What your business is most interested in is delivering an amazing customer experience for your customers. Customer experience drives the bottom-line, which is the key metric to the success of the overall business. Whether you are monitoring customer experience KPIs, product costs, errors, retention rates, or SLAs the common objective is to better the bottom- line of the business. But measuring, monitoring, and understanding your customer’s experience is challenging.
Every day, every business department is faced with good and bad challenges. Each department measures and monitors their own challenges in hopes to better their impact to the overall business. IT operations obsess over the infrastructure and application performance, but struggle to make sense of its direct impact to the overall business. That is why it’s so difficult to fully understand the customer experience, and as long as AIOps only obsesses over the infrastructure and application performance, it will fail to give a more meaningful understanding of its impact on the overall business.
Not understanding how the customer is experiencing a business’s products or services will eventually break the business as its reputation and revenue will slowly degrade. So why do businesses and their operations go so long without figuring out a solution to measuring, monitoring, and understanding the customer experience, especially in a time where data is so readily available?
The goal for every organization trying to achieve DevOps is automation for all things operations and deployment. As new technology continues to emerge such as micro- services, containers...etc, systems continue to become more and more complex and the need for automation and DevOps continues to grow with it. These systems have grown past the ability for humans to be able to maintain and sustain. Performance metrics, logs, trace data continues to grow and the ability to correlate and monitor all that data is prone to errors and is tedious.
AIOps aims to replace the tedious and error prone work by operationalizing data and analytics with data science. AIOps consumes all the data from the various services into a central repository to break down the silos between those services to bridge the information problem of IT, the cloud, software and hardware.
Data science can be layered on top of all the system data to find correlations and relationships between the different systems and infrastructure data. This allows for machine generated insights into correlated incidents, root cause analyses, and automated workflows to speed up remediations actions
Image credit: https://www.gartner.com/en/documents/3893494/use-aiops-for-a-data-driven-approach-to-improve-insights
At its core, AIOps is automation for operational tasks. It relies on machines using advanced machine and deep learning to find relationships and correlations between infrastructure and application data.
IT operations continue to evolve and grow incomplexity. DevOps and Service Reliability Engineering continue to grow in popularity stressing SLAs, SLOs, and SLIs and Time to Resolution. Although DevOps and Service Reliability Engineering is the correct direction to take for organizations, the demand can be overwhelming and create more problems if not handled correctly.
The issue for organizations today is 79% of them report that adding more IT staff to manage operations is not an effective solution. The complexity of their systems is growing so large that it has grown beyond human’s ability to maintain it, negating the benefit of hiring more staff. But the average cost to a performance incident continues to rise currently averaging
$427 per minute for a smaller business and $9,000 per minute for a larger business. Incidents are not going away, but the ability to manage those incidents with staff alone is going away.
These issues are a problem facing all tech companies today:
• DevOps: Developers are given more and more responsibility over the product but are still measured primarily on feature output. Teams are expected to build new features and maintain their own infrastructure but don’t have the resources to do both.
• Complexity of Cloud Computing: The dynamic nature of cloud computing and the introduction of micro-services and containers have created a surge in events and information from multiple, different sources making it impossible for humans to manually correlate and find relationships between systems and in the data.
• Data Overload: More complex systems, more logs, more metrics all from more different technologies. Data is being collected at a high rate from multiple, different sources making it difficult to keep track of. On top of that, different tools silo the data making it difficult and time consuming to stay on top of it all.
• Service Assurance: SLAs, SLOs, and SLIs continue to grow in popularity increasing the expectations of faster time to resolution. Most organizations don’t have the means to measure their own SLAs while customers expect systems to be continuously available.
AIOps cannot replace humans today. Instead, it can be a tool to help current DevOps or SRE teams to fill in the gaps created by the increasing complexity of dynamic cloud computing and the complex systems it creates.
AIOps ingests operational data into a single repository where it is layered with data science such as machine and deep learning to find relationships, correlations, trends, analyses, and anomalies. Then, depending on the AIOps tool, an insight is created and pushed into the existing organizations workflow to automate the entirety of the process to assist staff with meeting customer demands.
Image credit: https://www.gartner.com/en/documents/3893494/use-aiops-for-a-data-driven-approach-to-improve-insights
In short, AIOps is about the customer experience, and operationalizing an organization’s data and analytics to make that experience better.
AIOps is not business monitoring. Monitoring machines and monitoring business objectives are completely different things. Correlating business performance to the availability and reliability of a business’s complex systems is something completely different to the initial objective of AIOps.
As AIOps builds bridges between infrastructure and application monitoring, it creates its own silos away from the business and its objectives. AIOps ignores the larger business objectives that can impact the business’s bottom-line including revenue, costs, payment providers, traffic sources, partners, 3rd party technical partners and more. Business monitoring seeks to understand the customer and how she interacts with the product by analyzing data such as churn, retention, conversion, support, logins, usage, monthly active users, daily active users...etc. Machines and their relationships with each other tend to remain consistent. AIOps goal is to monitor the customer experience, but misses a large piece of the experience by not including business monitoring.
