# IT analytics maturity assessment

**Are you enabling insight-driven IT operations–or just reporting the past?**

In the world of rapid innovation and disruption, a mature analytics strategy is no longer a competitive advantage–it's a business imperative. This interactive assessment is designed for IT teams who want to benchmark and elevate their analytics capabilities. Measure your maturity across seven strategic pillars–and gain a clear roadmap to drive efficiency, optimize operations, and deliver transformative outcomes.

## Data availability and integration

Your approach to collecting, centralizing, and integrating IT operational data.

### 1. What is the current state of your IT data quality and consistency?

- IT data is inconsistent, siloed, and difficult to analyze
- Some usable data exists, though often isolated by function or process.
- Key data domains identified and are being unified through early integration efforts.
- Data is centralized, and its quality is enhanced through automated pipelines.
- Fully integrated, high-quality data used for real-time insights and decision-making.

### 2. How easily can you access and query cross-functional IT data?

- Querying is manual, with no standard tools.
- Querying is limited to CSVs and raw data exports, with cleanup needed before use.
- Standard dashboards and BI tools exist, but data is static or manually updated.
- Near real-time, interactive analysis on data from multiple systems.
- Real-time, self-service analytics with intuitive non-assisted reporting across domains.

## Tools and technology

The sophistication of your analytics platforms, tools, and technologies (including AI and ML) employed for analysis.

### 1. What analytics tools does your team primarily use for monitoring and reporting IT KPIs and events?

- Spreadsheet-based tracking and reporting.
- Built-in reports within individual tools like ITSM, ITOM, or EPM.
- Unified platform with custom dashboards and basic self-service.
- Proactive insights via predictive models and diagnostic tools.
- Prescriptive analytics tools generating automated recommendations.

### 2. How integrated is your IT analytics tool with your IT applications?

- Performs manual data exports; doesn't integrate with other tools or systems.
- Enables basic integration and data transfers; systems operate mostly in silos.
- Connects select tools via a central warehouse for a unified ITOps view.
- Collects data automatically from diverse IT sources; updates insights regularly.
- Fully integrates with the IT stack in real time; delivers automated data processing and intelligence.

## Analytics culture

Your IT's expertise, organizational commitment, and importance to data-driven insights, data governance, and security.

### 1. How would you describe your company's culture toward IT decision making?

- Decisions are made with minimal or no use of data.
- Data-driven insights are rarely considered for decision-making.
- Multiple KPIs are tracked and reviewed regularly, having some influence on decisions.
- Analytics routinely guide decisions with coordinated improvement efforts across IT.
- IT strategy is shaped by automated, data-driven insights and decision intelligence.

### 2. How do you handle sensitive or confidential IT data (e.g., passwords, PII, incidents)?

- Access to sensitive data does not require authentication.
- Access is manually restricted to admins.
- Sensitive data is hidden or encrypted.
- Sensitive data is anonymized and protected by strict security and access policies.
- Sensitive data is protected by automated data classification, masking, and audit logging.

## Analytics democratization and consumption

How analytics insights are shared across your teams and used to support IT decision-making.

### 1. How well do cross-functional IT teams collaborate around analytics?

- Analytics is siloed within individual teams.
- Collaboration happens only during or after major incidents or escalations.
- Teams leverage on-demand sharing of static reports, dashboards, and insights.
- Collaboration is embedded into workflows with live reports on critical KPIs.
- 360-degree analytics drive real-time, cross-functional decision-making.

### 2. How accessible are analytics and insights to non-technical stakeholders such as technicians, managers, and leadership?

- Limited tool access with little to no support or enablement offered.
- Static reports are manually shared for leadership meetings.
- Tool access with basic onboarding and prebuilt views.
- Self-service tools support report creation with actionable insights.
- Automated assistants surface insights, narratives, and recommendations to all stakeholders.

## Advanced analytics adoption

The incorporation of automation, AI, and ML within your IT analytics workflows.

### 1. How sophisticated are your analytical techniques across IT domains?

- Trends are tracked using simple charts and spreadsheets.
- Statistical methods like correlation are used for initial insights.
- Structured formulas and composite metrics enable in-depth RCA.
- Patterns are anticipated and anomalies identified proactively.
- Insights are automatically generated, enabling recommendations and proactive actions.

### 2. To what extent do you use automated RCA in ITOps and ITSM?

- RCA is carried out manually and in isolation.
- RCA is based on KB articles.
- Automated RCA addresses recurring events via pattern identification.
- ML models detect anomalies and correlate insights for probable causes.
- Predictive RCA insights enable self-healing remediation.

## Continuous monitoring and proactive incident intelligence

The maturity of your IT monitoring, alert enrichment, and incident correlation processes, with a focus on analytics usage to reduce noise, detect anomalies, and accelerate resolution.

### 1. How centralized is your incident monitoring and alerting across infrastructure, applications, and endpoints?

- Monitoring is domain-specific with limited cross-functional visibility.
- Data is consolidated across domains, but alerting remains fragmented.
- Central dashboards aggregate alerts for review, but triage is manual.
- Alerts are unified and delivered in real time with filtering and impact-based prioritization.
- Monitoring is centralized with intelligent alert correlation, and key drivers are surfaced automatically.

