR were all advantages of using event correlation.

Intruder Detection

Let’s imagine an employee account hasn’t been used in years. The logs suddenly show a large number of login attempts. In a short while, the account might start executing unusual commands. The security team might declare that an attack is ongoing using event correlation.

Let’s assume that after numerous failed attempts to log in, one did succeed. The system classifies this incident as “curious” in the correlation. Then we discovered that a system port had been checked fifteen minutes earlier. Now we see that both the port scan IP address and the login attempts share the same IP address. Here, the relationship between the occurrences alerts us by designating the significance of the stated event as being of significant concern.

We would need to rely on luck than on expertise if we tried to locate these occurrences using merely a manual correlation. But with event correlation, you can identify and resolve the issue in no time. 

Financial SaaS Provider For Businesses

Only 5% of events could be successfully resolved by this enterprise software as a service provider’s level one service team. The company particularly suffered as alert volume increased 100-fold while processing payroll on Friday. The level one team improved its resolution rate by 400%, cut MTTA by 95%, and cut MTTR by 58% in the first 30 days by applying AI-based event correlation.

Retail Chain

A national home improvement retailer had prolonged outages at its stores because point-of-sale activities were not being correlated. With event correlation, both the overall and average outage durations decreased by a combined 65 percent. The organization found that a high frequency of alerts, some of which were meaningless, made it difficult for the network operations center to identify important problems and caused resolution to be delayed. Major incidents decreased by 27% with a better event correlation solution, while root cause analysis improved by 226% and MTTR decreased by 75%.

Sporting Goods Manufacturer

Even after adopting some event correlation techniques, this large athletic shoe and clothing manufacturer was still overrun with alert information from its IT monitoring. The organization significantly increased its capacity to recognize major situations, take swift action, and do precise correlations by switching to a machine learning-based system. Its MTTA decreased from 30 minutes to 1 minute in less than 30 days as a result.

Event Correlation Approaches and Techniques

Event correlation techniques concentrate on establishing links between event data and determining causality by examining event features including the time, place, procedures, and data type. Today, AI-enhanced algorithms play a significant role in identifying these connections and trends as well as the root cause of issues. Here is a summary:

• Time-Based Event Correlation: This method looks for connections between the timing and order of events by analyzing what took place just before or concurrently with an event. For correlation, you can specify a time window or latency requirement.
• Rule-Based Event Correlation: Using specified values for variables like transaction type or user city, this method compares events to a rule. This method can be time-consuming and ultimately unsustainable because a new rule must be written for each variable.
• Pattern-Based Event Correlation: The time-based and rule-based strategies are combined in pattern-based event correlation, which looks for events that match a predetermined pattern without requiring the values of individual variables to be specified. Pattern-based event uses machine learning to improve the event correlation tool but is significantly less laborious than rule-based. The correlation program continuously increases its understanding of novel patterns with the use of machine learning.
• Rule-Based Approach: According to a predetermined set of rules, the rule-based method correlates events. The rule-processing engine examines the data until it reaches the desired state, taking into account the outcomes of each test and the interactions of the system events.
• Codebook-Based Approach: The codebook-based strategy is comparable to the rule-based strategy, which aggregates all events. It stores a series of events in a codebook and correlates them. Compared to a rule-based system, this approach executes more quickly since there are less comparisons made for each event.
• Topology-Based Event Correlation: This method is based on network topology, which is the physical and logical configuration of equipment, such as servers and hubs, as well as the nodes that make up a network, as well as knowledge of how those nodes are connected to one another. Users can more easily visualize incidents in relation to their topology using this technique because it maps events to the topology of impacted nodes or applications.
• Domain-Based Event Correlation: This method connects events using event data gathered from monitoring systems that concentrate on a particular area of IT operations. Some event correlation tools perform cross-domain or domain-agnostic event correlation by gathering data from all monitoring tools.
• History-Based Event Correlation: This technique looks for similarities between recent events and past occurrences. A history-based correlation is comparable to a pattern-based correlation in this regard. History-based correlation is “dumb” in that it can only link occurrences by contrasting them with similar events in the past. Pattern-based systems are adaptable and dynamic.
• Codebook-Based Approach: The codebook-based strategy is comparable to the rule-based strategy, which aggregates all events. It stores a series of events in a codebook and correlates them. Compared to a rule-based system, this approach executes more quickly since there are fewer comparisons made for each event.

