Modern IT environments are growing in complexity. They rely upon interconnected services and consist of diverse technology stacks. This complexity makes it difficult for operations, development, and security teams to manage systems, handle issues, and ensure reliability. Additionally, the increasing amount of time and effort required to troubleshoot issues, manage demand for resources, and secure access to assets and processes is also an important concern. IT teams need tools to improve outcomes and better manage an increasingly complex infrastructure.
What is workflow automation and why is it important?
A workflow is a series of tasks, processes, or actions undertaken to accomplish a specific outcome. Workflows are essential tools used by DevOps, network operations, security, and development throughout an enterprise’s technology stack. Moving data from one environment to the next, adding users to security or application groups, or connecting one service to another are examples of processes that can be managed through workflows.
Workflow automation refers to deploying rule-based logic or oversight to execute workflows without human intervention. Automation allows teams to combine or string together tasks through a central control tool or application. Automations can be kicked off through scheduling, alert messages, or in response to another automated request.
Modern IT environments comprise interconnected services that can include internal infrastructure and cloud-based technology stacks, as well as maintaining access and security. For DevOps and other operations teams, troubleshooting performance degradation or malicious activity within complex infrastructure can require time-consuming, manual processes. Teams often switch between tools, log in to different environments, collect information, and monitor processes to determine the necessary steps to overcome issues, mitigate risks, and resolve problems. Manual processes such as these can be moved to workflow automation and triggered by service alerts, by scheduling, or even by other workflows, saving time and improving reliability.
What different components are utilized by workflow automation?
Workflow automations can be assembled and managed via a script, text file, or a visual builder tool (see Figure 1). Through a builder tool, a workflow automation contains key components that manage or control the steps or tasks it undertakes, as described in Table 1:
| Component | Description |
|---|---|
| Triggers | Triggers are events (like an alert from a monitoring system or a scheduled interval) that initiate the workflow. |
| Actions | Actions are the steps a workflow takes, such as restarting a service, creating a Jira ticket, or sending a Slack notification. Note that workflow actions can operate across platforms, applications, and environments. |
| Conditions and Logic Operators | Conditions and logic operators control the flow of the automation, enabling workflows to branch based on data (e.g., if/else, loops), triggers (e.g., “when this happens, perform this step”), or events (e.g., “restart this service”). |
| Execution Environment | The execution environment is the system or tool (like Datadog Workflow Automation) that runs and monitors workflows. |
| Audit and Logging | Audit trails and logging track the input/output and status of each step for transparency and troubleshooting. Operations can review logs to ensure automations are working as expected while security can review auditing to track access roles and privileges. |
A use case example for a workflow automation may involve a DevOps team that receives an alert regarding high latency. An automated workflow triggered by this alert can collect related logs, notify teams through messaging apps, apply changes to increase scaling of affected systems, and block potentially malicious IPs—automatically.
What specific scenarios apply to workflow automation?
Workflow automations can be used to handle a wide range of situations and scenarios for DevOps, development, security, and other IT teams. These scenarios include:
Leverage context-rich insights to resolve disruptions faster
By interpreting data obtained through logs and monitoring tools, workflow automations can automatically initiate remediation tasks upon detection of service degradations, performance bottlenecks, security threats, and vulnerabilities.
When tied to data insights, workflow automations can access comprehensive full-stack observability and security data, including detailed service and team information, enabling responsible teams to obtain the necessary context for making informed decisions.
Workflow automations can respond promptly to issues by leveraging a range of extensive trigger options, including alert-based, scheduled, and platform-wide on-demand triggers.
Build complex workflows with ease
Datadog Workflow Automation empowers teams to create automated workflows using a visual low-code/no-code builder and over 550 out-of-the-box actions for a wide range of key integrations.
Forty-plus pre-built blueprints help teams automate essential processes across DevOps, security, incident management, remediation, and others.
Automate complex processes using advanced logic operators, branching, conditions, for/each iteration, custom JavaScript data operations, and HTTP requests to any API.
Maintain and secure workflows at scale
Security teams can apply workflow automations to implement granular role-based access control to restrict access, modify, and execute application or user actions.
Workflow automations facilitate collaboration across teams through reusable, pre-configured action steps to expedite and ensure consistent workflow creation.
Teams can use workflow automations to integrate human decision-making steps within workflows to effectively manage critical tasks. DevOps and other teams can apply workflow automations to get comprehensive visibility into every task execution, including its status and input/output data, through a detailed debugging view.
What shifts or changes in the industry affect workflow automation?
Rapid changes in the enterprise are affecting the deployment of workflow automation. These changes coincide with retiring manual processes used to manage IT operations, security, and development. System complexity, especially with infrastructure that includes cloud, microservice, and ephemeral-based architectures, has forced teams to move away from traditional workflows and manual processes. The adoption of platform engineering, along with DevSecOps, means teams must adopt tools that are collaborative, extensible, and built for hybrid, distributed environments.
The adoption of artificial intelligence (AI) and machine learning (ML) with workflow automation tools provides teams with intelligent data collection for enhanced decision-making, proactive issue analysis and resolution, and automating complex tasks across platforms, applications, and infrastructure.
What are the challenges of manual workflow techniques for the enterprise? See below for the challenges associated with manual workflow techniques and practices.
| Challenge | Discussion |
|---|---|
| Siloed tools lead to time-intensive context-switching. | Investigating and resolving issues is a significant challenge for DevOps and operations teams using fragmented tools and platforms. Responders must manually navigate dashboards to find root causes, switch applications to collaborate, and then perform remediation actions like restarting services, scaling instances, or blocking users. This disconnected approach wastes time, hinders efficient incident resolution, and can lead to errors, resulting in a higher mean time to resolution (MTTR). |
| Manual processes are inefficient and prone to errors. | Operations teams that carry out routine tasks manually find they lose significant time to address more critical tasks. During urgent situations, manual processes cause delays, are prone to errors, and prevent teams from quickly resolving issues. |
| Lack of context makes it difficult to identify remediation actions. | Siloed data and separate tools leave responders with little context when tackling an issue. As a result, operations teams spend considerable time gathering the necessary information to make informed decisions. Even with traditional automation solutions in place, data silos make it difficult to quickly access and act upon insights. |
What features should users look for when choosing a workflow automation tool?
Review the features shown in Table 3 when considering a workflow automation tool:
| Challenge | Discussion |
|---|---|
| Ease of use (interface and user experience) | The tool or solution should include a visual interface, workflow designer or builder tool for drag and drop design and low-code/no-code capabilities to create automations without coding experience. |
| Integration capabilities | The tool or solution should include pre-built connectors for integration with existing infrastructure, services, and platforms. These connectors should include API access and webhooks which can be triggered by application events or messaging. |
| Workflow customization and flexibility | Workflow automations should be easy to customize, including a script or app connector library for workflow connections, adding logic or flow changes, and incorporating role-based access permissions and controls. This way, only authorized users have access to view, edit, or approve workflow changes, especially for sensitive data. |
| Monitoring, reporting, and analytics | The tool or solution should contain a centralized dashboard to review and manage workflows, real-time tracking for viewing workflows in progress, and reporting and querying on workflow performance. The tool should provide reporting on metrics, cycle times, and error rates, and audit trails for security and compliance teams to review access and monitor workflow activity. |
Learn More
For more information on orchestrating and managing tasks and processes across your enterprise, review the Datadog Workflow Automation documentation.