Cloud-Based BI: Why Companies Are Migrating in 2026?

In 2025, over 80% of enterprises are projected to rely on Cloud-Based BI for data-driven decision-making, according to a Gartner report.

Yet many organizations still struggle with legacy analytics tools that can’t handle fast-growing data volumes, distributed teams, and real-time insight demands.

This blog explores why Cloud-Based BI is essential for leaders seeking a competitive advantage — plus practical migration strategies and a real-world success example.

With Andolasoft as your trusted partner, you can modernize your analytics and unlock faster value.

Cloud BI vs On-Prem BI What Drives Migration in 2026

Why Cloud-Based BI Matters for Modern Businesses

As data complexity increases, traditional on-prem analytics become roadblocks. Cloud-Based BI helps organizations:

Key Benefits

  • Scalability on Demand: Instantly scale compute and storage — zero hardware upgrades required.
  • Enhanced Data Security: Cloud-native encryption, identity management, and compliance controls.
  • Cost Predictability: Move from CAPEX to OPEX with flexible pay-as-you-grow pricing.

Industries Transforming Through Cloud BI

  • Healthcare: Real-time patient analytics for better outcomes
  • eCommerce: Personalized offers based on live segmentation
  • Logistics: Smart routing with real-time shipment + traffic data
  • Fintech: Continuous fraud detection automation
  • Education: Adaptive learning and student success analytics

Risks of Staying On-Prem

  • Manual reporting → slower decisions
  • Vulnerable security posture
  • Delayed insights → lost revenue
  • Outdated dashboards → poor user experience

Modern BI demands unified data, automated pipelines, and scalable architectures — and the cloud delivers exactly that.

How to Successfully Migrate: Best Practices & Frameworks

Migration requires planning. Use this proven roadmap:

Define Clear Business Objectives

Align KPIs across teams (e.g., reduced churn, faster operations).

Choose the Right Cloud Deployment

Model Best For
Public Cloud Scalability + low maintenance
Private Cloud Highly sensitive data + compliance needs
Hybrid Cloud Balanced security, cost & control

Adopt Modern Architectures

  • Lambda Architecture → real-time + historical data
  • Data Mesh → decentralized data ownership

Build a Modern Data Stack

  • Ingestion: Fivetran, Talend
  • Warehousing: Redshift, BigQuery, Synapse
  • BI Tools: Tableau, Power BI, Looker

Prioritize Governance & Security

  • RBAC, data masking
  • Automated compliance monitoring (GDPR, HIPAA)

Optimize for Performance & Cost

  • Auto-scaling clusters
  • Query caching & materialized views

Enable DevOps + Automation

  • IaC (Terraform / CloudFormation)
  • CI/CD for BI pipelines

Quick Wins

  • Migrate high-impact dashboards first
  • Introduce self-service analytics
  • Automated anomaly detection for data quality

How Andolasoft Accelerates Cloud-Based BI Adoption

We specialize in:

  • Custom Web & Mobile BI Interfaces
  • SaaS Platform Engineering
  • AI/ML-driven predictive analytics
  • Application modernization & data migration
  • DevOps, Cloud & automation services

Our end-to-end support ensures a seamless journey from discovery → deployment → optimization.

Explore BI & Analytics Services (internal link)

Customer Success Story

A leading healthcare provider partnered with Andolasoft to modernize patient analytics.

Impact Achieved in 3 Months

  • 70% faster reporting
  • 40% reduction in readmissions with predictive risk scores
  • Automated ingestion from 10+ EMR systems

Outcomes

  • Clinicians gained anywhere-access to real-time dashboards
  • Leadership achieved full operational visibility
  • IT costs dropped 30% and shifted from maintenance to innovation

Key Takeaways

  • Cloud BI is essential for scalability, security & real-time insights
  • Modern data frameworks (Lambda, Data Mesh) ensure efficiency
  • Strong governance + automation = long-term success
  • Staying on-prem creates avoidable risk and revenue drag
  • Andolasoft delivers expertise to reduce cost, risk, and timelines

FAQs

What is Cloud-Based BI and why is it important?

A cloud-hosted analytics solution delivering real-time insights, scalability, and cost efficiency for modern data demands.

How do I start migrating?

Set measurable goals, select a cloud model, modernize your data stack, and begin with high-value use cases.

Which industries benefit most?

Healthcare, eCommerce, logistics, fintech, and education see significant ROI through automation and predictive analytics.

What security measures are essential?

RBAC, data encryption, compliance automation (GDPR/HIPAA), and regular audits.

Is Cloud BI affordable for small businesses?

Yes — pay-as-you-go pricing makes enterprise-grade analytics accessible.

How does Andolasoft support BI transformation?

We deliver development, data, AI/ML, and DevOps expertise for secure, scalable implementations.

What pitfalls should we avoid?

Big-bang migrations, poor governance, and lack of stakeholder alignment — use a phased, strategic approach.

Top 6 Business Intelligence Service Providers in 2025–2026

In the rapidly evolving digital age, data is more than just a byproduct — it’s a vital asset. Every day, businesses across industries collect enormous volumes of information: operational metrics, customer behaviour data, supply-chain logs, financial performance, and more. However, raw data on its own rarely yields insights. Only when it is properly processed, analyzed, and visualized does it become a weapon for smarter decisions, faster strategies, and improved outcomes. That’s where Business Intelligence Service Providers come in. These firms help organizations design data strategy, build data pipelines, create dashboards, implement analytics, and deliver actionable insights — turning data overload into clarity.

Because BI is no longer optional — it’s essential. The right BI partner equips your business with real-time visibility, predictive insights, trend analysis, and strategic intelligence. As a result, companies using BI effectively gain agility, competitive advantage, better resource utilization, and improved decision-making.