Importance of Event Correlation

Some events among the network’s thousands of daily events are more significant than others. A server may have had a brief increase in demand, a disk drive may be beginning to fail, or a business service you depend on may be responding slowly. Without event correlation, it might be difficult to identify the problem. Perhaps you won’t even realize it until it’s too late.

Like that tenaciously determined investigator, event correlation software can sort through the signals and draw the connections required to correlate incidents to events quickly and to understand better what constitutes an issue and what constitutes a sign of a problem. Prioritizing and resolving incidents is a crucial step. 

Event correlation software completes this work more quickly and accurately than humans. Even so, certain legacy products from the past struggle to function in today’s cutting-edge IT environments. Because of this, more businesses are using AIOps for IT operations.

Benefits of Event Correlation

With the help of a series of related events, event correlation provides comprehensive context and logical analysis. Security analysts can then carefully decide how to respond and investigate in the future.

This entails using user-defined rules to transform unprocessed data into actionable alerts, alarms, and reports. The necessary course of action can then be taken. The following are some advantages of applying event correlation techniques:

• Real-time threat visibility: IT departments can benefit from active event correlation and analysis to quickly identify dangers. Business is impacted by errors, security breaches, and operational problems. These can be effectively avoided instead.
• Lowers the cost of operation: Tools for event correlation automate tasks like the analysis of extensive workflows to lower the number of pertinent warnings. So, the IT department can focus more on fixing immediate threats and spend less time attempting to make sense of it all.
• Monitoring for network security: The network could always be looked over. Impact failures, like those that impact business services, can also be found and fixed.
• Better time management: Modern event correlation tools are user-friendly and effective, thus fewer resources are required. Additionally, using SIEM technologies for event correlation and analysis can save a significant amount of time.
• Continuous compliance reports: There may be different levels of security and networked system compliance required by federal, state, and municipal authorities. Techniques for event correlation can be utilized to guarantee continuous monitoring of all IT infrastructures. The actions required to mitigate such risks can then be detailed in reports that describe security-related threats and incidents.

Event correlation approaches are designed for event detection, interpretation, and control action assignment. The value of correlation intelligence will keep growing as data complexity rises.

AI-Driven Event Correlation

Assembling and analyzing log data from many network applications, processes, and devices is made possible by the event correlation in SIEM solutions. This feature ensures that security risks and hostile activity patterns in business networks will be found that would otherwise go undetected.

Many firms are using a blend of artificial intelligence and human intelligence to increase the accuracy of event correlation. They have come to the conclusion that no strategy can be successful on its own. The employment of SIEM solutions that contain correlation engines, algorithms for machine learning, and artificial intelligence consequently serves as a before and after example of cybersecurity advancements.

Event Correlation in AIOps 

Initially, a procedure that required human engineers and developers, things started to change around 2010. The first significant development in event correlation was the introduction of statistical analysis and visualization.

Event correlation systems can now automatically create new correlation patterns by learning from event data thanks to machine learning and deep learning. This was the first time artificial intelligence had been used to correlate events.

The term “AIOps” was first used by Gartner analysts in 2016. Big data management and anomaly detection are two additional use cases for AIOps that go beyond event correlation. In 2018, Gartner listed the following as the primary duties of an AIOps platform:

• Incorporating data from several sources, irrespective of type or vendor
• Real-time analysis being carried out at the site of intake
• Analyzing historical data from stored sources
• Utilizing machine learning
• Action or next move being taken based on analytics

AIOps is the power to process a plethora of alerts for events, evaluate them quickly, derive insights, and identify incidents as they emerge before they develop into severe outages.

