• Data observability:

Over the last while, data has transformed almost everything we do, starting as a strategic asset and evolving the core strategy. However, managing data quality is the most critical barrier for organizations to scale data strategies due to the need to identify and remediate issues appropriately. Therefore, we need an approach to quickly detect, troubleshoot, and prevent a wide range of data issues through data observability, a set of best practices that enable data teams to gain greater visibility of data and its usage.

• Identifying Disparate Data Points:

Disparate data points refer to information that appears unrelated or disconnected at first glance. They can be derived from multiple sources, such as customer behavior, market trends, social media interactions, or environmental factors. The challenge lies in recognizing the potential relationships between these seemingly unrelated data points and understanding the value they can bring when combined.

• Unveiling Hidden Patterns:

Correlating disparate data points reveals hidden patterns that would otherwise remain unnoticed. For example, in the retail industry, correlating sales data with weather patterns may help identify the impact of weather conditions on consumer behavior. Similarly, correlating customer feedback with product features can provide insights into areas for improvement or potential new product ideas.

• Benefits in Various Fields:

The ability to correlate disparate data points has significant implications across different domains. Analyzing patient data alongside environmental factors in healthcare can help identify potential triggers for certain diseases or conditions. In finance, correlating market data with social media sentiment can provide valuable insights for investment decisions. In transportation, correlating traffic data with weather conditions can optimize route planning and improve efficiency.

• Tools and Techniques:

Advanced data analysis techniques and tools are essential to correlate disparate data points effectively. Machine learning algorithms, data visualization tools, and statistical models can help identify correlations and patterns within complex datasets. Data integration and cleaning processes are crucial in ensuring accurate and reliable results.

• Challenges and Considerations:

Correlating disparate data points is not without its challenges. Combining data from different sources often involves data quality issues, inconsistencies, and compatibility challenges. Additionally, ethical considerations regarding data privacy and security must be considered when working with sensitive information.

Getting Started: Correlate Disparate Data Points

Many businesses feel overwhelmed by the amount of data they’re collecting and don’t know what to do with it. The digital world swells the volume of data and data correlation to which a business has access. Apart from impacting the network and server resources, the staff is also taxed in their attempts to manually analyze the data while resolving the root cause of the application or network performance problem. Furthermore, IT teams operate in silos, making it difficult to process data from all the IT domains – this severely limits business velocity.

Data Correlation: Technology Transformation

Conventional systems, while easy to troubleshoot and manage, do not meet today’s requirements, which has led to introducing an array of new technologies. The technological transformation umbrella includes virtualization, hybrid cloud, hyper-convergence, and containers.

While technically remarkable, introducing these technologies posed an array of operationally complex monitoring tasks, increased the volume of data, and required the correlation of disparate data points. Today’s infrastructures comprise complex technologies and architectures.

They entail a variety of sophisticated control planes consisting of next-generation routing and new principles such as software-defined networking (SDN), network function virtualization (NFV), service chaining, and virtualization solutions.

Virtualization and service chaining introduce new layers of complexity that don’t follow the traditional monitoring rules. Service chaining does not adhere to the standard packet forwarding paradigms, while virtualization hides layers of valuable information.

Micro-segmentation changes the security paradigm while introducing virtual machine (VM) mobility, which introduces north-to-south and east-to-west traffic trombones. The VM, which the application sits on, now has mobility requirements and may move instantly to different on-premise data center topology types or external to the hybrid cloud.

The hybrid cloud dissolves the traditional network perimeter and triggers disparate data points in multiple locations. Containers and microservices introduce a new wave of application complexity and data volume. Individual microservices require cross-communication, potentially located in geographically dispersed data centers.

All these new technologies increase the number of data points and volume of data by an order of magnitude. Therefore, an IT organization must compute millions of data points to correlate information from business transactions to infrastructures such as invoices and orders.

Growing Data Points & Volumes

The need to correlate disparate data points

As part of the digital transformation, organizations are launching more applications. More applications require additional infrastructure, which always snowballs, increasing the number of data points to monitor.

Breaking up a monolithic system into smaller, fine-grained microservices adds complexity when monitoring the system in production. With a monolithic application, we have well-known and prominent investigation starting points.

But the world of microservices introduces multiple data points to monitor, and it’s harder to pinpoint latency or other performance-related problems. The human capacity hasn’t changed – a human can correlate at most 100 data points per hour. The actual challenge surfaces because they are monitored in a silo.

Containers are deployed to run software that is found more reliable when moved from one computing environment to another. They are often used to increase business agility. However, the increase in agility comes at a high cost—containers generate 18x more data than they would in traditional environments. Conventional systems may have a manageable set of data points to be managed, while a full-fledged container architecture could have millions.

