In today’s data-driven economy, analytics platforms aren’t just about dashboards — they’re about enabling smarter, faster decisions that fuel real business growth with ROI. Choosing between Qlik Sense (on-premise) and Qlik Cloud (cloud-native) isn’t simply a technical debate — it’s about how your organization can maximize ROI from data.
At Arc Analytics, we help businesses navigate these decisions daily. This guide breaks down the strengths of both Qlik options, showcases where Qlik Cloud creates new opportunities, and explains how a hybrid approach might unlock the best of both worlds.
The Core Difference: On-Premise Control vs. Cloud Agility
Qlik Sense (On-Premise): Best suited for organizations with strict security, compliance, or legacy systems. You retain full control over infrastructure while enjoying Qlik’s powerful associative data engine.
Qlik Cloud (Cloud-Native): A flexible, continuously evolving platform that delivers scalability, accessibility, and advanced analytics. Updates roll out automatically, reducing IT overhead and giving teams instant access to new features.
This core choice — control vs agility — frames today’s analytics strategies.
Why Businesses are Moving to Qlik Cloud
Qlik Cloud isn’t just Qlik Sense in the cloud. It’s a next-generation platform designed to enhance ROI and reduce friction in just about every phase of analytics.
🚨 Proactive Insights with Qlik Alerting
Set real-time, data-driven alerts to act the moment thresholds are crossed or anomalies appear.
📊 Advanced Qlik Reporting Suite
Automated, polished, and customizable reports that ensure insights are delivered to the right people, exactly when they need them.
🔄 Drag-and-Drop Data Flows
Reduce IT bottlenecks with visual data preparation for analysts and business users — no heavy scripting required.
👥 Seamless Collaboration
Enable true real-time co-authoring and dashboard sharing across teams, locations, and devices.
📈 Elastic Scalability
Scale instantly to meet spikes in data volume or user demand. No more waiting on hardware expansions.
🔒 Enterprise-Grade Security
Far from being a risk, Qlik Cloud meets rigorous security standards, often exceeding what smaller enterprise IT setups can provide.
🤖 AI + Machine Learning Insights
Go beyond dashboards with AI-powered predictions and ML-driven insights.
🌍 Broad Data Connectivity
Unify cloud and on-premise sources into one analytics environment.
Unlocking ROI with Automation, Qlik Answers, and Qlik Predict
One of the most transformative ROI drivers in Qlik Cloud is the ability to automate and modernize how users interact with data:
Qlik Automation connects processes, apps, and triggers, removing manual tasks from your team’s workload.
Qlik Answers lets users ask questions in natural language and get instant, contextual insights — expanding analytics adoption to the entire workforce.
Qlik Predict leverages machine learning to forecast trends and give businesses predictive power, not just reactive dashboards.
These SaaS-native tools go far beyond cost savings — they unlock entirely new value streams, driving adoption, speeding decisions, and creating competitive differentiation.
Migrating from Qlik Sense to Qlik Cloud can be daunting without the right expertise. This is where Arc Analytics’ Qlik Migration Services give you a competitive edge.
We specialize in:
Ensuring zero downtime migration.
Rebuilding complex Qlik apps in the cloud for performance gains.
Training teams for success in Qlik Cloud environments.
Notably, Qlik itself recently launched the Qlik Sense to Qlik Cloud Migration Tool (May 2025), giving organizations an official, streamlined path to migrate apps, data connections, and user roles. We combine this tool with our strategic approach for the smoothest possible transition.
Hybrid Approaches: Best of Both Worlds
For many enterprises, the smartest path isn’t choosing one — it’s choosing both.
Keep sensitive workloads in Qlik Sense on-premise for compliance.
Use Qlik Cloud for innovation, new projects, or global accessibility.
Minimize costs with licensing options that allow a hybrid setup at only ~30% additional cost.
This approach unlocks incremental ROI without forcing a “rip-and-replace” investment.
High-Level Licensing & ROI Comparison
Feature/Model
Qlik Sense (On-Premise)
Qlik Cloud (SaaS)
Licensing Model
Core-based (per CPU/core)
Capacity-based (data volume & users)
Infrastructure Costs
Requires hardware, maintenance, IT resources
Included in subscription (no infrastructure overhead)
Scalability
Limited to available cores & hardware
Elastic, scales on-demand
Updates & Upgrades
Manual patching & downtime
Continuous updates built-in
Security & Compliance
Controlled on-prem, internal governance
Enterprise-grade, built-in compliance frameworks
Total Cost of Ownership
High upfront + ongoing infra costs
Predictable subscription, pay for usage
ROI Focus
Infrastructure investment heavy
Data-driven outcomes & business agility
Takeaway: With Qlik Sense, ROI is partly consumed by infrastructure cost and IT overhead. With Qlik Cloud, that same investment is redirected toward automation, innovation, and user adoption — where business ROI is truly measured.