The “black box effect” is one of the main problems in AI. This has the effect of fostering user mistrust and slowing adoption because machine learning algorithms and their instructions are not transparent. This problem can be solved by using AIOps event correlation tools since they offer transparency, testability, and control. Users of the software may be able to build or alter correlation patterns, as well as view them and test them before implementing them in real-world settings.

Event correlation will provide pattern-based predictions and the identification of root causes that individuals miss as AIOps develops. Artificial intelligence-driven event correlation solutions will just plug in and instruct incident managers on how to proceed.

Analyst Recommendations on AIOps

The AIOps platform market was pegged by Gartner Research at $300 to $500 million annually in a market analysis from November 2019. 40% of DevOps teams would reportedly add AIOps technologies to their toolkits by 2023, according to their prediction.

The analysts at Gartner advised businesses to implement AIOps gradually. Events classification, correlation, and anomaly detection should be the first important applications that are implemented. They can employ technologies over time to shorten outage times, improve IT service management, become proactive in reducing impact, assess the value of patterns, and eliminate false alarms.

In its review of the AIOps market for 2020, GigaOm discovered that products on the market cover a range of AI adoption and forecasted a consolidation of vendors. According to GigaOm, many event correlation solutions have tacked on AI as an afterthought. Therefore, businesses must thoroughly examine the offers and comprehend all of their features, including compatibility, which has been a challenge for some tools. The decision between an on-premises or cloud-native, on-demand model is another factor.

Misconceptions about Event Correlation

There are various differences among the instruments that come under the event correlation category, as GigaOm pointed out. However, some misunderstandings are widespread. 

Real-Time Processing: According to many users, machine learning enables event correlation software to process and correlate new events in real time. Since it requires significant increases in computer power and breakthroughs in AI, no vendor currently offers this capacity.

Anomaly Detection: Users frequently misunderstand how anomaly detection and event correlation are related. Tracking and observability tools that glance at a single, isolated metric over time and can identify when this measure enters an anomalous state perform anomaly detection. Tracking and observability tools create events indicating the discovery of abnormalities when they do so. One of the data streams fed into the event correlation machine is this output. Anomaly detection is not currently a function of any event correlation solutions.

How to Choose the Ideal Event Correlation Tool for Your Business

Your IT Ops efforts can be made to produce higher business value with the help of the appropriate event correlation tool. But it might be challenging to determine whether the product is the greatest fit for your needs due to competing claims and complex technology.

A list of important characteristics and capabilities is provided below. You can compare providers based on many criteria that are weighted according to their significance to your business.

1. User Experience

• Convenience and safety of access: The ideal event correlation tool will enhance the user experience with ease of use to safely access business data.
• Intuitive navigation: Active event correlation tools should make the user experience better with an intuitive interface that helps navigate through the issues and come up with solutions smoothly. 
• A cutting-edge, clear user experience: The ultimate goal of any tool should be to improve user’s experience and an intuitive interface will help with that. 
• Integrated console: A single pane of glass to prevent incidents
• Native analytics: Simple to install and comprehend
• Easy integration of third-party analytics with best-in-class BI solutions

2. Functionality

• Sources of data ingested: It should be easy and quick to navigate the sources of the data collected to understand the issue and offer a solution immediately. 
• Platform for hosting data streams: Helps gather multiple data streams to detect a pattern in all of them.
• Correlated event types: Since all the event types will be different, the tool should be able to recognize multiple events with a single functionality.
• Interpretation and improvement of data to provide the best experience to the end users without any bugs or issues.
• Correlation methods used should be varied to identify different patterns and root causes of the event failures. 
• Root cause analysis that is fully automated to avoid wasting time on manual operations. 
• Ability to drill down and adjust root causes.
• Ability to view incidents in relation to topology and surroundings to determine the best solutions for that region. 

