Introduction
In today’s digital-first landscape, organizations are modernizing their Business Intelligence (BI) ecosystems to move beyond static reports and fragmented analytics. The modern data professional no longer just analyzes past performance but actively enables AI-driven, real-time, and predictive decision-making.
Co-authored by experts Sandeep Khemani, Principal PDM - Strategic Partnerships and Industry Value Network, at Google Cloud and Swapnil Srivastava, EVP and Global Head of Analytics at Evalueserve, this paper explores how businesses can modernize their BI strategy by leveraging Google Cloud’s advanced analytics, AI, and data infrastructure. By integrating Looker as the BI platform, BigQuery for scalable data storage, Vertex AI, and Gemini models for predictive analytics and GenAI, along with services like Dataflow, Dataplex, and Pub/Sub for real-time and governed data processing, organizations can build an intelligent, actionable BI ecosystem that enhances decision-making, operational efficiency, and business growth.
Here are the 10 imperatives for BI modernization, powered by Google Cloud’s data and AI capabilities:
01
Reporting Consolidation and KPI Rationalization
Organizations often struggle with dashboard sprawl—too many redundant reports, conflicting KPIs, and inconsistent data interpretations. The key to modernization is streamlining reporting through consolidation and rationalization.
- Looker unifies business metrics with a centralized data model, ensuring KPIs remain consistent across reports and dashboards.
- BigQuery’s federated queries allow businesses to pull data from multiple sources without duplicating datasets, improving data efficiency.
- Dataplex ensures data governance by enabling a single pane of glass for metadata, policies, and security across BI assets.
A leaner, more structured BI framework improves decision-making by eliminating noise and sharpening focus.
Traditional on-prem BI platforms struggle to keep up with the velocity, volume, and variety of modern data. BI modernization requires migration to a scalable, cloud-native architecture that enables real-time analytics.
- BigQuery’s serverless architecture eliminates the need for infrastructure management, allowing organizations to scale seamlessly.
- Dataflow (Apache Beam) enables streaming data pipelines, ensuring real-time data ingestion for up-to-date BI dashboards.
- Cloud Composer (Apache Airflow) automates ETL workflows, making data transformation more efficient and error-free.
02
Platform Migration
03
Enhance Design
An effective BI system is not just about data availability — it’s about usability. Poorly designed dashboards reduce adoption and diminish the value of insights.
- Looker's embedded analytics and API-driven customization allow teams to design intuitive, interactive, and mobile-optimized dashboards.
- Google Data Studio (Looker Studio) enables self-service reporting, reducing dependence on IT and accelerating decision-making.
BI modernization demands a human-centered design approach, ensuring users can easily explore, interpret, and act on insights.
BI must evolve beyond historical analysis to provide foresight-driven insights using AI and machine learning.
- Vertex AI enables automated machine learning (AutoML) for business users, turning descriptive dashboards into predictive engines.
- BigQuery ML allows analysts to build and deploy ML models directly within SQL, enabling seamless model integration into BI workflows.
Shifting from rearview analytics to foresight-driven decision-making is critical for maximizing BI's impact.
04
Descriptive to Predictive
05
What-If Scenarios
Decision-makers need to simulate different business scenarios before making critical choices.
- Scenario Planning: Enable users to adjust variables and predict outcomes dynamically.
- Monte Carlo Simulations: Provide probabilistic risk analysis for complex decisions.
- AI-driven Recommendations: Use reinforcement learning to optimize decision pathways.
By embedding What-If analysis into BI platforms, businesses enhance agility and resilience in uncertain environments.
The next frontier in BI is the integration of Generative AI and Intelligent Agents to act as decision-making assistants.
- Gemini AI can act as a conversational BI assistant, allowing users to query data using natural language prompts.
- Vertex AI Agents integrate with Looker and Google Chat to provide real-time, AI-driven insights for decision-makers.
- Google Cloud Search enables enterprise-wide search across BI reports and documentation.
Embedding GenAI transforms BI from a passive reporting tool to an active decision-making engine.
06
Embed GenAI and Agents
07
Data Quality
Data-driven decisions are only as good as the quality of data. Organizations need to embed data quality metrics into dashboards to ensure trust and accuracy.
- Dataplex automates data governance, ensuring datasets meet compliance and security standards.
- Looker Dashboards can include a Data Quality Scorecard, tracking accuracy, completeness, and consistency of KPIs.
- Data Catalog enhances metadata management, allowing teams to understand data lineage and quality trends over time.
A governed, trusted, and high-quality data foundation is non-negotiable for AI-driven BI. By embedding data quality insights directly within dashboards, organizations can enhance user confidence and drive continuous data improvement.
A well-documented KPI Playbook is essential for consistency, governance, and long-term sustainability.
- Looker's centralized semantic layer enforces standardized KPI definitions across departments.
- Google Cloud’s API-driven approach allows organizations to automate KPI governance and tracking.
By institutionalizing KPI management, BI modernization ensures alignment between analytics and business goals.
08
KPI Playbook
09
User Analytics
Adoption is the key to BI success. Organizations must measure how users interact with dashboards and continuously improve them.
- Looker Usage Analytics provides insights into which reports are accessed, how frequently, and by whom.
- Google Analytics 4 (GA4) for BI tracks user behavior across web-based BI tools, identifying drop-offs and engagement patterns.
- BigQuery’s Audit Logs help identify unused reports, ensuring BI investments remain cost-effective.
BI modernization must demonstrate tangible business impact—from operational efficiency to revenue growth.
- BigQuery FinOps tools allow organizations to track BI cost savings and infrastructure efficiencies.
- AI-driven insights in Looker help measure how BI influences revenue growth, cost reductions, and decision-making speed.
- AutoML in Vertex AI can optimize pricing strategies, marketing spend, and operational efficiencies.
For a deeper discussion on how high-quality insights drive business value, you can read my Forbes article.
10
Drive Business Impact
Inspiring Action, Not Just Informing Decisions
In one of the Harvard Business Review (HBR) issues, we came across the phrase: "KPIs should inspire, not just inform." I wholeheartedly agree. The goal of modern BI should not be limited to presenting numbers—it should motivate action, spark innovation, and create measurable business value.
By leveraging Google Cloud’s advanced analytics, AI, and BI ecosystem, organizations can transform their BI strategies from reporting tools to real-time, AI-powered decision engines.
Are you ready to modernize BI and unlock its full potential with Google Cloud?
About the Authors
Swapnil Srivastava
EVP and Global Head of Analytics at Evalueserve
Sandeep Khemani
Principal PDM - Strategic Partnerships and Industry Value Network at Google Cloud