The ROI Equation
Migrating to Qlik Cloud doesn’t replace your past Qlik investment — it amplifies it. By combining proactive alerts, advanced reporting, Qlik Automation workflows, Qlik Answers for natural language analysis, and Qlik Predict for machine learning insights, companies can:
Improve decision-making speed.
Reduce IT overhead and manual reporting.
Empower every department with data-driven culture.
Stay future-ready as Qlik continues innovating.
Ready to Maximize Your Qlik ROI?
Whether full migration or hybrid, Arc Analytics is your partner in unlocking more value from Qlik.
For IT leaders and cloud architects, scalability isn’t just about adding storage or compute—it’s about designing a data infastrucutre that can sustain velocity, variety, and volume without sacrificing performance, governance, or cost efficiency.
Most infrastructures that work in early stages eventually break under pressure: query latency spikes, pipelines slow, storage thresholds force hard data-retention decisions, and new integrations become brittle. This isn’t just an operational headache—it’s a systemic limitation that compromises data reliability and agility across the enterprise.
At Qlik, we see this every day: organizations that proactively design for scalability achieve not only data resilience, but the ability to expand analytics, machine learning, and real-time decisioning at enterprise scale.
Why Non-Scalable Data Architectures Fail
When data infrastructure isn’t built for scale, challenges multiply quickly:
Throughput bottlenecks – ETL jobs that run overnight now take days.
Data silos – Multiple ungoverned storage layers prevent reliable analytics.
Cost inefficiency – Ad hoc scaling without automation results in overspend.
Poor resiliency – Systems that stall or fail under peak workloads reduce trust in data.
For IT directors, the real cost here is not just performance degradation—it’s losing the ability
Core Principles for Scalable Enterprise Data Infrastructure
Technical leaders can insulate against these risks by designing around five fundamentals:
Elastic Compute + Storage – Native autoscaling for ingestion, transformation, and warehousing.
Decoupled Services – Avoid monoliths. Architect for loose coupling across ingestion, processing, storage, and analytics.
Pipeline Automation – Continuous integration and deployment (CI/CD) for analytics pipelines reduces manual operations while supporting rapid iteration.
Observability & Monitoring – Real-time metrics, lineage, and anomaly detection to pre-empt bottlenecks.
Economic Scalability – Design for TCO (total cost of ownership), not just uptime. Plan for the frameworks to evaluate trade-offs across providers.
👉 Arc Professional Services often helps organizations operationalize these principles through reference architectures, deployment accelerators, and governance frameworks across cloud and hybrid data ecosystems.
Reference Architectural Patterns
The building blocks of scalable infrastructure vary, but certain patterns consistently deliver at enterprise scale:
Cloud-Native Architectures – Managed elastic compute/storage (AWS, Azure, GCP) tailored via policies for autoscaling and failover. See our guide on Building a Cloud Data Strategy to align platform selection with scalability goals.
Distributed Systems – Leverage Spark/Dask for distributed compute, Kafka for real-time messaging, and distributed query engines (Presto, Trino) for federated analytics.
Microservices & APIs – Isolate high-throughput services (fraud detection, personalization) into independently scalable units; deploy via containers and Kubernetes orchestration.
Hybrid and Multi-Cloud Mesh – Where latency, regulatory, or locality requirements exist, Qlik’s integration solutions bridge on-premises and cloud-native stores into a cohesive fabric with data lineage and governance.
Technology Decisions That Drive Data Infrastructure at Scale
For IT decision makers, selecting the right scaling tools requires weighing trade-offs:
Storage – Object stores (S3, Blob, GCS) for scale-out economics; NoSQL DBs (Cassandra, MongoDB) for flexible schema and horizontal reads/writes; columnar/cloud warehouses (Snowflake, BigQuery, Redshift) for analytics concurrency.
Compute & Processing – Batch and micro-batch with Spark/Dask; streaming with Kafka + Flink; consider Kubernetes orchestration for elastic container scaling.