3. Machine Learning And Artificial Intelligence

• Automation at level 0 turns manual activities into automated workflows to accelerate incident response.
• Scalability: The tool should be able to scale the run-time correlation engine to monitor the cloud computing environment. 
• Performance that is agile-friendly to help teams offer value to their customers without consuming a lot of their time. 
• The ability to combine all event feeds using existing tools and integration technology.
• Integration technology and the capacity to include all event feeds without the cost of consultants.
• The ability to quickly connect all event feeds and integration technology.
• Extensibility to ingest monitoring alerts, alarms, and other event indications.
• Security to keep the data and data sources safe from fraudulent incidents.

4. Strategic Aspects

• Business Model Harmony: For identifying patterns that have common go-to-market, vulnerability, value-sharing, and commercial features
• Vision Alignment to help achieve a win-win situation for both concerned parties.
• Roadmap Alignment will help in achieving the goals of the company by ensuring that all the efforts are directed towards achieving that goal.
• Organizational Behavior: Helps the management make sense of the events and understand what changes should be made at the organizational level.
• Monetary security: The tool should be able to recognize which resource is a money-pit and which solution will help save money. 
• Customer satisfaction: The end goal of any company is to achieve customer satisfaction and the event correlation tool should be able to solve any issues that customers face. 

5. Partners

• Observability and monitoring tools integration vendors.
• Tools for the integration of topology and change vendors.
• Tools for collaboration and integration vendors.
• System integrators that link all the applications that could be used in solving the issues.
• Cloud service provider
• Resellers

6. Service

• Proof of Value: Proving value in the eyes of your customer by dealing with problems they face in a quick and efficient manner. 
• Training/education: The learning curve of the tool should be minimum with tutorials and training that help understand the features and its advantages. 
• Advisory for more effective intrusion detection
• Customer support SLA
• Customer success program

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Why Companies Choose Acure For Event Correlation

Acure is a simple yet powerful AIOps platform for event correlation and deduplication. It helps reduce the noise level of events coming from different monitoring systems by 70% and increases the productivity of IT Ops. Deduplication and correlation tools prevent incidents from reoccurring and allow you to prioritize alerts, which allows the IT Ops team to focus only on important tasks.

Acure developers are constantly listening to customers and upgrading the platform according to their wishes. So far, synthetic triggers are used for event correlation. Acure triggers process events received via the monitoring system. It is possible to create a trigger from a template or write it from scratch using scripts written in the Lua language. However, a platform update is planned soon: synthetic triggers will be replaced by signals driven by scripts on a low-code engine that is already used for automation scenarios. This is crucial for systems with a dynamic environment.

Event correlation in Acure is also topology-based. The topology graph displays the whole IT environment like a tree with links between each configuration item. You can see the health and state of the IT complex and each item individually.

And last but not least, Acure is absolutely free with 5 GB daily data, unlimited scenarios, configuration items and users.

Want to see how event correlation works in Acure in practice? Here is our event correlation & noise reduction use case for your IT department.

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To achieve these end-user expectations, IT service providers and businesses must increase the reliability and stability of backend IT operations, despite the inherent complexity of IT systems. To optimize the system’s dependability, we closely observe and monitor indicators and statistics relating to infrastructural effectiveness.

There are some striking parallels between observability and monitoring. Both need the acquisition of several data sets in some parts. Both aim at assisting teams in identifying issues with their software frameworks and providing an excellent experience for users.

Yet, they are not the same. Speaking about observability vs. monitoring, we are discussing two different aspects of information technology. Let us see some key differences between the two.

Observability vs. Monitoring

Following are the key differences between observability and monitoring:

Definition

The capability to evaluate a system’s internal states by studying its outputs is known as observability. If you can assess the current state of an IT system only using information from output datasets, such as sensor data, the system is deemed “observable.”