Given this backdrop, we have curated a list of the Top 6 Business Intelligence Service Providers for 2025–2026. We place Andolsoft at #1 (as per your direction), and then cover a mix of global leaders, agile consultancies, and firms with strong presence in India and beyond — such as Mphasis, Persistent Systems, Stefanini, and more. Our goal is to provide a balanced, practical guide to help businesses of all sizes pick the right partner.

Compare Top Business Intelligence Service Providers

How We Evaluated the BI Providers

Before diving into the list, it’s important to understand the selection criteria we used. Not all BI firms are equal, and different businesses have different needs. We looked at the following factors:

  • End-to-end BI capabilities — from data ingestion and ETL, to warehousing, analytics, dashboards, reporting, data governance, and maintenance.
  • Support for modern architectures — cloud BI, hybrid deployments, streaming analytics, real-time dashboards, scalability, and future readiness.
  • Analytics maturity & advanced analytics — including machine learning, AI-driven insights, predictive analytics, and data science capabilities.
  • Industry and domain experience — ability to serve different verticals (finance, retail, manufacturing, healthcare, logistics, etc.) and understand domain-specific data patterns.
  • Track record and credibility — proven client projects, documented success stories, recognized assessments or partnerships.
  • Flexibility, customization & cost-effectiveness — tool-agnostic approach, tailor-made solutions, accelerators/migration support for legacy systems.
  • Data governance, security and compliance — especially crucial for regulated industries and global organizations.
  • Support for long-term growth and data-driven culture — including training, documentation, change management, and scalable architecture.

With these lenses, we crafted the list below.

Top 10 Business Intelligence Service Providers

Andolsoft — Best BI Partner

As the top-ranked firm on our list, Andolsoft delivers comprehensive BI solutions tailored to each organization’s unique needs. They help businesses transform fragmented, siloed data into unified, actionable intelligence — enabling data-driven decision-making across functions.

Why Andolsoft stands out:

  • They design custom, tool-agnostic BI architectures, meaning you’re not locked into a single vendor or platform.
  • They support end-to-end BI lifecycle: data ingestion → cleansing/ETL → warehousing → visualization → automated reporting → analytics → support.
  • Their dashboards and reporting systems are built to support real-time and historical analytics, enabling both daily operations monitoring and trend forecasting.
  • For organizations seeking to embed BI into their culture, Andolsoft offers ongoing support, customization, and scalability.

Hence, Andolsoft is ideal for companies that value flexibility, long-term partnership, and BI systems designed to grow with their business.

Mphasis — Enterprise-Grade BI & Next-Gen Data Analytics

Mphasis, a long-standing Indian IT-services and consulting firm, has evolved its offerings to include advanced data management, analytics, cloud BI, and AI-powered data solutions.

What Mphasis brings to the table:

  • Their Next-Gen Data Services help enterprises migrate from legacy data systems to modern, cloud-based analytics infrastructure — enabling unified data storage, faster queries, and scalable analytics.
  • Mphasis invests in AI and machine-learning through platforms like DeepInsights™, enabling cognitive analytics, intelligent data extraction from unstructured sources (documents, PDFs, images), and advanced decision support.
  • For businesses dealing with high data complexity — multiple sources, structured and unstructured data, streaming data, hybrid workloads — Mphasis offers end-to-end data strategy, governance, warehousing, and analytics under one umbrella.
  • Their services cover a wide range of industries (finance, logistics, telecom, insurance, retail), making them a reliable partner for large enterprises with diverse needs.

Best for: Large enterprises or organizations undergoing digital transformation, dealing with complex legacy data systems, hybrid cloud environments, or requiring both BI and AI-enabled analytics.

Persistent Systems — Modern Data Stack & Analytics Modernization

Persistent Systems is another strong player, especially for companies looking to modernize their data stack or migrate from legacy reporting platforms. Based in Pune, India, the firm offers comprehensive data and analytics services including cloud migration, data governance, data science, BI modernization, and more.

Key strengths of Persistent Systems:

  • Their Data & Analytics Advisory practice helps companies define data strategy, implement governance, and align analytics with business objectives — ensuring data becomes a strategic asset, not just an operational tool.
  • They offer data stack modernization, enabling migration to modern cloud data warehouses or data lakes; they support tools like Snowflake and Databricks while also offering master data management, data cataloging, and BI modernization.
  • Their data science and ML capabilities let clients move beyond reporting and dashboards — into predictive analytics, AI-driven insights, data monetization, and data-driven products/services.
  • The firm has a robust partner ecosystem and proven track record across industries, making it adaptable for mid-market businesses as well as larger enterprises.

Best for: Organizations transitioning from legacy BI/reporting systems to modern cloud-based BI, or those seeking to embed analytics and data governance into their core operations.

Stefanini — Strategic BI with Data Governance & Analytics Services

Stefanini is a global technology consultancy that offers a broad range of digital services, among which data analytics, data science, BI consulting, and data-driven transformation stand out.

What makes Stefanini special:

  • They combine data strategy consulting + technical implementation + business alignment. In other words, they don’t just create dashboards — they help you build a data culture, align analytics with business goals, and ensure data-driven decisions across departments.
  • Their services include data architecture, data governance frameworks, data engineering, BI and reporting, data science & ML — enabling a full-lifecycle analytics approach.
  • With a global presence and experience across industries like manufacturing, consumer goods, finance, and services, they can serve multinational operations with diverse, distributed data sources.
  • Stefanini’s emphasis on cloud enablement, hybrid infrastructure, automation, and security ensures that BI is not only functional but also scalable, robust, and future-ready.

Best for: Organizations aiming for a strategic, governed, enterprise-wide BI rollout — especially those needing compliance, cross-functional analytics, and long-term data governance.

