What is AIOps
Artificial intelligence for IT Operations (AIOps) encompasses technologies that augment human decision-making with autonomous decision-making driven by AI and ML technologies that learn patterns and relationships from underlying datasets.
AIOps is gaining ground rapidly in modern enterprises that currently struggle with deriving insights from large data volumes, managing surplus alerts and event noise, handling complex and time-intensive IT resolution processes, the unpredictability of IT service degradations, outages and the unending IT complexity.
Core AIOps use cases include-
- Root cause inference
- Anomaly detection
- Alert noise reduction using event correlation
- Incident MTTR reduction
- Automated context enrichment
- Predictive analytics and forecasting
- Trends and baseline establishment
What is Data Fabric
Modern digital businesses are increasingly complex across legacy and cloud-native applications, multi-cloud distributed services with Kubernetes and microservices architectures. Enterprises need observability across the org for service assurance, which means they need to rein in the data deluge while making insights possible quickly.
This is where observability augments AIOps to automate contextualization, scaling and instant remediation of root cause while enabling business outcomes.
Observability today needs to go beyond MELTs in combination with AIOps and DataOps to enhance service delivery and accelerate innovation.
Gartner recently identified Data Fabric or Robotic Data Automation Fabric (RDAF) as one of the top 10 data and analytics trends for 2022. Gartner also states that “by 2024, data fabric deployments will quadruple efficiency in data utilization while cutting human-driven data management tasks in half.”
Why Data Fabric is Critical for Today’s Enterprise
Data Fabric allows an enterprise to execute and process data locally without the need to pull all disparate and diverse data into one data lake, situated in a central location, then run algorithms on it to lead to insights. Traditional systems render useless in such modern scenarios as they require the expensive operation of moving high-volume data and keeping it up to date in all copies.
Data Fabric makes distributed data processing and computation easy.
Data fabric lets you keep the data where it is- across departments- and connect all islands flexibly. This means data gets pulled on-demand and combined for analytics. As RDAF adds a virtual layer on top of data applications, end-users don’t worry about the origination of data anymore and derive insights cost-effectively.
Data Fabric makes the composability of workflows and democratization of data possible.
The use cases of data fabric are domain agnostic. CloudFabrix has helped businesses across FinTech, Healthcare, Marketing and Product Engineering reap the benefits of an agile and transformative IT ecosystem.
Data Fabric use case areas include:
- AIOps with alert enrichment and correlation, anomaly detection and IT change detection.
- ITSM/NOCOps with auto-remediation with RPA and automation scripts and NLP insights with KB recommendations.
- InfraOps with application to infra dependency mapping and infrastructure change impact analysis.
- Observability with anomaly detection, automated alerting from Edge devices and automated provisioning of observability pipelines.
- DevOps with container security and vulnerability analysis, integration of MLOps into CI/CD pipeline and microservices error message contextualization.
- SREOps with performance optimization analysis, automated data collection and analysis and updated KPI dashboards.
How AIOps and Data Fabric Improve an Enterprise
Here’s how CloudFabrix’s Robotic Data Automation Fabric enhances AIOps for a forward-looking enterprise:
RDAF injects AIOps and robotic data automation across an enterprise’s distributed edge using natural language processing (NLP), natural language generation (NLG), distributed messaging, unified query language and integrated development environments (IDEs). RDAF facilitates the creation of customized, on-the-go pipelines with low-code, inline AI/ML data bots and delivers the following benefits:
- Data Fabric accelerates value from AI and ML usage, bringing ROI faster.
- When enterprise data expands exponentially, RDA can help scale insights, decision-making and business growth with bots.
- As the reliance on manual labor reduces, it automatically leads to the containment of cost and resources.
- Enterprises can readily identify internal biases, errors and quality compromises even without data scientists with RDAF.
- Enterprises employing RDAF enjoy faster proposals courtesy of exploratory data analysis, what-if analysis and pre-trained bots.
- As a result of data fabric, field teams, services, implementation, and support departments are more productive.
- RDAF accelerates AIOps with a robust set of bots with a consistent interface that simplifies data handling in AIOps implementations.
The impact of RDAF on an enterprise is manifold.
Robotic Data Automation Framework- A Game Changer
Modern systems are disparate, dynamic and distributed, piling on complexity over observability. DataOps teams need to handle multi-source data collection, processing and storage to get to real-time, meaningful insights.
It can get really messy, really quick to comprehend interactions between systems. The solution is robotic data automation fabric (RDAF), which allows value extractions from unstructured databases by analyzing patterns with the help of ML algorithms.
Just like enterprises leverage Robotic Process Automation for mundane, linear business processes, Robotic Data Automation Fabric comes to rescue DataOps and AIOps to create an intelligent data ecosystem that accelerates value and transformation.
RDAF combines AI tools and automation to deliver composable IT workflows that employ software bots and low-code pipelines. RDA with AIOps Studio allows you to author pipelines, monitor and debug pipeline stages, visualize data, explore data bots, publish pipelines to production and more.