Data Movement & Integration – Use CDC (change data capture)–enabled pipelines for real-time data replication. This is where Qlik excels—providing low-latency ingestion with lineage and CDC at scale.
Visibility & Governance – Implement observability into every layer; Qlik solutions embed lineage and metadata management to avoid “black box” integrations.
📌 As Gartner notes in their Data Management Maturity Model, scalability isn’t just technology—it requires aligned governance, processes, and integration across the data lifecycle.
Scaling Strategies for IT Leaders
Scaling should be iterative and framed as a roadmap, not a single migration project. Consider these strategies:
Foundational First – Build around elastic storage/compute before layering complex processing systems.
Automation Everywhere – Autoscaling, IaC (Infrastructure as Code), CI/CD pipelines for ingestion and analytics.
Observability-Driven – Keep real-time monitoring/alerting across ingestion, storage throughput, query latency, and pipeline success rates.
Plan by Workload Models – Model current/future concurrency + workload shapes, not just raw data volume.
Continual Optimization Loop – Regular audits for both performance and cost.
🔧 Qlik’s Professional Services partner with IT leaders to design and operationalize scaling strategies—from elastic CDC pipelines to governed multi-cloud architectures. Our team ensures scalability paths are not only designed but also implemented with integration best practices.
Technical Scalability as a Business Enabler
For IT directors and cloud architects, scalable data infrastructure isn’t about keeping the lights on—it’s about enabling the organization to innovate, move fast, and trust its data under continuous growth.
By following proven architectural principles, choosing technologies designed for horizontal scale, and embedding governance + observability into every layer, you ensure that infrastructure doesn’t become tomorrow’s bottleneck.
With Qlik’s platform and services, enterprises can bridge cloud-native, hybrid, and distributed systems into a single governed fabric—delivering elastic scalability with integration and lineage built in.
That’s the difference between scaling infrastructure and scaling real business impact
In today’s data-driven economy, businesses depend on skilled professionals to turn raw information into actionable insights. Two of the most critical roles are the data engineer and the data scientist. While these titles are often mentioned together, their responsibilities, skills, and day-to-day work differ significantly.
If you’re considering a career path — or trying to hire the right talent — understanding the difference between a data engineer vs. data scientist is essential.
What Does a Data Engineer Do?
Data engineers are the architects and builders of an organization’s data infrastructure. They design, construct, and maintain the pipelines, databases, and platforms that make clean, reliable data available for analytics and business intelligence.
Core Responsibilities of Data Engineers
Designing and maintaining data pipelines (ETL/ELT processes)
Building and managing data warehouses and data lakes
Ensuring data quality, consistency, and scalability
Implementing security measures to protect sensitive information
Optimizing data systems for performance, cost, and efficiency
Key Skills for Data Engineers
Strong programming in Python, SQL, and Scala
Expertise in databases (SQL & NoSQL)
Familiarity with cloud platforms (AWS, Azure, GCP)
👉 Explore more on how we help clients build scalable infrastructures in ourData Engineering Services page.
What Does a Data Scientist Do?
While engineers prepare the data, data scientists dive into it to uncover insights, predict outcomes, and inform decision-making. They apply statistics, machine learning, and AI to transform raw datasets into actionable intelligence.
Core Responsibilities of Data Scientists
Cleaning, exploring, and preparing data for modeling
Applying statistical analysis and machine learning algorithms
Building predictive and classification models
Visualizing complex results for technical and business audiences
Framing business problems as solvable data questions
Key Skills for Data Scientists
Strong background in math, statistics, and machine learning
Programming with Python, R, or Julia
Proficiency in visualization tools: Tableau, Power BI
Experience with ML libraries (scikit-learn, TensorFlow, PyTorch)
Ability to communicate business-ready recommendations
While different, these roles are deeply interconnected. Data engineers ensure high-quality foundations; data scientists transform that foundation into insights.
Which Career Path Is Right for You?
If you’re debating between becoming a data engineer or a data scientist, consider:
Do you enjoy building systems, solving infrastructure problems, and optimizing performance? → Data Engineering may be your fit.
Do you prefer analyzing data, applying models, and storytelling with insights? → Data Science might be your direction.
Both paths are in high demand and offer strong growth opportunities. For organizations, the best results come when both roles collaborate closely.
(Curious how we guide talent strategy? Read ourData Careers Guide for insights.)
What Comes First: Data Engineer or Data Science?
The reality is — data engineering usually comes first. Without well-structured, accessible data, even the most advanced science and modeling will fail.