Agile & AI-First BI Consultancies (Emvigo, Specialist Firms, etc.)

Beyond large IT firms and global consultancies, the BI landscape includes nimble, agile consultancies and BI specialists who offer rapid deployment, flexible solutions, and cost-efficient services. These firms are especially relevant for startups, mid-size businesses, or companies with evolving data needs.

Why they matter:

  • They often deliver quick time-to-insight, with faster setup of dashboards, reporting, and analytics. This speed helps businesses test BI use-cases, iterate quickly, and scale gradually.
  • They tend to be tool-agnostic and flexible — able to integrate with cloud data lakes, hybrid data sources, and modern analytics stacks without enforcing a rigid infrastructure.
  • For companies needing predictive analytics, custom reports, embedded analytics, or AI-based insights without major infrastructure overhaul — such consultancies provide cost-effective BI transformation.

Best for: Startups, growth-stage companies, and mid-size enterprises looking for flexible, scalable, and affordable BI solutions without committing to heavy upfront investment.

Legacy & Global Consulting Firms – Enterprise-Scale BI

Large, global consultancies and legacy BI players remain relevant in 2025–2026 — especially for enterprises with complex data landscapes, global operations, regulated industries, and compliance needs. These firms offer broad domain expertise, rigorous data governance, and enterprise-grade BI deployments.

Advantages of legacy/global firms:

  • They bring stability, compliance support, regulatory readiness, and global delivery capabilities.
  • Their teams often include experts in data architecture, data warehousing, compliance, security, cloud migrations, and cross-region data governance.
  • They serve enterprises needing full-scale BI deployments, covering multiple business units, geographies, and compliance requirements.

Best for: Multinational corporations, regulated industries (finance, healthcare), and organizations requiring extensive governance, security, and enterprise-wide BI architecture.

How to Choose the Right Business Intelligence Service Provider for You

Selecting the “right” BI partner depends heavily on where your organization stands today — and where you want to go tomorrow. Here’s a practical checklist to help you evaluate potential partners:

  • Assess Current Data & Infrastructure Readiness
    • Do you have a data warehouse, data lake, or only spreadsheets?
    • Are your data sources scattered, structured, semi-structured, or unstructured?
    • Do you need real-time data ingestion or batch data processing?
  • Define Your BI Goals & Use Cases
    • What do you need — dashboards, reporting, real-time monitoring, predictive analytics, data governance, cloud migration, or AI/ML insights?
    • Which business functions should benefit — sales, finance, operations, HR, etc.?
  • Match Provider’s Strength to Your Needs
    • For custom, scalable, long-term BI: choose flexible, full-service providers (e.g., Andolsoft, Persistent, Stefanini).
    • For rapid deployment and cost-effectiveness: consider agile BI consultancies or smaller specialist firms.
    • For enterprise-grade governance and compliance: global/legacy firms win.
  • Check Tool & Technology Compatibility
    • Ensure providers support your preferred platforms: cloud BI (Snowflake, AWS, Azure), BI tools (Power BI, Tableau, Looker, etc.), real-time streaming (Kafka, Spark), data science & ML tools.
    • Confirm they support data governance, data security, compliance standards relevant to your industry.
  • Review Track Record & Domain Experience
    • Look for case studies, client testimonials, vertical-specific implementations.
    • Prefer providers with prior experience in your industry or similar scale & complexity.
  • Consider Cost, Timeline & Scalability
    • Balance cost vs value: cheaper providers may suit small-scale BI needs; bigger firms might deliver more robust long-term value.
    • Check how easily you can scale BI as data volume grows, or as you add new data sources.
  • Plan for Culture & Change Management
    • BI success requires more than technology — it’s about adoption, data culture, user training, consistent workflows, and management support.
    • Choose a provider who offers training, documentation, and long-term support.

Benefits of Working with the Right BI Provider

Partnering with a good Business Intelligence Service Provider can bring multiple benefits:

  • Accelerated decision-making — with real-time dashboards and consolidated data, leaders get timely insights.
  • Improved operational efficiency — BI helps highlight inefficiencies, drive process improvements, reduce costs.
  • Better forecasting and strategic planning — with historical data, predictive analytics, and trend analysis.
  • Enhanced data governance and compliance — especially useful for regulated industries.
  • Scalability — as the business grows, BI platforms and data architecture scale along, avoiding bottlenecks.
  • Data-driven culture — empowers teams across departments to make informed, data-backed decisions.

Ultimately, the ROI from a well-implemented BI solution — whether in productivity, cost-savings, or strategic advantage — often outweighs the investment many times over.

Final Thoughts

In 2025–2026, the role of data and analytics in business decision-making will only grow stronger. Companies that harness data effectively will lead markets; those that ignore it risk falling behind.

The firms above — from agile, AI-first consultancies to enterprise-scale global players — represent the best of what BI consulting has to offer today. By carefully matching your business needs, data maturity, and strategic goals with the right provider, you can build a robust, scalable, and future-ready BI foundation.

If you seek flexibility and customization, consider Andolsoft. If you manage complex enterprise data across legacy systems and cloud, firms like Mphasis, Persistent Systems, or Stefanini may be better fits. For smaller companies or quick deployments, agile consultancies and BI-specialist firms offer speed and cost-effectiveness.

Whatever your choice — remember: data without insight is just noise. The right BI provider turns noise into clarity, confusion into strategy, and data into decisions.

FAQs

Here are some frequently asked questions about choosing and working with BI providers:

1. What exactly do Business Intelligence Service Providers do?

They help collect, clean, integrate, store, analyze, and visualize data — turning raw data into actionable insights. This includes building data warehouses or lakes, ETL pipelines, dashboards, reporting systems, and analytics models.

