Only Consistent Cross-Domain Analytics Delivers Business Value

Why is Integrated Analytics so important?

Without integrated analytics that consolidates data from various business domains, it is nearly impossible to deliver meaningful insights or generate measurable impact. There are four commonly recognized types of analytics: 

• Descriptive - what happened, 

• Diagnostic - why does it happen, 

• Predictive - what to expect, 

• Prescriptive - what can be done.

Do you want to know more about types of analytics? Read this post. 

Among these four, only Descriptive analytics can operate effectively with data from a single domain as, in general, it simply aggregates past data, and users perform the main analysis and make decisions mentally.

When the goal is to understand why something occurred (Diagnostic analytics), a broader perspective is required. This involves analyzing historical data across multiple functions to uncover interdependencies and root causes.

For example, Descriptive analytics might show that sales have declined. But to identify the reasons behind this trend, sales data alone is insufficient. We need to examine related domains such as production, inventory, marketing campaigns, changes in product lines, supplier availability, logistics, distribution channels, customer service, etc. In addition, macro-level industry data can enhance this analysis. Comparing our sales performance against competitor trends helps to distinguish internal issues from external market influences.

Predictive analytics takes this a step further. It asks: What is likely to happen next? Here, we need to understand how the business environment is evolving and how internal capabilities can adapt. Predictive models benefit from the same integrated, semantically aligned data structure used in Diagnostic analytics.

However, Prescriptive analytics introduces a new level of complexity. It aims to answer: What should we do? To generate actionable recommendations, we must go beyond internal data and internal understanding of external data. Prescriptive analytics requires understanding how to collaborate or coordinate with external entities - suppliers, partners, regulators, and even competitors. This demands not only data sharing but also semantic alignment across organizational boundaries.

From a Business Value perspective Single Domain Analytics is useful for operational decisions, performance monitoring, and local optimization, while Cross-Domain Analytics supports tactical and strategic decisions, systemic improvements, and early detection of cross-functional risks and opportunities.

Why is Language so important?

When organizations set out to build integrated analytics capabilities, the instinct is often to focus first on data sources, pipelines, and dashboards. While these are necessary components, they are not sufficient. The real starting point is language—specifically, the shared semantics that define how data is understood across the enterprise.

1. Data Without Common Definitions Creates Misalignment

Each function within an organization may define the same term differently.

The same term could have different meaning in different departments:

• What is a "customer"? Marketing may define it as a lead, while finance sees it as a paying account.

• What is "revenue"? One team may include discounts and returns; another may not.

Or the same concept could have different names:

• HR systems might use "Employee ID", while Finance may use "Staff Number" in payroll systems, and in both cases it will be the same identifier.

The same situation we could have with classifications. Sometimes they are very similar or equal  by included items, but have completely different meanings and different usage. And sometimes classification items and names are completely different, but actually mean the same.

Even within Business Domain, but in different locations vocabulary and logic could differ due to different official reporting requirements and regulations.

Without a common vocabulary, attempts to integrate data across domains lead to:

• Conflicting reports

• Poor trust in analytics

• Inefficient reconciliation efforts

2. Semantics Is the Foundation of Data Integration

Before systems can be integrated, the meaning of their contents must be harmonized. This includes:

• Dimensions (e.g., time, product, geography)

• Metrics and KPIs

• Status definitions (e.g., "active", "on hold", "expired")

• Classifications (e.g., customer segments, product categories)

A shared semantic model ensures that data from sales, finance, operations, and customer service can be linked, compared, and analyzed accurately.

3. Cross-Domain Analysis Requires Shared Interpretation

Integrated analytics must answer questions that cross functional lines:

• Why did customer churn increase - was it due to a product change or a shift in the marketing campaign?

• How do supply chain issues affect customer satisfaction and revenue?

These insights require linking events and metrics across different domains. Without shared semantics, these links break or lead to misleading conclusions.

4. External Collaboration Requires Interoperable Semantics

Prescriptive analytics often involves external players—suppliers, distributors, partners. To optimize decisions across organizational boundaries, we must also align our language with theirs:

• Product IDs, delivery status codes, service-level definitions

• Regulatory classifications

• Industry-standard taxonomies

This semantic interoperability becomes critical for integrated planning, forecasting, and joint decision-making.

5. Language Is Stable, Data Structures Change

Data sources evolve. Systems get upgraded. Platforms change. But a well-defined semantic model provides continuity:

• It decouples business meaning from technical implementation

• It ensures long-term consistency in metrics and analytics

• It reduces cost of change when data platforms are modernized

Without consistent semantics integration project is set to fail

According to various studies, up to 70% of complex cross-domain analytics projects fail.

Official reports may list different reasons - high costs, poor data quality, silos, or lack of data literacy among business users. But these are only symptoms. The root issue is the lack of a shared and consistent business vocabulary.

When we start from the data, data architects and engineers begin by exploring datasets, looking for similarities and connection points. Then, they approach business domain specialists, typically one domain at a time, to validate their assumptions. Business experts identify inconsistencies. In response, data specialists try to adjust the models. But because existing models are often already in production, they introduce mapping layers or new transformation pipelines.

This becomes a costly and time-consuming process, requiring high-effort collaboration between both business and data teams.

Unfortunately, the result often falls short of expectations. Models that start with technical assumptions, and are later refined separately by domain, tend to suffer from semantic inconsistencies. The knowledge transfer from business experts to the data team is rarely perfect. Data professionals often model semantics as they understand them, but lacking deep subject-matter expertise, errors and misalignments are common.

The result? Conflicting or inaccurate reports. The data team revisits mappings, pipelines, and business rules, and may make slight improvements, but at a high cost. Eventually, they shift focus to data quality efforts. Data quality improvement is valuable, but it cannot resolve broken semantics. Even perfect data cannot produce reliable analytics if the underlying business meaning is misinterpreted. Worse, if semantic understanding is flawed, data validation rules themselves will be incorrect.

When projects follow this trajectory, business users lose trust. Project costs escalate. After several redesigns, the situation may improve slightly, but rarely enough to justify continued investment. Final reports may state the project failed due to cost, poor data, or lack of business engagement. But the real reason is this:

If you don’t start integrated analytics with semantic alignment, the project is set to fail.