### 2. How do you currently track IT health across domains?

- No formal process for tracking infrastructure health is used.
- Health indicators are manually tracked via spreadsheets or static reports.
- Individual monitoring tools are used, but with no centralized visibility.
- Centralized dashboards track health KPIs in near real time.
- A unified platform highlights anomalies, forecasts IT issues, and offers proactive insights.

## Business alignment and decision making

How your IT analytics align with business goals and deliver data-driven insights to guide strategic decisions, optimize operations, and improve IT outcomes.

### 1. What role does IT analytics play in shaping your organization's strategic planning and decision-making?

- Decisions are made independent from IT analytics.
- Analytics insights are occasionally presented with limited influence.
- IT analytics are periodically reviewed to support planning and track IT goals.
- AI-powered forecasts and insights shape planning and budgeting.
- Forecasted trends and prescriptive recommendations shape IT strategy and innovation.

### 2. Is there a centralized dashboard for CXOs to track business-impacting incidents?

- No centralized dashboard available.
- Critical KPIs are shared manually via periodic reports.
- Dashboards exist, but rely on frequent manual updates and revisions.
- Live dashboards on key IT tenets are accessible to leadership staff.
- Predictive insights and real-time recommendations are provided by dashboards on demand.

## IT analytics maturity levels

### Level 1: Analytics beginners

For analytics beginners, IT insights are unstructured and reactive, with decisions largely based on intuition. Data is manually collected, siloed, and poorly organized, with minimal access or collaboration across teams. Reporting is ad hoc, using basic in-product tools or spreadsheets, and data-driven decision-making is rare. While there's awareness of analytics, no formal strategy or governance is in place.

### Level 2: Reactive analysts

Reactive analysts have begun to recognize the value of IT analytics and are taking early steps towards a structured analysis process. Dashboards and visual reports are used within departments for general intelligence, and efforts are underway to break data silos through creation of central data lakes or repositories. Basic automation support for data collection exists, but integration gaps, RCA inefficiencies, and limited analytics skills still hinder broader adoption.

### Level 3: Emerging proactive analysts

Emerging proactive analysts recognize the value of proactive analytics and intend to make advancements in that direction, moving beyond "what happened" to an in-depth exploration of "why it happened". Self-service analytics is emerging, empowering more users with data-driven insights. Systematic diagnostic analysis, cross-functional correlations, and scenario analysis is performed to understand the root cause behind IT events and issues.

### Level 4: Predictive and AI-enhanced organizations

Predictive and AI-enhanced organizations are highly data-driven, leveraging advanced analytics platforms and widespread AI adoption. Predictive models, AIOps, and MLOps are actively used to forecast trends, detect anomalies, and drive proactive decision-making. Analytics plays a central role in anticipating issues and optimizing both IT and business outcomes.

### Level 5: IT analytics champions

IT analytics champions represent the pinnacle of IT analytics maturity. Analytics is viewed as a strategic asset and deeply embedded across all IT and business functions. AI is leveraged to its full potential through automated root cause analysis, no-code ML models, and advanced decision intelligence. The focus shifts from predicting outcomes to prescribing optimal actions–enabling continuous optimization, innovation, and autonomous IT operations.

## Strategies to move up the maturity scale

### Level 2: Reactive analysts

- **Establish a vision for data-driven IT**  
  Begin by identifying a centralized analytics platform to unify reporting across IT functions.
- **Break down silos**  
  Encourage cross-functional collaboration and leadership buy-in for a shared analytics strategy.
- **Start integrating key IT data**  
  Connect core systems like ITOps, ITSM, and endpoint management into a common repository to enable basic visibility and insight.

### Level 3: Emerging proactive analysts

- **Streamline and expand access to data**  
  Refine your existing data pipelines and management to ensure IT data is more easily accessible to wider users across teams.
- **Move beyond static reporting**  
  Shift from reactive static reports to embedded, proactive diagnostic analytics that help uncover patterns, bottlenecks, and causes.
- **Empower self-service**  
  Simplify and democratize the analytics process by adopting an intuitive analytics platform that enables all IT stakeholders, including technicians, service owners, and managers, to query and explore data with minimal training.
- **Standardize shared KPIs**  
  Automate analysis of common IT metrics, such as SLA compliance, alert volumes, utilization trends, and patching success rate, across departments for consistent visibility.

### Level 4: Predictive and AI-enhanced organizations

- **Strengthen your data foundation**  
  Implement enterprise-wide data quality practices, centralized governance, and standardized access controls to prepare for predictive and automated analytics workflows.
- **Operationalize AIOps and DataOps**  
  Introduce frameworks to automate data movement, enhance infrastructure observability, and ensure secure, scalable access for analytics and ML processes.
- **Adopt advanced AI- and ML-driven analytics**  
  Begin exploring NLP-based narrative summaries, automate RCA and anomaly detection, and introduce custom predictive modeling to minimize manual analysis and enable faster, more accurate decisions.
- **Enable scalable, no-code ML**  
  Leverage AutoML tools to develop tailored models for forecasting, risk scoring, performance prediction, and more, without needing dedicated data science teams.