2. Which industries benefit most from BI?

Virtually all industries — finance, retail, healthcare, manufacturing, logistics, telecom, tech, services — benefit. BI helps with operational visibility, customer analytics, forecasting, risk management, and strategic planning.

3. How long does a full BI implementation take?

It varies. A basic BI deployment with dashboards might take 1–3 months. A full-scale enterprise BI rollout — including data migration, architecture overhaul, analytics, governance — could take 4–9 months or longer, depending on complexity.

4. Does BI always require cloud infrastructure?

Not always. Some business intelligence solutions can run on-premise. However, cloud BI is increasingly popular because it offers scalability, flexibility, easier data collaboration, and lower infrastructure overhead.

5. Can BI providers help with unstructured data (e.g. documents, images)?

Yes. Leading providers increasingly support unstructured data analytics, leveraging tools like AI/ML, natural language processing (NLP), cognitive computing to extract insights from documents, images, logs, social media, etc.

6. What BI tools are commonly used?

Popular tools include Power BI, Tableau, Looker, Qlik, Sisense, Domo, along with true cloud data platforms like Snowflake, Databricks, AWS/Azure/GCP data stacks, streaming tools (Kafka/Spark), and custom ML/AI solutions.

7. What’s the difference between BI and advanced analytics/data science?

Business intelligence traditionally covers descriptive analytics — reporting, dashboards, historical trend visualization. Advanced analytics and data science add predictive, prescriptive analytics, machine-learning models, forecasting, anomaly detection, pattern recognition — going beyond “what happened” to “why it happened” and “what will happen.”

8. How do I know when my business needs BI?

If you face data overload, multiple data sources, delayed or inconsistent reporting, manual spreadsheets, lack of insight-driven decisions, or want better forecasting — it’s time to adopt BI.

9. What makes a great BI partner?

A great partner offers end-to-end services, aligns with your business goals, supports scalability, handles data governance, delivers clean and user-friendly dashboards, ensures timely support/training — and adapts as your data needs evolve.

10. Does BI adoption guarantee success?

BI is a tool — its success depends on data quality, adoption by teams, consistent governance, and using insights in decision-making. With the right strategy and partner, BI greatly increases chances of success.

Dual-Axis Line Chart Plugin for Superset 4.1: Unlock Dual Perspectives in Dashboards

In a data-driven world, organizations are not looking for more dashboards — they are looking for deeper clarity. A single metric rarely tells the complete story:

  • Revenue must be evaluated against cost
  • Application usage must be compared to latency
  • Inventory must be analyzed alongside demand

Yet, BI tools often restrict how many perspectives can be visualized together.

That’s the gap our Dual-Axis Line Chart Superset plugin solves.

Why Dual-Axis Visualization Matters More Than Ever

High-growth teams in fintech, NBFCs, SaaS, logistics, and manufacturing rely on understanding how metrics influence each other.

A Dual-Axis Line Chart provides:

  • Two independent Y-axes
  • Clear comparison of two metric trends
  • Distortion-free visual correlation
  • A single chart that narrates multi-layered insights

However, Superset does not provide a native dual-axis line chart. So — we built one.

Dual-Axis Plugin for Superset Key Capabilities

Introducing Our Dual-Axis Line Chart Plugin for Superset

Built using ECharts and fully integrated with Superset 4.1, our plugin enables:

  • Seamless native behavior in dashboards
  • Visualization at scale with high performance
  • Accurate multi-axis comparison for analytics teams

It’s engineered for enterprise BI — without compromising Superset’s performance model.

Crafted Using ECharts for Maximum Flexibility

ECharts powers advanced visual control including:

  • Smooth animated transitions
  • Independent Y-axis formatting (currency, %, units)
  • Custom theming to match your brand
  • Tooltip synchronization
  • Dynamic legend interactions

Just drop it into your dashboard — no manual configuration required.

Built for Superset 4.1 and Beyond

This Dual-Axis Line Chart Superset plugin:

  • Works natively with Superset’s latest plugin architecture
  • Adapts to filters and cross-filters
  • Supports all standard query APIs
  • Efficiently handles large datasets
    • Remains compatible with Superset 6.x visualization roadmap

Your analytics experience stays future-ready.

Perfect for NBFC & Financial Analytics

NBFCs rely heavily on correlated metric intelligence.

This plugin enables powerful financial comparisons like:

Metric Compared With
Loan Disbursement Collections Trend
Portfolio Growth Risk Exposure
Overdue Ratio Receivables
Customer Acquisition CAC
Interest Income Operational Cost

Compliance + risk analysis become faster and more accurate.

Easy Deployment for Enterprise Environments

We ship everything needed for smooth deployment:

  • Packaged plugin bundle
  • Optional CI/CD automation scripts
  • Full DevOps documentation
  • Compatibility validation with your Superset setup

Supported setups include:

  • Docker/Kubernetes
  • Multi-instance Superset clusters
  • Cloud-native deployments
  • Secure, air-gapped environments

Install → Restart → Enable → Start visualizing multi-axis insights

Why This Dual-Axis Plugin Matters for Your Organization

Better insight leads to better decisions:

  • Reduces dashboard clutter
  • Strengthens executive reporting
  • Enhances decision-making clarity
  • Unlocks BI capabilities beyond native Superset
  • Supports multi-dimensional data narratives

Your teams shift from reactive analytics to proactive strategy.

More Superset Possibilities for Your Enterprise

This plugin is just the beginning. We help enterprises build:

  • Custom visualization plugins
  • Domain-specific KPI widgets
  • Industry-focused dashboards
  • Superset upgrades & migrations
  • Advanced analytics UI experiences

If Superset doesn’t support it — we will engineer it.

Bring Multi-Axis Intelligence to Your Dashboards

Your data has more stories.