Think of it like constructing a building: you wouldn’t hire interior designers before architects and builders lay a solid foundation. Similarly, no data science project succeeds without a trusted, scalable infrastructure in place.
👉 This is why many companies start by investing inModern Data Infrastructure before scaling analytics initiatives.
Future of Data Roles
Both roles are evolving with emerging technologies:
Data Engineers: Focus on cloud-native architectures, data governance, and security
Data Scientists: Sharpen expertise in deep learning, natural language processing (NLP), and explainable AI
Automation tools are accelerating workflows, but the demand for human expertise in designing systems and interpreting results will only grow.
Organizations that foster close collaboration between these two functions will be best positioned to leverage AI and data for competitive advantage.
For more perspectives on where business data is heading, check out our recent post onThe Future of AI in Business.
Forward Thinking
The distinction between data engineers and data scientists isn’t about competition — it’s about collaboration. Together, they form the backbone of any modern data team.
Businesses that want to succeed in the data economy must invest in both infrastructure (engineering) and analytics (science). For individuals, both career paths offer rewarding opportunities to shape the future of how organizations harness information.
Frequently Asked Questions (FAQ)
Is data engineering harder than data science?
Not necessarily. Data engineering leans heavily on programming, system design, and cloud infrastructure, while data science requires a deep understanding of math, statistics, and modeling. The difficulty depends on your background and interests.
Who earns more: Data engineer or data scientist?
Salaries vary by industry and experience, but historically data scientists earn slightly higher median salaries due to their specialization in machine learning and AI. However, demand for data engineers is rising quickly as companies recognize the importance of solid infrastructure.
Do you need data engineering before data science?
Yes. Without a reliable and scalable data infrastructure, data scientists cannot work effectively. That’s why many organizations invest in data engineering first, then scale into analytics and advanced modeling.
Which career path should I choose?
If you enjoy building systems, working with databases, and solving infrastructure problems, pursue data engineering. If you’re more interested in analytics, machine learning, and storytelling with data, consider data science. Both fields are in high demand.
Are data engineers and data scientists replacing each other?
No — these are complementary roles. Data engineers build the foundation, while data scientists analyze and interpret the data. Together, they drive data-driven decision-making.
Schools and universities run on many systems—SIS, LMS, assessments, finance, alumni, and clinical programs. Without data integration, insight stays trapped, reports conflict, and decisions slow down. With the right data integration plan, these systems tell one story about students, programs, and resources.
Different definitions for attendance, course completion, or program status lead to “dueling dashboards.” Establishing common definitions, validation rules, and routine data quality checks aligns reports across campuses and terms. Governance gives everyone confidence in what the data means.
• Shared definitions and validation rules end report drift
• Routine quality checks catch errors before they spread
• Data lineage explains where numbers come from
3. Slow Financial Visibility
Funding, grants, tuition, purchasing, and budgeting often sit in separate systems, making reconciliation slow.
• Connect accounting, grants, procurement, and planning for one finance model
• Tie spend to objectives and refresh KPIs quickly
• Streamline audits with consistent structures and controls
Student, parent, faculty, and alumni surveys hold valuable signals, but mixed tools and formats make comparisons hard. Standardize surveys and join responses to SIS/LMS data. Suddenly, a shift in satisfaction aligns with schedule changes, program redesigns, or resource gaps, and action is clearer.
• Standardize instruments so results compare term to term
• Join surveys to SIS/LMS data to see cause and effect
• Track changes over time to inform program design
5. Clinical Programs Kept Apart
Nursing, medicine, and allied health track EHRs, clinic software, and simulation data separately from academics. Secure connectors merge clinical hours, competencies, and outcomes with the academic record. Education data integration shortens accreditation reporting and gives faculty a complete picture of progress.
• Secure connectors sync clinical hours, competencies, and outcomes
• Unified records show skills, progress, and accreditation evidence
• Faculty gain a complete view of each learner
6. Manual Work and Spreadsheet Stitching
Exports, copy‑paste, and one‑off scripts drain time and add risk. The payoff is faster cycles and fewer late-night fixes.
• Managed pipelines to replace ad hoc work
• Change data capture keeps apps current where freshness matters
• Documented schedules and runbooks reduce midnight fixes
7. Security and Governance Gaps
As systems connect, risks rise. Define stewards, publish data dictionaries, and track lineage from source to dashboard. Encrypt sensitive data, enforce least‑privilege access, and audit regularly. With governance embedded, integration becomes safe and repeatable rather than fragile.
