Seven reasons why specialised analytics tools are a must for IT departments

Analytics helps visualise and present data in a way that simplifies decision-making, making it an integral part of organisations’ application stack. But IT departments have little say in analytics initiatives, even though IT systems generate a lot of data that can be used for making crucial decisions. This can help organisations improve their operational efficiency by increasing revenue and cutting costs, among other benefits. 

Compared to its business counterpart — the primary focus for analytics initiatives — IT data is vast, complex, and generated at high velocity. Which is why the number of organisations that practically ignore IT data analytics is not surprising. Rather than being caught unaware, let’s explore seven reasons for why companies need specialised IT analytics tools.

1. Domain Expertise For Complex Data

IT teams are typically made up of several departments: IT services (hardware and software problem-solvers), help-desk, network-operations, and endpoint management and security. These departments have different areas of focus but work together for business continuity.

 
IT departments generate and analyse large volumes of complex data that is handled by subject matter experts. Their collective efforts will determine the overall IT health of an organisation. Doing this requires a deep understanding of how these functional areas influence each other. 

For example, IT analytics software should have the built-in intelligence to know that a major network outage will flood the help desk with tickets from employees who can’t access resources for daily duties. This will allow the software to issue advance warnings.

Deep domain knowledge of IT operations is key for highlighting the right metrics at the right time. Since generic business-intelligence and analytics applications take a build-it-yourself approach, IT teams need more time and effort to understand complex, raw data, discern underlying correlations between various departments, and build the analytics framework. But specialised IT analytics software can look for important metrics, find data correlations, and highlight important indicators for making the right calls.

2. Out-Of-The-Box Connectors For Popular IT Tools

IT department generally use 10-15 applications to run and manage operations. These hold valuable information that can be used to:

• Develop an overarching view of operational health.
• Analyse operational efficiency.
• Look for security loopholes or breaches.
• Monitor IT staff performance.
• Find cost-saving avenues.
• Calculate IT budget needs.

IT applications are usually fine-tuned for data-collection and storage. But they aren’t the best for making data available in real time, or for historical analysis. The time and effort needed to understand data-extraction APIs and use them on these applications is a major barrier for IT data analysis. 

An IT analytics application with out-of-the-box connectors for popular IT applications can extract data, model it, and generate pre-built reports and dashboards. This can reduce analysis time up to 80 per cent. Since data is available in an easy-to-understand format, CIOs, IT managers, and supervisors can create organisation-specific reports and dashboards, without relying on analysts or database administrators.

3. AI-Enabled Auto-Interpretation

IT analytics applications that use AI and NLP can often answer a variety of direct questions without requiring users to dig through the underlying raw data. By filtering the results, AI-powered analytics tools can shield sensitive and confidential corporate data from end users. This can also help analytics be implemented in parts of organisational hierarchy that it previously wasn’t in, and open channels for data democratisation. 

AI can remove another aspect of analytics that predominantly requires human effort: interpretation. Effective data analysis has two components: a robust analytics application and a discerning end user. However, human interpretation can be subjective and biased, and is prone to cognitive distortions. 

An AI-enabled IT analytics solution overcomes this by auto-interpreting data and offering direct insights. End users only need to take appropriate actions based on the latter.

4. Scalability For Large Data Volume

IT management applications monitor hundreds of network devices, log thousands of security events, and create millions of log-lines. This scale requires an analytics application capable of processing, storing, and analysing millions of data-rows every few minutes.

Though they can analyse large volumes, off-the-shelf analytics applications can’t keep up with the speed at which modern IT applications generate data.

Specialised applications use intelligent algorithms, batch processing, and auto-prioritisation of data based on criticality. High-risk activities like security events are immediately reported. Event-based thresholds can be set for notifications. This keeps important metrics from being inundated in data.

5. Real-Time Analytics and Alerts

IT metrics are derived from either historical or real-time data. 

An example of historical analysis: scrutinising help-desk incidents to finalise technician strength for smooth operations. An example of real-time analysis: displaying details of privileged user-account access during non-business hours.

Traditional applications are good for analysing historical data, not real-time analysis, since the latter involves high data velocity. IT analytics applications are fine-tuned for this. Services like Live Connect allow direct, real-time connections to data sources, and bypass the traditional mode of periodic synchronisation and data storage. It also gives users real-time data. This lets IT administrators rectify issues instantaneously, leaving no room for exploitation of security loopholes. 

6. Custom Visualizations

Common visuals like bar and pie charts are available on run-of-the-mill analytics applications. But IT data needs custom formats. 

Consider companies like Walmart or Target that need to ensure their point-of-sale machines are connected to banking systems, their websites are responsive for processing orders, their help-desk systems are operational for customer complaints, and so much more. From an IT perspective, each is a business service. These should not only be operational, but also coordinate seamlessly. The best way to represent their availability and operational status is through a hierarchy chart of health and interconnectivity. 

7. Built-in Security

IT applications carry a lot of sensitive information—like IP addresses, CMDB relationships, and personal-user data—which all can be used for security exploits. Analytics applications gather them at a single location for cross-functional analysis. Many companies prefer to keep data on-premises for this reason. 

In this context, IT analytics applications should be flexible for deployment on-premises or in cloud. Functions like masking sensitive data, two-factor authentication, allowing role-based access, and data encryption are key elements of a well-rounded IT analytics application. 

The article has been written by Rakesh Jayaprakash, Product Manager, ManageEngine