Moreover, your dashboards deserve richer intelligence.

Your leaders need clear, contextual insights.

With our Dual-Axis Line Chart Superset plugin, you deliver exactly that.

The Role of Embedded BI in Enhancing SaaS Products

In a world where 90% of enterprise data goes underutilized, Embedded BI in Enhancing SaaS Products is no longer optional — it’s essential. Today’s customers demand real-time insights, personalized dashboards, and seamless analytics baked right into the applications they use every day. By embedding Business Intelligence (BI) directly into your SaaS offerings, you unlock higher user engagement, drive stickiness, and differentiate your product.

In this blog, you’ll learn why Embedded BI matters for SaaS leaders, how it solves industry pain points from healthcare to logistics, and the best practices for implementation. We’ll also share a real-world mini case study that underscores measurable gains. As a trusted technology partner, Andolasoft brings deep expertise in custom web development, SaaS product engineering, BI, AI, cloud, and DevOps. Let’s dive into why Embedded BI in Enhancing SaaS Products can be your next strategic advantage.

Key Benefits of Embedded BI for Modern SaaS

Business Need & Importance of Embedded BI

Embedding BI capabilities directly into SaaS products has moved from “nice-to-have” to “must-have” for modern enterprises.

Here’s why:

  • Rapid data democratization demands self-service analytics across roles. Development teams, product managers, and business leaders need insights without toggling apps.
  • Legacy systems create data silos, leading to inefficient decision-making, security vulnerabilities, and frustrated users. A report by Gartner reveals that siloed analytics can incur a 20% productivity loss.
  • Industries such as healthcare require compliance-ready dashboards to monitor patient outcomes in real time. eCommerce companies need embedded sales trend analysis to optimize promotions on the fly. Logistics providers rely on shipment analytics to reduce delays, while fintech platforms embed risk models for instant alerts.

Without strategic BI integration, companies face inefficiencies, increased security risks, and revenue leakage from poor customer experiences. In manufacturing, delayed insights can halt production lines. In education technology, slow reporting erodes instructor trust.

Rather than piecing together legacy point solutions, progressive organizations invest in Embedded BI to ensure seamless, scalable analytics. By partnering with experts, you mitigate technical debt, strengthen data governance, and future-proof your SaaS offerings.

Best Practices, Frameworks & Actionable Tips

Implementing Embedded BI in Enhancing SaaS Products requires a holistic, future-ready approach. Below are practical steps, frameworks, and tips:

Define Clear Use Cases

  • Start by mapping primary user personas and their analytics needs (e.g., CFOs want financial KPIs, marketers need campaign dashboards).
  • Prioritize high-value scenarios that deliver quick ROI and drive user adoption.

Select the Right BI Architecture

  • Choose between embedded widgets, iFrames, or SDK-based integrations based on performance and customization needs.
  • Adopt a microservices-based architecture to decouple analytics services from core business logic, enhancing maintainability.

Ensure Data Readiness & Governance

  • Establish a centralized data warehouse or lakehouse to maintain a single source of truth.
  • Implement robust data governance policies, including encryption, role-based access, and audit trails, to meet compliance.

Optimize for Performance & Scalability

  • Leverage in-memory data processing engines or columnar databases for sub-second query responses under heavy loads.
  • Utilize auto-scaling cloud infrastructure (e.g., Kubernetes, AWS Lambda) to handle surges in user activity without downtime.

Embed User-Centric Dashboards

  • Adopt responsive design techniques to ensure dashboards render flawlessly across web and mobile.
  • Provide customizable widgets and drag-and-drop builders so end users can tailor insights to their workflows.

Prioritize Security & Compliance

  • Enforce multi-tenant data isolation and encryption at rest/in-transit.
  • Align with industry standards such as HIPAA for healthcare or PCI DSS for fintech.

Integrate AI & Machine Learning

  • Incorporate predictive analytics modules to forecast trends, anomalies, or churn probabilities directly within the UI.
  • Use AutoML pipelines for continuous model retraining, ensuring your insights stay relevant.

Follow Agile & DevOps Practices

  • Implement CI/CD for your BI components to roll out features rapidly and with minimal risk.
  • Monitor performance through automated observability tools, enabling proactive issue resolution.

Avoid Common Pitfalls

  • Don’t overload users with data: focus on actionable metrics.
  • Avoid hard-coding visualizations; instead, use dynamic query builders for flexibility.
  • Refrain from monolithic BI platforms that hamper agility—favor modular solutions for easy upgrades.

Achieve Quick Wins

  • Integrate single KPI widgets on high-traffic pages to showcase immediate value.
  • Roll out mobile-friendly reports for on-the-go decision makers, delivering early user delight.

How Andolasoft Helps You Execute

  • Custom Web Development: Tailor embedded analytics interfaces with modern JavaScript frameworks.
  • Mobile App Development: Craft native or cross-platform mobile dashboards for executives on the move.
  • SaaS Product Engineering: Build multi-tenant, scalable BI modules aligned with your roadmap.
  • BI, AI & Machine Learning Solutions: Embed predictive analytics to uncover hidden patterns.
  • Data Analytics: Establish clean data pipelines and warehousing for reliable insights.
  • Application Modernization: Migrate legacy reporting tools to modern, cloud-native architectures.
  • Enterprise IT Services: Ensure compliance, data governance, and hybrid-cloud strategies.
  • DevOps, Cloud & Automation: Enable rapid, risk-free deployments with automated testing and monitoring.

By selecting Andolasoft as your development partner, you leverage a proven delivery framework, cross-industry expertise, and a collaborative approach that ensures Embedded BI in Enhancing SaaS Products delivers measurable business value.

