Turn Data into Decisions with Modern Analytics

What is Analytics & Business Intelligence?

Analytics and Business Intelligence (BI) encompass the technologies, practices, and tools that transform raw data into actionable insights for business decision-making. BI provides the dashboards, reports, and visualizations that make data accessible to everyone in an organization—from executives tracking KPIs to analysts exploring trends to operations teams monitoring real-time metrics.

Modern analytics goes beyond static reports. It enables self-service exploration, embedded insights in applications, and increasingly, AI-powered analysis that surfaces patterns and recommendations automatically.

The goal of analytics and BI is to create a data-driven organization where decisions are informed by evidence, everyone has access to the insights they need, and data is trusted across the business.

Why Analytics & BI Matters

In today's competitive environment, organizations that leverage data effectively outperform those that don't. Effective analytics enables:

• Better decisions: Move from gut-feel to evidence-based decision making at all levels of the organization.
• Faster response: Real-time dashboards surface issues and opportunities immediately, enabling rapid response.
• Organizational alignment: Shared metrics and dashboards ensure everyone is working toward the same goals.
• Competitive advantage: Data-driven insights reveal market opportunities, customer needs, and operational improvements.

Modern BI Platforms

The BI market has evolved significantly, with modern platforms offering capabilities far beyond traditional reporting. Today's leading platforms combine powerful analytics with intuitive interfaces, governance, and collaboration features.

Enterprise BI Platforms

The major enterprise platforms each have distinct strengths:

• Looker: Google's platform excels at data modeling through LookML, enabling consistent metrics and strong governance. Ideal for organizations that want to centralize business logic and enable self-service.
• Tableau: Known for powerful visualization and intuitive drag-and-drop interface. Strong for exploratory analysis and creating compelling visual stories.
• Power BI: Microsoft's platform offers excellent integration with the Office ecosystem and strong enterprise features. Popular in organizations already invested in Microsoft technology.

Modern Analytics Tools

Alongside enterprise platforms, modern tools offer lighter-weight, often developer-friendly alternatives:

• Metabase: Open-source platform that's easy to set up and use. Great for teams getting started with self-service analytics.
• Apache Superset: Powerful open-source option with rich visualization capabilities. Good for teams wanting flexibility and customization.
• Sigma: Cloud-native platform that feels like a spreadsheet but queries data warehouses directly. Appeals to users familiar with Excel.

The Semantic Layer

A semantic layer (also called metrics layer or headless BI) sits between your data warehouse and BI tools, defining business logic, metrics, and relationships in one place. It ensures that whether you're viewing a dashboard in Looker, querying via API, or building reports in Excel, you get consistent answers.

Why Semantic Layers Matter

Without a semantic layer, metric definitions live in individual dashboards, reports, and SQL queries. Different teams calculate revenue, churn, or other KPIs differently, leading to conflicting numbers and eroded trust. A semantic layer solves this by:

• Centralizing definitions: Business logic defined once, used everywhere. Change a metric definition and it updates across all reports.
• Enabling consistency: Everyone works from the same numbers, eliminating the "whose data is right?" debates.
• Simplifying access: Business users query metrics without understanding complex SQL or data model details.
• Supporting governance: Access controls, documentation, and lineage are managed centrally.

Semantic Layer Technologies

Several approaches exist for implementing semantic layers:

• LookML (Looker): Looker's modeling language defines dimensions, measures, and relationships. Tightly integrated with Looker's visualization.
• dbt Semantic Layer: Define metrics in dbt and expose them through APIs or BI tools. Brings semantic layer to the transformation layer.
• Cube: Open-source semantic layer with caching, pre-aggregations, and API access. Works with any BI tool.

Dashboard Design Best Practices

Effective dashboards are more than just charts on a page. They tell stories, guide attention, and enable action. Good dashboard design requires understanding both data visualization principles and the needs of the audience.

Design Principles

• Start with the question: Every dashboard should answer specific business questions. Define the purpose before building.
• Prioritize clarity: Choose visualizations that make data easy to understand. Avoid chart junk and unnecessary complexity.
• Enable drill-down: Start with high-level metrics and let users explore details. Overview first, then zoom in.
• Maintain consistency: Use consistent colors, layouts, and conventions across dashboards. Reduce cognitive load.
• Design for the audience: Executive dashboards differ from operational views. Tailor complexity and detail to users.