Further, it enables teams to examine what’s going on in context across multi-cloud setups to identify and fix the root causes of problems.

On the other hand, monitoring is the process of gathering, evaluating, and interpreting data to follow a program’s progress toward its goals and make managerial decisions. 

Monitoring is concerned with keeping track of specific metrics. Monitoring is indeed an action you take to improve your system’s observability.

Determining What Is Important

Monitoring necessitates anticipating what needs to be monitored. 

On the other hand, observability enables one to figure out what is vital by viewing how the system operates over time and asking critical questions.

Fixing The Wrongs

Monitoring notifies you when something goes wrong. You only know something is faulty if you monitor it, not how to correct it. A monitoring tool, for example, may inform you that your application’s response rate is no longer appropriate. 

Whereas, observability goes a step further by assisting you in determining what is wrong and how to correct it. 

So, in our example, observability determines which microservices inside the application are causing the issue. You can use an incident management platform to plan and execute an adequate response to dependability issues based on such information.

Choosing Between Process and Potential

Monitoring is the act of observing something. When monitoring takes place, the events and applications the system will track have already been decided. 

Simply put, monitoring identifies specific occurrences throughout the system and generates artifacts and reports that are incorporated into overall metrics.

On the other hand, observability allows monitoring of various events throughout the pipeline including the entire software development process.

Kind Of Concept: Broad Or Wide

Monitoring has long become a known concept and idea that allows you to evaluate trends, dashboards, and alarms by providing a broad perspective of system activity and performance. 

While observability complements monitoring. It offers a deep state of all system components by employing multiple telemetric channels.

Sustainability Of The Approach

Observability is a long-term and sustainable strategy because it improves incident management and quality control. It assists you in obtaining actionable information for better decision-making and a quicker feedback chain. As a result, it contributes to superior product generation. 

Meanwhile, you could monitor without observing. However, this would limit your capacity to identify and fix issues. Thus, monitoring is a limited approach that cannot function alone.

Single Plane Or Map Approach

Observability is the ability to map an environment and then navigate that map smoothly to gain a better knowledge of what the situation is. Finally, you can fix it then.

For the most part, you can apply monitoring on a single plane. When a known scenario occurs, you put up rules, aggregations, and alarms for the same.

Reliance On Data

Observability relies on sampling data. It employs sample data to determine if a system will generate enough useful data for personnel to deduce its internal state from its exterior outputs.

On the other hand, monitoring relies heavily on the raw dataset. Monitoring software collects data, displays a specified set of metrics and logs, and detects known issues such as errors, traffic, slowness, and saturation.

Use Of Metrics

In this context, let us first see what metrics are. Metrics refer to measures taken over time. There are a few types of metrics as mentioned below.

Firstly, cumulative metrics track changes over time, such as the amount of API function call failures returned in the last hour. Then, one uses delta metrics to capture variations between prior and current data, such as a change in throughput since it was measured previously. 

So, observability generates these metrics for the system whereas monitoring involves collecting these metrics.

Importance

Even with a complicated microservice design, observability enables us to understand the internal components of a system. Thus, it is easier to go from the effects to the cause. It assists you in locating solutions for performance bottlenecks and request execution in the existing system. 

Monitoring is a critical component of developing and operating microservice-based systems. If your monitoring rules and metrics are simple and focused on the data, you’ll get a good picture of your system working.

What Is Better? Observability vs. Monitoring

Monitoring displays overall system performance because it focuses on metrics like utilization rates and throughput. However, monitoring usually provides a skewed perspective of system data, focusing on specific indicators. When the failure mechanisms of a system are sufficiently understood, this strategy is sufficient. 

Database managers and engineers with a lot of experience can notice trends that lead to difficulties. Thus, monitoring is beneficial when we understand how systems fail. 

However, failure modes become more complex as applications become more advanced. It’s cumbersome to forecast how apps will fail in most cases. You can comprehend the internal status of a system by observing it and then figuring out what isn’t operating correctly and why.