Customer Success Example

For example, FinSecure, a leading fintech company, partnered with Andolasoft to build embedded compliance dashboards. Within just four months, they achieved:

  • 60% faster report generation through in-memory analytics optimization.
  • A 45% reduction in manual audit preparation by automating compliance checks.
  • 30% higher user engagement as customers accessed real-time risk scores directly in their portal.

Here’s what changed: FinSecure’s product team replaced static, PDF-based reports with interactive visualizations.

End users could drill down into transaction anomalies, set custom alerts, and share insights seamlessly.

The result was improved customer satisfaction, reduced support tickets, and a clear competitive edge in a crowded market.

Key Takeaways & Closing

  • Embedded BI in Enhancing SaaS Products transforms data into actionable insights that drive user engagement and revenue growth.
  • Modern analytics architectures, coupled with AI-powered models, deliver real-time, predictive capabilities at scale.
  • Avoid data silos and legacy patchwork by adopting a strategic, modular approach to BI integration.
  • Prioritize user-centric dashboards, robust security, and automated DevOps workflows to ensure performance and compliance.
  • Partnering with Andolasoft means gaining end-to-end expertise — from data engineering to cloud-native deployments — for lasting, measurable impact.

Adopting Embedded BI in Enhancing SaaS Products now positions your organization for sustainable growth, stronger customer loyalty, and data-driven decision-making. Let’s turn your analytics vision into reality.

FAQs

What is Embedded BI in Enhancing SaaS Products?

Embedded BI involves integrating analytics capabilities directly into SaaS applications, enabling users to access dashboards, reports, and insights without leaving the product interface.

How does Embedded BI boost user engagement?

By offering real-time, context-specific insights, Embedded BI keeps users within the application, reduces context switching, and increases feature adoption and customer stickiness.

Which industries benefit most from Embedded BI integration?

Healthcare, fintech, eCommerce, logistics, manufacturing, and education all leverage Embedded BI for compliance dashboards, predictive maintenance, sales trend analysis, and personalized learning analytics.

What security measures are critical for Embedded BI?

Key practices include multi-tenant data isolation, encryption at rest/in-transit, role-based access controls, and adherence to industry standards like HIPAA or PCI DSS.

How long does it take to implement Embedded BI?

Implementation timelines vary, but organizations can often achieve “quick wins” within 4–6 weeks by embedding core KPI widgets, followed by full rollouts in 3–6 months.

What are common pitfalls when embedding BI in SaaS?

Overloading users with too much data, hard-coding dashboards, and relying on monolithic BI platforms can hamper flexibility, scalability, and user adoption

Why partner with Andolasoft for Embedded BI?

Andolasoft offers end-to-end expertise — from data pipeline design to cloud-native DevOps — ensuring your embedded analytics solution is scalable, secure, and aligned with business objectives.

Scalable Mobile App Development for Business Growth

Mobile presence is a must-have tool for businesses to succeed in today’s market. It is an integral part of today’s web presence. Be it for building a new application for your startup or scaling an existing app, your business needs app scalability for long-term success.

As your use base grows you need to scale your app to accommodate the new users. If your app is not designed for scalability your application will face various performance and security issues which will impact your user experience.

To ensure that your latest mobile app development handles the increased number of users, requests, and data we will explore the principles and best practices for building scalable mobile apps that can stand the test of time.

Scalable Mobile App Development for Business Growth

What is Scalability in Mobile App Development?

Scalability is the ability of the app to handle an increasing number of users, requests, and data while at the same time maintaining performance and functionality.

A scalable mobile app development is designed to grow seamlessly along with the growing business. It can easily scale up in complexity and traffic without compromising its speed, reliability, and user experience.

In simple terms, scalability suggests that the applications’ user base and features expand so that the infrastructure can handle the increased load without slowing down the performance.

Scalability is not just about adding more resources, it’s about facilitating your app to perform efficiently and effectively as your business and user needs evolve.

Key Principles of Building Scalable Mobile Apps

1. Modular Architecture

The first thing to do to ensure a scalable mobile app is to build the app in modular architecture. This modular approach involves breaking down the app into smaller and more independent components. In this process, each model can be developed independently and when integrated together the complete app works as a single entity.

This process also ensures that you can scale up or down the application easily simply by adding or reducing the module in the app.  For example, If the payment system becomes more complex, you can scale it independently without affecting the rest of the application.

When developing the backend of the application you should use microservices where each service is responsible for the specific task and it can be scaled independently.

2. Optimize Performance Early On

Performance optimization is another important aspect of the mobile app development process. To achieve a high-performance mobile app you need to focus on both frontend and backend efficiency. You should also ensure that the app user interface is responsive and loads faster even with limited resources.

Backend development best practices include optimizing database queries, caching data, and reducing server load. You should optimize your images, videos, and other media files to reduce the size. You should also consider implementing lazy loading and minimizing network requests by implementing data transfer protocols such as HTTPS.

3. Cloud Infrastructure and Scalability

Implement cloud platforms such as Amazon Web Services(AWS), Microsoft Azure, and Google Cloud for cost-effective solutions. This way you scale resources on-demand which can ensure that your app handles traffic spikes without downgrading performance.

Choose a cloud service that enables your application resources to efficiently distribute the traffic and access multiple servers. They can also provide tools for monitoring, analytics, and automated scaling.

You must choose a cloud solution with auto-scaling features and leverage content delivery networks to reduce latency for users.

4. Use a Robust Database System

Along with your mobile app, the amount of data you need to manage also increases. Using the right database is essential for ensuring that your application can scale effectively.

The database should be capable of handling large amounts of data and delivering fast read and write operations even when your user base expands.

There are two primary types of databases:

  • Relational (SQL)
  • Non-Relational (NoSQL)
  • SQL databases are perfect for structured data with relationships
  • NoSQL databases excel at handling large volumes of unstructured data

When you choose a database you should consider your application’s data requirements such as how much data it needs to store how often data can change and how you will query it.