Common Dashboard Types

• Executive dashboards: High-level KPIs and strategic metrics. Focus on outcomes and trends, not operational detail.
• Operational dashboards: Real-time monitoring of business processes. Enable immediate action on issues.
• Analytical dashboards: Support exploration and analysis. More interactive, with filters and drill-down capabilities.
• Tactical dashboards: Monitor specific initiatives or projects. Track progress against goals.

Self-Service Analytics

Self-service analytics empowers business users to answer their own data questions without depending on analysts or engineers for every request. It's the key to scaling analytics across an organization and enabling true data-driven culture.

Enabling Self-Service

Successful self-service requires more than just deploying a BI tool:

• Curated data models: Pre-built, documented datasets that business users can explore without understanding raw tables.
• Training and enablement: Users need to learn the tools and develop data literacy. Ongoing training programs drive adoption.
• Governance guardrails: Controls that prevent misuse while enabling exploration. Row-level security, approved datasets, and certified metrics.
• Support model: Office hours, documentation, and analytics champions help users succeed.

Common Pitfalls

Self-service initiatives often fail due to:

• Complexity: Exposing raw data models overwhelms users. Pre-aggregated, business-friendly datasets are essential.
• Poor data quality: Users lose trust when they encounter errors or inconsistencies.
• Lack of training: Tools deployed without adequate training gather dust.
• No governance: Without guardrails, self-service leads to proliferating inconsistent metrics and reports.

Embedded Analytics

Embedded analytics brings data insights directly into applications, portals, and workflows where users already work. Rather than switching to a separate BI tool, users see relevant data in context—whether that's a customer viewing usage metrics in your product or an operations team seeing KPIs in their workflow tool.

Use Cases for Embedded Analytics

• Customer-facing analytics: Let customers see their data, usage, and insights within your product. Adds value and stickiness.
• White-labeled reporting: Provide branded reports and dashboards to clients or partners.
• Operational integration: Embed insights into internal tools like CRM, support, or operations systems.
• Data monetization: Sell analytics as a feature or standalone product.

Implementation Approaches

Several approaches exist for embedding analytics:

• BI platform embedding: Looker, Tableau, and Power BI all offer embedding capabilities with SSO, row-level security, and customization.
• Headless BI: Query a semantic layer API and build custom visualizations in your frontend. Maximum flexibility.
• Purpose-built platforms: Tools like Cube Cloud or GoodData are designed specifically for embedded use cases.

Building an Analytics Team

Successful analytics requires the right team structure and skills. The analytics function has evolved from isolated reporting teams to embedded, cross-functional roles that partner closely with the business.

Key Roles

• Data Analyst: Answers business questions, builds dashboards, and supports decision-making. Deep domain knowledge combined with SQL and BI skills.
• Analytics Engineer: Bridges data engineering and analytics. Transforms raw data into clean, modeled datasets using dbt and modern practices.
• BI Developer: Specializes in BI platform development, building complex dashboards, data models, and maintaining the BI infrastructure.
• Data Scientist: Applies statistical methods and machine learning for advanced analytics like prediction, segmentation, and experimentation.
• Analytics Manager: Leads the analytics function, prioritizes work, and ensures analytics delivers business value.

Team Structures

Organizations structure analytics teams differently based on size and needs: centralized teams provide consistency and expertise; embedded analysts sit within business units for domain knowledge; hub-and-spoke models combine both approaches. The right structure depends on your organization's size, maturity, and how data is used across the business.

Why Salt for Analytics & BI?

Salt brings deep expertise in analytics and business intelligence combined with the practical experience to make your analytics initiative successful. Here's what sets us apart:

Platform Expertise: Our team has implemented and optimized analytics on Looker, Tableau, Power BI, and modern alternatives. We bring hands-on experience with semantic layers, embedded analytics, and the full analytics stack.

End-to-End Capability: From strategy to implementation to training, we handle the complete analytics lifecycle. Our Data & AI Pods provide dedicated teams that own your analytics delivery.

Business Value Focus: We don't build dashboards for dashboard's sake. Every engagement starts with understanding your business questions and designing analytics to answer them.

Adoption-First Approach: Beautiful dashboards are worthless if nobody uses them. We prioritize training, change management, and user experience to drive adoption.

SPARK™ Framework: Our SPARK™ framework brings structure to analytics initiatives. Clear phases, quality gates, and transparent communication ensure predictable delivery and stakeholder alignment.

Knowledge Transfer: We build your team's capability alongside the dashboards. Documentation, training, and embedded collaboration ensure your organization can own and evolve analytics independently.

Ready to transform your analytics capabilities? Schedule an analytics assessment with our team to discuss your goals and how Salt can help you build analytics that drives business results.