### Level 5: IT analytics champions

- **Embed AI into everyday decision-making**  
  Foster a culture where AI-driven insights guide actions at every level–from leadership strategy to technician-level interventions.
- **Advance decision automation**  
  Optimize decision-making processes, especially for well-established operations. Make analytics the basis for innovation and overall development, even for complex decisions where optimization may still involve manual oversight.
- **Deploy prescriptive analytics across IT**  
  Implement decision intelligence frameworks that recommend next best actions for key scenarios and incidents across the IT landscape, including incident management, workload balancing, capacity planning, and remediation.

### Level 5: IT analytics champions (continuous optimization)

- **Continuously evolve with emerging technologies**  
  Stay ahead by monitoring and adopting cutting-edge techniques like GenAI, deep learning, agentic AI, and autonomous analytics frameworks to meet shifting IT demands.
- **Expand and refine closed-loop automation**  
  Extend your AI-driven systems to handle complex and high-impact decisions, triggering actions autonomously based on real-time recommendations and forecasts.
- **Adopt autonomous AI frameworks**  
  Explore intelligent agents capable of independently identifying issues, recommending solutions, and executing actions–minimizing human intervention while maximizing operational speed and precision.
- **Invest in innovation and R&D**  
  Dedicate efforts toward experimentation, custom model development, and strategic use of AI and ML to lead your industry in data-driven IT operations.

## Recommended resources

### Level 2: Reactive analysts

- [E-book on 5 inefficient IT practices](https://www.manageengine.com/analytics-plus/e-book-on-5-inefficient-it-practices.html)
- [Whitepaper on analytical capabilities for IT survival](https://www.manageengine.com/analytics-plus/whitepaper-on-analytical-capabilities-for-it-survival.html)
- [Analytics Plus data sheet (PDF)](https://download.manageengine.com/analytics-plus/docs/data-sheet.pdf)

![Recommended resources](https://www.manageengine.com/analytics-plus/images/scorecard/resources-recom1.svg)
![Recommended resources](https://www.manageengine.com/analytics-plus/images/scorecard/resources-recom2.svg)
![Recommended resources](https://www.manageengine.com/analytics-plus/images/scorecard/resources-recom3.svg)

### Level 3: Emerging proactive analysts

- [Whitepaper on analytical capabilities for IT survival](https://www.manageengine.com/analytics-plus/whitepaper-on-analytical-capabilities-for-it-survival.html)
- [E-book on five hidden IT correlations](https://www.manageengine.com/analytics-plus/e-book-on-five-hidden-it-correlations.html)
- [ROI calculator](https://www.manageengine.com/analytics-plus/roi-calculator.html?utm_source=Resources&utm_medium=Webpage)

![Recommended resources](https://www.manageengine.com/analytics-plus/images/scorecard/resources-recom2.svg)
![Recommended resources](https://www.manageengine.com/analytics-plus/images/scorecard/resources-recom4.svg)
![Recommended resources](https://www.manageengine.com/analytics-plus/images/scorecard/resources-recom5.svg)

### Level 4: Predictive and AI-enhanced organizations

- [E-book on AI and ML solutions to greatest IT challenges](https://www.manageengine.com/analytics-plus/e-book-on-ai-ml-solutions-to-greatest-it-challenges.html)
- [E-book on getting started on AIOps](https://www.manageengine.com/analytics-plus/e-book-on-getting-started-on-aiops.html)
- [E-book on cutting down infrastructure costs with ML-based capacity planning](https://www.manageengine.com/analytics-plus/e-book-on-cutting-down-infrastructure-costs-with-ml-based-capacity-planning.html)

![Recommended resources](https://www.manageengine.com/analytics-plus/images/scorecard/resources-recom6.svg)
![Recommended resources](https://www.manageengine.com/analytics-plus/images/scorecard/resources-recom7.svg)
![Recommended resources](https://www.manageengine.com/analytics-plus/images/scorecard/resources-recom8.svg)

### Level 5: IT analytics champions

- [E-book on AI and ML solutions to greatest IT challenges](https://www.manageengine.com/analytics-plus/e-book-on-ai-ml-solutions-to-greatest-it-challenges.html)
- [Webinar: Gen AI-powered analytics for ITOps excellence](https://www.manageengine.com/analytics-plus/webinar-gen-ai-powered-analytics-for-itops-excellence.html)
- [Whitepaper on analytical capabilities for IT survival](https://www.manageengine.com/analytics-plus/whitepaper-on-analytical-capabilities-for-it-survival.html)

![Recommended resources](https://www.manageengine.com/analytics-plus/images/scorecard/resources-recom6.svg)
![Recommended resources](https://www.manageengine.com/analytics-plus/images/scorecard/resources-recom9.svg)
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