In current applications, associations between a few indicators are frequently insufficient to pinpoint issues rightly. Therefore, these modern and complicated applications necessitate greater system visibility, which is possible to achieve through a mix of observability and more effective monitoring tools.

What is Application Performance Management?

As the volume of data that companies generate and process continues to grow, it has become increasingly important to monitor and manage that data in real-time. Application Performance Management (APM) is a vital tool for monitoring data and ensuring that data-intensive applications are performing optimally. By providing real-time insights into application performance, APM can help organizations make informed decisions about how to allocate resources and optimize data usage.

APM is essential in data monitoring because it provides visibility into how data is being processed and how applications are performing. Without APM, organizations would be blind to any issues that may be impacting data processing and application performance. By monitoring key performance indicators (KPIs), such as response times, throughput, and error rates, APM can help organizations identify performance bottlenecks, pinpoint the root cause of issues, and resolve those issues before they impact the user experience.

APM can also help organizations identify patterns and trends in data usage, providing valuable insights into usage patterns and capacity planning. With APM, organizations can understand how their data usage changes over time and adjust their infrastructure and resources accordingly. For example, if a company sees a sudden spike in data usage, APM can help identify the cause and provide recommendations for how to manage that spike, such as scaling resources or optimizing data processing.

In addition, APM can help organizations ensure that their data is being used effectively and efficiently. By analyzing application performance data, APM can identify areas where data is being underutilized or wasted. This insight can help organizations optimize their data usage and make better decisions about how to allocate resources.

One of the most significant benefits of APM in data monitoring is that it enables organizations to make informed decisions in real-time. By providing real-time insights into application performance, APM can help organizations take immediate action to resolve issues and prevent downtime. This can help organizations avoid costly downtime and lost revenue.

Overall, APM is an essential tool for any organization that is responsible for processing and managing data-intensive applications. By providing real-time insights into application performance and data usage, APM can help organizations optimize data usage, ensure that resources are allocated effectively, and prevent downtime. As data continues to grow in volume and complexity, APM will become even more critical for ensuring that organizations can effectively manage and process their data.

Final Note: Observability vs. Monitoring

Production apps fail for a multitude of reasons. There will always be something that goes wrong, no matter how much work you put in. You’ll have difficulty troubleshooting production issues if you don’t effectively instrument your application’s components. 

Even observability and monitoring do not have all the answers at times. Therefore, there is no end to the observability vs. monitoring debate right now.

To get answers to known and unknown production difficulties, having correct data is the foremost requirement. Thus, you must constantly assess the dataset you have to determine its utility.  

Moreover, you must constantly improve your system’s instrumentation until it is appropriately observable and monitorable to the extent that you can receive answers to any questions you may have to support your program at the degree of performance you desire.

DevOps engineers strive to keep applications highly available and scalable because these complex systems behave in unforeseen ways. Furthermore, problems arise from causes that aren’t always obvious.   

They don’t take in enough data or provide enough insight into the state of apps to know how to fix the problems quickly.  

Thus, observability and monitoring need a next-generation approach so that one can utilize the information infrastructure in the best way possible.

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The successful aggregation of data can help target information at individual and group levels, especially with the ever-growing volume of information available online. It also means that data that has already been aggregated can be stored and itself become a coherent data source without reverting to the granular and massive core data.

Data Aggregation in Focus

We live in a time where more data is easily reachable than ever before, but volume and complexity can make its practical use more challenging than it should be. In the corporate world, consumer data is one of the fundamental building blocks to future success. As well as the size and scale of data expanding, the demand for it to be processed and presented coherently is growing too.

Aggregation creates a prism through which data can be viewed more meaningfully. Think of it like using a very powerful telescope to survey the night sky, focusing down on just one group of stars, rather than the entirety of the cosmos.

