Data Analytics Modernization: Strategy, Benefits & Examples

Introduction

Like every facet of their app, SaaS providers must modernize their analytics. Data analytics modernization brings many benefits to end users and SaaS providers alike.

In this blog, we’ll provide helpful guidance on the problems and challenges which must be overcome. We also share examples of successful data analytics modernization in practice, along with a best practices reference chart listing Dos and Don’ts.

What Data Analytics Modernization Really Means

Data analytics modernization describes the process of updating and an organization’s data infrastructure, tools, and processes to use more advanced analytics capabilities. Modernizing analytics enables more data-driven decision-making. 

Modernizing analytics can be accomplished many ways, including: 

• Improving analytics by incorporating more data, such as unstructured data
• Embedding analytics as an inherent part of other applications used daily
• Making analytics more customizable to surface better insights
• Providing more employees with access to analytics 

Benefits of Modernizing Data Analytics

Benefit #1: Data-Driven Decision-Making

With modernized analytics, such as real-time insights and alerts, organizations can make more data-driven decisions, leading to greater efficiency and productivity. And even small, incremental changes can have huge impacts, particularly for larger enterprises. When your customers benefit from modern data analytics, they’re more likely to continue using your app, boosting your retention rates.

Benefit #2: Boost Productivity with Workflows & Automation

It’s great if users can discover new insights with analytics – but better yet if the analytics tool does the discovering for users. Workflows and automations can be triggered as new data is received or when user-defined metrics and thresholds are met. All-new types of data-driven applications can be created.

Benefit #3: Improved Collaboration

Modern analytics put insights into the hands of every employee more effectively. Collaboration is improved by empowering every team member with instant access to the same dashboards and reports. Consensus can be achieved more quickly, enabling organizations to act faster.

Benefit #4 Faster Time to Action with Real-Time Alerts

It’s helpful to be informed when established thresholds are met, such as inventory levels dropping or the output of a machine declining. Such alerts inform a business of the need to reorder products or fix a machine obstruction. 

Alerts must be immediate to be most valuable. Products must be ordered promptly to guarantee supply, and manufacturing problems must be dealt with immediately to stop a loss of production and inability to deliver to customers. 

Enterprises need analytics in real-time. To be effective, managers need to make decisions based on the most up-to-date information. Modernizing data analytics can deliver these vital alerts in real time, enabling organizations to act fast.

Benefit #5: Including More Data Points = More Comprehensive Insights

More than 70% of all business data is never used for analysis because most traditional analytics tools only work with structured data. To gain vital insights, you must be able to integrate all of your data, including semi and unstructured data sources like forms and images.

Benefit #6: Enable Users to Get Personalized Insights

Democratize data by empowering users to configure their own datasets for personalized reporting. With self-service interfaces, you can enable users to personalize their dashboards and analytics to match their individual preferences and use cases.

Current Data Analytics Problems

Problem #1: Data Complexity

Many SaaS providers face numerous data challenges, including tackling multiple data processing pipelines. Additionally, data warehouse solutions do not offer native multi-tenant security, so it requires a large development effort to consolidate databases while implementing granular security controls.

Qrvey’s single analytics data lake eliminates the need for multiple data processing pipelines.

• Built-in data connectors and a data ingest API
• Sync data to Qrvey or connect to live data
• Ingest structured, semi and un-structured data
• Create data transformations to match your needs

Problem #2: Lack of Scalability 

Your users demand applications that are always available with 100% uptime and near-instantaneous performance. Legacy analytics platforms can fail to meet these demands, risking customer frustration and churn.

Scalability challenges can be overcome with cloud-native technology, but growth can still pose a problem for SaaS providers if the pricing of their analytics tool rises excessively and disproportionately.

Problem #3: Implementation Time

Since most legacy analytics platforms weren’t built for embedding, they don’t let your team build analytics the same way they build your core application. Legacy analytics generally lack:

• A robust API
• Prebuilt widgets
• Ample training, tutorials, and examples to get you started
• Multiple environments for development, testing, and production

All of these shortcomings slow down development efforts and create hassles for your DevOps team.

Problem #4: Inability to Analyze All Data

Modern data analytics must enable analysis of all of your data, including semi and unstructured data sources like forms and images. Unfortunately, legacy analytics lack the ability to connect to any data type. Users miss out on the possible insights to be gleaned from your dark data.

Problem #5: Disjointed User Experience

Over 95% of business intelligence and business analytics software available today isn’t suitable at all for embedded analytics within SaaS apps. Plenty of BI companies claim their tools are embeddable, but in fact, only a few features can be embedded. And when “embedding” entails merely placing a chart inside an iframe, the impact is a user experience that feels disconnected from the host application.

Instead, every chart, font, and color should be easily customizable to perfectly match your existing application.

Key Drivers Behind Analytics Modernization

The following are the top reasons enterprises update legacy analytics systems.