Best Practice:

  • When you have an app with structured data and complex queries you should use a SQL database such as PostgreSQL and MySQL.
  • When you have applications with large amounts of unstructured and semi-structured data you should use a NoSQL database such as MongoDB and Cassandra.
  • You should implement database sharding i.e. to split data across multiple databases, this will improve performance and scalability.

5. Implement Caching Mechanisms

Caching is a powerful technique to improve the performance and scalability of the application. By storing these data in memory, you can reduce the load on the server and database which enables the app to serve content faster.

It also helps you minimize the need for repeated requests and improves the overall user experience.

There are multiple styles of caching mechanisms that you can implement including server-side caching, client-side caching, and reverse proxy caching.

Choosing the right types of caching depends on the application’s architecture and data access patterns.

Best Practice:

  • Use in-memory caching systems such as Redis or Mem-cached for fast access to frequently requested data.
  • You can also cache static content such as images and JSON responses. On the client side, you can use the technologies such as service workers and local storage.
  • You can set the right expiration times for cached data in order to ensure that the users get the most up-to-date information.

6. Asynchronous and Background Processing

Handle long-running tasks and processes, as it is essential for maintaining a responsive and scalable mobile app.

If your app needs features that involve data processing, notifications, and 3rd-party integrations, it is essential for the app to be able to process these tasks in the background so that they don’t block the main thread or user interactions.

Asynchronous processing can enable your app to continue functioning normally, even when handling resource-intensive tasks. You can process user requests in the background and notify users once the task is completed.

Best Practice:

  • Enable message queues like RabbitMQ or Apache Kafka to handle background tasks.
  • For mobile applications, you can utilize background processing frameworks such as Firebase Cloud Functions and Native background services for iOS and Android.

7. Plan for Load Balancing and Redundancy

Ensure that your app traffic is evenly distributed. Load balancing is one such feature that lets your application scale up where traffic is evenly distributed across multiple servers. This can prevent any single server from becoming overwhelmed. Redundancy is also important for ensuring high availability.

The best practices include integrating services like AWS Elastic Load Balancer and Nginx. You should also implement database replication and backup strategies which will ensure that the data you implement is safe across multiple servers and locations.

8. Prioritize Security at Scale

When it comes to mobile app scalability, security is the primary concern. When the application grows it brings in a ton of users as well. This also makes your app attractive to cyberattacks. Implement authentication, encryption, and authorization mechanisms to secure app data and user interactions.

Use authentication such as OAuth, two-factor authentication, and single sign-on. Also, encrypt data using protocols such as HTTPS and AES.

Best Practices for Scaling Mobile Apps Effectively

  • Plan for Future Growth

Plan by choosing scalable technologies and tools from the beginning. You should keep the app’s long-term vision and choose a platform that can accommodate future growth.

  • Use Analytics and Monitoring Tools

Monitoring the application’s performance and user behavior is important for identifying the bottlenecks, errors, and areas for improvement. You can use tools such as Google Analytics, Firebase Analytics, and New Relic to help you track key metrics and respond proactively.

  • Build for Offline Capabilities

You need users who will have a reliable internet connection. Ensure a smooth experience by implementing offline capabilities such as local data storage, caching, and sync functionalities.

  • Test at Scale

Before you launch the application you should test its performance under high load to ensure that it can take on the traffic spikes. Implement tools such as LoadRunner, Apache JMeter, or BlazeMeter to simulate heavy traffic and identify potential issues.

  • Consider Cross-Platform Development

Support both iOS and Android, by using cross-platform frameworks such as Flutter, React Native, and Xamarin.

These frameworks enable you to write most of your code once and deploy it on both platforms which saves time and resources while maintaining scalability.

Conclusion

Building a scalable mobile app requires careful planning, and for that, you need the right technology stack for which you need to adhere to its best practices.

Following principles such as modular architecture, optimizing performance, and using cloud infrastructure can leverage caching. You can ensure that the app will handle increasing traffic, data, and user demands.

Security, load balancing, and background processing are essential for building an app that is reliable and efficient to grow.

Security, load balancing, and background processing are also essential to building an app that remains reliable and efficient as it grows. Scalability is about future-proofing your mobile app.

When you build the application with scalability in mind, you can ensure that the app can evolve alongside the business. This provides a seamless experience for users and meets the demands of the market.

How Scalable Infrastructure Drive Transformation?

Digital transformation is not just a buzzword it’s a necessity for businesses that want to stay competitive and outshine in today’s fast-paced digital era.

The need for agility, speed, and innovation has never been higher, and at its center lies scalable infrastructure. Digital transformation is not just about adopting a new technology but about creating a foundation that can adapt, grow, and evolve with the demands of the market.

Without scalable infrastructure, even the best digital strategy can falter, thereby leading to inefficiency and bottlenecks. But what does it take to build such an infrastructure? And how can organizations future-proof the operation?

Scalable infrastructure is more than just a concept, it’s a strategic necessity that enables businesses to thrive in an ever-changing environment. It empowers businesses to process large amounts of data, deploy apps, and deliver a seamless experience to customers.

In this blog, we will explore what is scalable infrastructure, its role in digital transformation, and the key components that make it successful.

We will also dive into actionable strategies and emerging trends that can help your businesses stay ahead in the competitive world.

What is Scalable Infrastructure?

Scalable infrastructure refers to an organization’s ability to handle increased workloads and continue to grow without compromising performance.

It’s not just about adding more servers or storage; it’s about designing a system that can expand seamlessly even while maintaining efficiency, reliability, and cost-effectiveness.

Why Scalability is Crucial for Digital Transformation?

