Automated monitoring helps reduce human error and provides real-time visibility for critical business data. It also makes it easier for your team to focus on other priorities.
With machine learning capabilities, Signals focuses alerts on your most pressing issues and prioritizes relevant insights. It means users receive the most relevant and personalized alerts first.
Prevent Downtime
An automated system to collect and analyze data enables you to monitor for issues, anomalies, or errors more efficiently. It is done by generating alerts and notifications when a problem or potential issue is detected and by analyzing the collected data for long-term trends via analytics.
Unplanned downtime is a significant problem that costs industrial manufacturers billions of dollars annually. This type of downtime negatively affects production, worker efficiency, and inventory management.
Automatic monitoring solutions help prevent downtime by detecting problems, analyzing them for root causes, and automatically triggering work tickets. It reduces the time IT support and engineering teams spend analyzing logs and alerts and allows them to focus on more pressing business concerns. It is essential when deploying new hardware and software.
Detect Potential Issues Before They Happen
Modern APM / IT monitoring tools allow users to specify and configure alerts for specific network devices or services. It means that users are only notified of problems they are interested in rather than being inundated with notifications (notification spam).
Additionally, many APM / IT monitoring systems with advanced reporting capabilities can automatically create and send reports to distribution lists. It eliminates the possibility of someone forgetting to run a report, being out of work, and not receiving it in time.
Unlike dashboards that compare operational performance to known indicators, automated business monitoring can detect trends or anomalies before they impact business processes and operations. For example, a Yellowfin customer that offers healthcare and beauty products leveraged ABM using the capability to detect stock shortages of certain items in advance, reducing annual losses from stock-outs to almost zero.
Reduce Unnecessary Expenses
Users can take less time to interpret and share valuable surfaced insights using a monitoring solution with automation capabilities. That means they have more time to act on their reports and avert or adapt to business changes before it’s too late.
For instance, mining companies can use automated monitoring to identify a leak or other problem that can affect the safety of employees. It helps reduce expenses related to lost production and unplanned downtime.
Energy-intensive industries can also save money, improve their reputation, and combat climate change with automated monitoring systems. These are designed to detect problems and ensure compliance with regulatory requirements. The system will automatically alert the appropriate personnel via an alarm, dialer, or pager. It will also provide a real-time view of meter data.
Increase Productivity
In addition to detecting and preventing issues, automatic monitoring can enhance productivity. Data from real-time sensors and machines can help businesses predict equipment issues, reducing costly maintenance downtime.
Employees who know they’re being monitored may be more likely to focus on work, meet deadlines, and prioritize tasks. They can also identify areas where they struggle (such as time management, working from home, or lacking motivation) and brainstorm ways to improve their productivity.
The heightened agility delivered by automation capabilities enables users to respond to changes, deviations, trends, and correlations faster.
Prevent Fraud
Fraud prevention is a crucial aspect that businesses need to take seriously. The impact of fraud can be disastrous for the business, leading to financial losses and reputational damage. However, businesses can take proactive measures to mitigate the risk of fraud by implementing fraud monitoring systems, such as fraud detection and risk scoring software tools, and by establishing a dedicated fraud team to investigate suspicious activity. By adopting such measures, businesses can create a culture of transparency and accountability and foster a safe and secure environment for their stakeholders.
Detecting fraudulent activity requires advanced algorithms to identify patterns in preprocessed data and alert the user of potential risks. Often, these systems use machine learning to improve their accuracy over time.
It can be a powerful tool for banks, fintechs, and neobanks who want to ensure compliance with regulations like customer due diligence (CDD) and anti-money laundering (AML). However, the system must also balance out false positives, such as good customers who look fraudulent to the software and are denied service or pushed into higher-risk segments.
