Elevating Telco Operations Through Data‑Centric AIOps

The backbone challenge: massive data, limited, backwards looking insights

Modern network backbones emit massive volumes of telemetry—logs, traps, metrics, flows, syslog messages and streaming telemetry. Alongside informal sources such as spreadsheets, Visio diagrams, and slide-based data dictionaries.

Large operators often generate tens to hundreds of terabytes every day. Such volumes quickly overwhelm storage and analytics budgets. Industry surveys report that log and observability data volumes have grown 500% over the last three years, driving total observability costs commensurately. To control costs, teams resort to dropping or sampling the majority of raw telemetry. Common sampling configurations keep only 10% of logs and discard the remaining 90%.

This is justifiable, but can create a risk vector. If your event isn’t flagged, it might not be within the sample, and even point in time analysis can become difficult. It is also focused on a retrospective analysis, making trend based, and AI based analytics difficult.

While this approach suits traditional operations paradigms, it breaks down as data volumes accelerate and downtime costs escalate (IT downtime at telcos costing ~ thousands per minute).

This downtime is not inevitable, recent case studies have shown that AIOps is able to cut down on downtime (primarily by reducing MTTR) by ~60%. And models for AIOps are improving on a day-to-day basis, as well. But, without a data foundation capable of feeding AI/ML models, most organizations remain locked into manual, retrospective and deterministic network operations, meaning high mean‑time‑to‑detect (MTTD) and mean‑time‑to‑repair (MTTR), persistent outages and mounting operational costs.

The promise of AIOps, towards proactive Operations

AIOps applies machine learning, statistical inference and large language models to improve IT operations. For network operators, the goals include:

• Faster MTTD / MTTR: predictive models can flag anomalies before they become outages.
• Moving to predictive maintenance: instead of rigid schedules, models recommend maintenance windows based on actual device health.
• Noise reduction & richer context: correlation engines and natural‑language assistants provide summarized root‑cause analyses and highlight affected services, cutting through alert fatigue. They also allow your network engineers to intuitively interact with the data that they are a subject matter expert on.
• Automated remediation: AI‑driven workflows can trigger rollbacks, reconfigurations or ticket creation without human intervention.

AIOps delivers tangible economic benefits. However, these gains are only realized when AI has access to complete and high‑quality data. And data that is in a form performant to the needs of AI (we’ll get back to the role of OLAP, Online Analytical Processing, later in this whitepaper).

What “AIOps‑Ready” Data Looks Like

For AI to move from theory to reality, data must be engineered specifically to address the challenges inherent in high-frequency, high-cardinality analytics and machine learning.

• Unified and complete: telemetry, trouble tickets, configuration files and inventory must live in a single foundation. Fragmented sources cause missed correlations and duplicated effort.
• Time-indexing simplifies pattern recognition and anomaly detection, while managing high-cardinality dimensions (e.g., device, interface, timestamp) through thoughtful partitioning and key design. The data must be modeled coherently to how it is going to be queried.
• Accessible at AI speed: AI workloads require low‑latency, high‑throughput access. Row‑oriented OLTP systems (e.g., Postgres, MongoDB) are optimized for transactions, not analytical scans across billions of records.

Unlike deterministic queries, LLM-based copilots execute iterative, exploratory queries—often 4–20 per task—as they reason toward an answer. A two-minute query becomes a one-second query with proper schema design, cardinality optimization, and OLAP best practices, saving up to 40 minutes per prompt. Across dozens of prompts per operation, these savings compound dramatically. And such savings are absolutely realistic—for more, see Fiveonefour’s heuristic benchmarks: https://github.com/514-labs/LLM-query-test, and ClickHouse’s general performance benchmarks: https://benchmark.clickhouse.com/.

Why Current Systems Fall Short

Telco IT organizations have invested heavily in network management systems, OSS/BSS platforms, CMDBs and log aggregators. Yet these systems are primarily OLTP‑oriented, and accordingly:

• Are perfectly performant for a retrospective use-case (like “retrieve logs related to X machine at y timestamp), where you are dealing with a tiny subset of data that must be retrieved
• Work fine with log parsing and analysis toolsets

But when running the type of analytic queries that you AI tools do (and that your operators will want to do using conversational interfaces), the write-optimized, schema on read approach breaks down, becoming prohibitively slow and operationally costly.

The result is a growing gap between what legacy systems can support and what AI-driven analytics demand.

Data Engineering is the real AIOps Bottleneck

Despite advances in AI, the primary constraint on AIOps success is not model performance but data readiness. Most AIOps initiatives fail to scale because the underlying telemetry and operational data lack the consistency, timeliness, and structure required for analytics at scale.

Building AIOps-ready datasets is fundamentally a data engineering challenge—transforming heterogeneous, high-frequency, high-cardinality streams (common in telco environments) into unified, queryable, and trustworthy analytical data foundations. Three bottlenecks typically stand in the way:

1. Fragmented Ingestion Pipelines
   Data arrives from diverse systems—streaming telemetry, SNMP traps, Kafka topics, CMDB exports, file drops, APIs—each with its own cadence, schema, and reliability.

