ROLE PURPOSE
The Service Data Analytics Specialist is accountable for building the analytical and technical capability that turns the SDM's operational data into predictive, diagnostic insight — going beyond reconciliation and status reporting into trend modelling, anomaly detection, and forward-looking risk signalling. This role exists to shift the department from reactive reporting ("what happened") to predictive positioning ("what's about to happen and why it matters") — giving the SDM early warning on service degradation, capacity strain, or client risk
What This Role Owns (Outcome Commitments):
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Predictive & Diagnostic Analytics — Trend, pattern, and anomaly analysis across service performance data (incidents, SLA/SLO performance, capacity, XLA/experience metrics) that surfaces risk before it becomes visible operationally.
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Analytics Tooling & Dashboard Infrastructure — Design, build, and maintenance of dashboards, automated reporting pipelines, and analytical tooling that reduce manual reporting effort and increase reporting frequency/accuracy.
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Statistical Rigour — Application of sound statistical and analytical method (not spreadsheet-level approximation) to service data — ensuring conclusions are defensible, not directional guesses dressed as insight.
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Early-Warning Risk Signalling — Proactive identification of deteriorating trends (SLA drift, capacity strain, recurring incident patterns) communicated to the SDM in time to act, not after the fact.
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Data Model & Source Integrity for Analytics — Ensuring the underlying data models and pipelines feeding analytics are structurally sound, distinct from the BA's day-to-day data reconciliation for reporting purposes.
ROLE ACCOUNTABILITIES / KEY ACTIVITIES
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Predictive & Diagnostic Analytics
Accountable for: Surfacing forward-looking risk from service data before it becomes an operational or client-facing issue.
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Build and maintain trend analysis across incident volumes, SLA performance, and capacity data to detect early signs of service degradation
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Apply statistical methods (e.g. variance analysis, forecasting, correlation analysis) to distinguish genuine risk signals from normal operational noise
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Conduct pattern analysis on recurring incidents to identify systemic risk that individual RCAs may miss in isolation
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Deliver early-warning signals to the SDM with sufficient lead time to act — not retrospective confirmation of what already happened
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Analytics Tooling & Dashboard Infrastructure
Accountable for: The technical infrastructure that makes predictive and diagnostic analysis possible and repeatable.
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Design, build, and maintain automated dashboards for SLA performance, capacity trends, and XLA/experience metrics across the SDM's portfolio
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Develop and maintain data pipelines that reduce manual data pulling and consolidation effort, increasing reporting frequency and accuracy
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Ensure dashboard and tooling outputs are structured for the SDM's actual decision-making needs — not generic BI output disconnected from operational reality
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Maintain version control and documentation on analytical models and tooling logic, so methodology is auditable and repeatable.
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Statistical & Methodological Rigour
Accountable for: Ensuring analytical conclusions are defensible under technical scrutiny, not directional approximation.
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Apply appropriate statistical technique to each analytical question — avoid overfitting simple trend lines to complex operational reality
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Validate data quality and sample sufficiency before drawing conclusions; flag where data volume or quality is insufficient for a reliable signal
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Distinguish correlation from causation explicitly in any diagnostic output — never imply causal relationships the data doesn't support
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XLA & Experience Data Integration
Accountable for: Extending analytics beyond traditional SLA metrics into end-user and digital experience data, in line with the department's XLA maturity direction.
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Integrate digital experience monitoring and sentiment data sources into the analytics view where available for the SDM's accounts
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Build the analytical bridge between traditional operational metrics (MTTR, availability) and experience-based metrics (end-user satisfaction, sentiment trends)
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Support the department's shift toward XLA-informed reporting by piloting experience-data analysis on suitable accounts
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Flag where XLA data collection or tooling gaps limit the department's ability to report on experience-level trends
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Data Model & Source Integrity for Analytics
Accountable for: The structural soundness of the data models and pipelines underpinning analytics
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Define and maintain data models that support analytical use cases (trend analysis, forecasting) rather than only point-in-time reporting
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Coordinate with the Business Analyst to ensure analytics-layer data models remain aligned with reconciled source-of-truth data, avoiding two competing versions of the same metric
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Identify structural data gaps that limit analytical capability (e.g. missing historical data, inconsistent tagging/categorisation) and drive resolution
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Ensure analytical tooling and models comply with data handling, particularly where experience/sentiment data involves personal information
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Advisory Input to the SDM
Accountable for: Translating analytical findings into decision-relevant input for the SDM, not raw technical output.