The amount of data to be correlated to support digital transformation far exceeds human capabilities. It’s just too much for the human brain to handle. Traditional monitoring methods are not prepared to meet the demands of what is known as “big data.” This is why some businesses use the big data analytics software from Kyligence.

That uses an AI-augmented engine to manage and optimize the data, allowing businesses to see their most valuable data, which helps them make decisions. While data volumes grow to an unprecedented level, visibility is decreasing due to the complexity of the new application style and the underlying infrastructure. All this is compounded by ineffective troubleshooting and team collaboration.

Ineffective Troubleshooting Team Collaboration

The application rides on various complex infrastructures and, at some stage, requires troubleshooting. Troubleshooting should be a science, but most departments use the manual method. This causes challenges with cross-team collaboration during an application troubleshooting event among multiple data center segments—network, storage, database, and application.

IT workflows are complex, and a single response/request query will touch all supporting infrastructure elements: routers, servers, storage, database, etc. For example, an application request may traverse the web front ends in one segment to be processed by database and storage modules on different segments. This may require firewalling or load-balancing services in various on and off-premise data centers.

IT departments will never have a single team overlooking all areas of the network, server, storage, database, and other infrastructure modules. The technical skill sets required are far too broad for any individual to handle efficiently.

Multiple technical teams are often distributed to support various technical skill levels at different locations, time zones, and cultures. Troubleshooting workflows between teams should be automated, although they are not because monitoring and troubleshooting are carried out in silos, completely lacking any data point correlation. The natural assumption is to add more people, which is nothing less than fueling the fire.

An efficient monitoring solution is a winning formula.

There is an increasingly vast lack of collaboration due to silo boundaries that don’t even allow you to look at each other’s environments. By the design of the silos, engineers blame each other as collaboration is not built by the very nature of how different technical teams communicate.

Engineers say bluntly, “It’s not my problem; it’s not my environment.” In reality, no one knows how to drill down and pinpoint the root cause. Mean Time to Innocence becomes the de facto working practice when the application faces downtime. It’s all about how you can save yourself. Compounding application complexity and the lack of efficient collaboration and troubleshooting science create a bleak picture.

How to Win the Race with Growing Volumes of Data and Data Volumes?

How do we resolve this mess and ensure the application meets the service level agreement (SLA) and operates at peak performance levels? The first thing you need to do is collect the data—not just from one domain but from all domains simultaneously. Data must be collected from various data points from all infrastructure modules, no matter how complicated.

Once the data is collected, application flows are detected, and the application path is computed in real-time. The data is extracted from all data center points and correlated to determine the exact path and time. The path visually presents the correct application route and over what devices the application is traversing.

For example, the application path can instantly show application A flowing over a particular switch, router, firewall, load balancer, web frontend, and database server.

**It’s An Application World**

The application path defines what infrastructure components are being used and will change dynamically in today’s environment. The application that rides over the infrastructure uses every element in the data center, including interconnects to the cloud and other off-premise physical or virtual locations.

Customers are well informed about the products and services, as they have all the information at their fingertips. This makes the work of applications complex to deliver excellent results. Having the right objectives and key results (OKRs) is essential to comprehending the business’s top priorities and working towards them. You can review some examples of OKRs by profit to learn more about this topic.

That said, it is essential to note that an issue with critical application performance can happen in any compartment or domain on which the application depends. In a world that monitors everything but monitors in a silo, it’s difficult to understand the cause of the application problem quickly. The majority of time is spent isolating and identifying rather than fixing the problem.

Imagine a monitoring solution helping customers select the best coffee shop to order a cup from. The customer has a variety of coffee shops to choose from, and there are several lanes in each. One lane could be blocked due to a spillage, while the other could be slow due to a training cashier. Wouldn’t having all this information upfront before leaving your house be great?

Economic Value:

Time is money in two ways. First is the cost, and the other is damage to the company brand due to poor application performance. Each device requires several essential data points to monitor. These data points contribute to determining the overall health of the infrastructure.

Fifteen data points aren’t too bad to monitor, but what about a million data points? These points must be observed and correlated across teams to conclude application performance. Unfortunately, the traditional monitoring approach in silos has a high time value. 

Using traditional monitoring methods and in the face of application downtime, the theory of elimination and answers are not easily accessible to the engineer. There is a time value that creates a cost. Given the amount of data today, on average, it takes 4 hours to repair an outage, and an outage costs $300K.

If revenue is lost, the cost to the enterprise, on average, is $5.6M. How much will it take, and what price will a company incur if the amount of data increases 18x? A recent report states that only 21% of organizations can successfully troubleshoot within the first hour. That’s an expensive hour that could have been saved with the proper monitoring solution.

There is real economic value in applying the correct monitoring solution to the problem and adequately correlating between silos. What if a solution does all the correlation? The time value is now shortened because, algorithmically, the system is carrying out the heavy-duty manual work for you.