Legacy Analytics Pain Points	Modern Data Analytics Benefits
Slow performance and latency issues	Real-time data processing and instant insights
Rigid, one-size-fits-all reporting	Customizable, self-service dashboards and reports
Siloed data across systems	Unified data models and centralized analytics
Heavy reliance on IT for report generation	Empowered business users with low-code/no-code tools
Outdated, batch-based ETL pipelines	Modern, event-driven and streaming data architectures
Limited scalability with growing data volumes	Cloud-native, elastic infrastructure that scales automatically
Poor user adoption due to disparate systems and clunky interfaces	Intuitive, embedded analytics experiences within SaaS applications
Lack of actionable insights	Workflows and automations as well as advanced analytics, AI/ML capabilities for predictive decision-making
Inflexible licensing and deployment models	API-first, modular platforms with flexible deployment optionsAnalytics pricing that includes unlimited users
Security and compliance concerns in aging systems	Built-in governance and role-based access

Core Pillars of Data Analytics Modernization

The transition from legacy reporting to modern, embedded analytics requires a strategic approach grounded in key architectural and operational principles. Below are the core pillars that guide successful transformation efforts.

Core Pillar #1: Cloud-Native Architecture

Modern analytics platforms are built for the cloud, enabling elastic scalability, high availability, and seamless integration with modern SaaS ecosystems.

Core Pillar #2: Embedded & Self-Service Analytics

Empowering end users with intuitive, self-service tools embedded directly into the applications they already use improves adoption and reduces reliance on technical teams.

Core Pillar #3: Real-Time Data Processing

Modern systems prioritize real-time or near-real-time insights, enabling faster, data-driven decisions without waiting for batch-processed reports.

Core Pillar #4: Unified Data Access & Integration

Breaking down silos by unifying structured and semi-structured data sources under a single analytics layer ensures a holistic view of business performance.

Core Pillar #5: API-First and Extensible Platforms

An API-first design allows analytics capabilities to be seamlessly extended and customized to meet specific application and user needs.

Core Pillar #6: Advanced Analytics 

Incorporating AI/ML, natural language querying, and automated insights elevates analytics from descriptive to predictive and prescriptive.

Core Pillar #7: Governance, Security, & Compliance

Modern platforms provide record- and column-level security, allowing administrators to restrict data access at granular levels in a dataset, so each user gets only the information they are authorized to see. 

Security tools and features must also  support multi-tenant analytics within SaaS applications and ideally will inherit your security model, including all of your rules and policies.

Core Pillar #8: Developer & DevOps Friendly

Modernization includes support for CI/CD pipelines, configuration as code, and rapid deployment, enabling agile iteration and innovation.

Examples of Data Analytics Modernization in Practice

Example #1: Impexium

Read about how Qrvey allowed Impexium to go to market quickly and get analytics into the hands of their customers. The company needed to replace their legacy analytics platform with a modern solution with self-service capabilities, responsive design, and data automation.

Example #2: JobNimbus

JobNimbus, a CRM and project management platform tailored for exterior home renovation contractors, was struggling with churn due to inflexible legacy reporting modules within its platform. The self-service, drag-and-drop simplicity of the Qrvey platform allowed the JobNimbus product team to quickly build custom reports and dashboards addressing their customer pain points. Within months of deploying Qrvey, JobNimbus achieved:

• 70% adoption among targeted large enterprise users
• Meaningful increase in product market fit score
• Significant reduction in customer churn due to reporting limitations

Example #3: Workflow Management Software TrueContext 

TrueContext enables insight-driven decision making with powerful analytics. Users can capture in-the-moment insights on their critical assets and provide visibility to key stakeholders.

Reports and analytics, such as the real-time form submission dashboard, are available to all users, Advanced analytics, including customizable reports, are offered only at the “Advanced” and “Enterprise” levels.

Best Practices for Long-Term Success 

Do	Don’t
Prioritize user experience with embedded, intuitive analytics	Don’t assume users will adopt tools that require jumping into a separate app
Design with scalability and flexibility in mind	Don’t build rigid systems that can’t evolve with business needs
Empower business users with self-service tools	Don’t rely solely on IT for every report or force users to work with analytics that fail to meet their unique needs
Choose a proven embedded analytics platform to accelerate time-to-market	Don’t waste resources building analytics from scratch unless it’s your core product
White-label analytics to match your application’s brand experience	Don’t embed dashboards using iFrames—it breaks UX and limits flexibility
Choose platforms with modern APIs and integration capabilities	Don’t adopt closed or proprietary systems that limit extensibility
Implement real-time or near real-time data capabilities	Don’t settle for outdated batch-processing that delays insights
Focus on unified data governance and access control	Don’t overlook security, compliance, and data trust
Continuously evaluate and optimize performance	Don’t “set it and forget it” once analytics are deployed
Align analytics strategy with product and customer goals	Don’t treat analytics as an afterthought or disconnected project

Build a Future-Ready Data Culture

Modernizing your analytics stack isn’t just about upgrading technology—it’s about empowering your users, accelerating innovation, and driving real business outcomes. Qrvey was built specifically for SaaS companies looking to embed powerful, flexible, and fully white-labeled analytics into their applications without the overhead of building from scratch. If you’re ready to move beyond the limits of legacy reporting, Qrvey offers the tools and architecture to help you build a scalable, future-ready data culture.

Want to see what modern embedded analytics looks like in action? Request a demo or read our guide on Choosing an Embedded Analytics Platform.