   Impact: AI models operate on incomplete or stale data, producing misleading predictions.
   Remedy: Implement automated ingestion frameworks capable of handling both real-time (streaming) and near-real-time (CDC, file-based) flows, with built-in lineage and latency monitoring.

2. Unoptimized Data Shape
   Many organizations treat AIOps datasets like logs—stored in document databases or row-based OLTP systems. However, AI and analytical workloads thrive on wide, columnar, time-indexed tables, not nested JSON or sparse key-value pairs.

   Impact: Even simple correlation queries take minutes, and LLM-based copilots—issuing 10–20 exploratory queries per task—become cost-prohibitive.
   Remedy: Adopt OLAP-first schema design: few, wide, strongly typed tables modeled around domain entities (e.g., Device, Interface, Region) to support high-throughput scans and low-latency responses.

3. Limited Observability of the Data Layer
   When pipelines silently fail or data drifts unnoticed, the entire AI stack loses credibility. AIOps cannot infer meaning from missing context.

   Impact: Predictions degrade silently; trust in automation erodes; adoption stalls.
   Remedy: Enforce data contracts, SLAs, and completeness checks—treating data quality as production SLOs for the AI system.

In short, AIOps maturity is gated by data engineering maturity. Without automated ingestion, optimized schema design, and robust observability, even the most advanced AI models will underperform—trapped in proof-of-concept purgatory, unable to deliver production value.

Code‑Defined OLAP stack with Fiveonefour

A code-defined OLAP backbone provides the performance, consistency, and observability that AIOps systems require. FiveOneFour’s MooseStack and Boreal provide a blueprint for an AIOps‑ready backbone. At its core is a code‑defined OLAP stack:

• Columnar analytics engine: MooseStack uses ClickHouse, whose column‑oriented architecture delivers orders of magnitude faster than traditional row‑based databases, document stores and file based log storage; and supports managing tables, materialized views and migrations through code. ClickHouse is optimized for real‑time analytics and can process billions of rows per second.
• Schema management and migrations: MooseStack tracks schemas through version‑controlled migrations, mitigating schema drift and ensuring reproducible analytics pipelines.
• Deployment flexibility: Boreal Cloud offers managed deployments of MooseStack, providing the infrastructure, CI/CD and observability to run analytical backends securely. It connects to existing ClickHouse and Kafka installations or provides a fully managed stack.

Together, MooseStack and Boreal enable telco operators to codify their analytical backbone, accelerate AIOps readiness, and ensure that data pipelines remain performant, reliable, and transparent—forming the foundation on which AI copilots and autonomous agents can operate effectively.

Operational Intelligence with AIOps

Once an AIOps-ready data system is established—unified, normalized, and optimized for analytical performance—organizations can seamlessly integrate their preferred operations copilots or agent interfaces to accelerate value realization.

With a robust OLAP foundation in place, AI copilots transform complex telemetry into actionable intelligence:

• Natural-language interaction: Operators can query complex telemetry in plain language (e.g., “Which region experienced an increase in CRC errors over the past 24 hours?”), eliminating the need for specialized query syntax or SQL knowledge.
• Event summarization and root-cause analysis: AI copilots automatically generate concise incident summaries, correlate metrics, logs, and configurations, and surface probable root causes—significantly reducing investigation time and improving mean time to resolution (MTTR).
• Dynamic visualization: Natural-language interfaces enable the generation of dashboards, anomaly reports, and recommendations on demand, providing immediate visibility into performance trends and emerging issues.
• Predictive analytics and maintenance: By applying machine-learning models to historical and real-time data, these systems forecast potential failures, capacity constraints, or performance degradations, allowing teams to schedule interventions before service impact occurs.
• Autonomous remediation and closed-loop control: Acting on predefined policies, autonomous agents can initiate corrective actions—such as traffic rerouting, device resets, or configuration rollbacks—under human oversight, closing the loop between detection and response.

By combining a code-defined OLAP backbone with AI copilots, network operators gain a powerful framework for real-time anomaly detection, predictive maintenance, and closed-loop automation—transforming operations from reactive response to proactive intelligence.

Actionable steps

The path to AIOps success starts with data readiness. Telco IT leaders should:

1. Consider whether discarding telemetry is still best practice. Instead, evaluate whether investing in scalable, columnar storage to retain full‑fidelity (or greater proportions of the) data.
2. Build AIOps‑ready data systems. Adopt code‑defined OLAP stacks that scale with increasing data production and increasingly heavy demands on data driven by AI.

By shaping data for AI, MSOs can unlock proactive operations, reduce costs and deliver superior network reliability. The combination of a unified analytical backbone and AI copilots positions telco operators to move from reactive monitoring to predictive, AI‑driven operations.