Digital transformation involves adopting new technologies, changing processes, and delivering top-notch customer experiences. Here are a few reasons why scalability is needed.

1. Handling Increased Data Volumes:

Digital transformation involves integrating IoT devices, creating big data analytics, and using AI to manage data. Scalable infrastructure ensures that this data is processed and stored efficiently.

2. Supporting Business Growth:

As businesses expand, their IT requirements also increase. This is why scalable systems need to grow alongside the organizations to eliminate the need for frequent overhauls.

3. Enhancing Customer Experience:

Business scalability also ensures consistent performance even during traffic spikes. It furthermore results in a seamless user experience.

4 . Cost Optimization:

Scalable systems enable businesses to pay only for what they use which significantly reduces waste and optimizes budgets.

 Key Components of a Scalable Infrastructure
Key Components of a Scalable Infrastructure

To build a scalable infrastructure you need more than just a one-size-fits-all approach. It needs a combination of technologies, best practices, and strategic planning.

1. Cloud Computing

The cloud is the most important aspect of scalable infrastructure. By leveraging cloud services, businesses can achieve the following.

  • Scale resources based on demand.
  • Reduce capital expenditures.
  • Improve performance and reliability.

Cloud providers like AWS and Microsoft Azure offer a range of services customized to different scalability needs.

2. Microservices Architecture

Microservices is one of the latest in IT technologies, and businesses are shifting to microservices instead of building monolithic apps. These modular components can developed, deployed, and scaled independently which makes them ideal for handling fast growth.

3. Containerization and Orchestration

By using technologies such as Docker and Kubernetes businesses can make scalability a game-changer. These container package applications ensure that they run consistently across various environments. You can take a step further by automating deployment, and container management.

4. Elastic Storage Solutions

Data storage needs to evolve with growth. Storage solutions such as distributed file systems can ensure that businesses can store and retrieve data efficiently no matter how large the datasets become.

5. High-Performance Networking

A scalable infrastructure needs robust networking solutions. It can easily handle increased traffic without bottlenecks. Today it is propagated more by SD-WAN and 5G networks.

6. Automation Tools

Scalable infrastructure thrives on automation. Tools such as provisioning, monitoring, and streamlining operations can reduce manual intervention and enable businesses to scale easily.

7. Security Frameworks

As systems scale up so do their vulnerabilities. These scalable infrastructures must include security measures such as:

  • Automated threat detection.
  • Multi-factor authentication.
  • Regular security audits.
  • End-to-end encryption.

Steps to Build Scalable Infrastructure

Creating scalable infrastructure needs a strategic approach. Here are the steps to guide you:

1. Assess Current Infrastructure

Before deciding on scaling up your infrastructure you need to evaluate your existing systems. You need to identify bottlenecks, determine if the technology is outdated, and find areas of improvement. You also need to conduct performance testing to understand how your infrastructure can handle stress.

2. Define Scalability Goals

Before deciding on scaling up your business you need to define clear objectives such as:

  • Determining the number of growth in users
  • Determining data size
  • Reducing downtime

3. Adopt a Cloud-First Strategy

To leverage on-demand scalability you need to move workloads to the cloud. Hybrid cloud solutions are the perfect option for businesses to transition gradually.

4. Implement Modular Systems

You can break down applications into smaller more manageable components. You can use APIs to connect the module as well as ensure its flexibility and scalability.

5. Optimize for Performance

Use tools such as load balancers, and CDNs to optimize performance and reduce latency.

6. Invest in Automation

You could deploy tools that automate infrastructure provisioning, scaling, and monitoring. This minimizes human error and accelerates scalability.

7. Monitor and Iterate

Scalability isn’t just a one-time task, it is all about continuously monitoring systems, gathering user data, and making iterative improvements to keep pace with business needs.

Real-World Examples of Scalable Infrastructure

1. Netflix

Netflix transitioned from on-premises data centers to a cloud-first approach with AWS. By adopting microservices and automation, Netflix could easily handle billions of hours of streaming each month even during peak times.

2. Uber

Uber today supports millions of rides every day across the globe. Its scalable infrastructure relies on containerization, real-time data processing, and distributed architecture to ensure seamless user experiences.

3. Slack

Slack is one of the world’s leading collaboration platforms, and they scaled their infrastructure to accommodate millions of simultaneous users. By leveraging cloud computing and real-time messaging frameworks, Slack delivers consistent performance.

Challenges in Building Scalable Infrastructure

While scalability provides numerous benefits, it comes with its own set of challenges:

1. Cost Management: Scaling up resources dynamically can lead to unpredictable costs if not monitored closely.

2. Technical Complexity: Transitioning to scalable systems such as microservices and containers needs experience and planning.

3. Legacy Systems: Integrating and replacing outdated systems with scalable solutions is a lengthy and resource-intensive process.

4. Security Concerns: One of the biggest security concerns is that expanding infrastructure can expose vulnerabilities and make robust security essentials.

Future Trends in Scalable Infrastructure

The landscape of the scalable infrastructure is constantly evolving. Here are a few trends that are shaping the future:

1. Edge Computing: Process data accurately to reduce latency and enhance scalability.

2. AI-Driven Scalability: Machine Learning algorithms optimize resource allocation and predict scaling needs.

3. Serverless Computing: Serverless architectures provide unparalleled scalability.

Conclusion

Building a scalable infrastructure is the foundation of successful digital transformation. By adopting cloud-first strategies and leveraging modern architectures, you can prioritize performance and security.

While challenges exist, the benefits of enhanced efficiency and superior customer experience outweigh its disadvantages.

As technology evolves, scalability will remain the cornerstone of the business looking for a digital transformation strategy. Now is the time to invest in infrastructure to make your business future-proof.