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Present analytical findings in business-impact terms — what the trend means operationally and commercially, not just the statistical result
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Support the SDM's client service reviews and escalation responses with predictive/diagnostic input where relevant.
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Recommend where deeper analytical investment (tooling, data collection) would materially improve the SDM's risk visibility
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Flag emerging risk patterns proactively to the SDM, even where not specifically requested
COMPETENCIES (KNOWLEDGE, SKILLS AND ATTRIBUTES)
Technical & Domain Competencies
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Statistical & Quantitative Analysis — Working fluency in statistical methods (trend analysis, forecasting, variance analysis, correlation analysis) applied to operational data — not spreadsheet-level approximation
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Data Modelling & Pipeline Design — Ability to design and maintain data models and automated pipelines that support analytical use cases, distinct from simple reporting extraction
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Dashboard & BI Tooling Proficiency — Hands-on capability building and maintaining dashboards and visualisation tools (e.g. Power BI or equivalent) that translate complex data into decision-ready views
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ITIL 4 Continual Improvement Practice — Understanding of how predictive analytics feeds into Continual Improvement and SLM processes, not analytics as a standalone technical exercise
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XLA / Experience Data Literacy — Working knowledge of digital experience monitoring and sentiment analysis, and how experience-level data differs from and complements traditional SLA metrics
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Data Handling Compliance — Understanding of compliance requirements where analytics involve personal or sentiment data, particularly in experience-level analysis
Analytical & Judgement Competencies
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Signal vs Noise Discrimination — Reliably distinguishes genuine risk trends from normal operational variance; does not raise false alarms or miss early warnings
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Correlation vs Causation Discipline — Never implies a causal relationship the data doesn't support; explicit about the limits of what the analysis shows
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Forward-Looking Orientation — Defaults to predictive and diagnostic framing ("what's likely to happen") rather than purely descriptive reporting ("what happened")
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Methodological Defensibility — Structures analysis so that methodology — not just the conclusion — can withstand scrutiny from a data-literate stakeholders
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Data Quality Judgement — Recognises when data volume or quality is insufficient to support a reliable conclusion, and says so rather than presenting a weak signal as strong
Relationship & Influence Competencies
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Technical-to-Business Translation — Converts statistical findings into business-impact language the SDM can act on, without requiring the SDM to interpret raw analytical output
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Cross-Functional Coordination with the Business Analyst — Works closely with the BA to keep analytics-layer data models aligned with reconciled source-of-truth data, avoiding duplicate or conflicting metrics
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Proactive Risk Communication — Surfaces emerging risk patterns to the SDM unprompted, rather than waiting to be asked for analysis
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Influence Through Evidence — Drives adoption of predictive insight into SDM decision-making through the strength and clarity of the analysis, not positional authority
Behavioural Competencies
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Intellectual Rigour — Does not present a directional guess as a statistically supported conclusion; comfortable saying "the data doesn't support that yet"
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Curiosity & Pattern-Seeking — Actively looks for patterns and anomalies in data rather than only analysing what's explicitly requested
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Structured Documentation Habits — Maintains version control and documentation on analytical models and methodology, consistent with departmental audit and governance standards
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Composure Under Technical Challenge — Able to defend methodology calmly and clearly when findings are questioned by technically sceptical stakeholders.
QUALIFICATIONS & EXPERIENCE
Minimum Qualifications
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Bachelor’s degree in data science, Statistics, Computer Science, Information Systems, or a related quantitative field (essential)
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Formal training or certification in a BI/analytics tool (e.g. Power BI, SNOW) — essential
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ITIL 4 Foundation certification — advantageous, given the role's alignment to Continual Improvement and SLM
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Formal statistics, data analytics, or data science certification (e.g. relevant coursework, professional certification) — strongly preferred where not covered by degree specialisation
Minimum Experience
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4–6 years in a Data Analyst, Business Intelligence Analyst, or Service Analytics role, ideally within an IT/Telecommunications or Managed Services environment (essential — pure academic/statistical background without applied service-data experience will require a steeper ramp-up)
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Demonstrated experience building and maintaining dashboards and automated reporting pipelines that reduced manual reporting effort
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Direct experience applying statistical or predictive analysis to operational data (incident trends, SLA performance, capacity data) — not purely descriptive reporting
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Proven ability to identify and communicate early-warning risk signals that were later validated by operational outcomes
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Experience working with large or messy operational datasets, including data quality assessment and structural gap identification