• Assign stewards and publish a data dictionary
• Encrypt sensitive fields and enforce least‑privilege access
• Audit regularly; track lineage from source to dashboard
8. Choosing an Approach to Data Integration
Match patterns to needs rather than forcing a one‑size‑fits‑all solution.
ETL to Warehouse
Curated reporting, historical trends
Clean, conformed data
CDC/Event Streams
Operational syncs, near real-time
Low-latency updates
Data Virtualization
Fast access across sources
Minimal data movement
• Pilot a narrow use case, prove value, then scale
• Balance freshness, complexity, and cost
• Reuse standards and components across projects
How to Get Started with Data Integration
Map today’s flows, agree on shared definitions, and pick one high‑value pilot—unify SIS and LMS for early alerts, or connect finance for grant tracking. Build with maintainability in mind, train the team, and expand to the next priority. When you’re ready, we’re here to help.
Most businesses run on three core systems: ERP for operations, CRM for customers, and BI for insights. Without ERP, CRM, and BI Data Integration, data gets trapped in silos and critical context is lost. Effective data integration connects these systems so information flows in real time, reducing manual work and errors. When your tools share a single source of truth, teams make faster, smarter decisions and deliver a smoother customer experience. This is how you turn disconnected activity into coordinated growth.
Picture this: Your sales team closes a big deal in the CRM, but your warehouse doesn’t know about it until someone manually updates the ERP. Meanwhile, your BI dashboard shows last week’s numbers because it can’t pull real-time data from either system.
Sound familiar? Here’s what data silos are costing you:
• Duplicate work and manual data entry
• Inconsistent reports across departments
• Delayed decisions based on outdated information
• Frustrated teams working with incomplete data
• Missed opportunities to serve customers better
This fragmented approach doesn’t just waste time—it actively hurts your ability to compete and grow.
Operational Excellence: When Data Integration Works Together
Imagine a different scenario. A customer places an order through your sales team, and instantly:
• Inventory levels update automatically in your ERP
• Production schedules adjust if needed
• Shipping timelines appear in real-time
• Customer service gets full order visibility
• Finance sees revenue impact immediately
This isn’t wishful thinking—it’s what happens when your systems are properly integrated. The result? Smoother operations, fewer errors, and teams that can focus on strategy instead of data entry.
When your CRM and ERP share data, something powerful happens—you see the complete customer story:
CRM Data
ERP Data
Combined Insight
Sales interactions
Order history
Customer buying patterns
Marketing campaigns
Shipping details
Campaign effectiveness
Service tickets
Payment history
Customer satisfaction drivers
Lead sources
Product preferences
Best acquisition channels
This unified view lets your team:
• Personalize every customer interaction
• Predict what customers need before they ask
• Identify upselling and cross-selling opportunities
• Resolve issues faster with complete context
Strategic Decisions: BI That Actually Works
Your BI tools are only as good as the data they can access. When connected to integrated ERP and CRM data, your dashboards transform from pretty charts into strategic weapons:
• Track real-time KPIs across all departments
• Spot trends before your competitors do
• Measure the true impact of marketing campaigns
• Understand which customers drive the most profit
• Make decisions based on complete, accurate data
For example, integrated data might reveal that customers acquired through social media campaigns have 40% higher lifetime value—but only if they purchase within their first 30 days. That’s the kind of insight that drives real business growth.
Making Data Integration Happen
Getting your systems to work together doesn’t have to be overwhelming. Here’s how successful organizations approach it:
Assessment & Planning
Start by mapping your current data flows and identifying the biggest pain points. Where are teams spending the most time on manual work? Which decisions are delayed by missing data?
Choose Your Integration Approach
Native integrations: Use built-in connections when available
Middleware solutions: Deploy integration platforms for complex scenarios
Modern data platforms: Leverage cloud-based tools for scalability
Focus on Business Value
Don’t integrate everything at once. Start with the connections that will have the biggest impact on your operations, customer experience, or decision-making.
Need help getting started? Contact our team to discuss your integration strategy.
The Bottom Line for Data Integration
Breaking down data silos isn’t just about technology—it’s about unlocking your organization’s potential. When your ERP, CRM, and BI tools work together, you get:
Faster operations with automated data flows
Happier customers through personalized experiences
Smarter decisions based on complete information
Competitive advantage through data-driven insights
The question isn’t whether you can afford to integrate your systems—it’s whether you can afford not to. Start your integration journey today and discover what your data can really do.