Using a clear focus on a defined point and processing the maximum amount of data available about those stars collectively, an astronomer can make draw meaningful conclusions about the way that cluster of stars behaves in a way they could not if they were staring into the vastness of space with the naked eye.

This process is also known as data aggregation in Database Management Systems. It identifies key trends and delivers ‘big data‘ crunching down to more manageable and understandable forms.

For a commercial business, this might be working out which product is selling where, what are the growing or decreasing demographics of its customers, or even analyzing the services of a competitor or rival. In political terms, it can be about voter identification or a better understanding of voter turnout against crucial metrics like age, location, gender, and ethnicity.

The identification and presentation of those trends are thus more meaningful than the hugeness of the data itself since aggregation has provided understandable snapshots that are commercially and socially valuable.

What Sort of Data is Aggregated?

Any form of public data can be aggregated using algorithms, including hard numbers like voter turnout and social media postings, and browsing history online. The comprehensive sources are sometimes known as the Internet of Things. Every action we take online can create a valuable data point for aggregation processes. That’s why companies like Facebook have faced challenging times over their own data aggregation application.

Data Aggregation Is Not New

The Scale is New

Modern humanity has always tried to make sense of the world by applying scientific analysis, so the roots of data aggregation are centuries old. What is new is the scale and complexity by which this can now be done by utilizing Artificial Intelligence and Machine Learning as tools to sort, interpret and analyze that data.

Software tools called data aggregators to undertake this activity far more effectively than the human brain ever could, meaning we can process more data more meaningfully with every technological advance. The signs of societal and human development indicate this will only increase.

Methods and Techniques of Data Aggregation

There are several methods and techniques used in data aggregation, including:

Manual Data Aggregation

This method involves collecting data from different sources, such as spreadsheets, reports, and databases, and manually entering the data into a single document or dataset. While this method is time-consuming and prone to errors, it may be necessary when dealing with unstructured data or when dealing with small datasets.

Automated Data Aggregation

This method uses software or tools to automatically collect and combine data from multiple sources. This can include web scraping tools that collect data from websites, APIs that access data from different platforms, or data integration tools that combine data from different databases.

Rollup or Drill-Down Aggregation

This technique involves summarizing data at different levels of granularity. For example, data may be aggregated at a high level, such as by month, and then drilled down to a lower level, such as by day or hour. This technique is useful for analyzing data trends and identifying patterns at different levels of detail.

Grouping and Filtering

This technique involves grouping and filtering data based on specific criteria, such as product category, customer segment, or geographic region. This technique can help identify patterns and trends in specific segments of the data.

Statistical Aggregation

This technique involves using statistical methods to summarize data, such as calculating means, medians, and standard deviations. This technique is useful for analyzing numerical data and identifying trends and patterns.

Time Series Aggregation

This technique involves analyzing data over time and identifying trends and patterns in the data. This can include analyzing data by week, month, or year and identifying seasonal or cyclical trends in the data.

Overall, the methods and techniques used in data aggregation will depend on the specific data sources, the data types, and the desired outcomes of the analysis. It’s important to select the appropriate method and technique for the specific data being analyzed to ensure accurate and meaningful results.

The Practical Application of Data Aggregation

You, therefore, will not be surprised that an increasing number of businesses have now moved into aggregation, both in terms of those doing the data crunching and those commissioning the aggregation. Successful data aggregation businesses aren’t just the ones that use the right focuses and process the information in the best way to illustrate demand, opinion, and of course, trend.

They are also the ones that present the data aggregation report most accessibly and in a way that lends itself easily to utilization. 

Therefore, any large-scale marketing strategy or product launch will always involve a process of data aggregation to capture and crunch opinions before moving forward.

Think of data aggregation as a chain. The first and heaviest link is the core data in all its vastness. Then comes the aggregation process, which leads to the following link: reporting and pinpointing. 

Another link must be present for a commercial organization: the precise application of that reporting to business improvement. It’s all about not just giving meaningful analysis but delivering significant application of that analysis so that it can make a difference.

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