Business monitoring can be more complex due to the volatile context of the data. Relationships between the product and customer can change due to the day of the week or the season of year. It requires continuous correlation with multiple different metrics to find anomalies in those volatile contexts.
The difficulty with measuring business metrics:
• Human-dependent: Business metrics are human centric and influenced by human behavior. This makes business metrics subject to interpretation due to the need to understand the relationship between humans and the changes in their environment. Environments are seasonal and rely a large number of parameters to understand their influence over time on human behavior.
• Variable: Human behavior is not consistent. The rate at which sample data can be collected varies making it challenging to store and monitor that data. It requires a system that is adaptive to learning behavior and seasonality trends in real-time.
AIOps can tell us what the customer experience is while using the product, but it cannot tell us the impact it is creating for the business. It still lacks the overall ability to tell the business why it is important to the customer and why it is important to the business.
Customers interact with product and services on many different levels. At the foundation is the infrastructure and application followed by the customers interactions and touch points with the business. Each level is broken into its own journeys and funnels making it hard to track and understand the overall customer experience. The entire customer experience can be broken if any level has a problem. Good customer experience is the objective of every business regardless of its vertical, and any flaw to that experience can create massive hits to the businesses bottom-line.
To protect the customer experience, and the businesses reputation and bottom-line, a full view of each level of that experience must be monitored and tracked. But as mentioned above, the complexity of monitoring only the infrastructure and application already prove to be too complex for humans to handle and becomes even more tedious looking for correlations and relationships. Adding the additional levels of business monitoring deepens that already existing problems.
To fully understand the complete impact to your business’s customer experience, a marriage of AIOps and business monitoring is a necessity. FojiSoft delivers a suite of applications that deliver full monitoring at every level of the customer experience by operationalizing data and using machine and deep learning to monitor and find relationships and correlations between the multiple levels of the customer experience. FojiSoft monitors data in real-time to find anomalies in that data from the infrastructure, application, and business creating insights that automatically alert existing workflows to protect the businesses bottom-line. By creating a marriage of AIOps and business monitoring, FojiSoft delivers insights that can’t be found anywhere else.
FojiSoft delivers these unique insights by:
Real-time monitoring across the customer experience: Anomaly detection of the customer experience requires monitoring across every level of the business, and to learn the volatile context of the data from the customer, real-time data ingestion for real-time monitoring of data correlation is required. FojiSoft centralizes all data breaking down monitoring silos to find anomalies and correlations in the data across the entire customer experience.
Trend detection and forecasting: FojiSoft uses machine and deep learning to learn and construct an understanding of the customer experience to find trends and to generate forecasts to inform the business of incidents before they happen. As an example, FojiSoft can forecast a change in customer behavior due to upcoming Black Friday sales, and inform the business of needed infrastructure updates before experiencing any incidents.
Machine and deep learning: Humans cannot possibly keep up with all the data being created by a business. Leveraging machine and deep learning to monitor the business, application, and infrastructure, the business can reduce the toil of manually attempting to monitor and correlate all incidents. FojiSoft utilizes both unsupervised and supervised models to learn the systems and customers behavior to reduce alert fatigue, predict incidents, automate operational workflows, reduce time to resolve, and more.
Incident scoring: The incidents that matter the most are the ones that impact the bottom-line the most. Most IT organizations are still lost when it comes to prioritizing incidents usually relying on some arbitrary system of severity. FojiSoft scores incidents based on correlated data across the entire business from revenue, customer impact, impact to the application, and more.
Service assurance monitoring: Customers now expect continuous availability from the products and services they utilize, and the demand for SLAs, SLOs, and SLIs continues to increase. Service assurance monitoring isolates the customers pain points across the customer experience and gives a view into the business everyone can understand. FojiSoft makes it easy to create and monitor SLAs, SLOs, SLIs in real-time to give the business a clearer view into their customers experience.
Incident correlation and root cause analysis: FojiSoft correlates anomalies in real-time and identifies events and different factors from data at every level of the customer experience to quickly determine root cause for each incident.
FojiSoft’s suite of applications is designed to protect the business from the incidents that can cause damage to the businesses bottom-line.
It eliminates the volatility and complexity that causes human error while monitoring, searching, and resolving incidents. FojiSoft does this while also stitching together every level of the customer experience to create an intelligent marriage between AIOps and business monitoring. With FojiSoft, you’ll never have to suffer through another business damaging incident or business opportunity due to blind spots in your observability. By leveraging FojiSoft’s built-in data science, your business will gain actionable insights, achieve faster time to resolution, and protect its bottom-line.
Better business observability with FojiSoft is just a click away and you only pay for what you use!